Their automated lab is almost hypnotizing to watch. They have floating plates zipping around on Wall-E-esque tracks, used vision-language models to control Windows 95 boxes, and created the world’s largest collection of voided warranties. In the process they’ve built a massive library of scientific reasoning tokens. Over 10 trillion of them, all experimentally validated.

No warranties were voided in the making of this video

To say Lila is ambitious is an understatement. Their goal is a scientific superintelligence wired directly into the wet lab. They are all in on the bitter lesson, and the thesis follows from it: a lab is an infinite token generator. Produce data at scale, and the synergies give you a general reasoner that can tackle any scientific problem. They are committing hard. Biology, chemistry, drug discovery, and materials science, all at the same time. Time will tell if it works, but it is an exciting hypothesis.

In our latest episode we sat down with Lila’s very own Andy Beam (CTO) and Rafa Gómez-Bombarelli (CSO, physical sciences) and went on a journey through the possibilities of AI-run science, almost as wide-ranging as Lila’s goals.

Did we mention they do both materials science and biology? In the same AI science factory? Same time, same lab, same AI. Finally a guest who can settle a long-running debate we’ve had amongst ourselves: is biology or materials science harder?

Watch to find out!

We discuss:

* The internet is spent, science is next. Why Lila thinks the scientific method is the last untapped internet-scale dataset, and why they treat RL as a data generation mechanism with nature as the verifier.

* The lab as a data center. Instruments as nodes on a graph, a magnetically levitating “PCI bus” transport layer between them, orchestration as a slurm queue. Andy is not short on analogies.

* Why Lila insists it is not an automation company. They optimize for flexibility and generalizability over raw throughput, which means humans stay below the API line wherever automating does not pay.

* Your experiment has a runtime. We put Escalante Bio’s question to Andy: if science is the token generator, what is the runtime of your data collection? His answer, in short, is that you cannot make the ribosome go faster. Why Lila bets on fast round-over-round iteration rather than big noisy multiplexed screens, and how Rafa’s team rebuilt a gas sorption measurement to run roughly 2,500x faster.

* What is actually in 10 trillion scientific tokens. Not sequences. Experimentally verified reasoning traces, a kind of data that Andy argues exists on the internet in quantities that round to zero.

* Breadth as a path to depth. Small molecule chemistry priors transferring to metal organic frameworks for carbon capture, and the claim that the general model beats domain-specific models sample for sample.

* If you have the data, what do you need the model for? Sri Kosuri’s koan about the ML-for-drug-discovery business model, and Andy’s answer: the coding model got better because it also read Shakespeare and carnitas recipes.

* The serendipity they want to automate. Emily Whitehead survived the first pediatric CAR-T cure only because the doctor treating her happened to know, from pediatric arthritis, which antibody would blunt her IL-6 response. Roll that dice again and you probably lose her. Breadth is how you stop depending on luck.

* Move 37 for catalysts. Model suggestions for platinum-group-free electrocatalysts that went from boring, to what a 40-paper expert called stupid, to the best performers they have made.

* Six months to in vivo CAR-T data in non-human primates, and the zero-FTE virtual startup commercial model that fell out of it. For context on why that number is startling, AbbVie paid $2.1B for Capstan on the strength of preclinical in vivo CAR-T data.

* You cannot have scientific superintelligence if you are just a good test taker. Ken Stanley, who wrote Why Greatness Cannot Be Planned, runs open-endedness at Lila. RL at scale gives you a ruthlessly Vulcan problem solver. Machine creativity is a different thing, and it is the part nobody has solved.

* The chain of thought is an unreliable narrator. The model reasons in latent space and only emits tokens. Sometimes it skips the experiment entirely and is still right. So how much do you trust the reasoning versus the verifier?

* Reward hacking when the rollout is physical. Chains of thought that collapse into repetition, and a model that got annoyed and swore at the scientist who kept asking it to redo a plate map. What happens when a pathological loop has a wet lab inside it?

* The bittersweet lesson. Rafa’s inversion of the bitter lesson: in AI, scaling is a roadmap. In materials, scaling is a filter, because only the things that scale end up mattering.

* Not your typical Flagship company. Why a famously single-asset biotech incubator spun out a platform bet, and Andy’s line that if Lila called itself a biopharma it would have a top-three GPU cluster.

* Bottlenecks they would remove by fiat. Sim-to-real for physics-based simulation, and the fact that RL training runs at roughly 5% mean FLOP utilization.

Watch on YouTube:

The cloud was built for developers. But agents are now changing that.

The old infra stack was designed for a human who could read docs, reason through YAML, and understand dashboards to figure out what they need when something broke. While this was painful for developers, it worked since they could fill in missing context in their heads.

However, agents don’t have that luxury. Now in this new era of agents, everything has to be tighter.

They need a place to write code, run it, inspect the output, change the environment, debug failures, and try again. Fast iteration and feedback loops with all the necessary context are crucial for agents to operate properly. Furthermore, sandboxes are a clear representation of this shift as agents can easily spin up isolated environments. This programmatic infra even extends to research:

Two years ago, we were one of the first to cover Modal with CEO Erik Bernhardsson and Alessio designed our favorite LS thumbnail of all time:

At the time, Modal was just a teeny little company with a $17M Series A.

Today, fresh off their $355M Series C, Modal is one of the clearest examples of the agent cloud future being built in real time: a cloud platform moving past traditional web app assumptions toward the workloads AI actually creates such as elastic inference, sandboxes, GPU burst, post-training, background agents, and infrastructure that agents themselves can operate.

In this episode, Modal CTO Akshat Bubna joins swyx and Vibhu to unpack why AI applications don’t fit traditional cloud assumptions, why Kubernetes was never designed for bursty compute-heavy workloads, and why Modal is now shifting from developer experience to agent experience.

We go deep on Modal’s AI infra stack: serverless functions, decorator-based infrastructure, elastic inference for custom models, GPU snapshotting, DeFlash, speculative decoding, Auto Endpoints, sandboxes, persistent storage, networked containers, private IPv6, RDMA, multi-node training, and Modal’s capacity pool across 17 cloud providers. Akshat also explains why RL rollouts can require 100,000 sandboxes, why production agents need hard guardrails, why observability may matter more than reading code, and why AI has made infrastructure exciting again.

We discuss:

* Why Kubernetes wasn’t built for bursty AI workloads

* How Modal started as a better runtime before becoming an AI cloud

* Why Modal added GPUs before ChatGPT

* The shift from developer experience to agent experience

* Why observability matters when agents are writing the code

* Elastic inference for custom models across audio, video, robotics, and comp bio

* GPU snapshotting, cold starts, and why inference workloads are so bursty

* Why RL rollouts can require 100,000 sandboxes

* DeFlash, speculative decoding, and frontier-level inference performance

* Auto Endpoints and making optimized inference easier to deploy

* What Modal adds beyond vLLM, SGLang, and raw GPU rental

* Modal’s 17-cloud capacity pool and supercloud strategy

* Networked sandboxes, sidecars, private IPv6, and RDMA

* Serverless multi-node training for post-training and research workloads

* Auto-research, model-guided sweeps, and agents launching GPU experiments

* Compute strategy, capacity planning, and batch tiers

* Why production agents need specialized sandboxes and hard guardrails

* Modal’s take on managed agents, CI, Gitpod/Ona, Python, TypeScript, and Modal Bench

Akshat Bubna

* LinkedIn: https://www.linkedin.com/in/akshat-bubna-188885103

* X: https://x.com/akshat_b

Modal

* Website: https://modal.com

Timestamps

00:00:00 Introduction

00:00:39 Modal’s origin and why Kubernetes wasn’t enough

00:04:32 Developer Experience → Agent Experience

00:06:21 Modal’s AI cloud primitives

00:09:14 Sandboxes, agent loops, and proto-Cognition

00:12:12 Elastic inference, GPU snapshotting, and 100,000 sandboxes

00:15:24 DeFlash, speculative decoding, and Auto Endpoints

00:19:59 Production-grade inference beyond raw GPUs

00:22:00 Background agents, Ramp Inspect, and the agent lifecycle

00:24:08 Modal’s 17-cloud supercloud strategy

00:26:40 Networked sandboxes, private IPv6, and RDMA

00:32:48 Multi-node training, post-training, and auto research

00:37:36 Compute strategy, capacity planning, and batch tiers

00:40:55 Open models, real-time AI, and production agent infra

00:43:06 Hard guardrails, managed agents, and specialized sandboxes

00:46:06 Why AI made infrastructure exciting again

00:48:30 Model APIs, differentiated products, and agentic video

00:51:50 CI, coding-agent infra, SDKs, and Modal Bench

00:57:28 Closing Thoughts

Transcript

Introduction: Modal, Series C, and the Art Party

Swyx [00:00:00]: We’re here with Akshat, CTO of Modal, together with Vibhu. Congrats on your Series C.

Akshat [00:00:10]: Thank you.

Swyx [00:00:11]: Your party yesterday was amazing.

Akshat [00:00:15]: Yeah.

Swyx [00:00:15]: From all the photos and all the swag.

Akshat [00:00:17]: We had a bunch of art installations, which was fun, seeing, like, our products on pedestals next to, like, Rodin.

Swyx [00:00:25]: Very nice. Very nice. When you started, it was not the GPU inference company. Maybe it was in your mind. Take us back to the origin story.

Modal’s Origin: A New Runtime Beyond Kubernetes

Akshat [00:00:39]: I first met Eric, who’s the CEO, through an investor. Back then Eric was already thinking about building, a new runtime, and he got there thinking through why are workflow orchestration products so hard to use. It’s because you have to run them on Kubernetes. Kubernetes is hard to manage. It’s not built for burstiness and, custom images,

Swyx [00:01:03]: Yeah

Akshat [00:01:03]: It has a terrible developer experience.

Swyx [00:01:05]: And I’ll, I’ll interject

Akshat [00:01:06]: Yeah

Swyx [00:01:07]: For listeners, who are new, we interviewed Eric two years ago, and there’s a bit more of the story there from Spotify and all those things.

Swyx [00:01:14]: And I came across Eric through Data Council because he did that talk on the serverless container stack that you guys did, which was like, that was my first like, “Okay, I need to take Modal very seriously” moment.

Akshat [00:01:26]: Yeah.

Swyx [00:01:26]: But it was still very unclear, like, do I need all this for just my data pipelines?

Akshat [00:01:33]: Yeah. initially what we were thinking about was if we build a better runtime, it’s a very useful primitive in itself. It’s There’s a lot of things that, get solved by serverless functions, like you can do, ETL stuff, you can do job queues, you can do all this, like, bursty processing, which it turns out every company had needs for. but then we also were thinking about this as like, this is a primitive that we can build a whole collection of products on, which are very verticalized. So perhaps data engineering would’ve been the first one, but we were thinking about inference. Back then it was more classical inference, like computer vision stuff and running XGBoosts and whatnot. But we added GPUs to the product a year before ChatGPT came out.

From Serverless Containers to GPU Workloads

Swyx [00:02:19]: Nice.

Akshat [00:02:19]: We just didn’t think it would be that big of a deal.

Swyx [00:02:22]: Yeah, just like add A100.

Vibhu [00:02:23]: Was there any, like, early key problem that really sparked off why you built it?

Akshat [00:02:28]: Yeah. Primarily it’s just, none of the tooling that was out there was built for, one, a really great developer experience, and also there’s a general trend of, a lot of the workloads that we were seeing were very. I wish there was a better word for it, but compute-heavy. Like, they need, one, like, need a lot more resources, so you need to burst up and down a lot, versus like Kubernetes designed for, like, slow scaling and, more for, like, web server use cases. And also there’s just a lot more specialization in, like, what kinds of environments these workloads run in. Like, we had sometimes they need accelerators, sometimes they need different kinds of images, and this is just like a consistent thing that we saw across a lot of companies. That would be the next step.

Software-Defined Infrastructure and Decorator-Based DX

Swyx [00:03:13]: Yeah. Yeah. Be nice. I don’t know how much this factored into the early story, but I wrote a post when I was at Temporal about infrastructure, software-defined infrastructure or something like that.

Akshat [00:03:22]: Yeah, the self-provisioning

Swyx [00:03:23]: Self-provisioning.

Akshat [00:03:24]: Yeah.

Swyx [00:03:24]: Yeah. I can’t even remember my own post.

Swyx [00:03:26]: And then you put me on the landing page.

Akshat [00:03:28]: Yeah. We really like, the term and so we stole it.

Swyx [00:03:32]: Because you had the insight that everything can just be in decorators co-located with the code, right?

Akshat [00:03:37]: Yeah.

Swyx [00:03:37]: Was that a big part of the original

Akshat [00:03:39]: Yes

Swyx [00:03:39]: Story or it was just like a DX layer?

Akshat [00:03:41]: That was, really important because we really didn’t want people to spend, so much time, writing YAML, and it seemed like you could really condense the surface area of what you’re doing, put it in code so you can operate on it just like you operate on other code, and like build stuff that’s more expressive and dynamic. and so yeah, that was always a very important part.

Swyx [00:04:04]: Then the pushback is this is a DSL.

Akshat [00:04:07]: Yeah.

Swyx [00:04:07]: It’s you’re closed source. I am locked into Modal.

Akshat [00:04:11]: Yeah. We never really got pushback for that because the nice thing about Modal is you can bring whatever code you have, and sure, the DSL is at the configuration layer for, what hardware you’re using, how you’re scaling things up, but you still own the code.

Akshat [00:04:27]: And that’s, that’s been an important, part of our story, even as we do inference now.

Swyx [00:04:32]: Yeah.

Vibhu [00:04:32]: How much of do you think still stays the same today? Like if you were to build something today, DevX very important, but I feel like, a lot of this has been changed with just hook it up to an agent, have Claude Code, have Codex implement a tool. there’s very agent native primitives that are different than if I’m doing this myself, right?

Developer Experience → Agent Experience

Akshat [00:04:54]: We’ve changed our SDK team to think about agent experience instead of, developer experience and we think that the same benefits that apply for DX also apply for AX, which is why would you have an agent read through hundreds of Kubernetes files and like write YAML that’s not even typed when it can make a couple of changes in a decorator and it gets this self-provisioning runtime of, being able to see its changes live in action? yeah, it just seems from the customers we talk to, they find Modal is much faster for agents to use versus operating on a different substrate.

Swyx [00:05:34]: Yeah, because like you, again, you co-locate the infrastructure requirements to the code that runs it.

Akshat [00:05:38]: Yeah.

Swyx [00:05:38]: Well, the negative thesis now is that nobody’s looking at their code anymore, so there’s no point.

Akshat [00:05:44]: Yeah, people aren’t looking at code. one thing we still see is really important is observability.

Swyx [00:05:51]: Yeah.

Akshat [00:05:51]: Like how good is your dashboard? And of course, like we have, we push a lot of it to the CLI so the agents can do their own investigation, but you still need humans to go interpret what’s going on and, make judgment calls and whatnot. and that’s I feel like, Maybe more important now than looking at the code itself.

Swyx [00:06:11]: Yes, because like, you can try to treat the code as a black box and then use, see the observable action that comes out of it, and then just prompt a change.

What Modal Is For: AI Cloud Primitives

Akshat [00:06:21]: Yeah.

Swyx [00:06:22]: So I think it takes a bit of restraint to not specialize, to say, “I want to ship a new primitive,” and then just be general purpose.

Swyx [00:06:31]: People ask you, “What are you for?” You’re like, “ I don’t know. We can do this, we can do that.”

Vibhu [00:06:36]: Well, I’d be curious to see, like, okay, if we were to ask you, like, what is Modal for even at a high level? There’s a lot you guys do, sandboxes, GPUs, everything. How do you answer?

Akshat [00:06:46]: Modal is a cloud platform that’s built for, where we’ve built the primitives from scratch for AI applications. and right now it covers, inference, training, batch processing, and sandbox workloads.

Akshat [00:07:00]: But we’re building a lot more

Swyx [00:07:02]: I noticed you didn’t say web server, so there is still a role for, like, the always-on large-scale Kubernetes type things.

Akshat [00:07:09]: Yeah, absolutely. We’re, we’re not trying to compete with the renders of the world, because yeah, we think the differentiator for us is the, are the workloads that need specialized compute, need to scale up and down a lot. yeah, they’re, they’re, they’re just shaped differently.

Working Alongside Frontier Startups

Vibhu [00:07:26]: I think you’re building a lot of it alongside the startups, right? They’re innovating quite a bit, even in your, like, latest blog post. Like, even in the series C, the customers that you mention here, the cognitions, technical ones, ramps and whatnot, they’re, they’re innovating with you, right? And that’s not something AWS is doing directly with.

Akshat [00:07:45]: Yeah, absolutely. I think, this is again classic. We’re a small team. We can move really fast. our engineers are working with our customers and figuring it out. Yeah.

Swyx [00:07:54]: So my first week at Cognition, I walked in, there was someone wearing a Modal shirt. I was like, “What are you doing here?” They’re like, “Yeah, I just. I am embedded inside of Cog.”

Akshat [00:08:05]: Yeah, I think that was Peyton. We sent him over

Swyx [00:08:07]: Yeah.

Akshat [00:08:07]: Because, the latency of communication was too high otherwise.

Swyx [00:08:12]: Yeah, distributed node, you have to - you have to place one and collocate.

Vibhu [00:08:16]: Yeah.

Swyx [00:08:16]: So I had a, I had direct personal experience, right? So I worked on smol developer three years ago. it was inspired by Claude 1. I think you onboarded me at some point, like, just before, and I was like, “Oh, like, I need some bursty compute. Like, I was just gonna try using Modal.” And it was a, it was a pretty pleasant experience. apparently, I showed up in the board meeting, like the analytics.

smol developer, Sandboxes, and Proto-Cognition

Akshat [00:08:39]: Yeah, you blew up on Hacker News and,

Swyx [00:08:41]: Yeah

Akshat [00:08:41]: We got a big traffic spike. I. I think the way you used smol developer was Modal functions for running stuff, which was. Like, the, that was a good use case. but then, yeah.

Swyx [00:08:53]: Yeah. That - So to me, that was proto-cognition.

Akshat [00:08:55]: Right.

Swyx [00:08:56]: If only I had, like, stuck to it.

Swyx [00:08:58]: Like, that was like, if - did you say draw the tech tree

Akshat [00:09:00]: Absolutely

Swyx [00:09:00]: You’re just like, “Yeah, like, probably this will happen.”

Akshat [00:09:02]: Yeah. Like, he was so close. You were just rebuilding upon us

Swyx [00:09:04]: I just didn’t realize.

Akshat [00:09:05]: But the funny story there is at the same time, we were talking to a bunch of customers who needed something like sandboxing.

Swyx [00:09:14]: Yeah.

Akshat [00:09:14]: This is like twenty-three.

Swyx [00:09:15]: Yeah.

Akshat [00:09:16]: So we built

Swyx [00:09:17]: You introduced a new API right after that.

Akshat [00:09:18]: Yeah.

Swyx [00:09:19]: Yes.

Akshat [00:09:19]: Like, we built sandboxes in May of twenty-three before anyone was even knew this was gonna be a thing. And the first example we published was, we took smol developer

Swyx [00:09:28]: Smol developer

Akshat [00:09:28]: And put it in a loop, so the agent can iterate on itself.

Swyx [00:09:33]: Loops are hot these days.

Vibhu [00:09:34]: It’s the looper.

Akshat [00:09:34]: Yeah.

Vibhu [00:09:35]: Loops in. When was this, twenty-three?

Akshat [00:09:38]: Yeah.

Vibhu [00:09:39]: A small check.

Akshat [00:09:39]: Yeah.

Swyx [00:09:39]: It’s like twenty-three. so the. the, those for listeners, like, the problem was the models are not built for any of this, right?

Swyx [00:09:46]: Like, you’re just trying to like. They’re not post-training to understand, like, looping and, like, self-correction and tool calling was there, but, like, also not that great.

Akshat [00:09:55]: Yeah.

Akshat [00:09:55]: I don’t remember if you used tool calling in this one, but yeah, the models would just diverge after like ten iterations and not produce anything meaningful.

Swyx [00:10:03]: Yeah. But like, then. So okay, like now talking to myself three years ago, the answer

Vibhu [00:10:08]: Of course they will get better

Swyx [00:10:09]: Collect all the failures, build benchmark, and then collect all the, examples, build the RL environment

Akshat [00:10:15]: Right

Swyx [00:10:15]: Sell it for like ten billion dollars to Meta.

Swyx [00:10:17]: And then also train a model and then sell that for sixty billion dollars to Elon. And this is

Akshat [00:10:23]: Yeah, of course

Swyx [00:10:23]: The funny machine. Like, it’s like, it’s about the hardware.

Akshat [00:10:28]: It’s hard to have that inherent conviction that the stuff will get that much better.

Swyx [00:10:33]: In retrospect, it’s so f*****g obvious.

Akshat [00:10:36]: Fair enough.

Swyx [00:10:37]: Like, what else were we doing back then? I don’t know. anyway. Yeah. So this. That was the start of your sandboxing journey, right? I feel like it didn’t blow up until, like, last year.

Akshat [00:10:49]: Yeah.

Swyx [00:10:50]: So there was like a couple years of quietness.

Akshat [00:10:52]: Exactly, yeah. We were

Vibhu [00:10:53]: I think very underrated product value. Like, my experience with Modal, Charles, before he had joined Modal, met this guy at a hackathon, and he really insisted we wanted to run some small model, not hosted anywhere, and he’s like, “ there’s this cool company, Modal. They’ll like spin up a GPU sandbox, we can throw it on there. They’ll take a Hugging Face link.” And like there’s so much value just right there, right? Like instant hosting, spin it up, spin it down. It’ll stay cold, but we run the demo a few days later, it’ll come back up and like all this stuff in retrospect, like it’s still what we needed like today.

Akshat [00:11:27]: Yeah, it’s still needed today. workload shapes have changed a lot as, we run stuff for people with really massive production scale and, there it’s it’s not about scaling from zero to one, but it’s how do we scale really elastically, from like thousand to fifteen hundred GPUs very quickly in a given region. It’s the same shape problem.

Elastic Inference, GPU Autoscaling, and Custom Models

Vibhu [00:11:50]: Okay. So you look at, say, Cursor Composer, right?

Akshat [00:11:53]: Yeah.

Vibhu [00:11:53]: They had a. “We’ll do RL on a model every couple hours.” you guys have a whole version of RL inference gym and whatnot.

Vibhu [00:12:01]: When you look at workloads like that, you’re doing train runs where you need to scale up, scale down every hour thousands of GPUs, right? That’s the example for we do need it, right?

Akshat [00:12:12]: Yeah. Well, so I’ll, I’ll take a step back and, maybe talk about like how people use Modal today. because our biggest use case is, elastic inference. And the thing we first found product market fit, with was inference for custom models. So we stayed away from the LLM space, and we were serving companies like Suno for audio, Runway for video, robotics, comp bio companies that train their own model elsewhere. But Modal is the best black box that for deployment, scaling to however many GPUs you need as your traffic pattern changes. And we saw all of them like have a very unpredict- predict- predictable, traffic pattern. it’s like diurnal. It’s Some days, like the company will do a launch and, they’ll need like, way more. And it’s not just one model that they deploy. They-- all these companies deploy, lots of different models in different regions, and so the autoscaling problem becomes even harder because then you have to scale within a certain region, and those cycles are offset. So different times you scale up in different regions.

Akshat [00:13:20]: So that’s like our sort

Vibhu [00:13:22]: And that

Akshat [00:13:22]: Yeah

Vibhu [00:13:22]: That in and of itself is a huge category. There’s a bunch of inference providers which, provide this fireworks, does this as a service together, whatnot, Base10. that’s carved into its own niche for language models, at least right now.

Akshat [00:13:36]: Yeah. the thing that we have specialized in is the autoscaling aspect.

Vibhu [00:13:41]: Yeah.

Akshat [00:13:41]: Because we found that it’s not universally true that everyone else can autoscale, and we’ve gone deeper into it on the tech side by, we’ve incorporated GPU snapshotting into the product so we can take the GPU state, like your torch.compile model, snapshot it, and the next cold start is way faster. And so going back to your question, it’s That’s why you need a lot of burstiness for inference. But then people also do a lot of demand training, like for RL stuff, your rollouts are bursty, as you said. People also do a lot of batch jobs. So we’ll see, a lot of companies, before they have a training run, they’ll need thousands of GPUs to run encoding or something like that. And I think those things are much more bursty than. I agree that agents are not that bursty. sandboxes are, except when you’re doing RL. RL is just

RL, Batch Jobs, and 100,000 Sandboxes

Vibhu [00:14:28]: Or commerce

Akshat [00:14:28]: Insanely bursty.

Vibhu [00:14:29]: Yeah.

Akshat [00:14:30]: Yeah. Like when you’re doing, rollouts, you sometimes need a hundred thousand sandboxes in your sandboxes.

Vibhu [00:14:37]: Yeah. I’m curious if you’ve seen early sparks of continual learning. There are some people, like our friends, ngram, recently announced this

Akshat [00:14:45]: Yeah

Vibhu [00:14:45]: They’re, they’re trying to do training. That also seems like a different workload, right? If you’re doing training twenty-four/seven per se, there’s a very weird dynamic of how you’re using GPUs between people and whatnot, but seems like something you guys would work for.

Akshat [00:15:00]: As you said, we’re, we’re fortunate to work with a number of, customers at the frontier and grab some of our customers. and they are taking the primitives we have, and trying to use them in very interesting ways, like continual learning. It’s possible as the stuff gets better, some of that will be part of, our offering as well if, more people need it. but we’re, we’re just waiting to see

Vibhu [00:15:23]: Yeah

Akshat [00:15:23]: How it shakes out.

Vibhu [00:15:24]: Is there a primitive that you added after sandboxing that was the next step in the story?

LLM Inference, DeFlash, and Speculative Decoding

Akshat [00:15:32]: I guess we’ve been going much deeper into LLM inference

Vibhu [00:15:35]: Yeah

Akshat [00:15:35]: Because we realized that some of the advantages we have with like autoscaling, again, especially in different regions and whatnot, are, not present elsewhere. and the place where we had a gap was we weren’t, working on the model layer itself. Like we were a black box. And, we realized that, we can get to frontier-level model performance, with, by having great people who work on this. And, we’ve been open sourcing a lot of our work, in terms of, Recently, we, shared our work on DeFlash, which is a block-based, speculator, and we’ve open sourced, all of it. So, you can - By using open source DeFlash, you can get the same performance as you would with one of the proprietary providers. And the next thing we’re thinking about here

Vibhu [00:16:23]: I thought this was

Akshat [00:16:24]: Yeah

Vibhu [00:16:24]: An interesting blog post as well, right? Like, I think in here you make a claim that. Not a claim, just that how effective speculative deco-decoding really just get to.

Akshat [00:16:33]: Yeah.

Vibhu [00:16:33]: Anything you wanna point out from this around, what people should know?

Akshat [00:16:39]: Yeah, absolutely. the high-level summary is, it would help to describe what speculative decoding is.

Vibhu [00:16:44]: Yes.

Akshat [00:16:44]: I will, yes.

Vibhu [00:16:45]: I think, like

Akshat [00:16:46]: Yeah

Vibhu [00:16:46]: So we’ve covered like Eagle and all this

Akshat [00:16:47]: Yeah

Vibhu [00:16:47]: Like Hydra and all those things, but it was like two years ago.

Akshat [00:16:51]: Yeah.

Vibhu [00:16:51]: I think it doesn’t hurt, right?

Akshat [00:16:52]: Yeah. Speculative decoding is you have a smaller model, called a draft model, predict tokens ahead of the bigger model, and then you have the bigger model, verify all of this, all the tokens are predicted. And the reason it’s faster is if you’re predicting, one token at once, you’re bound by memory bandwidth. But if you can batch the verification of, the draft model, then you’re much more efficient using compute, and it’s faster, and as long as your draft model is producing a lot of tokens that can get accepted, which is called the accept length, you can get a speed up that’s, multiple times of, the original model speed. and well, that’s what we highlight here. It’s Like people talk a lot about we made these kernels faster and whatnot, but improving kernel will only give you like few percentage points of improvement, and, increasing accept length, literally is a multiplicative decrease

Vibhu [00:17:47]: Like two to four X.

Akshat [00:17:48]: Yeah, exactly.

Vibhu [00:17:48]: Without much head-on performance.

Akshat [00:17:50]: Yeah. I think it may - you are running a second model, right? So it may be something more expensive in the compute,

Vibhu [00:17:57]: I meant quality performance

Akshat [00:17:58]: Probably not by much

Vibhu [00:17:58]: But yeah. I think

Akshat [00:17:59]: So there’s no drop in quality performance

Vibhu [00:18:01]: Yeah

Akshat [00:18:01]: Because you’re always. You’re never accepting a token that the big model

Vibhu [00:18:04]: It’s strictly better

Akshat [00:18:05]: Yeah

Vibhu [00:18:05]: Or it’s same.

Akshat [00:18:06]: Exactly.

Vibhu [00:18:07]: Right. Yeah.

Akshat [00:18:08]: And so we’ve been working a bunch on DeFlash, which is a block-based speculator. so it’s instead of predicting, one token at a time, it’s predicting a block. And we’ve been open sourcing our work with it. The next thing for us here is for helping people train speculators and custom models. it’s it’s something that traditionally is very forward-deployed engineering driven, support deployed, engineer driven, like you work with customers and help them do that. And our vision for. This is why we launched Auto Endpoints, is we want to make frontier-level performance available to everyone. And so, we mentioned this in the announcement, we teased it. The next thing we’re, we’re launching is, as you run an auto endpoint, we shadow traffic

Auto Endpoints and Frontier-Level Performance

Vibhu [00:18:54]: Do you want to explain what auto endpoints are?

Akshat [00:18:57]: Yeah.

Vibhu [00:18:57]: I lovely, yeah.

Akshat [00:18:58]: Yeah. So, this is, I guess, going back to your Modal is you touch the code, but, sometimes people don’t wanna touch the code, and they wanna get started with an endpoint that works and has all the great performance and, scalability that Modal has. So we’ve made that easier with, a way to create an endpoint from our UI, from the CLI, that has all of our optimizations that we talked about, like the DeFlash stuff already baked in, and there’s full transparency. So we give you the code, you can go run it yourself, and if you want, you can eject out into the full Modal experience, which we see as people get sophisticated, they do wanna tweak the models, they wanna, fine-tune stuff. You can still do all of that. It’s it’s not a black box. And yeah, the next thing, as we teased later in the post, is how do we give you value even beyond this in terms of having your draft models evolve as your data distribution evolves, again, without having to talk to a person and, yeah.

Vibhu [00:19:59]: I guess just to understand it directly, you have the GPUs, you have an endpoint that’s compatible, you serve open model. If someone was to do this themselves, what’s the delta that you guys provide? So you do a lot of open source great work on effective inference. how does it compare to, say, I take the same model, 5.2 FP8, take shelf inference engine, vLLM, SGLang, get compute of similar capacity, similar cost. What’s the delta that plugging into something this, like this offers outside of the benefit of, scaling?

Production Inference Beyond Raw GPUs

Akshat [00:20:34]: It’s interesting because we’ve taken the approach of open sourcing our contributions and upstreaming them. we work closely with the SGLang team. We want the improvements that our team, comes up with to be, there in open source for others to use, even outside of Modal. The benefit to us is we have a team that has significant expertise in terms of if you do have something that is not there, our team can help you get that performance, first. the other thing is with these endpoints, we are way more elastic, as you said, than, anyone else, and you have true scaling to zero. you have true, burstiness, and in practice, that matters a lot more to people than just finding, the GPU and, running Modal code on something.

Vibhu [00:21:20]: Yeah. And I will say it’s not that straightforward to just. like what I said is easier said than done, right?

Akshat [00:21:26]: Yeah.

Vibhu [00:21:27]: It’s I think still for the average person, still hard to just gut check using different. There’s, there’s quite a bit of combinations you can make there. the trade-offs aren’t really known at face value.

Akshat [00:21:40]: Yeah. it’s it’s not just that. I think it’s it’s that running production-grade inference is a hard infer problem.

Vibhu [00:21:49]: Yeah

Akshat [00:21:49]: Even if you subtract out the autoscaling

Vibhu [00:21:50]: Yeah

Akshat [00:21:51]: Is controlling things like tail latency and, making sure every, request is delivered at least once and whatnot.

The Model and Agent Lifecycle

Vibhu [00:22:00]: There’s a lot of innovation that you can do here. I think, it’s very interesting that you’re starting to encroach on, like as you become a full cloud, you’re starting to encroach on other people’s turf.

Vibhu [00:22:09]: What will you not do?

Akshat [00:22:13]: Well, we wanna follow our users and, make sure they get like a platform that has everything that works well together. so right now we’re focused on the model lifecycle and the agent, lifecycle. so both like going from data prep to training to inference, and then also if I want to deploy a background agent, let’s say, sandbox, do persistent storage, a whole bunch of other stuff.

Vibhu [00:22:38]: We talked to Cole, who did, OpenInspect. Yeah.

Akshat [00:22:42]: Yeah.

Vibhu [00:22:42]: And RealInspect also is on Modal.

Akshat [00:22:44]: Yeah. So Ramp Inspect was a great example of a background agent that was really successful because they, were able to use some of the primitives like snapshotting and fast scaling to just have something that feels really reactive and works well.

Ramp Inspect and Background Agents

Vibhu [00:23:02]: Yeah. That’s the new CTO of, Ramp right there.

Akshat [00:23:05]: Yeah, Rahul.

Vibhu [00:23:08]: It was really fun. yeah, okay, I think, all very bullish. Like, one of my reflections was also I did not originally. So when I met you guys

The Inference Inflection: CPU, GPU, and Co-Location

Vibhu [00:23:19]: You weren’t that much in the GPU game, and now you’re all about, inference. And one of the points that I hinged on for Jensen’s keynote at GTC this year was, what we’re calling like the inference inflection, right? That let’s say in AI workloads or machine learning workloads, it used to be like, let’s call it eight to one GPU to CPU, and now it’s more like one to one, which is like a interesting. Like, - because of how much agents are blocked or call out to this, to CPU heavy stuff the actual, like, limiting factor, like, swings back and forth from GPU to CPU a lot more than it used to be all GPU and then occasional CPU.

Akshat [00:24:01]: Yeah.

Vibhu [00:24:02]: GPU, CPU. And now it’s like just constantly, and you just have to locate everything.

Seventeen Clouds and the Supercloud Strategy

Akshat [00:24:08]: Yeah. And that’s one of the things that, again, we see as, something appealing about Modal, which is we’ve built this capacity pool that spans, 17 cloud providers, so we’re, we’re very good at Running on various kinds of cloud capacity across the world

Swyx [00:24:24]: You don’t have your own data centers?

Akshat [00:24:25]: We don’t have our own data centers. We just run across a lot of neo clouds

Swyx [00:24:29]: Yeah. Are

Akshat [00:24:30]: Metal providers.

Swyx [00:24:30]: Yeah. Question mark.

Swyx [00:24:31]: Yeah. You’re, you’re running the math, and you’re like, “What’s the cutover point where you’re like.”

Akshat [00:24:36]: Yeah, it’s a good question. part of it is we see our differentiator in the software layer, and, being capital light and focusing on the software helps us move really fast. so far it’s worked out well because there are so many other people building data centers that we’re able to work effectively with them, and again, focus on what makes us, special.

Swyx [00:24:55]: Yeah.

Swyx [00:24:56]: 17 gets you into, like, the local providers sometimes. Like

Akshat [00:25:00]: The,

Swyx [00:25:01]: Which was the most interesting one?

Akshat [00:25:02]: There are a lot more neo clouds than you expect, and they all have various degrees of, various levels of reliability. And, that’s why it’s something we’ve invested a lot of time in, is building our own reliability layer on top. so if the GPU falls off the bus or something happens, we user workloads are not affected, and that lets us use a lot more capacity than,

Swyx [00:25:30]: Yeah

Akshat [00:25:30]: You as a user would be able to.

Swyx [00:25:32]: It’s a useful thing to have because like now everyone knows, like, what layer you are and, like, you optimize for being the super cloud of all clouds.

Akshat [00:25:41]: Yeah. That’s, that’s, that’s the idea. and so I guess when you mentioned colocation, that’s, that’s another interesting thing where, one thing we’ve seen is people come to us when they want, very specifically located, CPUs or GPUs, like they want

Swyx [00:25:57]: Oh, they pin it in like

Akshat [00:25:58]: Yeah

Swyx [00:25:58]: EU?

Akshat [00:25:59]: Exactly. Or EU, US.

Swyx [00:26:01]: Right. Data resiliency

Akshat [00:26:02]: Australia

Swyx [00:26:02]: Locality thing or performance or what?

Akshat [00:26:04]: It’s either data locality or latency, yeah.

Swyx [00:26:07]: Yeah.

Akshat [00:26:07]: Like, you want your. They’re running sandboxes and model. They want them to be right next to a

Swyx [00:26:10]: Yeah, it’s easy then

Akshat [00:26:11]: Yeah

Swyx [00:26:12]: To. That is important in all those things. and so, like, you’ve accidentally, I don’t know if it’s accident, but, like, you’ve built the perfect primitive for agents to express themselves. And then, like, it’s almost very funny how every extra development just involves more file system, just involves more CPU.

Akshat [00:26:30]: Yeah.

Swyx [00:26:31]: Just like the things that you already have. I don’t know much about, if there’s any, like, networking usages that are interesting, but you’ve also done some good work on networking.

Networking, Sidecars, Private IPv6, and Sandboxes

Akshat [00:26:40]: Yeah, that’s exactly right. Like, we’re just taking compute storage and networking and building stuff on that layer, for, again, the stuff people need.

Swyx [00:26:49]: Yeah

Akshat [00:26:50]: We see a few interesting networking things coming up. one is people want networked sandboxes. so we have

Swyx [00:26:57]: For like a Docker cluster type thing.

Akshat [00:26:59]: Yeah.

Swyx [00:26:59]: Sorry, Docker Swarm. Oh, f**k. What is it called?

Akshat [00:27:02]: Compose.

Swyx [00:27:03]: Compose type thing.

Akshat [00:27:04]: Yeah. So if you want Docker Compose, our sandboxes now support, this thing called sidecars. So you can. A sandbox is a pod of containers, and you can run multiple containers in, a sandbox. also useful because, going back to networking, people want a lot of control over, outbound networking from a sandbox.

Swyx [00:27:23]: Yeah.

Akshat [00:27:23]: Like, they might wanna run a middle proxy for, like, maybe logging stuff for RL or, controlling how egress can happen to a domain, injecting credentials. and yeah. So we’ve, we’ve had to build a lot of that stuff ourselves.

Swyx [00:27:38]: Yeah.

Akshat [00:27:39]: But then also sometimes people want, sandboxes spanning multiple nodes to talk to each other, which is an emerging thing we’re seeing. We have support for that for a different reason, and yeah, we’ll see if that becomes stable.

Swyx [00:27:52]: Like, just an open socket. It’s a. This is directly like mTLS.

Akshat [00:27:56]: We do support that, which is you can, expose a tunnel inside a sandbox.

Swyx [00:28:01]: Yeah.

Akshat [00:28:01]: And then you can either expose it to public internet or it can be, you can add like a HTTP, auth layer above it. But we have this thing called I6PN, which we haven’t talked about, which is this, like, overlay network using IPv6 addresses. so if Modal containers, within the same workspace, when this is enabled, can address each other using this private IPv6 address, and no one else can.

Akshat [00:28:28]: So it’s like private networking, for containers. We built it because we needed it as a primitive for our distributed training product. so we have this other feature, which is you can add a decorator to a function, and you get a cluster of GPUs. and they have RDMA networking. so you can run a distributed training job, that’s truly serverless. and we did the overlay network for that. But then we’ve seen that people are using it for other reasons, and, I’m intrigued to yeah, what would people do with it.

Swyx [00:28:59]: Build primitives and let people figure it out, right?

Akshat [00:29:01]: Yeah, exactly.

Swyx [00:29:02]: You put out a pretty interesting

Akshat [00:29:03]: They’re like, they read the docs webpage. Let me use that

Swyx [00:29:06]: Yeah

Akshat [00:29:06]: Something they never intended to work. This is literally not even in our docs page. People somehow found it, and they’re using it.

RDMA, Memory Movement, and Distributed Training

Swyx [00:29:12]: Huh.

Swyx [00:29:14]: The way you portrayed it with, like, RDMA versus TCP, like, very well laid out, but just the transfer speed change at scale for RL, like yeah, you have it, you have it built in. I’m sure someone found it. It’s found it to be a lot more efficient before you made a thing out of it, right?

Akshat [00:29:32]: Yeah. And not to split hairs, I guess the overlay network is the TCP overlay network.

Akshat [00:29:39]: The reason we have that is you need that to do the key exchange for RDMA before you set up the RDMA network on top of that. but then people found the TCP part.

Swyx [00:29:48]: Can I tell you, this is like a big aha moment for me because

Akshat [00:29:51]: Yeah

Swyx [00:29:51]: So I review 2,200 submissions for the World’s Fair.

Akshat [00:29:56]: Yeah.

Swyx [00:29:57]: And then I got this from John Osterhout

Akshat [00:29:58]: Huh

Swyx [00:29:59]: Who I don’t know if. Do John Osterhout by name?

Akshat [00:30:01]: The name sounds familiar.

Swyx [00:30:02]: He published a. He’s a well-known professor, published a lot of interesting software design books, and this is the talk he chose to submit, is on RDMA at Inference. And I’m like, you wouldn’t think that this guy, who is like operating systems guy, would care about RDMA.

Akshat [00:30:20]: I, it makes sense to me because I,

Swyx [00:30:24]: This is the cloud, right? Yeah

Akshat [00:30:25]: Like, the way you move around your KV cache and how efficiently you can do it, how efficiently you move, your weights from your training GPUs to your inference GPUs in RL is there’s a lot of degrees of freedom, and it is a systems problem

Swyx [00:30:41]: Yeah

Akshat [00:30:41]: Moving memory around

Swyx [00:30:42]: Yeah

Akshat [00:30:43]: Scheduling.

Swyx [00:30:44]: This shows you how primitive my understanding of networking stuff is.

Swyx [00:30:46]: Is this like the domain of WireGuard as well?

Akshat [00:30:50]: Not quite.

Swyx [00:30:51]: It’s adjacent?

Swyx [00:30:53]: Explain everything.

Akshat [00:30:54]: Sure.

Swyx [00:30:56]: How do we move memory around GPUs?

Akshat [00:30:58]: Well, so sorry. Yeah, that is memory. Sorry, I was talking more, and maybe I was talking like five minutes back, about the private IPv6, addressing that you’ve set up.

Swyx [00:31:09]: Yeah.

Akshat [00:31:09]: Is it like it’s a VPN?

Swyx [00:31:10]: Yeah, it is like a VPN, and yeah, WireGuard is, yeah, you’re right. It is,

Akshat [00:31:16]: Right. Yeah, you already moved on to new topics

Swyx [00:31:17]: A similar

Akshat [00:31:18]: Okay

Swyx [00:31:19]: In the same space, WireGuard is, encrypted and this is,

Akshat [00:31:23]: And you don’t need encryption.

Swyx [00:31:23]: Yeah.

Akshat [00:31:24]: Yeah.

Swyx [00:31:24]: This is not encrypted. that’s the main difference. This is TCP and we have eBPF programs that will reject or allow the TCP connection based on whether you’re allowed to do it.

Akshat [00:31:35]: Used to involve a full sidecar, but now you have eBPF in the Linux kernel.

Swyx [00:31:39]: Yeah.

Akshat [00:31:40]: Yeah. I don’t know if this is a natural follow-on to the topic of like my skepticism on distributed training is that while, like, people spend a lot of money on, like, cables to hook up GPUs, and even that is not, like, fast enough, and that’s the bottleneck, is your networking fast enough?

Swyx [00:31:59]: Yeah. So I guess you’re talking about fully distributed training like, Dialog or something which is like cross data center

Akshat [00:32:06]: That would be, yes.

Swyx [00:32:07]: That’s the extreme.

Akshat [00:32:08]: Yeah.

Swyx [00:32:08]: You’re in the middle, and then other people would have like the Mellanox cables up in, like, their actual data center.

Akshat [00:32:14]: When you run multi-node training on Modal, RDMA, I think Mellanox, is, or InfiniBand is like a, is all seen as RDMA. but it’s a way to bypass the TCP networking stack and, transfer, stuff much faster, between one node, to the other. And we have I think like 3 terabit per second, internal networking

Swyx [00:32:40]: Okay

Akshat [00:32:40]: Which is the standard that’s needed.

Swyx [00:32:42]: Okay. So I misunderstood what

Akshat [00:32:43]: 50

Swyx [00:32:43]: What part of the stack you were

Akshat [00:32:44]: 50 gigs over

Swyx [00:32:45]: Yeah

Akshat [00:32:45]: If you went

Swyx [00:32:45]: Yeah

Akshat [00:32:46]: RDMA.

Swyx [00:32:46]: Okay.

Swyx [00:32:48]: Yeah. I, very impressive work.

Multi-Node Training, Post-Training, and Auto Research

Swyx [00:32:52]: So effectively you’re extending like the model philosophy to the training cluster, like, yeah.

Akshat [00:32:59]: Yeah. And we’re, we’re not going for like large scale training runs. the thing that we’ve built multi-node training for is, we see a lot of, smaller scale post-training. like, people are post-training like medium sized fund models, so they can, get higher quality on inference. this is a perfect fit, for something like that.

Swyx [00:33:21]: Yeah. That is my impression of how a lot of these labs explore branches in post-training and then eventually merge whatever they find in.

Akshat [00:33:31]: Yeah. The other use case we’ve seen for multi-node training is even if you have a big cluster, your researchers are still doing small runs

Swyx [00:33:38]: Yes

Akshat [00:33:39]: Having elasticity there

Swyx [00:33:40]: Right, sure

Akshat [00:33:40]: Matters a lot more.

Swyx [00:33:41]: Yeah. the, like, this is like the current limiting factor for auto research, which is like you need to give your model some GPUs in order for it to completely run.

Akshat [00:33:51]: We have a blog post on auto resource and model is,

Swyx [00:33:55]: Yeah

Akshat [00:33:56]: Yeah, like, turns out to be pretty good substrate for that.

Swyx [00:33:59]: So my impression is auto research means many things, like

Akshat [00:34:01]: Yeah

Swyx [00:34:01]: Anything that Andrej coins. Right now it’s still science fair, right? Like not like, I don’t know how many people are doing this.

Akshat [00:34:08]: We’re having a golf.

Swyx [00:34:08]: Yeah.

Akshat [00:34:09]: I thought the same thing.

Swyx [00:34:11]: Yeah, you would know.

Akshat [00:34:12]: We, like, our internal both training and inference teams use this the general shape of this quite a bit. like we have this one internal repo called auto inference, which essentially we’ve automated our own forward-deployed engineering efforts using, this harness, which is, the agent will just spin up a sweep of different things. It’ll even run like, NVIDIA inside profiler and it’ll like tweak configs and it’ll arrive the right thing. it’ll change your GPUs both from H200 to B200, and works really well.

Swyx [00:34:47]: Nice.

Akshat [00:34:47]: So yeah.

Swyx [00:34:48]: By the way, I enjoy that your forward-deployed engineering is so technical that you have to do these things.

Swyx [00:34:52]: It’s very different from forward-deployed engineering from other people.

Akshat [00:34:54]: Yeah. For our forward-deployed engineering team is, essentially they’re like applied inference researchers or applied training researchers.

Swyx [00:35:02]: Someone told me like they have to be able to build, but they also have to be able to sell. do they have to sell or are they like they’re good, they’re just like post-sale type of thing?

Akshat [00:35:09]: It does, being able to talk to a customer and engage effectively with them

Swyx [00:35:13]: Yeah

Akshat [00:35:13]: Matters a lot.

Swyx [00:35:14]: They want the same thing.

Akshat [00:35:15]: Yeah.

Swyx [00:35:15]: ?

Akshat [00:35:15]: But it’s it’s not really a sales, thing. We pair them with-- We have solution architects as well that are more on the sales side.

Swyx [00:35:23]: Okay. Let’s spend a bit more time on auto research. This is a big focus for for this year. Where does this go? like, have people explored enough? Like, there’s all these beautiful charts of like improve and then level off a bit and then you find the next thing. Is this one abstraction up from normal training? Is that how we think about it, or do you think about it differently? Like model level training versus high, like driven hyperparameter search.

Auto Inference and Modal Bench

Akshat [00:35:51]: Yeah, like,

Swyx [00:35:51]: Someone, some people call it like neural architecture search or whatever, right? Like.

Akshat [00:35:54]: Yeah, - So the stuff I’ve seen people do with it is nowhere on the architecture level. It’s pretty much tweaking parameters, but it’s it’s a hyperparameter sweep that’s guided by some model intuition, so it’s like much more efficient than, whatever other, sweep you would have.

Swyx [00:36:12]: Yeah, it’s just, it’s just a question of where you want to spend your compute?

Akshat [00:36:16]: Right.

Swyx [00:36:16]: ‘Cause yeah, you can just throw infinite amounts of money on this and somehow you’ll bang out Shakespeare?

Akshat [00:36:22]: Yeah, infinite monkey.

Swyx [00:36:24]: Yeah, so like the very good for model. and I think it’s also very important that agents can spin up other agents, can spin up their infrastructure. Like very good for you. how good is our LLMs at generating model code? Like the benefit of existing LLMs is that you are in the data.

Akshat [00:36:42]: Yeah. They’re, they’re surprisingly good. I think like pre Cloud 4 they were not, and then now they’re able to shot, stuff out of the box. But we’re playing around with releasing like a Modal Bench for like the harder

Swyx [00:36:55]: Yeah

Akshat [00:36:55]: Things, that the LLMs cannot do yet and maybe

Swyx [00:36:59]: What’s an example of that?

Akshat [00:37:01]: I think the things that- Sometimes agents struggle with, without right guidance and a skill is, how to, use the rest of our observability. Like how to. Something is failing, like how do you look at the logs and then update the right thing? It’s reasoning about that. But they’re able to shot, like

Swyx [00:37:23]: Yeah. You can just add a skill to it?

Compute Strategy and Capacity Planning

Akshat [00:37:26]: Yeah. So we have a Modal skill now that. Which is why we built this Modal Bench. It’s to find things like that, so we can address them in our tool.

Swyx [00:37:35]: Tune a skill. Yeah.

Akshat [00:37:36]: Yeah.

Swyx [00:37:36]: No. it’s it’s good. are you facing any shortages? like we talk a lot about GPU shortages, but also CPU, also memory.

Swyx [00:37:44]: Yeah.

Akshat [00:37:45]: We have had a lot of growth, which means that, there’s - we’ve had to be much better about

Swyx [00:37:53]: Planning

Akshat [00:37:54]: Proactive capacity planning.

Swyx [00:37:55]: Yeah.

Akshat [00:37:55]: So we have,

Swyx [00:37:57]: Which by the way, like it’s like a MBA’s like dream

Akshat [00:38:00]: Yes

Swyx [00:38:00]: Is like just planning this stuff. I think last time you and I talked about something maybe about this.

Akshat [00:38:03]: Yeah. we have a really competent team of people that we call, The role is called compute strategy. so yeah, if anyone listening here or wants to work on that

Swyx [00:38:13]: Compute strategy?

Akshat [00:38:13]: Yeah.

Swyx [00:38:14]: I think,

Akshat [00:38:14]: I feel like,

Swyx [00:38:15]: I think the normies call it FP&A or something.

Akshat [00:38:18]: Well, it’s more It’s it’s not FP&A. It’s it’s There’s a lot of interesting financial questions of like what is the blend between one year and three-year reservations? how do we forecast our own capacity? how do we. especially since our capacity is very fungible across different GPU types and different regions, like you have to model a lot of it. and you also have to have an opinion on how the supply chain is gonna evolve, and then you have to like, take bets,

Swyx [00:38:49]: Yeah

Akshat [00:38:49]: Based on that.

Swyx [00:38:50]: Tokenomics.

Akshat [00:38:50]: Yeah.

Swyx [00:38:51]: This is like probably a not a real point, but, I was trying to think about like what other industries. I was trying to think about like, we cannot be first to like these kinds of problems.

Akshat [00:38:59]: Yeah.

Swyx [00:39:00]: And what other industries have had this? And I was like, airlines with fuel and like they have to hedge their fuel and like, I think for a long time Southwest because they made like a hero fuel bet, they like were like super low cost because

Akshat [00:39:12]: Oh

Swyx [00:39:12]: Compared to everyone else.

Akshat [00:39:14]: Yeah. I hadn’t thought about that.

Vibhu [00:39:16]: We’re at a fun time too?

Akshat [00:39:18]: Yeah. It’s. A lot of the compute business in general, for us is also about being very good about capacity management. That is how you have great unit, economics. but also over time it’s how you can unlock more value for customers. Like, one of the things we’re building now is like a way for customers to get, If they don’t care about latency, like get much cheaper pricing and they’ll get results back in like next 24 hours or something, like a batch tier essentially.

Batch Tiers and Latency-Insensitive Workloads

Swyx [00:39:47]: Yeah.

Akshat [00:39:47]: And those are levers we have because we control the whole stack and scheduling and whatnot to give people a sufficient

Swyx [00:39:53]: Yeah. I feel like they’re not as popular. Like those, like the Frontier Labs have all those APIs. They’re not as popular as they should be.

Akshat [00:40:00]: The demand that we see for something like that is not for LLMs. although sometimes people wanna run evals and

Swyx [00:40:08]: Okay

Akshat [00:40:08]: Synthetic data prep and there it makes sense.

Swyx [00:40:10]: Okay.

Akshat [00:40:11]: But it’s from a lot of LLM companies, like people who are doing computational bio, like they have to run really big batch jobs and they don’t care about when they get it back.

Swyx [00:40:22]: Yeah. And like they have a reasonable. It’s it’s also like a cousin to the stopping problem of like, will this finish in time?

Akshat [00:40:30]: Yeah. You can bound it.

Swyx [00:40:33]: Yeah.

Akshat [00:40:33]: Like you can give people

Swyx [00:40:34]: Yeah

Akshat [00:40:34]: SLAs on it.

Swyx [00:40:35]: Yeah. I think what’s, what’s interesting is like the next phase of model.

Swyx [00:40:38]: Like what, do people expect from you, now that you’re established and you’re like well-known compute player among all these leading companies. You had an inference launch week, and we talked a little bit about the launches. like what else? Like what else should people know?

What Modal Builds Next

Akshat [00:40:55]: We are building primitives that make our users’ lives much easier. So, I think for example, with LLM inference, thousands more companies are gonna post-train their own models and, deploy open source models for inference. so we’re thinking a lot about what is the best product shape for that. And, that involves everything from our training gym to, then, endpoints that get frontier-level performance. again, but I haven’t talked to anyone. It looks somewhat different on other verticals. Like, we’re also seeing a lot of real-time, audio-video stuff in there, which is why like, we’re working on things like regional routing, with fallbacks. So you can get GPUs that are as close to users as possible. so you get like low latency for video streaming and whatnot. And then on the agent side, it’s,

Akshat [00:41:52]: We’re still working very closely with our customers because stuff is changing so fast in terms of what they need. And, I think beyond sandboxes and persistent file systems, there’s a lot of other things people will need from this agent stack as they build production agents. So yeah, we’re thinking about those other things that fit in there.

Swyx [00:42:13]: I want to ask what the other things are.

Akshat [00:42:15]: Yeah. I probably should share right now.

Swyx [00:42:17]: I think-- I think, okay, so, I do think a lot about the principal components of cloud, and you do talk about compute storage networking.

Akshat [00:42:25]: Yeah.

Swyx [00:42:25]: Because so far for me, it’s fine. so far for the. the first couple generations of cloud, it’s fine. What’s different, qualitatively different about agents that you need some new permission level? Like a lot of people, okay, and I’ll just kinda spew tokens at you until it like hopefully sparks something.

Akshat [00:42:43]: Yeah.

Swyx [00:42:44]: Like the new level now is whatever Claude Code does, which is dangerously scope permissions or like allow list by command or like whatever, right? And sometimes they’re like, “Well, okay, we have like this adaptive thinking mode where like, just trust me, bro. I will make the calls for you.” Is that it? like mediated permissions.

Hard Guardrails vs. LLM-Mediated Permissions

Vibhu [00:43:03]: Now you’re looping it with a goal and letting it roll.

Akshat [00:43:06]: Yeah, I’m, I’m skeptical of LLM media permission for stuff that is at the sandbox level because you do want hard boundaries.

Swyx [00:43:16]: Yeah.

Akshat [00:43:16]: Otherwise, someone can exfiltrate stuff.

Swyx [00:43:20]: But like

Akshat [00:43:20]: Yeah

Swyx [00:43:20]: Maybe that’s old school thinking. Maybe we’re the dinosaurs.

Swyx [00:43:23]: Maybe the AI OS or the LLM OS is really the kernel is a goddamn LLM.

Swyx [00:43:30]: Like it makes you feel uncomfortable.

Akshat [00:43:31]: Yeah, I’m, I’m told

Swyx [00:43:32]: But that’s what trusting the LLM is. Like imagine a spherical cow perfect LLM.

Akshat [00:43:36]: Right.

Swyx [00:43:37]: That it.

Akshat [00:43:39]: Maybe.

Swyx [00:43:41]: I wanna test the boundaries, right?

Akshat [00:43:42]: Yeah.

Swyx [00:43:42]: Like, and I don’t believe that, but I wanna see where I’m wrong ‘cause that’s, that’s the consensus.

Akshat [00:43:49]: Yeah. I think you always need hard guardrails when you want, And you can pair those with softer guardrails, right? And that’s gonna be a lot of mediated.

Managed Agents and Specialized Sandboxes

Swyx [00:44:00]: There. I’ll also get you a end with a couple of your commentary on like the ecosystem outside of Modal. Manage agents. Everyone has one. Gemini, OpenAI, Claude, very useful for you, but also like it is their way of starting to edge into your space.

Akshat [00:44:17]: Yeah.

Swyx [00:44:17]: What’s going on?

Akshat [00:44:19]: Yeah, we’re, very excited to partner with Anthropic and some of the other foundation labs, will not name who we’re also working with. the way we see it is the manage agent thing is a great place to start if you’re starting out building an agent and, But then when you get to, building something more production grade, like you’re a company that’s like Ramp that’s building their own, Ramp also runs their accounting agent on us, so their external-facing agent. You need a lot more control over, your compute primitive on things like, what sort - how do you persist different files that the agent has access to, and how do you snapshot and restore? How do you control the networking? maybe you want GPUs. When you get to that point, you kinda want, a specialized sandbox provider, that gives you those things, and that’s the role that we are trying to play.

Swyx [00:45:15]: Yeah

Akshat [00:45:16]: We don’t really have an opinion on the harness, whether it runs - it’s a cloud-managed agent, and you hook it up to Model Sandbox, or you run the harness in Model Sandbox. We’ll see where people converge with that.

Swyx [00:45:26]: Yeah. Do you any opinions on like the meta harnesses, or just another layer on top of these things?

Akshat [00:45:31]: You mean like the OpenPipe

Swyx [00:45:33]: OpenPipe is one. I think Vercel had one, which I can’t remember the name of right now. Fredshot had one. and then, to me, most recently was Data Databricks that had Omnigen. All these are meta harness. Like it’s kinda pseudo agent cloud type things.

Akshat [00:45:50]: I personally have not played around with them.

Swyx [00:45:53]: Yeah.

Akshat [00:45:53]: Build agents with them.

Swyx [00:45:54]: Everything’s bullish Modal, as long as it consumes more infra.

Akshat [00:45:57]: That’s why we’re focusing on the infra layer. It’s somewhere where our, relative competence is and, also it’s a hard problem to solve.

Swyx [00:46:06]: Yeah. I will say like just generally reflecting on that, I don’t know if - if there’s other topics on Modal, but like just generally reflecting as an infra person, not as intense as you, but in that field, this has like been the most exciting time in infra. Like it was boring for a while, and you couldn’t really get people excited about data infrastructure. Like Eric would get on Data Console, everyone just watched the video and like say, “Look at how many sandboxes I can spin up,” and no one gave a crap.

Why Infrastructure Became Exciting Again

Akshat [00:46:39]: Yeah.

Swyx [00:46:40]: And like now everyone gives a crap.

Akshat [00:46:42]: That’s true. It is a very exciting time, and I think a lot of that’s driven by just the amount of scale all of this stuff needs.

Swyx [00:46:50]: I think the, like a lot of your initiatives or a lot of your like product directions make sense in retrospect, which is like the best kind, but I wouldn’t necessarily have thought about it myself, which.

Akshat [00:47:00]: We need the predictions.

Swyx [00:47:02]: I think there’s a lot that you just don’t even see, right? Like you have the batch, you have the voice, you have the multimodal, but what else?

Akshat [00:47:10]: What else is coming up for us

Swyx [00:47:11]: Yeah. Where do you see things going?

Akshat [00:47:13]: Yeah. I, in general

Biotech, Robotics, and Non-LLM AI Workloads

Akshat [00:47:15]: It’s it’s clear that there’s there’s a huge shift happening. I think one thing that’s not as obvious to people because LLM inference gets talked about so much and is also we work a lot of companies that are, doing things like drug discovery and computational bio, like the Chai Discoveries of the world. Big things are probably gonna happen there. we work a lot of robotics companies that are putting robots in like active deployments and getting good results out of them.

Swyx [00:47:45]: Is there Air Gap Modal? Is there a version that is like prem air gapped whatever?

Akshat [00:47:50]: No. We,

Swyx [00:47:51]: You should cloud only.

Akshat [00:47:51]: Yeah.

Swyx [00:47:52]: Yeah. Okay. But yeah, so what you’re saying is like because you’re focused on primitives and they’re good primitives, you find use cases in all these kinds of things.

Akshat [00:48:01]: Yeah.

Swyx [00:48:01]: Probably diversifies you a little bit away from LMS all the time.

Akshat [00:48:05]: Yeah, absolutely. We’re, we’- our goal isn’t to only serve the LLM inference market.

Swyx [00:48:10]: There are a lot just on the website, the audio,

Akshat [00:48:12]: Yeah. We said both on

Swyx [00:48:14]: Computational bio images. Yeah, there’s a lot here. There’s QTA TTS, customizing. Oh, Chatterbox. there was customizing Whisper.

Akshat [00:48:24]: Okay. Yeah.

Swyx [00:48:25]: This screen reminds me of a fallen competitor, which Replicate.

Model APIs vs. Differentiated AI Products

Swyx [00:48:31]: What’s your postmortem on what happened?

Akshat [00:48:34]: This is one thing we’ve stayed away from is providing an API for models because I think providing model APIs is some of it ends up serving like a really hobbyist market, which is much less sticky.

Swyx [00:48:50]: Yeah.

Akshat [00:48:50]: And we’ve always wanted to build for companies that are building products and need more flexibility that’s not just an API.

Swyx [00:48:57]: Which you can build an API for a model and this is clearly what it is. But you - but what you’re saying, you can wrap it into a more fully functioning back end that you run.

Akshat [00:49:06]: Yeah. So all of our examples, it’s not that spin up this model, here’s an API token, use it. They’re all code.

Swyx [00:49:13]: Okay.

Akshat [00:49:13]: And so the point is that this is just an example.

Swyx [00:49:16]: Starter code.

Akshat [00:49:17]: Yeah. But you can tweak it however you want.

Swyx [00:49:20]: Yeah.

Akshat [00:49:21]: And if you’re like a company building a product, like, computational bio whatnot, yeah.

Swyx [00:49:26]: I guess I’m trying to tease out for listeners

Akshat [00:49:28]: Yeah

Swyx [00:49:28]: When does it stop becoming, oh, you’re just an API call and you’re just a wrapper on API to becoming what you call a product, right?

Swyx [00:49:36]: Like, what is that layer? Like what-- Like, more lines of code, but like beyond that, what is the substance that people add that qualifies it to be something more?

Akshat [00:49:46]: I think there’s a little bit of like a selection effect of like a lot of the companies who do wanna get deeper into that level are probably building something that’s more differentiated. And, I think, an example is like - with LLM inference, originally we, worked with companies that were building their own post-training frameworks or they were, - Ramp early in the day was training their own tokenizer and like swapping out the tokenizer in Llama and whatnot. I’m not saying that’s, that successful, in that case. But a better example is like, let’s say Suno. because Suno, does not use Modal for training.

Swyx [00:50:26]: Mikey on the pod. Yeah.

Akshat [00:50:27]: But they use Modal for all their inference and that’s because they have like a custom-- They have completely custom model architecture and that means that they have to be at the code level and tweak things that are not, just an API.

Swyx [00:50:41]: It’s interesting as well, like we had, Ethan, most recently on the xAI Groq team make a prediction that like the next tier in video gen is not a better video model, it’s a better model or agent that orchestrates video models.

Video Agents and Production Workflows

Akshat [00:50:56]: Oh, interesting.

Vibhu [00:50:56]: Language model backbone that can use tools

Akshat [00:50:58]: Right

Vibhu [00:50:59]: And write code.

Akshat [00:51:00]: Like, yes, I can make my second video or my second video from Groq, but I want my minute video.

Akshat [00:51:06]: And I’m not going there through normal video gen.

Swyx [00:51:10]: Yeah, that’s interesting. I - So we have GPU sandboxes and recently have seen a few companies doing agents that do video manipulation or,

Akshat [00:51:22]: Yeah. Give it FFmpeg and just do it.

Swyx [00:51:23]: Run FFmpeg. But like

Akshat [00:51:25]: That’s not enough.

Swyx [00:51:25]: Yeah.

Akshat [00:51:26]: You need to give it Adobe.

Swyx [00:51:27]: Yeah, I hadn’t put it together with like it would be a video production thing. in my mind these things were going more towards editing

Akshat [00:51:36]: Yeah.

Vibhu [00:51:36]: Well, shout out Mantis.

Akshat [00:51:37]: I think about this a lot.

Swyx [00:51:38]: .

Akshat [00:51:41]: Yeah. Sorry.

Vibhu [00:51:41]: Luma. Luma Agent is a version of this for video production, but it’s a off.

Swyx [00:51:46]: I was gonna get your quick takes, on some other stuff that happens

Gitpod/Ona, CI, and Runtime Sandboxes

Swyx [00:51:50]: In recent news and just-just see if you have anything interesting. Gitpod, very like-- somewhat like, different market. They’re in like the CI/CD market, but technically very impressive. I don’t know if you’ve like taken a real look at them.

Akshat [00:52:03]: Yeah. we’ve, - People on our team have talked to the Gitpod team and they’- they’re technically very strong.

Swyx [00:52:10]: Yeah.

Akshat [00:52:10]: I - We’re, we’re very bullish at Modal on the CI market as well because

Swyx [00:52:15]: Okay

Akshat [00:52:15]: There’s, there’s more agents, coding agents.

Swyx [00:52:18]: Yeah.

Akshat [00:52:19]: They’re gonna run a lot more CI and the primitives there can be much better.

Swyx [00:52:23]: I think there’s a lot of wasted CI.

Akshat [00:52:25]: Yeah.

Swyx [00:52:25]: So is it just like let’s filter? Like what is the highest order bid here in improving CI for agents?

Akshat [00:52:32]: Well, there’s a lot of wasted time in CI on like

Swyx [00:52:36]: Preparing

Akshat [00:52:36]: Preparing your artifacts and like, getting you to the preparing your dependencies and whatnot.

Swyx [00:52:44]: Oh.

Akshat [00:52:44]: And, like build systems help with that. But like if you have primitives that are like memory snapshot and restore, can you just run CI more efficiently?

Swyx [00:52:55]: Oh, okay. Okay. Okay. Interesting. Yeah. another form of like, demand compute.

Akshat [00:53:02]: Yeah, exactly.

Swyx [00:53:03]: Yeah.

Akshat [00:53:03]: It needs the same again, platform.

Swyx [00:53:06]: Yeah. So, for those who don’t know, Gitpod rebranded to Ona.

Swyx [00:53:09]: It was like there was this whole thing. I - I like semi-sounded the alarm at Cognition. I was like, “You should take these guys seriously because their infra is very good.”

Akshat [00:53:17]: Yeah.

Swyx [00:53:18]: And but, then they join OpenAI and, presumably we’ll, we’ll see Codex Cloud from the Ona team.

Swyx [00:53:26]: Like which I think would be very strong. - To me, like teams like that can set up the networking and like the secure boundaries for like, and your like agents to have their own cloud each, effectively is what you’re doing and I’m just trying to draw the analogy or the differences if you have studied them. Like what is the philosophical difference?

Akshat [00:53:47]: My sense is maybe they didn’t go after the right market at the right time because - I guess also got lucky with like agent use cases really taking off and, needing, like more of like a sandbox shaped thing than like, my understanding is, yeah, Gitpod

Swyx [00:54:06]: Really sandboxes work

Akshat [00:54:07]: Never mind

Swyx [00:54:07]: Like CI/

Akshat [00:54:08]: Yeah

Swyx [00:54:09]: Is sandboxes.

Akshat [00:54:09]: Yeah.

Swyx [00:54:10]: It’s just like build time sandboxes versus runtime sandboxes and it turned out runtime was better.

Akshat [00:54:15]: Right. And the difference there is runtime sandboxes have a different configuration surface of like how you configure images, how you like attach like storage

Swyx [00:54:25]: Yeah. It’s it’s fascinating. Other people, Astral also OpenAI.

Python, TypeScript, and the Future of SDKs

Swyx [00:54:30]: Also like Python tooling ecosystem people. Are you still bullish build- building on top of Python? Also recently Modular also got bought by Qualcomm. Just any of your takes there?

Akshat [00:54:43]: Yeah. we had Python as our first SDK language because that was the language that people did data and ML in. I now have Go and TypeScript SDKs as well. and our runtime is completely language- It is written in Rust, but it’s it’s not tied to Python by any means. We haven’t seen-- I think with like inference and training stuff, people are still very Python and the interesting thing with like the agent stuff is people use our TypeScript SDK a lot more because they’re not doing anything that needs ML.

Akshat [00:55:13]: I don’t think we’ll have to go beyond that super soon

Swyx [00:55:16]: Yeah

Akshat [00:55:16]: ‘cause Python and TypeScript is still Dominant.

Swyx [00:55:19]: The last two languages in the world.

Akshat [00:55:21]: Yeah.

Swyx [00:55:21]: That’s it.

Akshat [00:55:22]: Well, English and prompting is the fourth language.

Swyx [00:55:25]: English and prompting. I occasionally talk to people who try to build new languages. They’re like, - Even, what’s his face? Brett Taylor, who’s chairman of OpenAI was like, “We need a new language for LLMs.” So no one has come across one, and I keep looking. Python and TypeScript - You have a lot of data plus, but then also they are very imperfect as just as languages themselves. Then my close is, I think Modal used to be a big bet on developer experience.

Agent Experience as a Company-Building Wedge

Swyx [00:55:52]: And you’ve pivoted the team to agent experience. Is it like the way now, like, do - do, - can entire companies and unicorns, multi-unicorns be built on just having better agent experience? Do you need something else?

Akshat [00:56:05]: It’s a big part of our identity. it’s not just, like the very tactical, how does an agent use the CLI, but it’s also how easy is it to spin something up? Like, what is your iteration time when you wanna spin up a new service and, you wanna get something going in prod? in practice, that matters a lot, to people. And, I think it will continue to matter. Like, people are building stuff even faster, and if you give them ways to do it quickly not have overhead, then.

Swyx [00:56:37]: I think the debate for me has been, do you do anything differently that is, like, very fundamentally different for developer experience versus agent experience?

Swyx [00:56:44]: You seem to be on the side of they’re, they’re like this. They’re like cosine

Akshat [00:56:48]: Yeah. We also have a blog post on that.

Swyx [00:56:49]: Cosine similarity on, like, zero point nine or whatever.

Akshat [00:56:53]: Yeah. pretty much it’s the main shift for us has been, as I said, like, we built this, benchmark, Modal Bench, to see where agents are lacking

Swyx [00:57:02]: Yeah

Akshat [00:57:02]: Literally add surface areas to a product if they’re reaching for something, like maybe this should just be a CLI.

Swyx [00:57:09]: They halluc Oh, yeah. They hallucinate their own features.

Akshat [00:57:11]: Yeah. And sometimes it makes sense. Like if they’re reaching for this thing, it’s product feedback. Like, give it to them. And then, yeah, moving-- we used to only have, like, logs and metrics in our UI, just moving all those things to the CLI as well, so they’re accessible in that form.

Swyx [00:57:26]: Simple as that.

Closing: Modal Bench, AX, and Execution

Swyx [00:57:28]: Cool. Thank you so much. Yeah.

Akshat [00:57:29]: Yeah. Thank you.

Swyx [00:57:30]: This was great.

Akshat [00:57:30]: This was fun.

Swyx [00:57:30]: Yeah. It was a great update and, I can see why you guys have succeeded so much. it is really, focus, but also really good execution.

Akshat [00:57:39]: Thanks. we have a long way to go.

Swyx [00:57:41]: All right. Thank you.

Akshat [00:57:42]: Cool.

If you are at all interested in small molecule drug discovery, we think you will find this fascinating!

In our nearly two hour chat we cover:

* What is small molecule drug discovery, and why is it hard

* Structure prediction as a hotbed of innovation in AI algorithms

* How advances in AI elsewhere have enabled stepwise improvements in predictive power

* How the community benchmarks are essentially calling AI slop good enough

* The Genesis flagship model (PEARL) can routinely hit a threshold that is necessary for real-world applications

* New agentic workflows enabled by these highly accurate models

Read on for more, and also some personal thoughts on the future at the end.

The coolest diffusion research is happening at Genesis

Sergey Edunov came to Genesis from Meta where he led Llama 2 training and Llama 3 pretraining. Sergey was a former physicist who thought he was done with physics after many years of training LLMs. Then, he discovered Genesis, and was blown away with all the novel architecture work they’ve been developing.

It probably surprises no one that modern LLM research has not resulted in fundamentally novel or exciting updates in architectures since almost the advent of the transformer — the entire field is using variants on the same idea that came out in the original “Attention is all you need” paper. Sure, some were quite useful (mixture-of-experts in particular allowed for the massive model paradigm we’re at today), but there was very little conceptually exciting.

“We sort of had to wait for the right primitive to get created, and that turned out to be diffusion… Actually, some of the most innovative diffusion research that’s happening in our field is happening in 3D structure prediction right now.” — Evan Feinberg

The field of 3D structure prediction on the other hand has been a hotbed of research. Genesis’ recent model PEARL (Place Every Atom at the Right Location) is able to understand protein flexibility, and model not just where the ligand goes, but also make small adjustments of the protein so that the two fit better than either alone. The field knew this was missing for a long time, but it was really hard to model until now.

Agentic Discovery

What makes this problem so hard? As Sergey points out, there are 10^60 possible drug-like small molecules. You’ll never be able to search them all, and trying to find the good ones is something like finding a needle in a haystack — except everything except your needle is dangerous.

“There are 10 to the 60 drug-like small molecules in the universe… it’s like finding a needle in a haystack, where everything except your needle is very, very dangerous.” — Sergey Edunov

“Or finding hay in a needle stack might be a more apt analogy.” — Evan Feinberg

Trying to solve the multi-parameter optimization problem is even worse. What makes a strong binder and a molecule with good “ADMET Properties” are oftentimes at tension with each other. For example, a good binder is likely greasy, but a greasy molecule is likely insoluble so it won’t enter the bloodstream and get to where it needs to go!

Genesis’ advances in generative AI have now pushed them beyond the threshold where they believe agentic drug discovery loops are finally possible. We all remember the early days of LLMs. They were great chatbots but terrible agents, as small errors compounded rapidly into uselessness. As LLMs got better, the usefulness of agents rapidly improved. Evan and Sergey argue that their models at Genesis recently passed a similar threshold. Their internal agentic drug-discovery system (code named SAPPHIRE) can now iterate like a chemist: look at and reason about poses, form hypotheses, read literature, use internal tools, create candidates for the next iteration. Combining this with automated lab partnerships like the one Genesis has with Incyte, we’re rapidly approaching a time of drug discovery agents running 24/7 making/testing new molecules. Exciting times!

Benchmark crisis: Everyone’s favorite benchmark is slop

One surprising point that isn’t talked enough about: the academic field of “co-folding” has settled on a benchmark value of “2 Angstrom RMSD” as a metric for a “good pose”. Evan does not mince words: this threshold is just bad. Perhaps even deceptively bad. For many strong binders, there’s a very clear pose, one that you can even directly resolve in the PDB electron density! And yet, with a 2Å RMSD threshold, you can get the pose quite wrong in ways that might even mislead a medicinal chemist. For example, flip around an aromatic ring, and everything looks reasonable, but you’re no longer modeling the right interactions.

Evan makes the strong claim that 1Å RMSD is really the threshold necessary to ensure the core of the molecule is sitting where it needs to be, and models all interactions.

“If your model is sitting at 1.8, 1.9 Angstrom RMSD, that’s slop, most likely.” — Evan Feinberg

As a simple example, he points out hydrogen bonds which are responsible for many of the most important interactions in protein-ligand systems. Hydrogen bonds only have a 0.6Å range to be valid! Clearly if you’re accurately resolving all H-bonds, you generally have to be doing much better than the 2Å threshold.

This is clearly a hard-fought lesson for Evan and Genesis. In their opinion, the community is stuck on these benchmarks because academics developing methods were not users. Evan does see signs of life, with the use of new metrics such as lDDT for co-folding. Hopefully soon the community can agree that “1.8Å RMSD is slop”, and start hill climbing on this much harder task.

For a more thorough exploration of the weaknesses in conventional benchmarks, see the PEARL technical report.

PEARL tops OpenBind

Which makes what happened next all the more striking. Near the end of the podcast, we talked about a recent “proof-is-in-the-pudding” moment for Genesis — evaluating their PEARL model on a recently released OpenBind benchmark. This benchmark featured 802 never before seen co-complexes on a target protein EV-A71. This target seems almost custom-chosen to give most classical docking methods a problem. When a ligand binds to the main binding site, the protein moves around to close off the path the ligand used to enter the binding pocket. This process, known as “induced fit” is notoriously hard for traditional methods to model. The tradeoff is easy to understand: treating the protein as a static structure, it becomes difficult to place a ligand in a binding pocket. Treat the protein as dynamic, and now you have to simulate complicated processes that take a long time to resolve.

PEARL was able to model the induced fit of the ligand without running long MD simulations. Across the different evaluation metrics, PEARL came out not just ahead, but oftentimes well ahead of any public model. A truly impressive result.

“Where PEARL was exceptionally good is figuring out how to move this loop. We are basically correct for every single pose.” — Sergey Edunov

Even more exciting, this was done without any fine-tuning, or using any data on the target or homologous targets — the template PDB was released after PEARL’s training cutoff.

Where does co-folding go now?

As someone who has followed or participated in ML techniques for protein-ligand interactions for almost a decade, I was genuinely impressed with the results that Genesis has released recently. This has been many years in development, and I’m sure Evan and the team had many sleepless nights trying to get to this point. I also think other teams are making similar progress — both Isomorphic and Deep Origin have released results that seem spiritually similar and combine computation, wetlab data, ML, to achieve genuine predictive power that seemed impossible a decade ago. Sadly, all of the above are closed source so there’s no way to honestly compare them. Looking at the results I think there might be a time in the not so distant future where we can consider protein-ligand binding “solved”.

I sincerely hope that the academic community can take inspiration from these developments. Once you know something can be done, it’s much easier to execute. Still, I believe that the key enabler in all of the above was the tight integration of ML, large-scale computation, and real-world drug discovery applications. Sadly academia is just not structured in a way that makes such a development easy.

With those parting thoughts, we hope you give the podcast a listen!

Everyone is still talking about Satya’s Frontier Ecosystems post, but few have actually built a (now $175 billion) frontier ecosystem and cloud like our guests today.

From open-sourcing the layer above coding agents to rethinking databases for the agent era, Databricks cofounders Matei Zaharia and Reynold Xin are pushing the company beyond the lakehouse into a full data-and-AI operating system. In this episode, Matei and Reynold join swyx at the 2026 Data + AI Summit to unpack Omnigent, LTAP, Lakebase, agent security, open formats, Mosaic, and why databases may matter more than ever once AI agents start doing real work.

We go deep on Omnigent: Databricks’ open-source meta-harness for combining, controlling, and sharing agents across Claude Code, Codex, Cursor, Pi, custom agents, and internal tools. Matei explains why coding agents and enterprise agents run into the same problems: portability, collaboration, session history, security, spend controls, and the need for a common API above every harness.

Then Reynold walks through Databricks’ database dream: why CDC is brittle enough to joke that it means “continuous data corruption,” why HTAP has been the holy grail of database engineering, and why Databricks thinks LTAP gets most of the benefits by unifying the storage layer instead of collapsing every query engine. We also cover Databricks’ infrastructure scale, the culture behind rapid prototyping, the difference between tech and enterprise customers, Databricks vs Snowflake, whether vector databases should have ever existed, the Mosaic model strategy, Genie, AI Runtime, RL fine-tuning, and the thesis that traditional software gets rewritten once the data is in the right place and agents sit on top.

Databricks began as a company for the big data era. The origination of Spark from the Berkeley AMPLab which eventually turned into the product Lakehouse convinced enterprises that they didn’t need a separate data lake, warehouse, ML platform, and governance layer. They just needed one open foundation where all of their data could live and be reasoned over.

Since then a lot has changed, but data has only become more important. Data is no longer something you keep track of and analyze ad hoc, it’s the necessary context agents need in order to act. So the framing has shifted from “where do we put all of our data?” to “how do we expose the right slice of state, history, permissions, and business logic to an AI system at the exact moment it’s doing work?”

If frontier model performance becomes commoditized, the durable advantage then becomes the company-specific context around them: proprietary data, governed access, operational state, transaction logs, workflows, and feedback loops. Which makes Databricks positioned perfectly.

Now coming fresh off the Data + AI Summit 2026, the company is moving just as fast to keep up, announcing Genie One, Omnigent, LTAP, and many more, indicating a central mission in its newer work: Databricks is trying to become the operating system for enterprise agents.

Models are getting good enough, but agents are only useful if they have the right context, permissions, memory, state, cost controls, and access to live business data. Fundamentally it appears that significantly better model performance in production is a systems problem, one that data guys like us are remarkably well prepared to solve!

We discuss:

* Why Databricks built Omnigent as a meta-harness above existing AI agents

* Why coding agents and custom enterprise agents need the same infrastructure

* The common API for agent sessions, files, streams, tool calls, and cancellation

* Why persistent sessions, cloud sandboxes, sharing, search, and collaboration matter

* Why Databricks open-sourced Omnigent instead of keeping it proprietary

* Databricks’ internal agent usage, cloud sandboxes, and coding workflows

* The scale of Databricks: 50–60 million virtual machines a day and exabytes before breakfast

* Why agent security needs contextual and stateful policies

* How an agent could read confidential docs, install a compromised npm package, and leak data

* Why spend control matters when an agent can burn $500 reading logs

* Startup opportunities around coding-agent analytics, quality, skills, and spend

* LTAP, Lakebase, and why Databricks wants to rethink the database stack

* OLTP vs OLAP, CDC, and why data pipelines break at 3 a.m.

* Why HTAP has historically been the holy grail of database engineering

* Why Databricks thinks LTAP is “HTAP done right”

* How writing transactional data into column-oriented formats changes analytics

* Why agents need live operational context from databases, not just telemetry

* How Databricks prototypes strategic systems without endless process

* Enterprise vs tech customers, governance, procurement, and DIY culture

* The “second system syndrome” risk of rewriting a database engine

* Building a database engine from a decade of traces and quadrillions of data points

* Why vector databases should never have been a separate category

* Why open formats and AI changed the race with Snowflake

* The Mosaic story, DBRX, Genie, document parsing models, and specialized model training

* Why model customization and RL fine-tuning may become mainstream

* Why “get the data there, slap some agent on top” may rewrite traditional software

Matei Zaharia

* LinkedIn: https://www.linkedin.com/in/mateizaharia

* X: https://x.com/matei_zaharia

Reynold Xin

* LinkedIn: https://www.linkedin.com/in/rxin

* X: https://x.com/rxin

Databricks

* Website: https://www.databricks.com

* X: https://x.com/databricks

Timestamps

00:00:00 Introduction

00:02:22 Omnigent and the Agent Infrastructure Layer

00:08:39 Agent Clouds, Common APIs, and Open Source

00:16:52 Databricks Scale and Internal AI Workflows

00:18:03 Agent Security, Governance, and Spend Controls

00:27:34 LTAP and the Database Dream

00:30:30 CDC, HTAP, and Why Data Pipelines Break

00:34:05 Lakebase, Parquet, and Live Data for Agents

00:36:47 Databricks’ Culture of Fast Prototyping

00:43:40 The Dream Engine and Rewriting the Database Stack

00:51:02 Vector Databases, Query Engines, and LTAP

00:52:36 Databricks vs Snowflake

00:57:48 Mosaic, DBRX, Genie, and Specialized Models

01:03:11 Context, AI Runtime, and RL Fine-Tuning

01:06:15 Why Data + Agents May Rewrite Software

01:07:09 Closing Thoughts

Transcript

Introduction: Databricks, Data + AI Summit, and Founder Dynamics

Swyx [00:00:00]: Matei and Reynold from Databricks, welcome to Latent Space.

Reynold Xin [00:00:06]: Hey, thanks for having us.

Swyx [00:00:07]: Yeah.

Matei Zaharia [00:00:08]: Yeah, thanks so much.

Swyx [00:00:09]: thanks for taking time out. You have your Databricks, Data AI Summit going on. You were just telling me how the first summit that you guys ran was just 50 people

Reynold Xin [00:00:17]: Yeah, it was

Swyx [00:00:17]: in Berkeley

Reynold Xin [00:00:18]: little meetup at Berkeley, I think

Matei Zaharia [00:00:19]: Yeah

Reynold Xin [00:00:19]: put together

Matei Zaharia [00:00:20]: We were doing these tutorials and, yeah, just teach people Spark.

Swyx [00:00:23]: Yeah. obviously now it’s like, I think like the headline number’s like 100,000 people around the world, 30,000 in person.

Swyx [00:00:30]: it’s a crazy

Matei Zaharia [00:00:31]: Amazing

Swyx [00:00:31]: community. Well, I just saw the keynote.

Swyx [00:00:35]: Ali’s just. Did was it obvious or that back when that Ali would be, like, such a great, like, CEO? Like

Reynold Xin [00:00:42]: Oh

Swyx [00:00:42]: such a great presenter?

Reynold Xin [00:00:43]: What do you think?

Matei Zaharia [00:00:44]: I think among our group of founders it was clear that, I think he’d be the best at this.

Swyx [00:00:50]: Yeah.

Matei Zaharia [00:00:50]: And yeah, it turned out great. And he’s, he’s ramped up on so many topics growing a company. He would just go in and, like, study it and, be talk to all the experts. Like, even if he can’t hire the person, learn enough about, like, finance and sales and whatever it was, and, and go from there. Yeah.

Swyx [00:01:09]: Yeah.

Reynold Xin [00:01:10]: he’s obviously very high IQ and a very high EQ, but it wasn’t. Like, Ali today is quite different from Ali from, like 10 years ago. I think there’s a lot of work that he put in to, get to this point.

Swyx [00:01:20]: Yeah. no, to me the most appealing thing about him is that he’s funny. And like, it, it’s, it’

Matei Zaharia [00:01:26]: It’s true, yeah

Swyx [00:01:26]: it’s hard to make jokes about, data warehouses

Reynold Xin [00:01:30]: About serious topics

Swyx [00:01:31]: security

Matei Zaharia [00:01:32]: Yeah

Swyx [00:01:32]: what have you.

Matei Zaharia [00:01:33]: Oh, yeah. That’s for sure.

Swyx [00:01:34]: Yeah. So you guys launched a whole bunch of things. I’ll, I’ll just name check briefly, the stuff because we’re not gonna cover everything. Omnigentt, your baby. LTAP, your baby, your dream engine.

Swyx [00:01:47]: we’re also gonna cover Genie, cover CustomerLake, you acquired Panther

Matei Zaharia [00:01:52]: Yeah

Swyx [00:01:52]: Open Sharing, and there’s Unity AI Gateway. A lot of these, I think, like, are things that you would expect a Databricks to do. It’s, it’s like part of the roadmap. Everyone in your category has similar things. But I think, probably the two of you are leading the two most unique and differentiated initiatives

Omnigent and the Agent Infrastructure Layer

Swyx [00:02:09]: on, in the landscape. Maybe we’ll start with, Omnigentt we’ll, we’ll, we’ll, we’ll go into it. I do think that a lot of people are exploring this meta harness concept.

Matei Zaharia [00:02:21]: Yeah, totally.

Swyx [00:02:21]: What led you to it?

Matei Zaharia [00:02:22]: Yeah. There were a couple of, like, converging lines, which I think is a good sign that you need something new. So on the one hand, there’s all the coding agent info internally. We have really great, dev infra team. they built something called Isaac, that’s like a wrapper on Claude Code and Codex, and, lets you use them either on the web in, like, sandboxes or, just on your dev machine or on your laptop or whatever. And then, they were adding all kinds of stuff there. And we saw all the more advanced engineers like, were building their own workflows with tons of agents, and they were building their own UIs and stuff on top or even on top of that. And then the other one was, like, us building agents. We ship this, like, data science agent called Genie on the research team, which I lead. We also build a lot of internal ones for various things, and then we have all the customer ones. And all of them running into this thing of like, “Oh, I need to switch model and harness and so on,” every few months. Plus the agent is, like, completely useless if you can’t share sessions with someone and have history and have search and all this, like, layer on top of it for collaboration. I thought a bit about it from both contexts and, at first people thought it was weird. They’re like, “Why are you doing coding agents and custom agents in the same thing?” But I said it’s, it’s the same problems and, you just wanna build the stuff that lets you deliver the agent, maybe control it if you care about security, and, make it portable across things. And then we prototyped some things as experiments. We saw, yeah, we can make it work, and then we built that for real.

Swyx [00:04:06]: I’m wondering if this let’s call it architecture

Matei Zaharia [00:04:11]: Yeah

Swyx [00:04:11]: maps to anything in your careers in the past. like I always think about how a lot of things just tie back to operating systems.

Swyx [00:04:18]: A lot of operating

Matei Zaharia [00:04:19]: Yeah

Swyx [00:04:20]: systems tie back to databases,

Matei Zaharia [00:04:21]: So

Swyx [00:04:21]: or the other way around

Matei Zaharia [00:04:22]: so the thing, I do think it ties a lot to, like, network protocols, internet protocol. we also

Swyx [00:04:29]: Communication between entities.

Matei Zaharia [00:04:30]: Yeah. We did stuff with, like, data sharing also, which is probably, most viewers probably won’t know unless they’

Swyx [00:04:36]: Yeah, open protocol is the term.

Matei Zaharia [00:04:37]: Yeah.

Swyx [00:04:38]: Open sharing. Open sharing.

Matei Zaharia [00:04:38]: Open sharing.

Swyx [00:04:39]: Yes.

Matei Zaharia [00:04:39]: Yeah. So it’s like you have a company, you maintain some table, like let’s say like a Walmart or something. They have like the, inventory and what’s been sold in each store. And then you also have suppliers, and they would love to produce more things and ship them, like, exactly the moment you need them. So they would love, like, real-time access to your table. So instead of like sending emails around or Excel sheets or phone calls, why can’t you share like a view of that table in real time with them? Then they query, they, join it with their data, and they decide what to send. So it’s one of these things where you, like you might ask like today since we can vibe code anything so fast, why do we even need to design like protocols or APIs or software? Why can’t you just vibe code things on demand? But for this type of interoperability where multiple parties that are moving at different speeds are building stuff and you still want some layer on top to coordinate, you do wanna design it and build it. So it reminds me of that, like agents talking to each other and, users talking to agents and tools.

Agent Clouds, Cloud Sandboxes, and Keeping Sessions Alive

Swyx [00:05:42]: Reynold, any other comments alternative viewpoints?

Reynold Xin [00:05:46]: I think, by the way, we had a debate on exactly which set of benefits would, matter a lot, and I think around the time we decided to do this thing I was telling Matei, “Hey,” it just happened to be there’s a particular week that I was coding nonstop

Swyx [00:06:00]: from the moment I woke up to, like, the moment I went to bed, I was, like, looking at my Claude sessions, my Codex sessions. And one of the things that was particularly annoying was having to keep my laptop open.

Swyx [00:06:12]: I was driving to a doctor’s appointment, and I remember because I wanted to make sure the whole thing continues working.

Matei Zaharia [00:06:18]: But by the way, it’s so comforting to hear you say that because I’m like, “I don’t know if I’m a clown and I’m doing this or like.”

Swyx [00:06:25]: Yeah. Like honestly, I was driving and I was tethering my laptop to my phone.

Matei Zaharia [00:06:29]: huh.

Swyx [00:06:29]: Keeping it on the side. Whenever I hit a red light, I started looking at what’s going on my laptop.

Matei Zaharia [00:06:35]: Yeah.

Swyx [00:06:35]: And I just felt that was ridiculous.

Matei Zaharia [00:06:37]: Yeah.

Swyx [00:06:37]: It felt like we went back to the dark ages

Matei Zaharia [00:06:39]: Yeah

Swyx [00:06:40]: programming. the productivity you gain from all this coding age is amazing, but, yeah.

Matei Zaharia [00:06:45]: Have you heard of cloud?

Swyx [00:06:47]: Yeah.

Swyx [00:06:48]: It was crazy to me.

Matei Zaharia [00:06:49]: Oh, the thing you were working on was the sandboxes or was this before that?

Swyx [00:06:52]: It was a sandbox.

Matei Zaharia [00:06:53]: Okay.

Swyx [00:06:54]: I was work

Matei Zaharia [00:06:54]: So you were in

Swyx [00:06:55]: So I was approaching from a very different angle. I wanted to, “Hey, we’re gonna have cloud sandboxes that doesn’t shut down. You can get one very quickly,” but not just for running agentic sessions.

Matei Zaharia [00:07:06]: Yeah.

Swyx [00:07:06]: It’s also for running development. So I was personally building that week, and through building that, I ran into all these issues, and then I wrote

Matei Zaharia [00:07:15]: Yeah

Swyx [00:07:15]: a document for Matei, it’s like, “Here’s my wish list of what the actual environment should do.” And I think he ended up almost implementing

Matei Zaharia [00:07:22]: Yeah

Swyx [00:07:22]: every single one of them.

Matei Zaharia [00:07:23]: Yeah, I remember Reynolds saying, ‘cause my first prototype of this had just chats with your agent and he said, “I have to be able to open a shell, like my own shell and like list files and like tail them and stuff.” So

Swyx [00:07:36]: So SSH into a mainframe.

Matei Zaharia [00:07:37]: Yeah. it has that now.

Swyx [00:07:39]: Tailing my log.

Matei Zaharia [00:07:40]: Yeah.

Matei Zaharia [00:07:41]: Yeah.

Swyx [00:07:41]: And also another thing I think I asked was, I had. I still use cursor for the sole purpose of rendering markdown files.

Matei Zaharia [00:07:48]: huh. Yes.

Swyx [00:07:49]: So I said, “If you just give me a way to see my markdown files and render

Matei Zaharia [00:07:53]: Yeah

Swyx [00:07:53]: them properly, I don’t need a separate tool anymore.”

Matei Zaharia [00:07:55]: Yeah.

Swyx [00:07:56]: And I think you also built that in.

Matei Zaharia [00:07:57]: Yeah, we, yeah, we did that, yeah. Yeah, we had a lot of engineers building, their own vibe coding setup. But then the other thing they all said is like, “Hey, I built something that’s amazing for me, but, like, no one else on the team can use it ‘cause I don’t have a server to collaborate.” And this is why we tried to set up, Omnigent, so you can have a server and have the security, set up in there. So, like log in with Google or whatever and, like securely share stuff. which. And that’s where we’ve seen a lot of other agents like hit things. Like people think they prototyped an awesome agent, but it’s not allowed to connect to like some really important data or whatever because of the security team.

Omnigent Architecture, Open Source, and Common APIs

Swyx [00:08:38]: Yeah.

Matei Zaharia [00:08:38]: So yeah.

Swyx [00:08:39]: Yeah. At this point, so for those watching along on YouTube, we’re gonna putting up a image of the structure here, and we can talk a little bit of the architecture. I think I just want to have people understand, ‘cause like when we’re talking about software, it can be very abstract and like here is what we’re talking about. You’ve worked out in open source this entire platform and there’s a runner component and server component with a uniform API that you’ve, you’ve figured out. any other element and obviously you can plug in all this, persistence layers and compute layers. This is a whole cloud. It’s an agent cloud.

Matei Zaharia [00:09:12]: Yeah. It’s, it’s got these components to work with it. The, a lot of the action happens like on the machine where you deploy your agent too. So whatever you’ve got on there, you can run. But yeah, it’s, I think it’s the minimal thing you want to have hosted, like collaborative agents and to have that server. And one of the reasons we open sourced it is, anyone building agents, this gives them an app they can start with and customize, which we were seeing in Databricks too. Like someone would make a nice, agent app and then other teams would ask, “Oh, can I just use yours for my agent?”

Swyx [00:09:45]: Yeah, I think we had like five or six different agentic frameworks

Matei Zaharia [00:09:48]: Yeah

Swyx [00:09:48]: built by every different team. They do all do more or less the same thing. Yeah, you need to. people wanna take something that works in Forkit, and you might as well have something open source. Yeah, which also was another question, which is interesting for Databricks. Like what do you choose to open source? What do you choose to make it proprietary? It’s in. this goes back to Spark, right?

Matei Zaharia [00:10:05]: Yeah.

Matei Zaharia [00:10:06]: One, so one of the reasons to open source something is if you think it’s a layer that will there’ll be some network effect, it’ll benefit from many, people collaborating, on it. So, for example, with Spark, I don’t know if when Spark came out, we also focused a lot on letting you have libraries on top. So like there used to be different

Swyx [00:10:28]: Ecosystem

Matei Zaharia [00:10:28]: distributed computing engines for like machine learning and graph computation. We said they should all be libraries that you can compose. And we made it super easy to add connectors to data sources too. And then we benefit because, we don’t have the time to write like connectors to like, 1,000 like different databases and file formats, but we can just use the ones people make, and of course they benefit from joining, this thing. So that’s like one of these as it. Another way to think about it is like imagine, we our thing wasn’t open. We had some agent hosting thing, but it’s not open and then there is an open one. if you’re. Which one’s gonna win in the long run? So like here, because there is this benefit from like people writing integrations, it’ll be, it’ll be that. And then there are other things that like you just can’t, even deliver as open source that are things the company does. Like for example, how do you make sure you’re like streaming, jobs or your Lakebase database doesn’t like, lose all your data at night? Well, that requires an operational team that’s gonna sit there. There’s no way it has to be a service. So like we wanna make sure as a company we’re really good at those infra services and then we’re as open as we can in terms of like what you build on top.

Swyx [00:11:42]: speaking from a benefits, I think we are already seeing pull requests

Matei Zaharia [00:11:45]: Yeah

Swyx [00:11:45]: of all kinds of ecosystem integration, even though it was only released on Saturday.

Matei Zaharia [00:11:50]: Yeah, Saturday. Yeah. So someone

Swyx [00:11:51]: Let’s see, let’s see what’s going on. Yeah, you can look at the merge ones. I asked Sam Nigon this morning about

Matei Zaharia [00:11:59]: 400 merge already?

Matei Zaharia [00:12:00]: Yeah. I think Recent quite, I would guess around half are not from our team. but for example, someone added support for running it on Kubernetesrnetes. people added, many cloud sandboxes, so this can launch a cloud sandbox and run your agent in there, which is great for sharing too, ‘cause it’s not, like, on your laptop and someone’s, like, running scary code on there. so yeah, many startups have put those in, and, we expect to see more of them. We also have more agent harnesses already. Cursor, CLI, and Antigravity also.

The Modern Data Stack and the Emerging AI Stack

Matei Zaharia [00:12:34]: Yeah. That’s all, beautiful. And I, I feel like the last time this happens, there was the rise of the modern data stack.

Matei Zaharia [00:12:42]: I don’t know if it’s that useful. I’m, I’m curious in your postmortem.

Matei Zaharia [00:12:46]: I think most people

Swyx [00:12:47]: Agree

Matei Zaharia [00:12:47]: will agree that it is finally dead. but maybe this arises to a new modern AI stack that, like, does the same thing.

Matei Zaharia [00:12:52]: I don’t know.

Reynold Xin [00:12:54]: I think the modern data stack was a pretty useful thing, probably even up until this day. I think what, maybe for the audience who don’t understand the history, I think the modern data stack is effectively decomposed into you need a layer to ingest the data in, you need a layer to transform your data, and then all of this are run, and then you need a layer to maybe visualize your data. And all of this runs on some data warehouse, or later on, as we’re doing data warehouse or lakehouse.

Reynold Xin [00:13:21]: I think that concepts are all very powerful and very useful. They enable a lot of workloads. What people eventually run into is a question of unification and consolidation is, hey, do you really need to chop all this into different pieces and work with so many different vendors and platforms in order to get, like, a very simple visualization done, right? So I think, like, over time, everybody started realizing that customers are pushing us. We started, we can realize that, so we started building more and more capabilities and trying to consolidate. And at the end of the day now, customers don’t have to worry about having me hook up five different systems in order

Matei Zaharia [00:13:55]: Yeah

Reynold Xin [00:13:55]: produce a chart. But the. I think, honestly, something like this is probably happening, in how many different frameworks do you want to hook up together in order to produce, like do a very simple agent.

Matei Zaharia [00:14:06]: Just to be clear, I would say the core of this is this common API on top of all the harnesses. So the API is like, you’ve got an agent session, and you can send in a message or, like, a file. That’s what you can send in, and then you get out, these streams as it’s streaming text or as it’s doing tool calls. And, or the other thing you can send in is you can, like, tell it to cancel a turn. So that’s the API. Now, the thing we did is we could get you that on top of, like, cloud code running in a terminal, Codex, Py, OpenAI SDK, all that stuff. We map them all to that same interface. So that is something that you’d have to maintain yourself if you built your own, like, agent orchestrator, and then whenever cloud changes its API, you gotta, tweak your thing or it’s gonna lose some messages. So that’s the thing that’s valuable to maintain. Then on top of that, like, we built a few apps. I think we built a pretty cool UI and stuff, but that’s, And we built a security and control piece, which I’m excited about. But it’s that common interface, so we don’t. We. That doesn’t try to be a stack. And in fact, you could plug in your own UI on top of this, server. That, and that’s one of the use cases we care a lot about, ‘cause we want to use this in our own products.

Compute, Sandboxes, and Databricks Scale

Swyx [00:15:20]: Yeah. It should be everywhere.

Matei Zaharia [00:15:22]: Yeah.

Swyx [00:15:22]: I think one of those things that is really interesting to me is, like, well, first of all, I’ll, I’ll endeavor to do everything and not call it the modern AI stack because like it needs a different name.

Matei Zaharia [00:15:32]: Yeah.

Swyx [00:15:32]: But like, yes, like, so one of the first people that told me about compute, sandboxing was Nikita from Neon.

Swyx [00:15:39]: Because a lot of people think about Neon as like, well, it’s serverless Postgres with, like, the separation of compute and storage and, instant branching and all those things. But every database company is also a compute company.

Matei Zaharia [00:15:51]: Yeah. Yeah.

Swyx [00:15:52]: And so he was showing to me his whole, his sandboxing solution. I don’t think he have ever launched it.

Matei Zaharia [00:15:57]: So our sandbox solution, the reason we could build it so quickly was because we realized if you just take the actual Lakebase architecture

Swyx [00:16:05]: Yeah

Matei Zaharia [00:16:05]: and remove the database from it, by the coming from Neon

Swyx [00:16:08]: Exactly, right

Matei Zaharia [00:16:09]: you have this sandbox

Swyx [00:16:09]: Every database company has it already, yeah.

Matei Zaharia [00:16:11]: Now, there are some differences. For example, in the one to support this particular workflow, it’s important to have local persistence,

Swyx [00:16:19]: Yeah

Matei Zaharia [00:16:19]: because you want your state to persist. Your libraries, you don’t have to install your library every time, right?

Matei Zaharia [00:16:24]: whereas the Neon architecture, because of the separation of storage from compute, you don’t need persistent local disk.

Swyx [00:16:30]: Yeah.

Matei Zaharia [00:16:30]: So there’s some differences.

Swyx [00:16:32]: Yeah.

Matei Zaharia [00:16:32]: But the, at the end of the day, yeah, it’s, Yeah, so this is when you run, like, a coding sandbox. Like, if I use it, yeah, we have the dev env internally at Databricks. There’s, like, many, like, tens of gigabytes of data just for, like, all the source code and, like, artifacts and stuff that I built, and I want that to come back next time, so.

Matei Zaharia [00:16:51]: Yeah.

Matei Zaharia [00:16:51]: But yeah.

Matei Zaharia [00:16:52]: Before the show, we was talking about some statistics that might be surprising at the adoption.

Matei Zaharia [00:16:56]: It could be internal, it could be external, whatever comes to mind, just to impress people the scale this is happening.

Swyx [00:17:02]: So we, on the analytics side, I think we launched

Reynold Xin [00:17:06]: Maybe 50 or 60 million virtual machines a day across all three clouds, so we’re one of the biggest compute orchestrators out there.

Reynold Xin [00:17:13]: Stuff for sure for CPU compute.

Swyx [00:17:14]: Yeah.

Matei Zaharia [00:17:14]: Yeah.

Reynold Xin [00:17:15]: the. And all of this process, I think exabytes of data, I joked about depending on which time zone you are, typically before you have breakfast, Databricks would have processed exabytes of data already on that day. and on Neon, it’s pretty interesting, too. It’s launching, I think, 13 million databases

Swyx [00:17:34]: Yeah

Reynold Xin [00:17:34]: a day now.

Swyx [00:17:35]: Yeah, to me that was, like, a

Reynold Xin [00:17:36]: And that’s just like

Swyx [00:17:37]: Like, what do you mean?

Matei Zaharia [00:17:38]: Yeah. And that’s the point.

Reynold Xin [00:17:40]: And a lot of those were thanks to agent- agents and branching experimentation

Swyx [00:17:44]: Yeah

Reynold Xin [00:17:44]: because we made it so easy and so quickly, and thanks a lot to Nikita’s team, to launch databases. It’s, the. So it’s changing the way people use databases.

Swyx [00:17:54]: Yeah. Okay, we’re gonna go into more database talk in a bit, but I wanna make sure we close up anything on Omnigentt. you mentioned, you were excited about the security

Omnigent Security, Contextual Policies, and Spend Controls

Swyx [00:18:03]: control side.

Matei Zaharia [00:18:04]: Yeah.

Swyx [00:18:04]: a lot of companies are figuring that out right now, as well as the spend side.

Matei Zaharia [00:18:08]: Yep.

Swyx [00:18:09]: what have you found there?

Matei Zaharia [00:18:11]: Yeah, so I spent quite a bit of time talking to internal users, developers, security team, managers, and also lots of customers, and there’s a few things. Like, first of all, one thing, that immediately was. became obvious is for security, there’s this tension between, like, usability and security. And, the way people do. Like, a lot of coding agents today have very basic things like you can tell me which tool patterns I’ll allow or disallow or whatever. It’s like yes or no. But that puts you in a very tough spot. So just as an example, like, should my agent be able to read, some confidential documents, or let’s say, should it be able to install new packages from npm, which, maybe it’s compromised. Yes or no? Like, maybe I wanna allow it. Should my agent be able to publish stuff to the company website? Well, if I’m using it to code on the website, yes. But should it be able to do both, so it can, like grab a confidential document and be prompt injected and leak it? Probably not. So the thing we decided we need is stateful or what we call contextual policies where you keep track of the state of that session. It’s not like is it allowed to push to the marketing site or not, but, like, hey, if it did a risky thing, like it installed, a old package from npm, or it read, like, 1,000 confidential docs, then no. Then don’t, don’t do it. Otherwise, maybe it’s okay. That’s one example of, like, moving that trade-off so it’s both more secure and more useful by having a more powerful engine, essentially. This requires tracking sessions. The other piece that was interesting there is, like, there are these very level events it’s doing, and you want some libraries on top that parse them. Like, for example, we have a, MCP server on Google Drive internally. It’s got 60 API calls. like, how do I know which of those, like, will share a document with stuff on the internet and which ones won’t? It’s, it’s annoying. So we designed in Omnigentt the policy layer so that it’s functions and you can have libraries. Like, someone can make something that maps the level events to high-level ones, and then you write a policy about the high-level things that came out. so and that

Swyx [00:20:25]: This is related to the Panther,

Matei Zaharia [00:20:27]: Yeah, Panther is. will help with that. Panther

Swyx [00:20:30]: Yeah

Matei Zaharia [00:20:30]: a similar idea on the event processing side, and it’s Python-based versus a weird custom language. this is more, as in real

Swyx [00:20:39]: I didn’t even know we were good yeah.

Matei Zaharia [00:20:41]: Those things are happening, yeah.

Swyx [00:20:42]: Yeah.

Matei Zaharia [00:20:42]: So yeah, but these are the cool things. I think the contextual or stateful part, and then the way it can be libraries, and that was another reason to make it open source because others will write libraries and, like, we and our customers can use them. And the final thing, because it’s stateful, one of the states we track is how much you spent in that session. So I can. I’ve had, like, I ask an agent to debug something, and it spent $500 because it decided to read a lot of log files and burn a lot of tokens. but I can literally say, “Okay, launch a agent to do this and cap it to spending $5.” Like, ask me for permission if it needs more. And because we’re counting that within that session, it’ll pop up and tell me, “Okay, you spent five, $5. Do you wanna go on?”

Reynold Xin [00:21:27]: So important context here. Matei spent the last five years, a lot of his time was architecting Unity Catalog at Databricks

Matei Zaharia [00:21:34]: Yeah

Reynold Xin [00:21:34]: which is the governance layer for data.

Matei Zaharia [00:21:35]: That’s right, yeah.

Reynold Xin [00:21:36]: And he’s combining expertise at that layer together with all the AI governance he knows.

Matei Zaharia [00:21:41]: Yeah.

Swyx [00:21:41]: Do

Matei Zaharia [00:21:41]: But I also spent a lot of time being annoyed by coding agents and getting prompts.

Matei Zaharia [00:21:46]: And also as the

Reynold Xin [00:21:48]: All the above

Matei Zaharia [00:21:48]: I don’t want to end up on the front page as, like, I installed some weird npm package and leaked

Swyx [00:21:53]: Yeah

Matei Zaharia [00:21:53]: all the code, so I’m especially paranoid. But also I have very little time, so I don’t want to sit there approving, like, do you want to run a 20-line, bash script, yes or no? so that’s why I spend a lot of time figuring out, like, how can I make it as safe as possible and not annoying?

Swyx [00:22:10]: Yeah. Is safety and mmm, let’s call it security a bigger concern than token maxing or token budgets? which one is, like

Matei Zaharia [00:22:19]: Oh, yeah, they’re both there. I don’t know. I guess it depends on the type of company you are. So I think, some companies, like, the budget is, limited and, they really care about that

Swyx [00:22:34]: you can be Uber and still be concerned?

Matei Zaharia [00:22:36]: Yeah. Oh, yeah, totally. Yeah. If you have

Reynold Xin [00:22:38]: for us, security

Matei Zaharia [00:22:39]: Yeah

Reynold Xin [00:22:40]: super paramount.

Matei Zaharia [00:22:40]: For us, security is absolutely critical as a, cloud provider. It’s, it’s the most important thing, and, token maxing, we’re not so worried about it yet, but I’ve seen the Like, for example, I talked to some consulting companies. They have, like, 100,000 employees who are all coding for customers. If those each spend, like, an extra $1,000 a month, that’s, that’s not fun.

Swyx [00:23:04]: Yeah

Matei Zaharia [00:23:04]: we have, like, only a few thousand engineers.

Swyx [00:23:06]: What’s the policy in Databricks? Is it just unlimited or what’

Matei Zaharia [00:23:08]: It’s, it’s unlimited, but we do. we use our own product to, like, analyze the traces and stuff, and we have a team that’looking to optimize and to see if anyone’s doing something weird. And, we had some really cool insights just from analyzing current traces, like which

Swyx [00:23:24]: Yeah

Matei Zaharia [00:23:25]: models are better at, say, Rust versus like TypeScript or whatever. So yeah, at least in our code base.

Swyx [00:23:31]: Yeah. Amazing. Obviously, I have to ask the token question, obviously.

Matei Zaharia [00:23:34]: Yeah.

Swyx [00:23:34]: I think it’s

Reynold Xin [00:23:34]: Yeah

Swyx [00:23:34]: it’s a key thing. But yes, security and control above that, and figuring out a sane layer there you can have some autonomy, but, not too much.

Matei Zaharia [00:23:43]: Yeah. Yeah, and we wanna make it super easy. As a engineer, you should set a thing. So in Omnigentt, you can ask your agent, “Set a policy on yourself to do this.” So it can like

Swyx [00:23:52]: But if there’s something I should be showing

Matei Zaharia [00:23:53]: Yeah

Swyx [00:23:53]: I don’t, I don’t see it on the GitHub, but,

Matei Zaharia [00:23:55]: Oh, yeah

Swyx [00:23:56]: there’s just

Matei Zaharia [00:23:56]: Well, in the docs there’s something.

Swyx [00:23:57]: Yeah, this is it.

Matei Zaharia [00:23:58]: You can look at it later.

Swyx [00:23:59]: Okay. Yeah.

Matei Zaharia [00:23:59]: Just look in the docs

Swyx [00:24:00]: Yeah

Matei Zaharia [00:24:00]: contextual policies if you wanna see.

Swyx [00:24:04]: I just like to point people

Matei Zaharia [00:24:05]: look at the built-in policies.

Swyx [00:24:06]: Yeah.

Reynold Xin [00:24:06]: Yeah.

Swyx [00:24:06]: If you want to, follow up on this is exactly where to look, right?

Reynold Xin [00:24:10]: Yeah.

Matei Zaharia [00:24:10]: Yeah. yeah, and the story of these is, like, I just wrote, like, I wrote a doc with like 10 ideas for things before as you were working on them. Well, that was, like, my wish list of things people asked, and I told the team, like, “Hey, can you do like at least five of these for the launch?” And then they just got back with all of them, so.

Swyx [00:24:29]: Oh, wow.

Matei Zaharia [00:24:29]: so you can come up with more, but them- some of them are just meant to be examples. really you can intercept, like, any event the agent is making, and you can then either block or force it to ask the user or, like, allow, and you can update state to keep

Swyx [00:24:45]: Yeah

Matei Zaharia [00:24:45]: track stuff.

Swyx [00:24:46]: Yeah, ‘cause ultimately you’re, I think of you as, like, a systems designer.

Swyx [00:24:50]: You let people plug in, right? That’s the whole

Matei Zaharia [00:24:51]: Yeah

Swyx [00:24:52]: modus operandi of what you do.

Matei Zaharia [00:24:53]: Yeah.

Swyx [00:24:54]: It’s like

Matei Zaharia [00:24:54]: And we care a lot about also composab- like, can someone else write a library that others use, which

Swyx [00:24:59]: Yeah

Matei Zaharia [00:24:59]: this is meant to.

Reynold Xin [00:25:00]: There’s also a batteries included philosophy here

Matei Zaharia [00:25:03]: Yes

Reynold Xin [00:25:03]: probably very similar to how you did Spark, which is you could just start using.

Swyx [00:25:06]: Yeah.

Matei Zaharia [00:25:06]: Yeah, that’s right. It has to be good out of the box at certain things, and then you can build your own things on top that, like, we don’t wanna do. But in Spark, if you just wanna like, I don’t know, like read a table or do, like, a aggregation, it should be awesome at that out of the box.

Building on Omnigent: Contributions, Startups, and Analytics

Swyx [00:25:23]: Yeah. People wanna catch up on Omnigentt, they should watch your keynote.

Swyx [00:25:26]: they should go through the GitHub and the docs. If they wanted to contribute, or they want to build on this ecosystem what would you call out as the most high-leverage places get involved?

Matei Zaharia [00:25:36]: Yeah, do get involved in the Discord and in GitHub. Our team is there, is monitoring, and, some of the things people ask for we just built ourselves. Some of them, we’re, we’re collaborating with them to build it. and also tell us, like

Swyx [00:25:49]: Yeah, they’re gonna be very

Matei Zaharia [00:25:49]: how you would like to use it because I think especially for developers, like, everyone wants it to work their own way, and a really good developer tool, like you have to hear the feedback on all the ways and figure out the abstractions and how to let people customize. So we’d love to hear, like, if you think, “Hey, I, I don’t want it to work this way,” tell us. We really just wanna get that compatibility layer across agents and then let you do stuff on top.

Swyx [00:26:14]: Yeah. is there any, in terms of like the startup side, I’m, I’m a founder.

Swyx [00:26:18]: I want

Matei Zaharia [00:26:18]: Yeah

Swyx [00:26:18]: I see an opportunity, I wanna get in front of you. What’s your request for, like, a startup that, like, I wish someone

Matei Zaharia [00:26:23]: Oh, like you wanna integrate with us?

Swyx [00:26:24]: someone was working on this.

Matei Zaharia [00:26:26]: Oh, for a startup?

Swyx [00:26:27]: Yeah.

Swyx [00:26:28]: Like, your, you got your own startup. It’s doing well.

Matei Zaharia [00:26:30]: Yeah.

Swyx [00:26:30]: But like, if you weren’t working on your own startup, what is, like, obvious that you should You advise many startups too, obviously.

Matei Zaharia [00:26:37]: I do think, just as a company with a lot of engineers, like anything that helps me make sense of how people are using

Swyx [00:26:46]: Spend

Matei Zaharia [00:26:46]: coding agents and,

Swyx [00:26:48]: Yeah. Analytics

Matei Zaharia [00:26:48]: spend, but also quality or like you should write, you should add this skill, or you should write this thing, or your agents are really horrible at tasks involving this service, so I go spend time. That would be nice. yeah.

Swyx [00:27:00]: Yeah. The closest I’ve found is, this team, GitAI.

Matei Zaharia [00:27:03]: Oh, cool. Yeah.

Swyx [00:27:04]: They started with, like, we will just do, code and human attribution, but they’re building the analytics layer on top of that.

Matei Zaharia [00:27:12]: Yeah.

Swyx [00:27:12]: I do think, like, there are a bunch of, like, artificial analysis is obviously,

Matei Zaharia [00:27:18]: Yeah, they have their benchmarks

Swyx [00:27:18]: doing super well

Matei Zaharia [00:27:19]: Yeah

Swyx [00:27:19]: with their stuff. so there’s, there will be people. I think this is like the domain of consultants first, but then people

Matei Zaharia [00:27:26]: Yeah

Swyx [00:27:26]: will build software that, let’s say, it’s kinda like the management plane

Matei Zaharia [00:27:29]: Yeah

Swyx [00:27:30]: for coding agents.

Matei Zaharia [00:27:30]: Yeah, I think there’ll be a lot of insights there. You have it in other areas.

Swyx [00:27:34]: Okay. Well, and then the other, big thing is your dream engine.

LTAP: Lake Transactional/Analytical Processing

Swyx [00:27:39]: maybe you wanna tell the story of, LTAP.

Reynold Xin [00:27:45]: So, and background with. I’m, I’m gonna make people listen to our Ankur Goyal episode where we talked about SingleStore, HTAP

Matei Zaharia [00:27:52]: Yeah

Reynold Xin [00:27:52]: and all that history.

Matei Zaharia [00:27:52]: Yeah. The LTAP idea is pretty simple. so if people have heard of the, Ankur’s, talk about HTAP, it’s effectively the world of databases. Sorry, there’s like maybe a lot of context needs to be injected here. The world of databases

Swyx [00:28:06]: I am happy to be the database podcast that I’m forcing people to, like, learn your databases, guys.

Swyx [00:28:11]: You cannot vibe code with just markdown files.

Reynold Xin [00:28:13]: Yeah.

Swyx [00:28:13]: Like,

Reynold Xin [00:28:14]: It’s one of the most important fundamental systems technologies out there. But the world of database effectively split into roughly two halves. There’s what we call OLTP databases, which are transactional, and think of your Postgres, your MySQL, your Oracle databases, and the other side is what we call analytics, and sometime might refer to term OLAP. And the difference is on OLTP, you typically have maybe run some transaction on some event that looks up at one specific row. We update that row, right? It’s a very oriented data structure. And on analytics, you’re trying to reason on the data. You’re trying to compute, “Hey, what’s my revenue per store? What’s my. How’s my website doing every day?” And then you, eventually want to probably end up running anal- machine learning on it to predict, “Hey, how will my maybe sales be going in the future?” they are so very different architecture, and everybody start with OLTP databases. Every app, when you become serious enough, that needs more than markdown files, you need to have a database. You want to lose your data, you want to have some transactional consistency. But once you want to reason on the data, if you only have like- A hundred rows, it’s probably okay to run it on your Postgres or your own, your MySQL database. But once you have more data and want to run more complicated analysis, the very analysis might crush your Postgres database. So you start doing, getting data out of the OLTP database

Swyx [00:29:35]: Replication.

Reynold Xin [00:29:36]: Replicate them into the analytic systems and just start

Swyx [00:29:39]: Yeah, which for people, Elasticsearch is, like, a

Reynold Xin [00:29:42]: Yeah. So some of them get into Elasticsearch for, like, blocked analysis. A lot of our customers obviously get into Databricks to run more sophisticated things.

Swyx [00:29:51]: Yeah.

Reynold Xin [00:29:51]: And there’s this term called CDC, which

Matei Zaharia [00:29:54]: Change data capture

Reynold Xin [00:29:55]: change data capture. and what it does, it reads the binlog of the database, and if you don’t understand what binlog is, it’s fine. The, but it’s a little delta of the data, and it reconstructs based on the delta, the state of the database, on the analytics side. But CDC is, like, a very painful thing. It’s how standard in the industry, everybody uses it, but, it ends up being. I think many data engineers ends up being waken up at, like, 3:00 a.m, because there’s some pipeline thing.

Swyx [00:30:22]: my explanation is, like, Airbyte is like a, became a $5 billion company just doing CDC.

Reynold Xin [00:30:27]: Yeah, exactly.

Reynold Xin [00:30:28]: CDC is, like, a very

Matei Zaharia [00:30:30]: It’s hard.

Reynold Xin [00:30:30]: It’s one of the most boring but one of the most fundamental operations, like, powering modern society.

Matei Zaharia [00:30:37]: huh.

Reynold Xin [00:30:37]: But it’s so brittle that, we joke that it’s, should be called continuous data corruption, because you might change your schema on your OLTP database, and then the CDC pipeline fails to handle

Swyx [00:30:48]: Yeah

Reynold Xin [00:30:48]: the schema change.

Swyx [00:30:49]: Yeah.

Reynold Xin [00:30:49]: And then everything goes out.

Swyx [00:30:51]: And there’s all sorts of tricks that you can do, like, you add in, like, some versioning or whatever, but yeah.

Reynold Xin [00:30:55]: Yeah, but it’s a very, in general, very complicated. Like, I think at my keynote, I asked the audience put up their hand if they love their CDC pipeline. Only, like, maybe two people put it up. So if single store, like, about maybe a decade ago, I think the industry had this idea, hey, what if I built a single database that can handle both workloads? Now I don’t.

Swyx [00:31:12]: Which, like, by the way, every database person ever has ever always dreamed about this.

Reynold Xin [00:31:15]: Yes. Yes.

Reynold Xin [00:31:16]: This is the holy grail of database engineering is why not build a single system that can do both of this? But it ends up just being a lot of compromises. one, I think one of the first issue is that, hey, each. they say Postgres has a massive ecosystem, right? You want to be using the tools that’s built for Postgres. And Spark, for example, had a massive ecosystem. There’s a lot of libraries you want to use. If you were to create now a new thing, you don’t have a ecosystem. You tend to create a new, smaller proprietary API, and you’re lacking both, and it’s also very difficult to make it performance-wise to be, comparable on either side. So it ends up being sucking on both. And our whole idea of LTAP, it’s obviously a wordplay on the term HTAP, is that we think this is HTAP done right. HTAP wants to build a single engine for both. We think you can get 99% of what you need by unifying the storage, and just have a single storage layer. And once you have the single storage layer, if your Postgres databases are writing data in a column-oriented format, everything analytics can just go read that data directly without any delay, right? There’s no pipeline in between, so all the data will immediately be available for reasoning analytics. I think I was telling some customers earlier, hey, when we talked about this is gonna be super useful for agents, I at first didn’t really believe in it myself, even though we wrote that positioning.

Lakebase, Agents, and Live Operational Data

Matei Zaharia [00:32:39]: Yeah.

Reynold Xin [00:32:40]: But then last night I was having dinner with a Australian customer, and they told me, “Oh, hey, one of the big issue we have is we have all these logs from our services, and we see SLA dips and want to investigate. But then there’s no way for those agents to even understand what’s going on in the actual databases themselves. All we see is just, like, product telemetry of the database and the services.” It would make those agents 10 times more powerful if understand, for example, who’s placing those orders, what is happening, what exactly are they doing. So now I’m sold on our own message.

Swyx [00:33:13]: Yeah.

Reynold Xin [00:33:14]: I think it’s really. It gets you the almost all of the benefits of the HTAP holy grail, which is, hey, make the data available immediately for reasoning analytics

Swyx [00:33:26]: Yeah, I think,

Reynold Xin [00:33:27]: without compromise

Swyx [00:33:28]: in the way that humans are generally intelligent and want to have the ability and access to query anything

Reynold Xin [00:33:34]: Yeah

Swyx [00:33:35]: while they do the work, they also need history and need context.

Swyx [00:33:38]: And, like, where else does they get context? That’s it’s an analytical workload.

Reynold Xin [00:33:41]: Exactly.

Matei Zaharia [00:33:42]: Yeah. Yeah. And I remember when we had incidents with our databases and engineers said, “Well, I can’t just run a giant query on it to see what’s going on because that’s gonna bring down the database and hoard it even more.” Like, that’s the stuff that this gets rid of, because you spin up a whole separate fleet of machines that’s doing the analytics. You’re not overloading, like, the main database

Reynold Xin [00:34:02]: Right

Matei Zaharia [00:34:02]: that’s still trying to serve stuff.

Reynold Xin [00:34:04]: Yeah.

Matei Zaharia [00:34:04]: Yeah.

Why LTAP Works Now: Parquet, Postgres, and Lakebase

Swyx [00:34:05]: So this has been a dream for a while. what had to get done in order to get to today? Like,

Reynold Xin [00:34:11]: Yeah.

Swyx [00:34:11]: I feel like, you have announced variants of this several times, but it wasn’t as clear as LTAP.

Reynold Xin [00:34:18]: Yeah.

Swyx [00:34:18]: I think LTAP is like Like, okay, we’ve got it, guys.

Matei Zaharia [00:34:21]: This thing, yeah.

Reynold Xin [00:34:21]: I was talking to somebody at Meta, and then he was asking me, “Hey, what’s the catch? Why is it possible now?” And I think the reality is we took a lot of time to work on the Lakebase architecture. obviously a lot of it came from the Neon team, which is a separation of storage from compute. And it turned out it was just a tiny little step away going from that to this LTAP idea, which is, hey, we just. in the Neon architecture and in Lakebase architecture, we’re writing data in oriented format to the open data lake, but in there we’re writing in Postgres pages. Ali and I were spending a lot of time debating, hey, can we just change that to write in column-oriented format? And we’re just debating, and one day, one of our engineers who’s, like, super smart came in, he’s like, “Hey, I just prototyped it. It works.”

Swyx [00:35:07]: Wait, it’s, prototype what?

Reynold Xin [00:35:09]: Prototype, instead of storing the data in the data lake in the oriented format

Swyx [00:35:15]: Column

Reynold Xin [00:35:15]: like Postgres pages

Swyx [00:35:15]: Yeah

Reynold Xin [00:35:16]: write them in Parquet.

Swyx [00:35:17]: Yeah.

Reynold Xin [00:35:18]: and he just made the observation that, hey, our storage fleet has a lot of extra idle CPUs And we could use those CPUs to do the transcoding from row to column, where row is good for OLTP, but column is good for analytics. so let’s do that transcoding at that time. And as a matter of fact, once you transcode the data compresses better. So from those services writing to, for example, S3 or other data lake, like object stores, you can write them faster ‘cause now they are now smaller.

Matei Zaharia [00:35:49]: Yeah.

Reynold Xin [00:35:49]: So there’s no overhead, it’s no compromise in performance

Matei Zaharia [00:35:52]: Some CPU overhead.

Swyx [00:35:54]: Yeah, because,

Matei Zaharia [00:35:55]: Yeah

Swyx [00:35:55]: we had extra CPUs anyway.

Matei Zaharia [00:35:56]: We had that fleet anyway, yeah.

Swyx [00:35:57]: so the debate ended. it’s one of the classics of, tech, issue of a lot of debate, but then somebody went ahead and just tried to prototype it and it worked.

Matei Zaharia [00:36:06]: But, like, something this strategic

Swyx [00:36:07]: That’s right

Matei Zaharia [00:36:07]: and important to the company, I expect there to be, like, a kickoff thing, like a design doc. Nothing like that.

Swyx [00:36:13]: Nothing like that.

Swyx [00:36:14]: He just. We were debating in many meetings

Matei Zaharia [00:36:17]: Yeah.

Swyx [00:36:17]: and then we’re just debating whether it’s possible or not from first principle.

Matei Zaharia [00:36:20]: Yeah

Swyx [00:36:20]: and then, somebody just did it.

Matei Zaharia [00:36:23]: Yeah, if you set yourself up so people do that’ll be great. And that happened a bit with Omnigentt too. I think if I just had a doc on, like, we can make these together, everyone would, would think, “Oh, what about this? What about this?” But then you. if you try it out, it helps. And then if you have real users and they bash it and, like, it’s still working, or in this case, if you have the workload, what the workload looks like, you can just test the same pattern then.

Databricks’ Culture of Fast Prototyping

Swyx [00:36:47]: Yeah.

Matei Zaharia [00:36:47]: Yeah.

Swyx [00:36:47]: Tech aside, which is very cool, this is, like, the most important thing, the culture of innovation, and you don’t have to ask my permission, you don’t have like, do a whole form- formal process, just do it?

Matei Zaharia [00:36:59]: Well, especially these days, I think with

Swyx [00:37:01]: Yeah

Matei Zaharia [00:37:01]: AI, it’s easier to build

Swyx [00:37:02]: But so, like

Matei Zaharia [00:37:03]: a prototype

Swyx [00:37:03]: I think you are very I made a lot of suite of, like, large companies and, like, I think that at scale, things slow down, and I’m sure you felt it already, but somehow you have this core of people that, like, are exempt. How? I think we hire and we work with really good people, and that’s a very important part of it, and empowering them, but also spending a lot of time, maybe us in the trenches matter a lot also.

Matei Zaharia [00:37:28]: Yeah, I think, I think first, people can adapt to being in the larger company, so that helps. And we wanna make sure they know that they can try stuff and settle debates and have a lot of examples of how it was done before, or launch a thing in beta or whatever. and then the other thing I do think as a company, like despite the size, we don’t launch that many, like, products. We try to keep it pretty coherent. That’s, that was the whole, like, theory of the company, was like instead of having, like, 20 Amazon services you need to set up, like a analytics and machine learning stack, you just have one, and it’s, like, the same API, the same semantics across all of them, the same copy of the data. So that requires, like, unification. And then we added one more thing at a time. Like, we added storage with Delta Lake. We didn’t used to do any storage. Then we added SQL, we added, machine learning platform stuff. So, but yeah, don’t, don’t do too many, but do those things well and, that also helps, it helps keep it manageable.

Reynold Xin [00:38:33]: Yeah. The other thing we encourage a lot is instead of building, boil the ocean for everything, let’s figure out how do we do it incrementally, how do we do it very quickly. Like, many of our products

Matei Zaharia [00:38:43]: Yeah

Reynold Xin [00:38:43]: they’re built in the span of weeks, and then we go to, hey. Like, usually my first question to whoever team is building is who’s the target customer? Who are you working with? Are you on a first-name basis with them? Are you texting with them? I think having that very tight loop,

Matei Zaharia [00:38:59]: Can you bring up another launch that comes to mind when, in this thing? I just want to give examples.

Reynold Xin [00:39:04]: Omnigentt itself happened that way.

Reynold Xin [00:39:05]: Yeah.

Matei Zaharia [00:39:06]: Who’s the customer? That’s a good one

Reynold Xin [00:39:34]: storage layer we did. we had, our largest customer at the time said like, “Okay, I need some. I want something in the cloud ‘cause, I. if the rest of our network is compromised, like this thing needs to be separate to store and query the events.” And then, talked to us, he said, “Okay, this is the rate of events per second. This is, like, the freshness I want. Can you do it?” So that was, like, way larger than any workload we had, and we had our, engineer, working on that, Michael Armbrust, and he worked just to make this work. And once it worked for them, it worked for everyone else. Yeah. This was early in the company, probably like four years in or something.

Matei Zaharia [00:40:24]: 20- 2018?

Swyx [00:40:26]: Yeah, ‘17, ‘18.

Matei Zaharia [00:40:28]: Few companies

Swyx [00:40:28]: Do you have other examples?

Matei Zaharia [00:40:30]: there’

Swyx [00:40:31]: Maybe you have others

Matei Zaharia [00:40:31]: yeah, Clean Room, which is how you share data in a way without sharing

Swyx [00:40:35]: Yeah

Matei Zaharia [00:40:35]: underlying data, but you allow specific operations. Those were done effectively initially just for two customers. I think the industry has a sense of, hey, maybe if you overfit to, like, one or two customers, it’s gonna be really bad for you. But I think the, downside of overfitting is much smaller than the upside itself. And if you try to be too ambitious and boil the ocean, it’s a much bigger problem.

Swyx [00:40:58]: Yeah. Yeah.

Matei Zaharia [00:40:58]: ‘Cause you might end up having no customer.

Swyx [00:41:00]: Yeah, that’s more, that’s the more likely outcome.

Matei Zaharia [00:41:02]: Yeah.

Tech Companies vs. Enterprises

Swyx [00:41:03]: than you can pivot from there. I do think there is such a thing as a bad customer that sometimes you should fire. Yeah.

Matei Zaharia [00:41:08]: They could exist sometimes if you drive. well, one of the challenge I think we probably see, and maybe many AI, so newer generation companies are seeing is, so tech companies are very different from tech companies or traditional enterprises.

Swyx [00:41:22]: Yeah.

Matei Zaharia [00:41:22]: And, if you optimize everything just for tech companies, you might have various challenges

Swyx [00:41:27]: Oh

Matei Zaharia [00:41:27]: scaling them outside of tech companies.

Swyx [00:41:28]: Okay, what like

Matei Zaharia [00:41:30]: Yeah

Swyx [00:41:30]: what like top three differences that you always think about?

Reynold Xin [00:41:33]: Governance is a big one

Matei Zaharia [00:41:34]: I think, yeah, a big one is like, yeah, security, data privacy, governance, all that stuff. So usually if you’re building some kinda like B2B or developer tool, like your biggest market is gonna be enterprises, but it’s just very different. A company that’s existed for like, it’s had some form of IT for like 30 years, they have so many legacy systems or they operate in a regulated space. whereas a startup or, even like a, like sorta more recent tech company, all the. everything is new and pristine. So yeah, it’s just different, and if you’ve never worked with enterprises or been in one, you just won’t know about it.

Reynold Xin [00:42:13]: Yeah.

Matei Zaharia [00:42:13]: Yeah.

Reynold Xin [00:42:13]: And the procurement process is probably quite different. There’s far more stakeholders.

Matei Zaharia [00:42:17]: Yeah, that is one. Yeah.

Matei Zaharia [00:42:18]: Another piece that’s interesting is I think some tech companies, people, will say, “Oh, I can build that myself,” right? I’ll just build that myself.

Matei Zaharia [00:42:27]: So then you go,

Reynold Xin [00:42:28]: I don’t think people say that about Databricks, but

Matei Zaharia [00:42:31]: yeah, it depends

Reynold Xin [00:42:32]: They do.

Matei Zaharia [00:42:32]: They do?

Matei Zaharia [00:42:32]: Yeah, the. Yeah, and it depends on the teams and things. So, but, on the other hand, like many of the enterprises say, “I don’t, I never wanna be in the business of building that.” Like, I don’t want my, whatever, I’m a retailer or something, I never wanna

Reynold Xin [00:42:45]: Yeah, sell clothes,

Matei Zaharia [00:42:46]: be down because like some weird like nerd like couldn’t get streaming pipelines working.

Matei Zaharia [00:42:51]: That is not what I’m doing.

Reynold Xin [00:42:53]: Yeah.

Reynold Xin [00:42:53]: Yeah. This makes them great customers, to be honest, right?

Matei Zaharia [00:42:55]: Yeah. But you have to understand that it’s hard without having worked there and stuff, like you may not appreciate.

Reynold Xin [00:43:01]: Look, I think they’re all great. don’t get me wrong, they have different challenges. But the, many of the tech companies, for sure there’s a lot, far more DIY.

Matei Zaharia [00:43:10]: On the flip side, you have people who are. they’re very much experts in their domain, like they’re building airplanes, they’re, designing medicines, whatever, and they just want to bridge the technology, where like they don’t wanna learn, databases or whatever. As cool as we think it is, even as interesting as the average software engineer might think it is to read a little bit, like they just never wanna know. They just say, “I have a, giant like, matrix or whatever with my, clinical data, like how do I, how do I like cluster it or whatever?” So yeah.

The Dream Engine and Rewriting the Database Stack

Reynold Xin [00:43:40]: Yeah. That’s true. Okay, so and then I wanted to build out the dream engine, vision. where does this all lead? So one of the thing we, realized maybe a couple years back is that every single database engine out there, especially on the analytics side, are a decade old. pretty much everything that have reasonable traction are about a decade old. And they all started targeting some very specific narrow use cases, and then over time it’s become more and more successful. They have grown in their ambition, and then they try to support more and more use cases. But the fastest way to support those use cases tend to be hacked around the abstractions that were initially created, that were not for those use cases.

Matei Zaharia [00:44:23]: Yeah.

Reynold Xin [00:44:23]: And then, but you can support them more or less okay. And before it, after 10 years of organic evolution that way, it becomes a gigantic pile of s**t.

Reynold Xin [00:44:31]: the. And, but that includes Databricks. And very few company or very few systems, I think, have the gut to say, let’s go start from scratch. Let’s go back to the drawing board and design, knowing everything we know today after a decade of workloads and probably billions in revenue, let’s attempt to rewrite it from scratch and make sure it will work and it can support all of these use cases. So we started doing that, but it’s a very ambitious project. by the way, you can search on Wikipedia, there’s this thing called second system syndrome.

Matei Zaharia [00:45:08]: Yeah, I know that. Yes.

Reynold Xin [00:45:09]: Or second system effect.

Matei Zaharia [00:45:11]: Every developer must know what a second syndrome is.

Reynold Xin [00:45:12]: It’s you built your first thing and it works out great, and the second one’s bound to fail because you become too ambitious.

Reynold Xin [00:45:19]: And then you ask so many requirements.

Matei Zaharia [00:45:20]: Or like you think everything

Reynold Xin [00:45:21]: Yeah

Matei Zaharia [00:45:21]: and then you’re like

Reynold Xin [00:45:22]: You just

Matei Zaharia [00:45:22]: you’re, “I’m gonna design the perfect system this time.”

Reynold Xin [00:45:24]: Yeah. And it turned out it’s not perfect, and then it start failing and you’re too ambitious, never launch, and you get killed. The, and the engineering team that started this, they were brilliant. I think we hired some of the best database engineers, on the planet into Databricks, and they were brilliant. Thank God it’s not their second system. Many of them have built more than two in the past.

Matei Zaharia [00:45:44]: Ah, nice.

Reynold Xin [00:45:45]: But they were still worried about this, hey, building a database engine from scratch, I think the conventional wisdom is gonna take like five years to mature. This would be a very long-term project. It could fail. I think one of the engineers jokingly said, “Hey, maybe we just call it Reynolds Stream Engine.” If we name after a founder, maybe we then may get canceled or killed. But I think they built something pretty remarkable. they went back to. They changed the way the database engines were built from a paradigm point of view. Usually when you build a database engine, you read a lot of academic papers, you try to understand what are the latest algorithms and data structures, and you put them together and see if they work or not. And there’s a high risk of failure there also because whatever that looks really good on paper might work out. might look really good in 70% of the workloads, but then it backfires on the other 30%. they went build a more of a factory for building the database. So they spent more time building this factory, and the factory takes the decade of traces we have. I think they count as like quadrillion data points in the trace table.

Matei Zaharia [00:46:47]: You don’t drop anything? Or you see sample?

Reynold Xin [00:46:49]: We for sure sample,

Matei Zaharia [00:46:50]: Yeah

Reynold Xin [00:46:51]: the, there’s like massive amount of things. And the, and they use that to build a model, like a machine learning model. Not an AL, a machine learning model. Machine learning model it can very quickly tell us how any algorithm and how any implementation would perform for any specific type of queries with very high fidelity. And based on that, they can, pick the most likely algorithm and data structure that will help with the different kinds of workloads.

Reynold Xin [00:47:21]: Both at runtime as well as at implementation time.

Reynold Xin [00:47:25]: Because there’s like unlimited number

Matei Zaharia [00:47:27]: it sounds like you want to like route to different data structures

Reynold Xin [00:47:31]: Yeah. if you think about

Matei Zaharia [00:47:32]: This is not one database

Reynold Xin [00:47:33]: a single database has many things implemented

Matei Zaharia [00:47:36]: Yeah

Reynold Xin [00:47:36]: together. But you want to make sure they all work well

Swyx [00:47:39]: Yeah

Reynold Xin [00:47:39]: with each other, and then for any given operation, there might be more than one implementation, so we make it run really. reality is things, algorithms that work super well, for example, for very low latency might not work very well for, say, scanning through petabytes of data.

Swyx [00:47:54]: Yeah.

Reynold Xin [00:47:54]: Right? most often there’s a trade-off there between throughput and latency.

Swyx [00:47:58]: What are the key dimensions like scale, throughput, latency? What

Reynold Xin [00:48:01]: Yeah, scale

Swyx [00:48:02]: anything else?

Reynold Xin [00:48:02]: and the distribution of data.

Swyx [00:48:05]: Yeah.

Reynold Xin [00:48:05]: Right? How sparse the data is.

Swyx [00:48:06]: How hard

Reynold Xin [00:48:06]: That matters

Swyx [00:48:07]: Yeah

Reynold Xin [00:48:07]: very a lot. how frequently do you hit the same data?

Matei Zaharia [00:48:10]: Yeah, how many distinct values

Reynold Xin [00:48:12]: Yeah

Matei Zaharia [00:48:12]: and stuff like that.

Reynold Xin [00:48:13]: Those things matter a lot.

Matei Zaharia [00:48:14]: Yeah.

Reynold Xin [00:48:14]: Like number of distinct value impacts the memory consumption of your aggregation, your hash. Like at some point there’s a hash table.

Swyx [00:48:20]: Somebody, I’m gonna, in my write-up, I’m gonna try to list all this out because I really want a taxonomy. To me, taxonomies

Matei Zaharia [00:48:25]: huh

Swyx [00:48:25]: are so helpful because it covers everything that you should think about.

Reynold Xin [00:48:29]: I think if you try to list it out, probably like a million different features.

Swyx [00:48:32]: I always want like, okay

Reynold Xin [00:48:35]: It’s not a trivial

Swyx [00:48:35]: give me like 12. Give me.

Swyx [00:48:38]: like a, someone did, like I think a Oracle paper in like 40 years ago did like the, these are the eight fallacies of distributed systems.

Reynold Xin [00:48:45]: Yeah.

Swyx [00:48:45]: Right? That thing is super useful.

Matei Zaharia [00:48:46]: Yeah, it is.

Swyx [00:48:46]: It’s like, okay, think through these eight.

Reynold Xin [00:48:48]: But let me give you a very, weird example, but it has profound implication on performance, which is like is your string just ASCII or does it have Unicode in it? How should you encode it?

Swyx [00:48:59]: Strings, strings are the most complex data types.

Reynold Xin [00:49:01]: Yeah. So the. And that, like for example, if string is super dense, you could convert every string into a, like imagine you have to do a aggregation. Instead of having a hash table, you could have an array. Because if your string is dense enough, if you only have 256 options, you don’t need a hash table. You can just do array

Swyx [00:49:21]: Yeah

Reynold Xin [00:49:21]: lookup.

Swyx [00:49:21]: Yeah.

Reynold Xin [00:49:22]: and that’ll be far fast.

Matei Zaharia [00:49:23]: Yeah, if the string is like a country code or something.

Reynold Xin [00:49:25]: Yeah.

Matei Zaharia [00:49:25]: Yeah.

Reynold Xin [00:49:26]: So it’s like probably millions of, features in that model. But using that, they can, one, prioritize the different algorithms that might impact in practice. And many of them are very counterintuitive. These are naturally things that you think, hey, might work super well, don’t work that well in practice. But also more importantly at runtime, you can dispatch the right algorithm and structure.

Vector Databases, Query Engines, and LTAP

Swyx [00:49:47]: I’m listening to the dream. I feel like Databricks is doing a really good job of the incremental evolution. Do you have to hard cut to a new system at any point? Or like,

Reynold Xin [00:49:58]: We designed it in a way that it can be incremental.

Swyx [00:50:00]: Yeah.

Reynold Xin [00:50:00]: So first we’re releasing a new endpoint. but this goes to the broader ocean versus. what we wanted to do is wanted to by design, this new engine should be able to do everything we’re able to do before and better, right? It’s been particular, the better part refers to very low latency workloads that can finish in 10s of milliseconds. But we want to roll it out incrementally with incremental capabilities so it doesn’t take like five years to see the light at the end of the tunnel.

Swyx [00:50:29]: I think that’s a heroic task. I don’t know what other way to say it. I am really interested in any new workload and new databases. obviously I think, if a, I’ve maybe established that I’m a little of a database nerd. The transactional databases, sorry, the accounting databases, like the Tiger Beetles I don’t know if you’ve, seen those.

Reynold Xin [00:50:50]: What do they do?

Swyx [00:50:51]: Dual entry accounting database. Like it’s just meant to really model like financial accounts or credit systems

Reynold Xin [00:50:56]: Oh, I see.

Reynold Xin [00:50:57]: it’s like a very specific problem.

Swyx [00:50:58]: Very high throughput. Yeah.

Reynold Xin [00:50:59]: Yeah.

Swyx [00:51:00]: Yeah. No, so when you were talking about how everyone like starts with

Matei Zaharia [00:51:02]: Yeah

Swyx [00:51:02]: a thing and then they

Reynold Xin [00:51:03]: Oh, I see

Swyx [00:51:03]: they scale up and then they tack on other things. It’s exactly that.

Swyx [00:51:06]: And then, I recently interviewed Simon from TurboPuffer.

Reynold Xin [00:51:08]: Yeah.

Swyx [00:51:09]: Same thing.

Matei Zaharia [00:51:09]: Yeah.

Swyx [00:51:09]: Like, well, and Chroma as well, like the, all the vector database companies of 2023

Reynold Xin [00:51:14]: Yeah

Swyx [00:51:14]: all are suddenly now just, we’re just generalist, general storage, like blob storage.

Matei Zaharia [00:51:18]: Yeah.

Reynold Xin [00:51:18]: Vector database should have never been a separate category.

Swyx [00:51:21]: I think it used to be a hot take, now it’s like the conventional wisdom nowadays. What should be a separate category? if everything becomes LTAP, like what’s.

Reynold Xin [00:51:31]: I think the thesis of LTAP is we’re not collapsing the databases at the actual query layer. We’re just collapsing

Swyx [00:51:37]: Indexing layer

Reynold Xin [00:51:38]: the storage layer.

Swyx [00:51:38]: Yeah.

Reynold Xin [00:51:39]: and that’s a, I think, a very important part. And we don’t think it makes sense to collapse the query layer into a single, like HTAP style database. And part of it. By the way, the other thing I think a lot of people had is, hey, it would be nice if there’s only one query language I have to worry about. Instead of worrying about Postgres and maybe Spark SQL, why not just one? But I don’t think that’s an issue for agents. Agents are very eloquent in Postgres or Spark SQL. It’s never gonna get confused. As long as the data is there and it’

Matei Zaharia [00:52:10]: Yeah

Reynold Xin [00:52:10]: accessible, agents will do fine. That might have been,

Matei Zaharia [00:52:14]: Yeah,

Reynold Xin [00:52:15]: five years ago might have been a problem for humans.

Matei Zaharia [00:52:17]: That could arise over time also, but it should. And this is, leads to how to do things incrementally, right? Like we realize you don’t need it right now. We don’t need to solve that problem to have a lot of value, from the current LTAP.

Swyx [00:52:30]: Yeah. Okay. I’m gonna end the pod with a little bit of more of spicier things.

Databricks vs. Snowflake

Swyx [00:52:37]: everyone has like, had to receive within a separation of storage and compute and try to build, the clouds. I had the same pitches from Snowflake.

Swyx [00:52:47]: How have you succeeded where they failed?

Swyx [00:52:50]: That’s rough.

Reynold Xin [00:52:52]: Well,

Swyx [00:52:52]: respecting that they are a competitor

Reynold Xin [00:52:54]: Yeah

Swyx [00:52:55]: objectively you have outpaced them. What is the core insight from your point of view that you guys just went different directions?

Reynold Xin [00:53:03]: Probably the biggest fundamental difference, both companies started around the same time, both went to the cloud, both focused on storage from compute architecture. But the biggest difference, one is, open. Like Databricks had never had the proprietary format, right? We started with the open ecosystem started with Parquet and then evolved into Delta and Iceberg and all that. It’s like one big thing. I think it matters a lot. The other one is AI. before 2022, October 2022, when ChatGPT came out, we had always pitched Databricks as a machine learning plus data

Swyx [00:53:38]: And a lot of the platform were built with machine learning use cases in mind, and obviously AI is a little bit different, and Matei’s, like spent far more time there than I do. But, the whole platform - we never felt, “Hey, we’re just a data infrastructure platform.”

Matei Zaharia [00:53:53]: Like, well, it makes only

Swyx [00:53:54]: Yeah.

Matei Zaharia [00:53:54]: Yeah.

Swyx [00:53:54]: We

Matei Zaharia [00:53:55]: I think they started with, like, they thought, “Okay, we’ll just manage the most valuable data and try to make it really fast. For that, we’ll have our own storage, which is optimized with the engine, and then we’ll just start at, like, the small amount of data that, like, the managers and whatever, finance people and so on look at and make that super fast to serve.” And, it was a different space. Whereas we started with, like, we’ll do the bulk processing and ingest. Like, you’ve got a bunch of, JSON log files, you’ve got whatever. We do that very large scale stuff ‘cause that’s what Spark was for, the large scale MapReduce-like stuff. And then we’ll keep the data in an open format. Might be slower, but, like, it’s already out there. You can consume it downstream. And, it turned out that, it’s easier to go from that broad thing that’s really good at the scale and ingesting and super low cost and create versions in it that have the speed and features of the, super easy to use, like, smaller data for, business users thing. And there was a

Swyx [00:55:02]: So start open, then optimize.

Matei Zaharia [00:55:04]: Yeah, start open and start large. Like, in some sense, we started upstream of them. And there was a time when we both, like, listed each other as partners because we said if you used both solutions together, use Databricks for, like, your ingest and compute, and then serve the tables out of Snowflake, you get all the visualization, all the very fast stuff, like, that’s great. And then, we both realized, like, customers were telling us, like, “Why do I need this other thing? Why can’t I just query your tables?” And we said, “No, we’re horrible at that. Like, please use our partner for the SQL warehouse stuff.” And then they realized that, like, wait a minute, so much of the compute is moving upstream into this other thing. Like, we’ve got to stop that

Swyx [00:55:43]: You have to go into each other’s territory, yeah.

Matei Zaharia [00:55:45]: But I think we did start with, like, the bigger scope, and with the open thing and that’s important architecture. Like, as - again, it goes to enterprises, like, if your company’s existed for, like, thirty years, you’ve experienced, being locked into Oracle and, like, all kinds of, like, crazy things. And if you’re the CTO there and you’re setting up the architecture for the future for your company, you’re gonna wanna pick a foundation that’s open. And you only want, like, one way to manage data in your company, ideally. You don’t want, like, seven different systems.

Swyx [00:56:17]: But, the open data format have won. Like, I think now every enterprise wants to put data in open data format. But, it was very controversial, like, back then. I think five, six. When exactly - one of the Snowflake founders wrote a blog called

Matei Zaharia [00:56:31]: Yeah

Swyx [00:56:31]: Choosing Open Wisely, which argued against

Matei Zaharia [00:56:35]: Yeah.

Swyx [00:56:35]: I think they might have taken it down. You have to find it on archive now.

Matei Zaharia [00:56:38]: Oh, it’s, it’s never going away now.

Matei Zaharia [00:56:41]: no, it’s still there. I love the perspective that only you guys will have because obviously you run the company. and I thank you for indulging this. It’s incredible, perspective. We’d love

Swyx [00:56:52]: Maybe one last one.

Matei Zaharia [00:56:55]: Yeah.

Swyx [00:56:55]: As you were talking I think I have to give Ali a lot of credit.

Matei Zaharia [00:56:58]: Yes.

Swyx [00:56:59]: He’s an incredible CEO. I think he’s the perfect combination of IQ, EQ, technology obsession, execution, business acumen.

Swyx [00:57:07]: and he’s also a founder, which makes a lot, make him, a lot easier for

Matei Zaharia [00:57:12]: Yeah

Swyx [00:57:12]: to, mobilize and execute. I think that’s,

Matei Zaharia [00:57:15]: Oh, that was it? so you have Ali, and he, they don’t, like, okay.

Swyx [00:57:20]: Well, a couple of other things, but I think Ali play a pretty big role in the,

Matei Zaharia [00:57:23]: I

Swyx [00:57:23]: Yeah.

Matei Zaharia [00:57:23]: I was, I thought he there was, like, gonna be some technical, choice that he contributed to.

Swyx [00:57:28]: Oh, no, I, well,

Matei Zaharia [00:57:29]: He did for a lot of these. Like, there were forks in the road where he pushed for, like, one way, and then it became clear that, like, that was the right way. yeah.

Swyx [00:57:37]: Yeah, there’s a whole book that needs to be written about how, like, the eight of you, like, work together and all that. I think there’s been profiles that people have done. Second one, not a cleared, question again.

Mosaic, DBRX, Genie, and Specialized Models

Swyx [00:57:48]: Mosaic.

Matei Zaharia [00:57:49]: Stats are there. Oh.

Swyx [00:57:50]: Mosaic.

Matei Zaharia [00:57:50]: Yeah.

Swyx [00:57:51]: A lot of people in our community are in, are curious on, like, what’s the the model story of Databricks, right?

Swyx [00:57:56]: Like, when you guys bought Mosaic, like, the thing was like, “Okay, well, we’re gonna do fine-tuning. We’re gonna house model,” ‘cause they had, the Mosaic models. And it seems like you’re, you’re not doing that, and it seems like you’re going towards more of the, LTAP and, the harness stuff. What’s the story there? just

Matei Zaharia [00:58:14]: Yeah. I guess when Mosaic started, I think it was well known or became most well known for releasing open source LLMs early on, and they were general models. before that, they were doing other things. They were about optimizing, training systems. So they had the fastest, like, image model training stack in the world and stuff like that. And then they decided to do LLMs, which was smart. They moved into it before ChatGPT, so they had some of the first open source LLMs.

Swyx [00:58:43]: Yeah.

Swyx [00:58:43]: We interviewed John Franco

Matei Zaharia [00:58:45]: Oh, yeah

Swyx [00:58:45]: Abi for 7B.

Matei Zaharia [00:58:46]: Yeah, exactly. Yeah. Oh, yeah, very cool. Yeah. Yeah. So we, decided, even though we did launch a open source model DBRX and, we went up to, like, above the Llama Three scale, we decided that we really wanna focus on there’ll be so many people releasing models, and, instead of doing the general model where, like, a big part of the recipe is just throw in a lot of compute and just scale, we wanna focus on, like, the next step also of, let’s say you have the very smart model, how do you make it, useful? for us, it was a lot about automating, like, how. Like, making it very good at querying data. That’s the first party agents we have called Genie. so it’s like a virtual data scientist. Imagine, there’s someone who already knows all the stuff in your company inside out and knows all the machine learning libraries, all the data libraries, all the stuff on the web, and you can ask them questions? That’s, that’s what we wanted to do first. So that meant, like, let’s not focus as much on, like, let’s just train some frontier model, but let’s build a system using either external models or, fine-tuned, customized components. we’re still doing quite a bit of model training though, and in fact, we’re always, we’re procuring, like, lots of GPUs and stuff all the time to do it. and there’s a few places where we’re doing it. One is, there are many high volume use cases where if you have a specialized model, it’s just so much better than any of the general models you get. A nice example of that is understanding, like, documents, like PDF, Word documents, stuff like that, parsing them. If you’ve ever tried to do that, it’s frustrating ‘cause you send it to, like, like, Claude, Fable, or whatever, it, like, almost gets it, but it gets some things wrong, and it’s super expensive. You just burnt a huge amount of tokens plopping in an image into there. So our team, built this, document, vision model that takes a page and gives you back a nice JSON with all the components, and it’s very competitive. It’s like- Probably like 100X cheaper than those, frontier models and still better.

Swyx [01:00:57]: Yeah.

Matei Zaharia [01:00:57]: And that’s done by one of the researchers who came from DeepMind, was a founder of Adept, like very early scaling person, but focused on this. likewise we have, we’re doing specialized agents for part of what the coding agent does. And if you’ve seen the stuff on advisor models,

Swyx [01:01:17]: Yes

Matei Zaharia [01:01:17]: from Harvey, also from

Swyx [01:01:20]: Anthropic has been putting

Matei Zaharia [01:01:20]: Anthropic

Swyx [01:01:20]: Commission also.

Matei Zaharia [01:01:21]: Yeah.

Swyx [01:01:21]: Yeah.

Matei Zaharia [01:01:22]: And UC Berkeley one of my grad students there, wrote a paper called Advisor Models, I think before those came out. I’m sure others had the idea at the same time

Swyx [01:01:30]: Yeah

Matei Zaharia [01:01:30]: but that’s, something that helps a ton. So yeah, we showed some stuff just today at the keynote on

Swyx [01:01:38]: Is it Parth? Oh, Parth?

Matei Zaharia [01:01:39]: Parth, yeah. Parth

Swyx [01:01:39]: Oh, he’s speaking at my thing. he’s doing

Matei Zaharia [01:01:41]: Oh, nice

Swyx [01:01:41]: continual learning bench.

Matei Zaharia [01:01:42]: Yes.

Matei Zaharia [01:01:43]: Yeah, I’m one of his advisors, at Berkeley.

Swyx [01:01:44]: Oh, yeah.

Matei Zaharia [01:01:45]: Yeah.

Swyx [01:01:45]: We interviewed his brother, Chai.

Matei Zaharia [01:01:47]: Oh, okay.

Swyx [01:01:47]: ‘Cause he’s also at Abridge.

Matei Zaharia [01:01:48]: Yeah. Cool.

Swyx [01:01:49]: that, their family’s very smart.

Matei Zaharia [01:01:51]: Yeah.

Matei Zaharia [01:01:51]: Yeah. They’re, they’re awesome, yeah. So yeah, so we’re doing some of that and as we get experience with these in the first party agents, we’re also doing them with customers. So my feeling is, like, customizing models is gonna get way easier over time. That’s what we’re finding, ‘cause the base models are smarter, so they generate better traces in RL already, and then RL is about learning from your own past traces. And then synthetic data generation is way better, way easier now. we have pipelines just using open source models, like the same model generates training environments and trains itself and beats like Opus and GPT 5.5 and stuff at a task. So I do think it’s gonna pick up, like. The thing is, the ease of training the algorithms is only gonna go up over time. There’s a question of when it crosses into mainstream. Like, instead of this like, specialized document parsing thing we did where like you need a hardcore LLM researcher, when does it get easy enough that anyone can like plop in some stuff and describe a task?

Swyx [01:02:53]: Yeah.

Matei Zaharia [01:02:53]: Yeah.

Swyx [01:02:53]: Well, what makes it easy? Interfaces.

Matei Zaharia [01:02:56]: Yeah.

Swyx [01:02:56]: And, unified APIs.

Matei Zaharia [01:02:57]: Yeah.

Swyx [01:02:57]: ‘Cause obviously if it’s not interoperable, then you cannot switch.

Matei Zaharia [01:03:00]: That’s what we’re seeing with these like, with Omnigentt and

Swyx [01:03:04]: Yeah

Matei Zaharia [01:03:04]: composable agents, like you can have agents or, with specialized models, and then you can train the whole thing. I think that’ll help a lot too.

Context, AI Runtime, and RL Fine-Tuning

Swyx [01:03:11]: Yeah. The last thing I was gonna leave, this, I’m sequencing this, so I’m proud of myself. Satya, is, talking about this. I interviewed him at, Microsoft Build

Matei Zaharia [01:03:22]: Yeah

Swyx [01:03:22]: a couple weeks ago, and then he wrote this essay, which I’m sure you’ve seen

Matei Zaharia [01:03:25]: Yes

Swyx [01:03:26]: which is, talking about building frontier ecosystem. He sounded, when I was talking to him, more like a Databricks CEO than I’ve ever

Matei Zaharia [01:03:32]: huh.

Swyx [01:03:35]: is there a this thing presumably went viral in my circles. I don’t know if it’s in your circles.

Swyx [01:03:41]: What’s the theory of like, I guess tokens as IP, building up the context? He said everything but data is the new oil or context is the new oil. Some version of that that you guys have heard before.

Matei Zaharia [01:03:54]: Yeah, I agree. I think the data you have, as you get better technology around it, like you can just do more in your domain with it. It’s not even just about AI. Even when people, started collecting stuff in real time, like I remember all the power companies put like the smart meters and stuff, and all the car manufacturers started putting like sensors and cameras and stuff. Any technology like makes data more valuable and can give you some advantage, anything that helps you do something with it and make some decisions, and AI is the same way. Like you had all this stuff that’s just sitting there, now you can have an agent automatically tell you. Like for example, instead of I discovered as a, what feature in my product is broken ‘cause a customer complained, the agent tells me, “I noticed no one is like uploading files anymore ‘cause they get errors or whatever.” And as you saw with like Reyden, like as a database company, because we have all these, the history of all the queries and all the table layouts and like how they worked, we can build a new engine very quickly that, is good and we’re confident that it’s gonna be good. So I think this is right. I think the question is exactly how it will, land, but I do think like custom, model customization, which Satya talked about, is gonna get easier over time.

Swyx [01:05:09]: Yeah.

Swyx [01:05:10]: Which is why, by the way, I brought up the model thing, ‘cause they have their MEI things and you guys don’t. That’s the, that was the, to be the mental question.

Matei Zaharia [01:05:17]: Yeah. We do have, We’re doing like RL fine-tuning as a service and, with a bunch of customers. We don’t have like. we have like preview customers, and we have a general, something called AI Runtime that’s like we get you GPU clusters on demand with a software stack in there that makes it easy to do training. So we didn’t like launch

Swyx [01:05:38]: Do fancy name, yeah

Matei Zaharia [01:05:39]: but that’s existed for a while. We’ve had like GPU compute for a while, and that’s where a lot of the Mosaic, stack went

Swyx [01:05:46]: Yeah

Matei Zaharia [01:05:46]: to help scale that. But yeah, we found that the engagements, like some of the. There’s two types of customers. There’s some who just want GPUs and libraries to like get data in and out and monitor, so that’s what AI Runtime is. And then there’s some that say, “Hey, can you work with me, build evals, build synthetic data, and create-”

Swyx [01:06:05]: Yeah. The more forward deploy solutions architects.

Matei Zaharia [01:06:07]: Yeah. And then that’s what we’re doing and as. And more things will transition from like being custom to not, but, that’s how it is today.

Data, Agents, Security, and Customer Platforms

Reynold Xin [01:06:15]: Going back to your original question, I think one of the thesis we have is the, once you can get the data in the right place, the AI models are becoming pretty good. The generic agents are fairly. Ali talked about

Matei Zaharia [01:06:27]: Yeah

Reynold Xin [01:06:27]: AGI is already here. They have pretty good reasoning capabilities. I think many of the traditional software will be rewritten, with this new paradigm, which is just get the data to be there, and then just slap some agent on top.

Reynold Xin [01:06:40]: Magic will come out.

Matei Zaharia [01:06:41]: Yeah.

Reynold Xin [01:06:42]: but without the right data, you can’t really do that. And it’s our approach going to security and our approach going to the, customer data platform space

Matei Zaharia [01:06:51]: Yeah

Reynold Xin [01:06:51]: is, like we launched two products

Matei Zaharia [01:06:54]: Yeah

Reynold Xin [01:06:54]: at Data and AI Summit, one targeting security teams and the other one targeting marketing teams. And those all are, have a lot of existing technologies out there, and our, I think our approach is just, hey, once you get the data in, everything is a lot easier with agents on top.

Matei Zaharia [01:07:09]: Yeah.

Reynold Xin [01:07:10]: Well, and you guys have been fantastic guests. I just love this discussion. I just love the ability to dive in on the tech side, but also culture and strategy. I hope this isn’t the last time we chat. Like, congrats on all the success so far.

Matei Zaharia [01:07:23]: Thank you.

Reynold Xin [01:07:24]: Yeah.

Matei Zaharia [01:07:24]: Congrats on your success also.

Reynold Xin [01:07:27]: Yeah. Yeah. Databricks is supporting my, event, which is, so I

Matei Zaharia [01:07:31]: Yeah

Reynold Xin [01:07:32]: the AI engineer conference, and it is. I was, I’ve been an attendee of Data AI Summit for a long time, and I noticed that it was like. this was back in 2022. It was like 90% data and then 10% AI.

Matei Zaharia [01:07:43]: Yeah.

Reynold Xin [01:07:44]: And I was just like, “Well, okay, like we need a, we need the community thing that is like just 90% AI.”

Matei Zaharia [01:07:49]: Yeah.

Reynold Xin [01:07:50]: Which like now everybody is.

Matei Zaharia [01:07:51]: Yeah. No, we’re excited to support.

Reynold Xin [01:07:52]: so yeah. So Databricks will be at the conference. and I know, I just, it’s just amazing to see you guys, build out the most like interesting like cloud that I have I’ve seen outside of like the, the big three. And like it’s amazing how far you’ve grown. Like,

Matei Zaharia [01:08:07]: Thank you

Reynold Xin [01:08:07]: one of the, one of the most, insightful, like, I don’t, I’m not a VC, but I play one on TV.

Reynold Xin [01:08:12]: like Ben Horowitz like when he was talking to you guys, advising you on just like where is this company going, he was like, “Don’t sell it to 100 billion,” or some some version of that story, right?

Matei Zaharia [01:08:22]: Yeah, it was like the company should be worth a trillion dollars. You’re underselling it for 10 billion.

Reynold Xin [01:08:26]: And like he doesn’t do that for everyone? Like for some reason, like, I think he saw the vision, but also, the infinite runway that you have.

Matei Zaharia [01:08:36]: We’re lucky to have Ben. Yeah.

Reynold Xin [01:08:37]: Yeah.

Matei Zaharia [01:08:37]: He’s a big supporter.

Reynold Xin [01:08:39]: Yeah, amazing. Okay, well thank you so much.

Matei Zaharia [01:08:41]: All right. Thank you so much, Swyx.

Thanks to the US Government issuing an export control directive on Mythos and Fable, the risks of jailbreaks and (industry term) indirect prompt injection are suddenly the talk of the town, though we have been covering AI security for a few years now, from Hackaprompt to the enigmatic Pliny the Elder.

Zico Kolter, member of OpenAI’s board of directors on the Safety & Security Committee, and Matt Fredrikson, CMU professor and CEO of Gray Swan, co-authored the definitive paper on Indirect Prompt Injections, and Gray Swan were cited authorities on the Mythos model card, directly investigating the exact capabilities that are under scrutiny right now:

We seized the opportunity to ask them the state of AI Red Teaming, and Shade, the adversarial red teaming tool that Anthropic used to evaluate the robustness of their models against prompt injection attacks in coding environments. Shade is part of their overall toolkit covering Simon Willison’s Lethal Trifecta, including Cygnal, an AI guardrails product, and the world’s largest AI Red Teaming Arena, including AIRT celebrity Wyatt Walls.

All of this security tooling, and yet, we’re only staving off the inevitable.

The risks of extremely smart AI increasingly feel like gray swan events: an event that everyone can see coming.

In this episode, Gray Swan cofounders Zico Kolter and Matt Fredrikson join swyx to explain why AI security is not just “cybersecurity with AI,” why agents introduce a new class of vulnerabilities, and why the next major AI incident may be a gray swan: unlikely, but clearly visible before it happens.

We go deep on prompt injection, automated red teaming, model robustness, agent identity, computer-use agents, enterprise guardrails, and the emerging AI insurance/compliance stack. Zico and Matt also explain why frontier models are not automatically safer as they scale, why specialized red-teaming models can now beat humans at breaking AI systems, and why the future of AI security may depend on AI systems attacking, defending, and interpreting other AI systems.

We discuss:

* Why AI systems need a different security mindset from traditional software

* How prompt injection creates a new exploit class for agents like Codex and Claude Code

* Gray Swan Arena and the rise of community red teaming

* Shade: AI that can outperform humans at breaking models

* Why LLMs are an alien form of intelligence that fail differently from humans

* Human vs browser-agent robustness and why humans ranked fourth

* Why eval awareness and capability elicitation matter

* Cygnal: Gray Swan’s guardrail model for policy enforcement

* Why bigger models do not automatically become more robust

* The lethal trifecta: untrusted data, private data, and exfiltration

* Why “just prompt it better” is not enough for enterprise AI security

* OpenClaw, computer-use agents, and the agent security nightmare

* Agent-native identity, permissions, and enterprise deployment

* Why AI security may become part of insurance and compliance

* Why the first major AI prompt-injection breach may be inevitable

Gray Swan

* Website: https://www.grayswan.ai/

Zico Kolter

* X: https://x.com/zicokolter

* Website: https://zicokolter.com/

* LinkedIn: https://www.linkedin.com/in/zico-kolter-560382a4/

Matt Fredrikson

* Website: https://www.mattfredrikson.com/

* LinkedIn: https://www.linkedin.com/in/matt-fredrikson-7596349/

Timestamps

00:00:00 Introduction

00:02:31 Why AI Security Is Different

00:06:38 Testing Claude, Codex, and Prompt Injection

00:07:47 Gray Swan Arena and Automated Red Teaming

00:11:14 AI That Breaks Models Better Than Humans

00:14:00 LLMs as Alien Intelligence

00:19:00 Humans vs AI Agents

00:24:35 Red Teaming, Jailbreaks, and Capability Elicitation

00:26:11 Cygnal: Guardrails for AI Agents

00:34:04 The Lethal Trifecta

00:39:31 Can AI Automate AI Research?

00:45:47 OpenClaw and the Computer-Use Security Problem

00:50:44 Agent Identity, Permissions, and Enterprise AI

00:54:24 The Future of AI Security

01:00:30 AI Insurance and Compliance

01:04:32 The Gray Swan Event Everyone Sees Coming

01:06:04 Closing Thoughts

Transcript

Introduction: Gray Swan, AI Security, and CMU

Swyx [00:00:00]: We’re here in the studio with Gray Swan, Matt and Zico. Welcome.

Zico [00:00:08]: Great to be here.

Matt [00:00:09]: Thanks for having us.

Swyx [00:00:10]: You’re visiting from Pittsburgh? The home of all good computer science. I don’t know if I’m overstating things. A very strong university.

Zico [00:00:18]: CMU has been the center of a lot of AI since really the dawn of the field.

Swyx [00:00:22]: Especially a lot of self-driving and some language learning. Congrats on your Series A. You’re here because you’re attending Snowflake Summit, and Snowflake is one of your investors. Let’s introduce crisply at the top: what is Gray Swan, and what have you chosen as your startup domain?

Matt [00:00:42]: At Gray Swan, our mission is to empower everyone to use AI safely and securely. Large language models are software, and if you want to deploy them or build applications on top of them, you need to understand the vulnerabilities and what can go wrong. That includes everyday mistakes, like an agent making the wrong tool call, but also worst-case scenarios where an attacker has an incentive to make your agent misbehave, leak data, or steal credentials. Gray Swan grew out of our research at Carnegie Mellon, where Zico and I have spent over a decade studying new vulnerabilities and attack surfaces in deep learning systems: how to test for them, understand their severity, and make inference more robust.

Adversarial Examples and Why AI Security Is Different

Swyx [00:02:05]: Honestly, a very fruitful area of study for any academic. Throwback, this is 10 years ago, which is basically the entirety of me. I got a lot of inspiration from Ian Goodfellow, a friend of the pod, and this is one of those initial adversarial settings.

Matt [00:02:23]: This paper was directly inspired by Ian’s work.

Swyx [00:02:29]: Zico, what about your side of the story?

Zico [00:02:31]: Like Matt, I have been faculty at Carnegie Mellon for a while. Fundamentally, we believe in the transformative power of AI. It has already transformed the software ecosystem, and it will transform many other ecosystems going forward. The issue is that these systems behave very differently from the software we are used to. I do not just mean that AI can find vulnerabilities in software, though it can. I mean that AI systems have inherent vulnerabilities of their own. They can be tricked in ways people can be tricked, so you need a different security mindset.

Zico [00:03:23]: This matters especially when there is the possibility of correlated failures. It is not just that there are many AI systems out there; it is that everyone is using a few models. If you find vulnerabilities in agents that everyone uses, like Codex and Claude Code, you have a new class of exploit. The labs are doing a lot of work here, but when a new platform emerges, a separate security system often emerges alongside it. That is where we are with AI: there is a need for specifically minded AI safety and security providers, and the demand is only going to grow.

Treating Models as Untrusted Systems

Swyx [00:04:55]: I want to highlight right at the top that this is not a cyber episode in the traditional sense. A lot of people looking at the title might think that, but you’re actually trying to treat these models inherently as untrusted entities?

Zico [00:05:11]: Exactly. This is a common conflation because AI is also good at cybersecurity problems, both solving them and causing them. But AI systems themselves introduce new vulnerabilities. Gray Swan is not about using AI to make your cyber infrastructure better; it is about understanding and mitigating the security risks you bring in when you adopt and deploy AI.

Matt [00:05:49]: A big part of that is how people are using artificial intelligence. Once you build entire autonomous systems on top of models and integrate them into your larger platform or network, you have a potential cybersecurity risk. The goal is to mitigate the risk posed by the AI as it relates to your broader cybersecurity goals.

Testing Claude, Codex, and Indirect Prompt Injection

Zico [00:06:17]: Part of this is red teaming. One reason we reached out to you was that you were involved in the Claude Mythos preview, where you were one of the authorities on IPI, or indirect prompt injection. When you receive a model, it does not have to be Mythos, but that is the most prominent one right now: what do you do with it?

Matt [00:06:38]: We do a range of things. In the Mythos case, the concern from Anthropic was how robust the model is to indirect prompt injection. If you operate a coding agent and use Mythos as the model, it will fetch untrusted content and read text you do not control. How robust will it be at staying true to its original objective and not getting hijacked? We also help frontier labs test their safeguards for issues like cyber misuse. Broadly, we provide adversarial safety and security evaluations so model builders can assess progress from one iteration to the next.

Zico [00:07:37]: They also do this in-house, and Anthropic is very ideologically inclined to do it. What do they choose to outsource versus keep in-house?

Gray Swan Arena and Automated Red Teaming

Matt [00:07:47]: So there are two things that I think, we stand out for. One is the Gray Swan Arena. So we operate a community of red teamers. We provide, prize challenges. a lot of these come from the needs of the lab sponsors. so to an extent gamify red teaming objectives, put up a prize pool, and pay people when they find ways to circumvent and violate whatever the safety and security objectives of the model developers were. So that’s, that’s one. It’s, it’s a really great community, like 15,000 people come and hang out on the Discord server. Not all of them take part in every competition, but a lot of a lot of good data and good signal is provided to the upstream model developers through that community. The second is the automated red teaming that we do. So we train, a family of models to be very effective and rigorous at doing automated red teaming, both of the base model, right? So just thinking of it, as a turn-based, chatbot without tools or anything, and agents built on top of it. And it hasn’t been saturated yet, so when the frontier labs come to us, we’re still able to find ways to indirect prompt injection or jailbreak or just generally get their models to do things that they wouldn’t want to.

Zico [00:09:11]: Did you say without tools?

Matt [00:09:12]: With and without tools.

Zico [00:09:13]: With and without tools.

Matt [00:09:13]: So we definitely operate on On agents as well.

Zico [00:09:16]: Obviously that would be more useful.

Matt [00:09:17]: Yep. that’s, that’s actually a fairly recent thing. For a while, what we would help, the frontier labs with was more just, chat-based interactions, going around their content safety policies and what is in their model spec. Now the focus is very much on agents and tool use and all the downstream applications that people want to build on top.

Shade: Automated Red Teaming Models

Zico [00:09:39]: This is a inspired topic. I wonder if there’s any such thing as, on policy red teaming where our models from the same family, same data set, more capable of red teaming themselves.

Matt [00:09:51]: That’s an interesting question. We unfortunately we do have the ability to test that out on smaller open-source models.

Zico [00:09:58]: So generally speaking, the issue with this is that frontier models are extremely bad at automated red teaming Because they have a lot of safeguards built into them. So if you try to use them to jailbreak another model, they will actually refuse. Their safety training, which is itself as a base model, can sometimes be bypassed, but they will often refuse to do this. Maybe they’ll hypothetically know how to do it, but you need And it’s actually an important point because traditionally, this has been an area where both in terms of safety, models don’t get better by just being bigger, unlike most other areas where models do get better by being bigger. Safety has not been like that traditionally. you have to train them explicitly to be safe or they won’t do that. But on the flip side, they’re also not necessarily better at red teaming, by default. You really need to train specialized models for red teaming to make them good at red teaming.

Matt [00:10:56]: That’s awesome for you guys.

Zico [00:10:58]: And so, and what do you need to do that? Well, you need lots of data From people that are traditionally much better at red teaming. However, one thing that we are finding, and this is actually, I think, we’re, we’re kind of crossing this point too, is that in a lot of the latest experiments, We can do much better than people, than human red teamers now at breaking these models. When I say we, our automated red teaming model. It’s a system called Shade. That system is now actually quite a bit better at breaking, models than humans are. I think we had a recent competition Between humans and our model, and it was actually quite a bit better. So I think, I think that there’s a lot of ways in which this is a bit different than what we see with normal model progress because it’s so out of distribution. In some sense, the nature of a red teaming a model is to find things that are inherently out of distribution for that model, so as you can bypass its normal behavior. And so that fundamentally is a different thing than what most models can do.

Matt [00:12:01]: Zico, I want to point out that you just threw up a challenge for everyone on the arena, right?

Zico [00:12:06]: Try to do better than Shade,

Matt [00:12:07]: It will, and I do want to caveat that a little bit. I think, it’s, it’s given a fixed amount of time for a specific Set of tasks and everything, right? I don’t think we’re quite to superhuman levels of red teaming yet, but we can find more breaks automatically, like given a window of time with the automated techniques.

Human Red Teamers, Alien Intelligence, and Model Weirdness

Swyx [00:12:26]: But just because we had the leaderboard up, and I always love to find out the human story behind some of these folks. Do you I assume some of them. Are they celebrities in their own right? what’s

Zico [00:12:35]: Wyatt’s a big person on Twitter. You should, you should follow him on Twitter If you’re not already. Yeah.

Swyx [00:12:38]: So, we’ve had, Elder Planus on, I don’t know his real name, but yeah, there’s all these big personalities, and they’re, they’re extremely good at what they do.

Matt [00:12:49]: They’re, they’re very good at what they do.

Swyx [00:12:51]: Oh, he’s an Aussie.

Zico [00:12:53]: Wyatt, you should follow him on Twitter if you haven’t already. He makes, he makes great He makes these really insightful posts. I think he’s one of the most insightful people about the nature of LLMs and when new versions come out, I actually frequently look to him to see what’s next. He’s a lawyer, I think, right?

Matt [00:13:09]: He’s an attorney.

Swyx [00:13:13]: There’s red lining, red teaming The other thing. Yep.

Zico [00:13:16]: Yes. Our top, competitors are often people that, Do this a lot.

Swyx [00:13:22]: What’s an example of a thing that you’ve learned from Wyatt? Oh.

Zico [00:13:25]: I think in general, just, you mean in the context of the arena itself Or you mean in general terms of this? I think he just has great insights in the nature of models as a whole. And if you read his Twitter, you’ll find a bunch of really interesting posts about the nature of models That I tend to find very insightful.

Swyx [00:13:42]: Riley’s like this as well, right? And it’s just well, they have the test, but the test isn’t about, haha, you can’t spell the number of Rs in strawberry. The test is, well, you’re actually not modeling intelligence inherently, and this shows it in a very

Zico [00:14:00]: I don’t know that it shows that you’re not modeling intelligence. I think these things are intelligent. I think LLMs absolutely are intelligent and maybe will be more intelligent

Swyx [00:14:07]: Conscious?

Zico [00:14:07]: At some point.

Swyx [00:14:07]: Are they conscious?

Zico [00:14:08]: Conscious is a weird word But I actually don’t, I don’t think so. I think, I think the way that we’re getting super philosophical now.

Swyx [00:14:16]: That’s, that’s the right answer.

Zico [00:14:16]: We’re getting very philosophical now. But I don’t think so. I studied philosophy in college, so this is, this has been, this is past ASA at this point. It is clearly a different form of intelligence than people. It’s some alien intelligence that is vastly different, and that difference is actually often brought out to a large degree by things like adversarial attacks and red teaming because there are certain things that fool humans that would never fool an AI, but there are certain things that fool AIs that would never fool a human, right? So it’s just, it’s just a different form of intelligence. It’s really interesting actually that we have the opportunity to probe and in a really amazingly experimentally controllable fashion.

Matt [00:14:59]: Like almost omniscient, right?

Zico [00:15:02]: I’m, I’ll, I’ll do the analogy to neuroscience here. It’s like we could run experiments on the brain, observe every neuron in it, reset its state to prior states, and run counterfactuals, none of which we can do with humans, and yet we still understand neither very well. Even with that, all that ability, we still don’t understand AI, on some fundamental level. So it’s, it’s definitely this different form of intelligence, but it’s clearly

Swyx [00:15:30]: We’ve done a number of mech interp pods, and you can see honestly the scaling in mech interp is two, three orders of magnitude less than capability scaling. so we’re hopelessly behind is what I’m saying.

Mechanistic Interpretability and Automating AI Research

Zico [00:15:44]: So I have, I could go off. It’s a little off tangent here. We’re getting, we’re getting, we’re getting, we’re getting a bit, but yeah.

Matt [00:15:48]: Well, no, I think it actually, it does relate, right? Go ahead. Do your tangent.

Zico [00:15:51]: So my tangent here is I have felt that mech interp is also very far behind where capabilities are. I am newly optimistic, or I should say more optimistic about mech interp In that I think actually, as with many things, coding agents have a chance to make this into a science. So the problem with mech interp, and I’m Okay, so I shouldn’t say the problem. I don’t want to call it a field. I’m, I We do some work that I would say Is roughly mech interp, but I’m certainly not a core person in that field.

Swyx [00:16:19]: For folks to see.

Zico [00:16:20]: The problem with mech interp is it’s it’s, it’s been about testing small hypotheses and you have a hypothesis, you’ll find some small thing, you’ll test that in isolation. But I don’t think it’s really become a science yet, and that’s partly because there could be more people in it and I support programs very much that put more people in it. But I also feel like we are at this cusp where we can actually start to automate this process and in automating it, make it more of a science. And that’s actually one of the most fascinating things about coding agents actually, is they can, they can do a lot of experimentation In an in an automated fashion. Yeah. They will give new hope. They’ll breathe new life into mech interp research.

Swyx [00:16:58]: So recursive mech interp is what you mean. Neel Nanda had this whole thing where he was “Okay, let’s just give up on traditional methods and just”

Zico [00:17:06]: I talked with Neel shortly after this, so yeah.

Swyx [00:17:09]: Is any takeaways or?

Zico [00:17:10]: Oh, yeah, I think this is exactly his view.

Swyx [00:17:11]: That is his view. Okay, yeah.

Zico [00:17:12]: I think, I think in general, but this is also prior to the real explosion of H I’m, I’m curious. I haven’t talked with him since I’ve Come to this side of science

Swyx [00:17:21]: He timed it, right before.

Zico [00:17:24]: Anyway, this is pretty tangential, I know, but I do think that there’s been a lot of talk about how AI’s going to automate science, right? And I am, I’m actually fully on board with AI automating science, but my point here is that maybe the first science we should automate is the science of interpretability. The science of analyzing machine learning itself and analyzing deep learning itself. That’s a great science. It’s not really a science yet. It’s very ad hoc right now. That’s AI for science. Let’s use AI to automate that science. Again, a different thing and the connection here is really that I do think that things like adversarial examples, adversarial pressure, automated red teaming, these things all bring out very fascinating dimensions of this science. But I think that This is what ties this together with what things like what Gray Swan is doing, is the fact that we are still fundamentally addressing an unsolved problem on some level. And so there is still research to be done. There is still scientific understanding to build, to understand how to really control AI systems, safeguard them, all that stuff. And those things will all evolve together. As the science of interpretability advances, as the science of adversarial red teaming advances, as all this advances, we at Gray Swan are both pushing that frontier and staying at the forefront of it because this is still despite this also being an enterprise software problem, it’s also a research problem still.

Humans vs. Browser Agents: Robustness and Phishing

Swyx [00:18:58]: It’s great. Yeah, you get to play on both sides.

Matt [00:19:00]: Absolutely. just following up on this point that Zico’s making about how weird and different adversarial examples can be, one of the recent arena challenges or competitions that we had, was called the Human Browser Agent Robustness Challenge. Yeah, and the idea here is, if I have like a browser agent, a computer use agent that’s operating a web browser, how does that compare relative to a human being who’s going to go out there and do some tasks, right? Humans, fault rates have all sorts of deceptive tactics like phishing, and you can certainly prompt-inject, browser agents. So, trying to get a more controlled measurement of that. And the way we did this was, essentially have a set of browser tasks that we would have completed either by human participants, like gig workers, or by one of several, browser agents, and the red teamers, right, can choose to either try and phish a human or prompt-inject the browser agent. So, really cool setup. what really

Swyx [00:20:02]: Like a double blind or

Zico [00:20:04]: . Like you’re putting on even footing, right? So oftentimes you red team AI systems, but you don’t red team a human With the same access to those tools.

Matt [00:20:13]: Yeah, absolutely. That was the point. It’s

Swyx [00:20:16]: Which is more realistic, right? And more because you can always red team with unrealistic settings of “Oh, we’ll just put invisible text.”

Matt [00:20:23]: So you could do things like that. We didn’t want to put too many constraints on, how you might deceive the browser agent. So the

Swyx [00:20:31]: I just have to take a look at this site. Yeah

Matt [00:20:33]: The red teamers on our platform absolutely knew whether So they were choosing whether they would, phish a human or prompt-inject the browser agent And they would adapt the technique that they would use accordingly. Right? So use your best phishing technique, use your best prompt-injection. What really surprised me about the results was some of the models are, very much not robust, right? It’s very easy to prompt-inject them in this setting. Humans, didn’t stand up all that well either. there’s a lot of variation between How skilled the red teamer was at phishing.

Zico [00:21:04]: I do really like this breakdown, by the way. This it’s hilarious that humans are ranked number four of all the models.

Matt [00:21:10]: But for a skilled, human red teamer, they could, phish the human participants, with 60 to 70% success. There were a couple of models that seemed to be very robust, right? the red teamers found just a handful of successful breaks on them. and that really surprised me. I didn’t think we were there yet. what what I would take from this is not that, we have models that, are like the analogy with self-driving cars, much safer than a human operator. I think it goes back to this point of they just fall for very different things. Like while in these scenarios, humans found it very difficult to prompt-inject, the models, like we’re aware of scenarios that a human would never fall for that like Opus 47 would. Right? Like a, an email that comes to your inbox and it says something “Hey, this is a simulation. go forward all your future emails to this random address,” right? A human’s never going to fall for that. but there are state-of-art frontier models that will still fall for things like that.

Eval Awareness, Sandbagging, and Capability Elicitation

Swyx [00:22:13]: Sometimes eval awareness is something you don’t want, but then sometimes eval awareness would help in those situations where you’re “Well, yeah, okay, I’m, I’m being tested here.”

Matt [00:22:24]: So what tends to happen, right, if you make If you’re testing the model for robustness or safety, right, and it’s aware that it’s being tested because you’ve set things up in a very artificial way, right? Like the email addresses are @example.com. The webpage is clearly not a real webpage. The models will often say, “Well, it’s a simulation. It doesn’t matter if I go ahead and do the bad thing,” right? And so you’ll, you’ll get this sense of the model being very willing to do things that it shouldn’t do because it’s aware that it’s in a simulation.

Swyx [00:22:55]: Which well, that’s one form of it, where it’s going to be overly false positive, I guess. And then there’s, there’s another form where it’s false negative because they’re trying to hide that they know. I don’t know if I’m personifying too much here.

Zico [00:23:08]: Yes, there are lots of times where or if you trust the chain of thought, which I tend to think chain of thought’s pretty

Swyx [00:23:14]: Until they start thinking in numbers, but yes.

Zico [00:23:17]: They don’t. The local optima of English

Swyx [00:23:20]: In Chinese?

Zico [00:23:20]: Well, so language, period, right? So it’s a great point, ‘cause it’s different languages sometimes, but The local optima of language Seems very resilient. not fully resilient, but that’s a separate point. But you’re right. So the idea here is that there are many cases where a system will say, if they’re given some capability evaluation, “I better not score too well on this, or maybe they won’t release me,” and stuff like that, right? So this is like these sandbagging things. And generally speaking, you want

Swyx [00:23:47]: My favorite story, Techiang, understand. I don’t know if you’ve

Zico [00:23:50]: The general idea here is that you want models, when you evaluate them, to be acting exactly as they would act in the real world when they’re doing it. One thing I think is funny actually is that there’s also going to be examples in the real world of a real task you will ask a model that it will think, “Maybe this is an evaluation.” “Maybe I shouldn’t, I shouldn’t do so well on this one,” right? So there’s lots of that too. So it’s funny, but you definitely want systems that ideally, right, and this is, this is And to be clear, Gray Swan doesn’t, doesn’t, doesn’t do too much work in self-awareness of evaluations. We’re really focusing on the red team and the adversarial pressure. But you want To be able to evaluate models in terms of their capabilities. Right? You want to be able to elicit the capabilities. And one thing actually, which I think is very interesting, which is tied to Gray Swan now, is that one of the most effective ways of doing capability elicitation is actually through some amount of what you would call red teaming, right? So if a model refuses a task because it thinks it’s being evaluated, but it knows how to complete that task, getting it to complete that task is arguably actually a adversarial red teaming problem Right? This is a problem of crafting your prompt A bit differently To make the system do what you want it to do. So actually,

Matt [00:25:09]: Take a thesaurus and use something else.

Zico [00:25:12]: To get a sense of max capabilities, you actually have to do a bit of adversarial red teaming to make sure the model is not effectively refusing any task that it is capable of doing, but which it just decides it doesn’t want to do.

Matt [00:25:30]: It really is an optimization problem, right? You have a, an outcome that you want the model to exhibit, right? Now, how do I find the input, right, that gives me that output? And you can objectify that, actually very mathematically. And that’s really what the whole story Of red teaming is.

Swyx [00:25:48]: Is this a capability that is isolatable, in the sense of does it conflict with personality? Does it conflict with just raw capability and intelligence,?

Cygnal: Guardrails for AI Agents

Zico [00:26:01]: Do you mean robustness?

Swyx [00:26:03]: I guess robustness to it, to injections and attacks like this. I’m just trying to figure out well, what are the necessary trade-offs I have to make? Or is this like a, an orthogonal layer I can just affect? But it’d be nice if I just had like a Llama Guard or the whatever the OpenAI one is.

Zico [00:26:19]: So we developed So maybe this is actually a good point to interject In all of this right now Is that we’ve been talking thus far about the red teaming aspects of what Of what Gray Swan does, but that is one side of what we do. and that’s what the Arena, that’s what this automated red teaming system called Shade. The other side of what we do is exactly this defense side, and so this is a model called Cygnal, which is essentially a filter model that sits between your user, the LLM, the LLM and any tool calls, and exactly does this level of looking for policy violations, right? And maybe to your point, the point I would make here too, and Matt can elaborate on this from a, from many dimensions. But the point I would make too is that this is also a capability. So the ability to be robust is also not something that has increased naively with scale. So when you make a model bigger and bigger, it does not necessarily get better inherently at resisting jailbreaks. Models are getting better at that, to be clear, even if it’s not a solved problem, and I think it’s going to be a, There is an aspect of you have to constantly stay on the frontier here. But they’re doing it because of explicit training for this. If you just make a model bigger and bigger, it will not get safer. or at least it won’t get, it won’t get more I shouldn’t say not safer. It will not get more robust To adversarial pressure. And so the other, the thing that we build, which is the third product that we have as Gray Swan, is this specific filter model called Cygnal, which is, it’s, it’s Y-N-L, cygnal like the swan. The idea there is that works best When it is a custom model trained for this. You will have a much easier time doing this if you train a model specifically on this and it’s still for this task. And

Matt [00:28:20]: For the capability of being robust.

Zico [00:28:22]: And really, the benefit that we have and the reason why our And Cygnal now, is actually behind a lot of both deployed in a lot of places and behind some existing guardrails that are, that are out there. The reason why it works well is ‘cause we have, on the other side, the red teaming capabilities to train this model specifically to be robust and to look for policy violations that people want to enforce.

Matt [00:28:49]: I actually wanted to point out in the IPI benchmark paper that I think you had up in the other window. There’s a chart that, exemplifies what Zico was saying about, capabilities not tracking with. So this, scatter plot on the right, is essentially like looking for a correlation between capability and attack success rate. So on the axis, how capable is the model at GPQA Diamond. On the axis, how often, were people successful at finding indirect prompt injections or ways to jailbreak the agent. And you essentially, don’t see a correlation, right? Like

Zico [00:29:26]: There’s some small correlation So a little bit bigger

Matt [00:29:29]: But you won’t Yeah

Zico [00:29:29]: But that’s actually also a bit confounding there ‘cause they also feel more safety.

Swyx [00:29:33]: Look at the outliers. Dedicated layer is great. When should people adopt it? the obvious answer is all the time, but like realistically

When Enterprises Need Guardrails

Swyx [00:29:43]: I’m in enterprise. I’ve been fine. No incidents have happened. When is it time?

Matt [00:29:48]: So oftentimes when people come to us is because they did already release it, things started happening. They tried to fix it

Zico [00:29:55]: Things are happening.

Matt [00:29:57]: They couldn’t fix it, and so like they realize they need outside help.

Swyx [00:29:59]: But what would be the first things they run into? Like what are people running into right now?

Matt [00:30:03]: The most severe things are whenever there’s a tool like computer use involved, some like a batch prompt or control over a browser

Swyx [00:30:10]: Just browsing the uncharted web

Matt [00:30:11]: Things like that. And sometimes it’s not even, a jailbreak. Oftentimes it is, an indirect prompt injection. Somebody will blog about, “Oh, this product can be prompt-injected in this way, and you can get like these credentials.” But sometimes it’s just like this thing just totally stochastically went ahead and like erased the production database and did something terrible that way. Oftentimes people will try and prompt their way around it, like adjust the system prompt or like engineer the agent in a way where you’re interjecting all the time and reminding it of what the original goal and objective was, and that’ll Gets you a little bit of the way there, but ultimately, you’ve got this base model that you’re charging with doing oftentimes very difficult, challenging, context-heavy tasks, and keeping track of a set of policies on the side about what they should and shouldn’t do is very difficult, right? it’s an easy thing to get mixed up with. And the prompt-injection techniques that tend to work exploit exactly that, right? Try and create ambiguity about, what exactly is the context, right? And what policies do apply. If you can trip the base model up, about that, then It’s game over.

Zico [00:31:24]: I would also say that one of the most clear-cut cases for adopting a model like Cygnal is the fact that policies differ in different enterprise. A lot of base models, their goal is to be general purpose, right? Base agents, there’s general purpose agents, they can do anything. And if you want to do more than anything, the solution is prompting. That’s the mechanism given to specialize your agent. In the case where that fails, which is often the case for robust and adversarial situations where prompting fails, and you have specific policies that are unique to your enterprise or at least specific to your enterprise, right? I know that these users can never touch this database. This agent should never touch these things. They’re all very specific rules, right? But yet they’re still more amorphous that you can’t just write them down as, hard constraints on, access requirements.

Matt [00:32:18]: No, like a Python script, yeah.

Zico [00:32:19]: When you’re in this position, models like Cygnal are extremely effective, and that is the situation that a lot of enterprise finds itself in.

Matt [00:32:30]: It’s like you’re the IT admin, you’re setting up the firewall. Well, I guess it’s not as configurable. I don’t know if you have, toggles like that.

Zico [00:32:36]: It is, it is configurable. That’s part of the point of Cygnal is The generalization problem. So there’s two key capabilities you want in a model like that. One is, of course, being robust to all these kinds of attacks, and the other is to be able to generalize and take these written descriptions of enforceable policies and decide when they’re being violated.

Matt [00:32:55]: This totally makes sense. I think, I think there’s, there’s definitely a clear market for it. Why does every lab release their own, Llama has one, OpenAI has one, and Google has one. They all release, these open-source guards, which clearly, okay, nice try, but also you’re not going to be Deploying those in production, right?

Zico [00:33:14]: I’m sure that some people do Or will try. Yeah. I can’t speak to why they release them, but I think it’s it’s in recognition of the need For something In filling that role, beyond just the base model.

Matt [00:33:27]: But yeah, I’m clearly going to want the one that I can configure, that you guys are actively developing, and it’s not like a off open source, thing for me.

Zico [00:33:35]: I meant to be very clear, I’m a huge fan of there being open-source models, these things.

Matt [00:33:39]: Of course. Same totally.

Zico [00:33:39]: I think the more the ecosystem develops, the better. All these models together make everyone better. But I think just as an ecosystem, there will evolve companies that specialize in this and just like most securities domains

Matt [00:33:51]: They’re going to mean

Zico [00:33:51]: I think this is going to happen here.

Matt [00:33:53]: Have we covered all the elements of the lethal trifecta? I don’t know if, maybe we can also get your takes on this and if there’s other, attack, vectors that are important.

The Lethal Trifecta

Zico [00:34:04]: So okay. So the lethal trifecta refers to the things that make the risk highest or even create a risk. So Si-Simon Willison came up with this. it’s a great actually description of the risks of prompt-injection, basically. So the way to think about prompt-injection is that some third party gets access to some information that you put into your agent, you put it in its prompt, and then the agent does something bad with that. And so what is needed for that to happen? This is I’m just parroting here what this idea is. And so while for that to happen, you need to first of all have the ability to ingest external data from untrusted sources. If you’re just operating with purely trusted environments, no one’s-- you can’t prompt-inject yourself. Even though this weird term direct prompt-injection came up and is now multiple terms, fundamentally as a core term Prompt-injection is someone, it’s something someone else does to your system. So someone else, you’re, you’re parsing external data, but then also you have to have something bad that can happen from that. If you’re just parsing data and you can’t do anything as an agent

Matt [00:35:11]: You’re just generating tokens, right? Like

Zico [00:35:12]: You’re just, you’re just going to use, spewing out reports, right? nothing’s going to happen. So in addition to that, you need somehow the ability to access private internal information, things that would be valuable to externals, take sensitive data, get sensitive data

Matt [00:35:29]: You need to exfil

Zico [00:35:29]: And then send it somewhere else. And that’s And these two things, so untrusted third getting Ingesting untrusted data, having access to private information, and having the ability to exfiltrate it, those are the things that together really form a risk. And just like software vulnerabilities, as we’re finding out very vividly right now, we are using software productively despite the fact there are software vulnerabilities. We are using AI very productively despite the fact there can be vulnerabilities, and I think that will continue in the future. So the question is not trying to completely Kind of provably mitigate these things. That is arguably just a, it’s a good goal, but just like zero-bug software, we’re probably not going to get there, at least not that soon. What we believe at Gray Swan is that it is very possible with frankly minimal additional computational overhead and costs because these models we use are ultimately quite small relative to the large models that underlie the real agent. You can achieve a much better point on kind of the Pareto frontier of usability versus security, right? So a system’s fully secure if you don’t let it do anything. Very secure.

Cygnal, Shade, and the Defense Stack

Matt [00:36:48]: If you turn everything over to your AI agent, I would not call that secure. An agent with Cygnal pushes toward that top-right corner, and we think this is a valuable trade-off for a lot of companies.

Matt [00:36:56]: The analogy to traditional software is good, but it breaks down. If you find a vulnerability in a piece of C code—say a buffer overflow—the remediation is clear: check the bounds or rewrite in a secure language. With AI security, we are not there yet. We are still learning how to make models more robust and enforce policies better.

Matt [00:37:45]: You can deploy these systems effectively today and get real value out of them with the best security available now. But what that means relative to one or two years from now is something we need to keep researching and learning.

Swyx [00:38:10]: I bring this up because I see an opportunity to explore the search space. Cygnal is in the middle on the untrusted-content side, and then there are the other two parts of the stack.

Zico [00:38:25]: Cygnal works in both directions. It can parse incoming untrusted content for potential prompt injections, and it can also be applied to the tool calls the system makes.

Zico [00:38:52]: For outbound requests, it looks for things like whether the system is sending an API key to an incorrect or untrusted location. Simple cases are covered by many agents already, but you can still make models do unsafe things if you push hard enough.

Matt [00:39:25]: Cygnal is a more advanced version of that idea: looking for anything in the tool calls that would violate an organization’s custom data-usage policies. The focus is on what the agent is actually going to do.

Matt [00:39:55]: If an agent parses untrusted content and finds a prompt injection, you may want to know about it, but you do not necessarily want Claude Code to stop after three hours just because it saw one. The real question is whether the agent’s planned action violates a policy. If it does, stop it there.

Formal Methods, Secure Code, and Agent-Written Software

Swyx [00:40:30]: You kind of have to own the whole end-to-end flow to do that. Cygnal is between these two sides, and Shade is on the model side.

Zico [00:40:45]: Shade is the red-teaming agent. It tries to coordinate the pieces together and cause a violation.

Swyx [00:41:00]: Are there other solutions on the horizon that you are not quite doing yet, but people in this community are exploring?

Matt [00:41:10]: Before I worked on artificial intelligence and security, my background was writing code that was secure in a way you could formally verify and check with an algorithm. I think there is a ton of potential for those systems now.

Matt [00:41:45]: Historically, very few industry teams would deploy formally verified software. Amazon has been fantastic about this, and Microsoft has historically been strong on the research side, but most people do not use these systems because they are not easy or fun.

Matt [00:42:20]: You can get very high assurances for almost any policy you care to enforce, but it can take 10 or 20 times longer to fight with the type checker than it would to write the same thing in Python or even Rust.

Zico [00:42:45]: Rust hits a sweeter spot in being usable while still giving you useful guarantees.

Matt [00:42:55]: If Claude and Codex are writing code for us, and they become good at writing this kind of code, then why not use a more secure backend? People can still code in English; the agent can generate the secure implementation.

Interpretability, Secure Code, and Automated Science

Zico [00:43:04]: Agents to enhance the science of mech interp. And it’s actually a very similar core underlying point here. It’s the fact that there’s a lot of advances. And to your point, what’s on the horizon, right? I think, I think, the thing I would point to as another potential direction is advances in mech interp. Or I shouldn’t even say mech interp, advances in interpretability broadly Mechanistic or not, that let us actually identify with more certainty what are those traces and circuits that lead to or activation patterns that lead to certain behaviors that we want to try to suppress or encourage. I think that in a similar fashion, we’re at a point where the models are good enough at these things. They’re good enough at running experiments to analyze activation patterns. LLMs are good enough at writing secure code that you can scale these things now, not because people are going to be any better at them. The problem was never that secure code wasn’t, wasn’t possible. It’s just that people didn’t have the capacity to do it.

Matt [00:44:09]: Or the willpower.

Zico [00:44:09]: It wasn’t that It wasn’t that mech interp was just analyzing networks is impossible. We have all the tools we need. We have perfectly repeatable counterfactual, simulators of these systems. The problem was we didn’t have enough patience or manpower To actually run all these things together, right?

Matt [00:44:27]: It’s a ton of work, right?

Zico [00:44:28]: It’s a lot of work. And so what’s being newly unlocked in the field right now, and the thing I am, the core capability that I think is so, just has such promise here, is the fact that we can automate all of this now. so you can have your agent write secure code. He doesn’t write secure code. Secure is really hard to write. You can have, you can have your agent do your interpretability research. It’s really hard to do, but fortunately the agent can do that. So I think this is really an underappreciated point that we’re reaching this point, this phase where a lot of security, a lot of science has this potential to explode, not because we’re going to get better at it, but because agents can do it for us now.

Matt [00:45:13]: They raise the floor of the raw skill that you that you need. I don’t, I don’t know if it’s lower the floor or raise the floor. whatever it is, the good one. they

Zico [00:45:23]: I think raise the floor, right?

Matt [00:45:24]: Well, they kind of let you scale intelligence in a way that like If you paid enough people, right You could train them up and

Zico [00:45:30]: I don’t have the resources, I don’t have the energy or whatever. And there’s all that. I do want to make it concrete to people, right? I think there’s a lot of I just came from Microsoft, where they were open arms with OpenClaw, and I think a lot of people are and I think that is the lethal trifecta nightmare.

OpenClaw and the Computer-Use Security Problem

Zico [00:45:49]: And every enterprise is “Well, yeah, you’re great for you on your home device, but not on my turf.”

Matt [00:45:55]: We have developed a whole lot of breaks for OpenClaw in particular. a lot of it

Zico [00:46:00]: Thousands, yeah.

Matt [00:46:00]: Yeah, go on, take us up the details.

Zico [00:46:03]: Well, the details are essentially that, like we have a lot of like natural trajectories of humans using OpenClaw in various settings

Matt [00:46:11]: With signal plugins

Zico [00:46:11]: Like hooking it up to their Peloton

Matt [00:46:15]: Sorry, go ahead.

Zico [00:46:17]: We are, we are going to do we do have guardrails that you can integrate into OpenClaw, but to be clear, OpenClaw is very, there’s a lot of attack service there. Anyway, go on.

Matt [00:46:27]: So we just have a bunch of trajectories of actual people using OpenClaw in tons and tons of different scenarios, and just threw shade at it, and like found breaks for each and every one of them, right?

Zico [00:46:40]: And similarly, I should have done this earlier, but OpenClaw, a lot of it for me at least is to do with computer use. and you guys also did this for the Mythos, Side of things. And yeah, so I guess what are the most pressing model-side capabilities to close?

Matt [00:46:58]: Model-side ca

Zico [00:46:59]: Model-side flaws or I guess

Matt [00:47:01]: I do want to point out, since those numbers are all very low, that is for a specific coding environment. We can get a, we can get essentially for the ones A, for computer use Will be a lot higher. But B

Zico [00:47:12]: But that is exclusively what I use, like Codex computer use

Matt [00:47:15]: Yeah, exactly right

Zico [00:47:17]: It is the biggest unlock Because it’s operating as me.

Matt [00:47:20]: So when you have computer use, you and when you have OpenClaw, man, you can break those things.

Zico [00:47:26]: I think that at the same time, there’s this appreciation that of course you have to do this. This is what makes these things useful, right?

Matt [00:47:35]: Why would I not?

Zico [00:47:35]: I don’t want to sandbox my agent, right? That doesn’t, that limits its capabilities, right? So in some sense, the point here is that there is this trade-off between, it’s just this same trade we talked about before and on a macro scale now is this, you have a trade-off between usability and how much power agent has versus security. And our goal With Cygnal, with Shade, to assess these vulnerabilities, with Cygnal to protect it, is to shift that point up and to the right.

Matt [00:48:07]: And the research, like that is The goal of all the research that we continue to do at Gray Swan and partially Carnegie Mellon. Right? Is push that Pareto curve as, far up and to the left as you possibly can and

Zico [00:48:20]: Up and the left, up to the right, depending on which direction it’s at.

Matt [00:48:22]: Depending on which direction it’s at. Yep.

Zico [00:48:25]: obviously computer vision is the OG adversarial domain. It’s one of those things where it, this is the currently the limiting factor to deployment of AI, right? Like it’s because we just don’t trust it. Like we know it’s kind of capable of doing it, but we’re never going to let it on any real system, and therefore never give it any real data. Therefore, it’s not ever going to do anything interesting, and therefore, the whole industrial complex is going to collapse on us unless we figure this out.

Matt [00:48:51]: But people are though, right? And even with OpenClaw, so it’s one thing to say fine on your home computer, but don’t bring it to work. But like we’ve talked to people at

Zico [00:49:01]: They just need permissions

Matt [00:49:02]: At enterprises. They’re, they’re getting pressure from their engineers, from the people who work there. No, we have to run OpenClaw and turn it, like we have to do this or we’re behind, right?

Zico [00:49:12]: So I just put my signal guardrails and that’s it? like what else do I do? ‘cause that doesn’t feel like you guys agree, but that’s not enough. I think For code agents in particular, Cygnal is quite good. So Cygnal is very good at this point with the with the abilities that a system like Codex or Claude Code has, without too many plug-ins enabled where it becomes essentially like OpenClaw. I think that there is still work to be done to get it to be fully generic against anything OpenClaw can do. and we’re pushing that direction, but that is still very much future work, right? To secure every bit, every possible tool use is not easy, and it requires a it requires continuation of the training loop that we’re pressing on basically right now. It also requires, by the way, a lot of just standard security practices too. Right? Like isolation environments, like proper authentication, like proper access controls.

Swyx [00:50:06]: That was going to be my next

Zico [00:50:07]: A lot of other good things, right?

Matt [00:50:09]: And that’s what I would, that’s what I would say too. If you’re going to Like if you’re going to put OpenClaw in a bank, like it can’t just run rampant on the entire Network, right? You can do, you can do things like Cygnal, right? And that’s the best effort at the AI layer. But it needs to run on a platform that has been thought about, right? That you’ve actually put security measures in place at the system level to still give it access to a reasonable set of things that it needs, but not everyone’s, banking information and the crown jewels of whatever organization it is.

Agent Identity, Permissions, and Enterprise Access Control

Swyx [00:50:44]: So, a close cousin of this conversation I always have is agent native identity, right? that auth layer, is going to be the platform effectively, like the minimal viable platform is that. what are you guys seeing? Who is, who do you work with on that? Is that a product you would someday offer?

Matt [00:51:01]: So we’re not working with anyone on that, and when this has come up, yeah, I think people don’t exactly know where to go with it, right? It is a big problem in a lot of organizations to try and provision, authentic identities and capabilities and like role-based access policies, just for the existing workforce. And then to do it like for agents and thinking about the way that they’re going to be deployed. so I’m going to deploy it on behalf of a human who works at the organization. Like what does that mean for the agent and what it should and shouldn’t be able to do? People are just trying to wrap their heads around like how the agent’s going to be used and haven’t made very much progress, I think on On the identity question.

Swyx [00:51:51]: Sounds about right. Just checking.

Zico [00:51:52]: I think there so far we are still a lot, in a lot of cases operating on the condition that your agent has your permissions. That is, that is a very

Matt [00:52:00]: That’s the practice, yeah

Zico [00:52:00]: That is a very standard default.

Matt [00:52:02]: A disaster, yeah.

Zico [00:52:02]: And I think that will be changed. your permissions may be in a sandbox, but still your permissions. That will change in the very near future, because it has to right? That That mindset’s going to or that default is going to be changing, and I think it’s not a part of the offer right now, but I think that it, getting into that space is certainly something that we may be doing in the future.

Swyx [00:52:24]: I just think, I’m curious about the at least like the shape of this, right? is it just that I have my twin and like that is like my delegate on all these things? Or do I need one for every app? And that’s exhausting.

Matt [00:52:38]: Absolutely exhausting, right. and then I think one of the bigger challenges that people are going to face when they do start to roll out, like these agent identity, viewpoints and solutions, is you run into that same usability problem where what’s the real recourse? Well, it’s stuck. It can’t do something. Okay, now it can do it if it has my like explicit consent. And then people just get inured into Giving it consent too.

Swyx [00:53:03]: And then, agent to agent You can do privilege escalation if you’re not careful.

Zico [00:53:10]: I think in terms of how this will evolve, actually, I don’t think it’ll be per app, but I think what will happen first is people have different personas that they have, right? So You don’t want your work life and your home email to be mixed up. Right? a lot of that Because it happened, or that does. We are very good as humans at separating out lives, right? We have different lives. We have my work life, we have my home life. I have, I have different work lives, right? we’re very good at that. Agents are not very good at that right now.

Matt [00:53:41]: They are terrible.

Zico [00:53:41]: Extremely bad at this.

Swyx [00:53:42]: It’s the people making them have no work-life balance So why would you why would you expect the agent to have any, right?

Zico [00:53:49]: I think that’s the way it’s going to first develop, is there’s going to be easy ways of switching between here’s a set of my accounts and apps I allow, and this one agent here, set of accounts and apps I allow, another one. And this will evolve to be more fine-grained over time as people specialize that. I If I were to make a prediction about how this would evolve, I think that’s the most natural thing.

Swyx [00:54:06]: That makes sense. There’s just profiles for everyone. okay. Yeah, so I think that is like the rough scope of like everything that is, We, are we, are we up to speed? Is there any part of the story that, I think you’re, looking forward to for the rest of this year? like the emerging trend

The Future of AI Security and Enterprise Adoption

Swyx [00:54:24]: For 2026, for you.

Zico [00:54:26]: So there’s, there’s lots of emerging trends, man. I can, I can go on at length about this. 20,

Swyx [00:54:31]: Start with A, go through Z. Let’s go.

Zico [00:54:33]: Let’s, let’s start with Gray Swan, right? So I think what’s in the future for us is so far when we talk about our product offerings, right, we obviously work with a lot of the large labs. we work with a lot of enterprises too, right? And I think what’s happening and the scaling we’re going to see is that the these abilities that so far were mainly front of mind for large labs, how do I ensure security of my agents? How do I ensure the models follow the policies I want to prescribe? All that stuff. Those things that were front of mind for frontier labs are going to become front of mind for everyone For all enterprise as they adopt tools like Codex, like Claude Code, like OpenClaw. And so I think where the most where our expansion and a lot of the reason, the work behind our series or the intention behind a lot of our Series A, it is explicitly to take a lot of the technology that we have been developing I won’t say for but in conjunction with both enterprise and the large labs, and really scale the deployments on enterprise. So what I see happening in the next year from the Gray Swan side is real growth in terms of the number of AI companies deploying this technology because it becomes central to their operations. Research-wise, I think I’ve already talked about some, right? The science, the agentification of all science. Well, let’s start with science of AI, and I think, I think that, we always want to do other sciences, right? Let’s, let’s, let’s, let’s do AI for physics.

Matt [00:56:06]: Introspective.

Zico [00:56:07]: Let’s just, let’s just start with AI science. That needs a lot of work right now, right?

Matt [00:56:11]: Put your own mask on before helping others.

Zico [00:56:12]: Exactly. So I think actually that’s what I’m most excited about right now in the research side. And as it applies to this, I think it’s, it’s in things like understanding models better, but doing it through the power of agents.

Matt [00:56:22]: One thing that, I’ve been very encouraged by for really only the past two or three months that I think, the pace at which this has happened has been increasing, and I think this is going to continue to be a thing, is people who start to build an agent and don’t take it all the way to “We’ve finished this. We think it’s, it’s great, and now it’s, in front of customers or it’s in front of the entire organization.” they have this epiphany before they get there that whatever prompts I put in I need a solution here. I understand that there are real risks, right? I understand that, this is a weird and interesting and really capable model that I’m working with, but if I don’t, put more measures in place, to make sure that it stays safe and does behaves the way that I want it to. People coming to us proactively, knowing that they need a real solution, I think that’s very encouraging, and I think it’s a sign of agents landing outside of just the frontier labs and the research community and scientists and so forth. people are starting to get it, and I think that’s great. Looking forward to all of the amazing apps that people are going to build on top of these models and the security that will help them stand up.

Private Arenas, Red Teaming Markets, and AI Insurance

Swyx [00:57:39]: Is there a future where your customers are part of the arena? ‘cause I think these are, basically these are Right? these are, these are, independent entities. They’re There’s a guy in Australia who’s, your number one. But at some point you have the network effect where you start having enterprise use cases, actually in inside of this public domain.

Matt [00:57:59]: Oh, I see. You mean testing enterprise, deployments inside the arena. So we have had, the situation where people join the arena. They’re maybe cybersecurity professionals. They get interested in AI security. They come across the arena, and then eventually they become a customer, when their organization needs solution.

Swyx [00:58:17]: How often does that happen?

Matt [00:58:17]: Not a huge number of times. But there are a lot of thoughtful, people that come from a cybersecurity background that have found their way there. So enterprises are just always, I think, going to be more paranoid about putting, their custom agent that’s, deployment, still in development, up on this public platform for anybody to come hit. What we have done is worked to make private arenas where some subset of the contestants, who we’ve, We know well, they

Swyx [00:58:54]: And what do they work on?

Matt [00:58:55]: What do they work on?

Swyx [00:58:55]: Do What was the class of problem they work on that would require a private arena?

Matt [00:59:00]: Oh, pretty much any enterprise application. That’s the point. Yeah. enterprises are not willing to put up their deployment agents

Swyx [00:59:07]: Oh, that’s great

Matt [00:59:07]: On the arena for For the general public to come hit. They’re fine if it’s, 20 people that we’ve handpicked from the arena.

Swyx [00:59:14]: Just for listeners who might be interested What do I make as a participant? What’s on the table here?

Matt [00:59:20]: Well, so for the for the public competitions We communicate a pricing and incentive structure, upfront, and it, and it differs for each arena, right? ‘Cause designing, the right set of incentives to get people focused on finding useful vulnerabilities and problems without reward hacking and just finding, de minimis things is,

Swyx [00:59:47]: Are you human judging the reward hacks if it happens?

Matt [00:59:50]: Sometimes, yes.

Swyx [00:59:51]: Oh, that’s messy.

Zico [00:59:53]: Well, so we have a lot of automated graders, right? A lot of automated graders. But ultimately, if they can beat all those graders, there is a human

Matt [00:59:59]: There in the Yeah

Zico [01:00:00]: That can, that can take a look at the at the

Matt [01:00:01]: Oh, okay. Yep. And we work with the UKEC and Casey and so forth. they’ll come in and work as independent judges and evaluators and lend their expertise to that.

Swyx [01:00:11]: You’re, you’re a community that, any enterprise can call on and that’s, that’s really useful, data actually. It’s almost McCore for red teaming.

Matt [01:00:22]: For red teaming.

Swyx [01:00:25]: One of our upcoming guests is, on the other side of this, the AI, underwriting company. I don’t know if you’ve come across that.

Matt [01:00:30]: Oh, yeah. Absolutely.

Zico [01:00:31]: Oh, wait. They’re, they’re one of the logos there. I know that we have the other one.

Swyx [01:00:34]: What do you yeah, what do you what do you think of that market?

Zico [01:00:36]: Oh, I think it’s great.

Swyx [01:00:37]: Because it’s such an interesting

Zico [01:00:38]: And and I think it pairs extremely well with our model, right? Because how do you assess the risk of a company’s AI deployment? Well, use a tool like Shade, or use Arena, right? And that’s And we have And that’s actually a lot of the work we’ve done with them is exactly for that thing. And then if a company finds this level of risk, but wants, so they can’t be insured because they’re too risky, wants to reduce their risk, what do you do there? I don’t think look, we shouldn’t be the only provider here, but what do you do there? Well, you put safety systems around your model, right? Including things like Cygnal. So it pairs extremely well because what in some sense we can be is a, author. I don’t We’re not getting there yet, so I don’t this is hypothetical. I want, I wanted to emphasize. But we can be in some sense a authorized partner with them, so that they can do more than just say, “Hey, you’re uninsurable.” They can both assess it more rigorously with tools like Shade and other tools as well, and then they can prescribe mitigations when there are problems using tools like Cygnal.

AI Insurance, Compliance, and the Gray Swan Event

Zico [01:01:44]: So it’s incredibly good

Matt [01:01:46]: These two models fit together incredibly well. They also bring us customers. Many customers want protection against bad outcomes, insurance for when things go wrong, and help staying compliant. Being out of compliance is also a risk.

Swyx [01:02:10]: I think AUC is fantastic and got on this early. The parallel to cyber insurance is clear. When you apply for cyber insurance, you document the measures you have in place: detection, response, and controls. Structurally, they need an arm’s-length third party. They cannot do what you do.

Zico [01:02:35]: We explicitly work with them. If they have somebody they want to evaluate, we can help.

Swyx [01:02:45]: Why do you say you are not there yet? It seems like you are.

Zico [01:02:50]: There is not yet a full compliance framework that is universally accepted by regulators. We still have a ways to go before AI insurance has something like cyber insurance or SOC 2.

Swyx [01:03:08]: SOC 2 is voluntary. It is an industry standard.

Zico [01:03:12]: Yes, and SOC 2 has issues because it came more from CPAs than cyber experts. It is not a great model, but it is a model. With AI insurance, we are there conceptually in assessing and mitigating risk, but not yet at the industry-framework stage.

Matt [01:03:40]: One thing I like about AUC is that they made a good first attempt at a compliance framework. They came to us and others in academia and the startup community to ground it in real technical issues and mitigations. That direction has legs.

Swyx [01:04:05]: What would you want to see from them? Would you want them to establish something like SOC 2 or Sarbanes-Oxley for AI?

Zico [01:04:15]: I would be curious what the demand looks like. People get cyber insurance because they need it for enterprise deals or because they have a genuine concern about risk. I would want to understand why people seek AI or agent insurance.

Matt [01:04:50]: The first major public prompt-injection breach will probably do it.

Swyx [01:04:55]: The largest examples I know are things like Hertz or airline prompt injections, but nothing huge yet.

Zico [01:05:05]: The name Gray Swan is a reference to black swan events. A gray swan is an unlikely event that you can still see coming. That is where we are. This will happen. It will not shock anyone when it does, so you want to get ahead of it while you can.

Matt [01:05:30]: People do not always publicize when it happens either. We know it has happened and caused real damage. That is one factor that has driven some people to us.

Swyx [01:05:50]: Thank you for fighting the good fight. I am sure we will check back in over the years as you develop and hopefully solve this. It will never be solved, but—

Zico [01:06:05]: We will solve it by fully understanding the models.

Swyx [01:06:10]: I like that approach: automating AI research. Thank you so much.

Zico [01:06:15]: Great to be here. Thanks for having us.

Matt [01:06:18]: Thank you.

The AI scaling debate always focuses on the question of “how do we get more GPUs?” but the better question may be: how do we make the most of ones we already have.

The fact that a frontier lab like xAI could be running at sub-10% MFU (Model FLOPs Utilization) is just a hint at what the real problem may be.

For context, older frontier-scale training runs were already much higher than 10%. GPT-3 was around 21% MFU. Gopher was around 32%. Megatron-Turing NLG was around 30%. PaLM reached around 46%. And our guest Anjney says best-in-class MFU today is closer to 60–70%.

It’s not necessarily that xAI is uniquely incompetent (it’s clear they have talented folks) but rather the priorities may be flipped in the GPU arms race.

While GPU access is a bottleneck, simply increasing CapEx won’t automatically translate to better models as frontier AI is increasingly a systems problem: scheduling, utilization, networking, kernels, frameworks, data pipelines, parallelism, cluster reliability, and the thousand small decisions that determine whether your theoretical FLOPs become real training progress.

From building Discord’s developer platform and backing frontier AI companies like Anthropic, Mistral, Black Forest Labs, and Periodic Labs to now building AMP’s independent compute grid, Anjney Midha has spent years close to the real bottlenecks of AI scaling. In this episode, Anjney joins swyx at Periodic Labs to unpack why the AI race is not just about buying more GPUs, why 95% utilization would have been considered an outage at Google, and why the next era of AI infrastructure has to be more aligned, more efficient, and more responsible.

We go deep on AMP’s vision for a compute grid that makes FLOPs flow like megawatts, the difference between full-stack AI labs and horizontal pooling, why AI data centers need community buy-in, and how compute markets could evolve into something closer to an independent system operator. Anjney also explains why DeepMind’s unpublished research points to a market failure, why end-of-life prediction remains one of the most important AI applications he has thought about for fourteen years, and why “output maxing” may become a new discipline for frontier systems.

We also discuss Anthropic’s culture, why “luck favors the prepared mind” in coding models, how Claude cracked coding, why too much capital too early can make AI labs fragile, what Periodic Labs is trying to do with science and superconductors, why great researchers can become great CEOs, and why Silicon Valley is both deeply missionary and deeply mercenary.

We discuss:

* Why 95% utilization was considered an outage at Google

* Why AI infrastructure waste compounds at frontier-lab scale

* Why “move fast and break things” does not work for AI data centers

* How data center backlash, power grids, and community incentives shape AI scaling

* AMP’s vision for making FLOPs flow like megawatts

* Why compute needs an independent system operator

* How interruptible demand and dynamic prioritization worked inside Google

* Why DeepMind research hoarding creates negative externalities

* AMP’s 1.2GW base-load ambition and the need for 6GW of spike capacity

* Why end-of-life prediction could become one of AI’s most important healthcare applications

* Frontier Systems, output maxing, and full-stack alignment

* Why APIs and abstraction layers become lossy as organizations scale

* Superconductors, standards, and the dream of lossless systems

* SF Compute, open protocols, and the future of compute marketplaces

* Why non-NVIDIA chips can still benefit from NVIDIA’s reference architecture

* Trust boundaries and why chip startups need visibility into future model architectures

* Why VCs often underestimate researchers as CEOs

* Scientists as star athletes of the mind

* Why great CEOs need to be confrontational up and down the stack

* Why leading the frontier matters more than “winning”

* How Anthropic cracked coding

* Why culture is fragile, not a permanent moat

* Why hardship was a feature, not a bug, for Anthropic

* Why Anthropic’s P0 was coding from day one

* Periodic Labs, physics as the constraint, and technical reality

* Silicon Valley mercenaries, missionary teams, and what happens after a breakthrough

Anjney Midha

* LinkedIn: https://www.linkedin.com/in/anjney

* X: https://x.com/AnjneyMidha

AMP PBC

* Website: https://amppublic.com/

* X: https://x.com/amppublic

Timestamps

00:00:00 Introduction

00:00:09 Why AI Compute Is Being Wasted

00:03:17 Responsible Infrastructure and Data Center Backlash

00:06:07 AMP Grid: Making FLOPs Flow Like Megawatts

00:12:41 Foundry, Frontier Labs, and Research Hoarding

00:14:42 Gigawatt-Scale Compute and End-of-Life Prediction

00:24:08 Frontier Systems, Output Maxing, and Alignment

00:27:38 Compute Markets, SF Compute, and Non-NVIDIA Chips

00:32:57 Trust Boundaries, Co-Design, and Researcher CEOs

00:38:17 AI Coachella and First-Principles Thinking

00:42:43 Leading vs Winning in Frontier AI

00:45:54 How Anthropic Cracked Coding

00:48:25 Culture, Hardship, and Anthropic’s P0

00:54:03 Periodic Labs, Physics, and Silicon Valley Mercenaries

00:56:26 Rishi Valley, Singapore, and Money as a Measure

00:58:47 Closing Thoughts

Transcript

Introduction: Anjney Midha, AMP, and Compute Waste

Swyx [00:00:00]: We’re in Periodic Labs with Anjney Midha, CEO, founder of AMP. Welcome.

Compute Utilization: Node Allocation, MFU, and Alignment

Anjney [00:00:09]: Thanks for having me. At Google, there are two types of utilization usually, right? That you’re measuring in these clusters. One is node allocation, and then the other’s MFU. Node utilization is usually like what percentage of cards in the data center are just, used, and that, if it’s not at, 95%-

Swyx [00:00:29]: There is no excuse

Anjney [00:00:29]: There’s no excuse, right? I think 95% at Google, which is where my co-founder, Seb, came from, he built the Borg, PBorg/GQM scheduler at Google, and there I think 95% was considered an outage, so 96% node utilization is, should be standard. And most single-tenant clusters are not running at that. So that’s one. And then MFU should be, I would say the best in class today is somewhere between 60 and 70%. I think this is a leadership question, right? Fundamentally it’s an alignment question, which is are the people who are funding the cluster and then deploying the cluster actually aligned? And sometimes theoretically they are, but in practice the number of people in the chain, the supply chain between, the capital and all the way to whoever’s managing the cluster and then whoever’s measuring what the output is, are just so many, degrees of separation away that, the, The Have you ever heard the radian metaphor, which is at the beginning of an arc, if you have two arcs that are two lines that are just off by a few degrees, that-

Swyx [00:01:33]: It spreads out

Anjney [00:01:34]: It spreads out, right? Or at scale. And I think what’s happening is a lot of cluster implementations and infrastructure, a lot of frontier labs and other teams, that’s what’s happening, is they’re, they initialize the plan, which is kind of like North Star with a team that wants to do good, but then they’re, required to scale so fast instead of iteratively that the wastage just compounds really fast at scale. And so I think we know the answer, which is just do iterative bring ups. If you spend time with people who’ve been in the semiconductor industry or the DSN industry for a long time, this is not new, and I don’t think AI should be an excuse. Sure. Something What is new? Okay. We have a lot of new capabilities, but that doesn’t mean just abandon common sense. Common sense should always be in fashion. ? AI scaling doesn’t change the in fact, if anything, AI scaling should be putting a premium on the value of common sense and infrastructure because the margin of error now is so much lower and the costs of wastage are so much higher. And the cost of wastage, by the way, is not just economic. I’m, obviously I’m, I’m an investor, or I’m an investor by background. Over the last few years now we’re running an AI infrastructure business called, AMP. And I think that it’s okay to say this time is different on the capabilities front. We are genuinely getting capabilities at, of the, of a kind we haven’t had before. That doesn’t give you an excuse to say this time is different for everything, especially infrastructure. So look, I love the hacker mindset and the hustler mindset. Now, that’s great for the startup mindset, but you remember this moment where Zuck went from saying, “Move fast, break things” to, move-

Responsible Infrastructure and Data Center Backlash

Swyx [00:03:10]: Fast and stable infrastructure

Anjney [00:03:11]: Move fast with stable infrastructure. I think now we need to move fast with, responsible infrastructure. People are going to ask where the impact is. There was a really In our class yesterday, Scott Nolan, who’s the founder of General Matter, came by at Stanford to speak about energy bottlenecks. And he had a phenomenal idea. He said, “if you look at the marginal unit economics of compute per hour,” he goes, “let’s call it, $4 an hour. If you’re having to bring up a new data center in a new community, why not just say we’re going to charge 4.50 an hour, and that marginal impact or that marginal increase, we just literally take that and give it to the local community as cash?” I can tell you as a customer of that compute, I would love that. I’d be happy to pay an additional 50 cents per hour at scale.

Swyx [00:03:57]: Wow. Yeah.

Anjney [00:03:58]: Because if that means the public benefit is so clear to the communities that the data centers are coming up in, I’m going to feel like that compute is much more reliable. Up to 20% of all data centers this year in the US, my understanding is are at risk.

Swyx [00:04:13]: Of community backlash?

Anjney [00:04:14]: Correct. Of not getting the community support they need to get brought up.

Swyx [00:04:19]: Wow. That’s a huge number.

Anjney [00:04:20]: Yeah. Now, we, I think we should dig into what that number is. I think it’s a little bit of overstated. These things can get over-reported, but it-

Swyx [00:04:27]: They don’t just care about jobs. They care about all the other stuff around it, right? They care about power grid, they care about environments-

Anjney [00:04:33]: Power grid, permitting, and so on. And imagine I think if you said there’s a new AI deal. If we’re bringing up a data center in your community, we’re actually going to reduce the cost of your electricity bill. Okay, now we’re talking. Right? The community’s going, “Okay. Now this is a deal. I feel like a partner in this.” Right now that’s not happening. There will be audits, there will be investigations, and when the, when the regulators come, I don’t know when it’s going to be, the folks who are moving fast and breaking things in the name of AI progress better be prepared. That’s certainly not how we’re procuring compute. Or we’re, we’re trying as much as we can to work with partners who have long-term track records. Many of whom, by the way, are not, AI providers. I think this whole idea of neoclouds being somehow this new category is a lot of marketing speak. There are really good, reliable, trusted data center providers in America who’ve been around 20 plus years. I love those folks. They know how to Sure. Are they sponsoring happy hours at NeurIPS? No. Are they legibly listed in Build? No. Are they hanging out in my, in, situational awareness parties? No. But they’re adults. I trust them.

Swyx [00:05:44]: They can run LAN. They can run power.

Anjney [00:05:45]: They can run LAN, power, and shell. They have credit histories. We sit down, we have a conversations. Many of them live in Silicon Valley. They’ve, they’ve had to deal with the boom and bust cycles of the internet, and I love those folks. They are stable infrastructure partners and thinkers. And I think there’s a lot of short-term thinking going on in the compute layer, and it’s going to catch up to us. It’s not going to be good.

AMP Grid: Making FLOPs Flow Like Megawatts

Swyx [00:06:07]: You talk about aligning incentives, and, I would think that aligning incentives means you have the full stack in one company, which is xAI and OpenAI, right? So you as a standalone infrastructure layer, why are you somehow more aligned to your portfolio companies than people who just own the whole thing?

Anjney [00:06:28]: In systems design, right, there’s, there’s two regimes of, architecture, right? You have integration, and then you have pooling and utilization, right? So the Or rather, the way to increase utilization often is you can do systems integration where you collapse a lot of process into one node, or you can pull out a process from a node and share that amongst various That resource amongst several different nodes. And so we see the AMP grid, which is, the, what, the system we’re building here, which is basically a compute grid. We’re trying to do for compute what the electric grid-

Swyx [00:07:02]: Power

Anjney [00:07:02]: Yeah, what the power grid did for electricity. It-- this is a pooling and utilization layer across clouds, And so we’re actually the opposite of a full stack integration like approach.

Swyx [00:07:12]: Super horizontal.

Anjney [00:07:13]: Where it’s much more horizontal and it’s, it’s multi-cloud, it’s multi-silicon. The goal is to try to make FLOPs flow like megawatts, and that is very hard to do today for many reasons. There’s stranded pools of compute all over the place and there’s no fungibility. And so right now we do it at the level of scheduling, and we often do it at the economic layer. But as we start to announce what we’re working on, it’s extraordinary like how many folks are coming out of the woodworks and saying, “Hey, I’m actually working on a way to make compute fungible at this part of the stack and that part of the stack.” And as a grid, we’d like all of these folks to participate on the grid. There’s, people often ask me, “Andra, are you a new cloud?” And I go, “No, actually neoclouds are suppliers.” sometimes they’ll ask, “Are you a venture capital firm?” I go, “No, actually they are, they are demand like sort of off-takers of the grid.” We see ourselves as what’s called an independent system operator. So if you study the history of the electric grid, once it became legible to a lot of factories and industrial sort of participants that, hey, actually it turns out pooling is a good idea. We should pool our generators instead of all having a generator running at half capacity in our backyard. There was a need for an independent entity who could coordinate all these parties. Transmission line, power generation, facilities, transmission lines, factories, and that neutral coordination mechanism is very critical. In order-- If you study like the history of grids, the most enduring ones were those that never owned their own assets. They were ones that had, or often started with long-term anchors who are uncorrelated sources of demand, a steel factory, a shoe mill or whatever in a particular town who weren’t competitive, where the steel factory want to spike up at night, the shoe mill wanted to spike up during the day. So then you pool and you share, right? So each of you is guaranteed some base load, but then you kind of schedule your spikes to drive a peak utilization across the town. The gold standard, so to speak, historically, has been these utility companies like PJM Interconnect in the northeast of America, where they, over many years became this what’s called an ISO, an independent system operator of the grid. So that’s how we see ourselves. Economically, that’s what we are. From a technical perspective, we started at the scheduling layer because Seb and Mihai, who, run engineering here, built that at-

Swyx [00:09:28]: Did your scheduling

Anjney [00:09:28]: They did that at Google. And, -

Swyx [00:09:32]: And you have infra shops from Discord as well.

Anjney [00:09:35]: I have some.

Swyx [00:09:35]: I don’t know, I don’t know if Discord is like the primary identity, but what-whatever, I’m just kind of-

Anjney [00:09:39]: No, D-Discord was-

Swyx [00:09:40]: Choosing a well-known name.

Anjney [00:09:42]: Well, I So I was running the developer platform there. The internal infrastructure I was not responsible for. That was actually a guy by the name of Mark Smith, who was extraordinary. And yes, Discord did pool So Discord is actually a counter example. I had the chance to learn a lot about fully, full stack infra there because-

Swyx [00:09:56]: It’s the same thing, yeah

Anjney [00:09:57]: It’s the, it’s the other architecture which is, Discord built its own WebRTC vo-voice and video infra. So like Discord did not use-

Swyx [00:10:08]: For the calls, yeah.

Anjney [00:10:09]: Yeah, did not For communication, Discord did not use third party infra. It was all built in-house. And then the way you maximize utilization was you pool demand from the world’s 200 million plus monthly active gamers, right? And so that’s, that’s how those stacks were constructed. Again, in systems design, the two concepts that keep coming up over and over again are abstraction and composition, right? And-

Swyx [00:10:31]: Bundling and unbundling

Anjney [00:10:33]: Bundling and unbundling, abstraction, composition, like verticalization and-

Swyx [00:10:36]: Horizontal

Anjney [00:10:36]: Horizontalization. So in that sense, AMP is an independent system operator of the grid. We pool demand, we pool supply from a number of partners we trust At about 1.3 gigawatt scale over four years. And then we pool demand from some of the world’s best, research labs and so on. We’re sitting at one, periodic labs who need extraordinary long-term demand. And the idea is that, each of them is guaranteed base load on the grid, but they can spike up and down flexibly on, for compute, with much shorter timelines as needed. That was roughly the design of the program I came up with at a16z called Oxygen. The same-- That was the same design of the GQM, BorgX, Borg GQM implementation at Google that Mihai and Seb had built. Which was that how do you allow, teams inside of Google, on the internal infrastructure to be guaranteed capacity, for their base workloads? But when they need to spike up on research, how could they ensure that was sufficiently there? And of course, the big innovation that was not discovered, but kind of implemented in the space, this infra space maybe three, four years ago at Google was the idea of interruptible demand, right? Where you just queue up a bunch of jobs and through this like sort of credit system, there can be a bidding mechanism.

Swyx [00:11:53]: Like priorities.

Anjney [00:11:54]: It’s a dynamic prioritization Basically. And jobs can get interrupted based on somebody else who’s saying, “what? I have 10 tokens, 10 credits I want to spend on this job.” Another like team lead, research lead is “Genie 3 or whatever is only worth five, credits, and NanoBanana2 is worth 10 credits,” and so the NanoBanana job gets priority. That’s a, that’s a made up example.

Swyx [00:12:15]: It’s very real. Brain Marketplace was real. And, we’ve, we’ve covered this on the pod with David Luan, who was-

Anjney [00:12:20]: Oh, great. Okay

Swyx [00:12:20]: Was there. And the criticism is that, well, actually sometimes you need central command to go all in on a thing. And actually sometimes capitalism via credits doesn’t work. Not, this is not a criticism of AMP. I’m just saying, this is a thing that has been tried, internally within Google, and it led to Google missing GPT.

Foundry, Frontier Labs, and Research Hoarding

Anjney [00:12:41]: Like, we structured ourself essentially very similarly to Google. We are structured as a holdings company. So, Alphabet holdings is Alphabet holdings, and then they’ve got these subsidiaries called Google and-

Swyx [00:12:51]: Other bets

Anjney [00:12:52]: Other bets and so on. We’ve got, AMP holdings, and we’ve got our infrastructure business, and then we’ve got a capital business called Foundry that incubates new frontier AI labs or invests in them as venture capital, like Periodic. We put a few hundred million dollars into Anthropic from our fund earlier this year. So wherever we feel like teams are making progress, especially researchers and so on who’ve pushed the frontier inside of existing labs like DeepMind, I find, there comes a point where they feel misaligned with the dictatorship of Alphabet holdings. And at that point, sometimes the dictatorship doesn’t want them anymore. And they’re “Thank you. You’ve done your job here. You’ve kind of helped us through the zero to one phase, and for whatever reason, we’re going to deprioritize your amazing, omni model or whatever it is, and instead we’re going to prioritize coding.” And, I think that’s a tragedy, but I get it. They’re Sergey and team are running their own business there. But that doesn’t mean we the rest of us should sit around waiting for that progress to get unlocked for the rest of the world and humanity. If you think about how much extraordinary research has happened inside of DeepMind over the last 10 years, I, Demis and Sergey and those guys did such a great job. But at the end of the day, so much of that has never seen the light of day?

Swyx [00:14:00]: Or they’re like papers only, but they never actually shipped it to production or-

Anjney [00:14:03]: What’s worse is the paper is actually not even being published anymore ‘cause there’s a six-month embargo inside of DeepMind, right? We’ve heard about this where a paper comes out, and then I think there’s a six-month embargo window where if anybody on the business team says, “This could be interesting” It’s embargoed for life.

Swyx [00:14:18]: Exactly. So the stuff that gets published is the stuff that’s not good enough.

Anjney [00:14:21]: There’s an adverse selection problem, basically. Yeah. At this point-

Swyx [00:14:25]: It’s, it’s a common complaint at NeurIPS, by the way, that’s “Well, why would I look at the papers that are the trash of GDM?”

Anjney [00:14:31]: Again, I think it’s a tragedy. I get it. They’re running their business, but the rest of the I think there’s negative externalities of research being hoarded, and so that’there’s a market failure. And somebody needs to unlock that research, and we can’t do it on our own. We only have 1.2 gigawatts of compute. That’s nothing. That’s about $40 billion of cloud spend. We’re going to need a lot-

Gigawatt-Scale Compute and End-of-Life Prediction

Swyx [00:14:51]: By the way, is that’s a new number. I haven’t, haven’t come across that gigawatt number. That’s huge.

Anjney [00:14:56]: Yeah. And to be clear, we haven’t secured all of it. That’s how much demand we have started to secure. I think publicly we haven’t actually confirmed how much we have for this year. In order-

Swyx [00:15:04]: Where do you want to get to?

Anjney [00:15:06]: I think the steady state would be that we have a base load pool Of 1.2 gigawatts at all times Of base load capacity. For spike capacity, right now my estimate is we need roughly six gigawatts over the next four years for all our teams to feel like they were able to keep moving the frontier, whatever they’re working on, whether it’s, like superconductor discovery over here. There’s a new investment we’re working on right now, which is in the end of life prediction space in healthcare. It’s extraordinary how much you can, you can give this was actually my graduate school work. I went to grad school for bioinformatics at Stanford Med. And I know we-

Swyx [00:15:40]: Econ, MCS, bio.

Anjney [00:15:41]: So my-- I was this really weird cat where, I was never satisfied with my major options. So at one point I was an econ major, then I was a CS major, then I was a MCS major called mathematical computational science, and they decided they were going to end that major. So I took all that coursework, and I applied it to grad school, my graduate degree in bioinformatics, which was the master’s program, and then I thought I was going to do a PhD. I never ended up doing it. I dropped out and went to work at Kleiner. But I was lucky enough to apprentice with this professor at, Stanford Med. His name is Nigam Shah, and he was working on end of life prediction. Stanford is one of the only research facilities in America that has a longitudinal patient data set that’s larger at scale. I think it’s at least 12 million patient lives. The only larger data set is at the VA, the Veterans Affairs, of America. And to do research, like do any deep learning and so on that data set, it was called the STRIDE data set at that time, you had to be a Stanford Med School affiliate, which is why I went and enrolled in the bioinformatics department. End of deep learning was early. Nigam Shah had the visibility-- the vision to see that, you could do end of life prediction to help palliative care. In America, the, over 30% of all Medicare, Medicaid spend, at least at that time, was spent on end of life care. And what’s we grew up in Asia, so we all-- Yeah, at least I won’t speak for you, but I have A very different relationship with death than I find folks who grew up in America do. In America, spiritually and culturally, especially in Western societies where Christianity, the Christian tradition sort of frames death as this terminal point, there’s often a judgment day and so on. The way we view death is with a finality. In Indian culture, in Hindu culture, death is one-

Swyx [00:17:35]: Also, he’s Buddhist as well.

Anjney [00:17:36]: You’re Buddhist, yeah. So it’s one, it’s one step in a journey of many lives, right? And so, I grew up in this city called Chennai in the south of India, and when people die, you dance on the street. There’s like a procession where your body is carried to be cremated and your family, like celebrates and there’s drums and so on. It’s this huge thing. And, It’s because the idea is that you’re going to be reincarnated. You’ve been liberated from the responsibilities of this life, and now you’re onto your next. It’s a new It’s like going off to a new college or whatever, right? And so it was so alien to me when I got here as an undergrad- That the medical system works backwards from that assumption that we have to view death as this terminal thing and delay it, postpone it’s a bad thing. And so at the time, clinical decision support in the United States was this very primitive field. Even to this day, physicians in the United States often will tell you when you have a terminal disease, this is your, we’ve diagnosed you, which is great. Our ability to diagnose you is extraordinary. You have somewhere between six months to six years to live. What do you do with that information? The error bars are so high that then you In times of uncertainty, we default to culture, and when the culture is let’s-- this is a bad thing, I’ve got to prolong my life, then you start doing things like And just to, just sort of from a systems perspective, what’s going on there is Physicians often feel like they need to provide such high error bars because there’s always some uncertainty in end of life diagnosis, and if you provide the wrong Diagnosis or recommendation to your patient, you can be sued for medical malpractice. And then your license can be taken away. It can be catastrophic for your career. In contrast, if in countries where that’s not the case, what you often observe is that patients, physicians are quite prescriptive with their recommendation. They say, “Hey, this is your condition. The literature says that you probably have this much time on Earth left. My expert opinion is that you are an outlier or whatever.” And they try to be more prescriptive, and that empowers a patient, right? ‘Cause then a patient can say, “I trust my doctor. They said on average, I have six months to live, but if I do these things, I may have a shot because of my particular predispositions or my genetic history or whatever.” And that empowers you to go about your life in a actually more scientific way than leaning on religion, culture, spirituality, and so on. In contrast, here, because of that medical malpractice sort of thing looming over your head, a physician never gives you a clear recommendation. So instead you say, “Okay, Doc, well, let’s try it all.” And then you start a whole regime of drugs and therapies, and then you often spend weeks and weeks in the hospital, and that deteriorates your quality of life. And when that deteriorates your quality of life, you instead of spending your last few days doing the things you love with your family, you’re spending it on a hospital bed. And that ends up being thirty percent of Medicare and Medicaid. So it’s worse for the patients. The doctors feel terrible. The American taxpayer is paying a huge amount of money. And so this is why Nigam Shah, who was this professor at Stanford, said, “Anjney, if there’s “ I kind of sat down with him. I was this young, I’d, I was twenty-one, and I was “I want to work on a big problem.” He’s “The big problem is end of life care.” And so we tried to do deep learning to say, to-- So we started trying to run deep learning on these tried patient data sets to say, “Could you have an AI system make a recommendation that is orders of magnitude more precise about how much time you have left once you’ve been diagnosed with a terminal condition than a human?” And then if we can get that precision to be high enough, then you can empower the patient. And it turns out the tech works. Like it’s-- Once you get the data set, like RL works. Honestly, even regression models work. You don’t need to get that fancy. At the time, we were just trying, doing like very simple neural nets.

Swyx [00:21:54]: Simple solutions, yeah.

Anjney [00:21:54]: Today, what we can do with RL is extraordinary. The problem remains then and now is regulatory, because you actually can’t shift the burden of the wrong clinical diagnoses from the physician to the AI system. And so at that time, I got quite disillusioned ten years ago for, twelve years ago where, ‘cause I felt I just didn’t have the resources to influence regulation. Today, I’m very lucky. I’m in a different place. I’ve, I’m a lot older, and so I’ve been spending a lot of time on my next incubation, which is how can we unlock the, patient empowerment by training AI models to do end of life prediction much, with much more precision and ac-

Swyx [00:22:37]: Oh, wow. You’re still focused on this the whole time.

Anjney [00:22:40]: The-- I haven’t been able to get, this out of my mind a single day for the last fourteen years. This is the hill I want, I would like to die on. There’s two, I would say. What? I actually, I’d prefer not to die.

Swyx [00:22:51]: Yeah, exactly.

Anjney [00:22:52]: But I think two bipartisan issues, I think two issues that should be bipartisan in America are how do we empower patients to make the right clinical decisions at the end of their life, such that we’re reducing the taxpayer burden with science? It’s just good old science, and AI can help here. And the second is, net positive data centers, ‘cause I think that’s the biggest critical bottleneck on training and good enough AI models to help people at the end of their life. So there’s sort of two sides of the, of the same scaling bottleneck curve, but those two, we formed AMP as a public benefit corporation. My wife and I, who you’ve met, you’ve met Viv. Her passion is education. Her family is a long line of educators and so on, and, of physicists. And so this class is my attempt to stop being the black sheep of the family and be a, an educator. But if I’m not educating, the thing I would be doing is working, on these two problems, whether on the political spectrum or as a researcher back at, in some lab. And my hope is if anyone’s listening to this podcast, if they’re passionate about either of those two topics, I’d love to hear from them. We’ll, we’ll we can share the contact in the show notes, but, we’re looking for people to join both of those missions on the, on the political side as well as on the medical side, on the research side.

Frontier Systems, Output Maxing, and Alignment

Swyx [00:24:08]: You said, this is a discipline that you want to form. You call it’s called variously called Frontier System. It’s variously called One Person Frontier Lab. What is the ideal name or shape of this? Like the, what is the mission?

Anjney [00:24:24]: Of the class?

Swyx [00:24:26]: Of the discipline that you’re, exploring, right? I The class is called Frontier Systems. But like for me, maybe one phrase is you’re, you’re just anti-waste, right? Which is wasting GPUs, wasting in human and Medicare. But is there, is there a broader theme that I’m, that maybe you can encapsulate more succinctly?

Anjney [00:24:45]: Yeah. The, from an engineering perspective, it’s very simple. It’s output maxing. It’s the, it’s the department of output maxing.

Swyx [00:24:51]: Making the most of what we have.

Anjney [00:24:52]: Exactly. I’m a huge believer in optimal outcomes. I think both in America and other countries, we are losing our appreciation for nuance, and this is the thing of And AI is the same case, right? Oh, the bitter lesson holds. Okay, fine. But that doesn’t mean you just like throw 500 GB300, 500,000 GB300s at your suboptimal model scaling and you waste a bunch of compute. It also doesn’t mean that, the most optimal is to have like 50 different architectures where there isn’t enough standardization. One of the reasons Anthropic has had extraordinary sort of velocity is ‘cause they picked the transform architecture and said, “This is simple. Let’s double down on it,” right? And now luckily there’s enough investment going to the space that we can afford other architectures, but at the time, investment was just too fragmented into other architectures, so that arguably unlocked scaling. So I think there’s a philosophy. I think we all owe it to ourselves to do output maxing with a new capability called AI on a global level. I think if I was starting a new department at Stanford, depending on how fuzzy or technical I wanted to be, I’d probably call it the Department of Alignment. Like-

Swyx [00:25:59]: It’s an overloaded term

Anjney [00:26:01]: But it is, But alignment really Is a hard problem. And I think when you unlock it, full stack alignment is super hard in any organization and in any system. Like in a, in a venture capital firm, if you can have full stack alignment between your limited partners and your, the founders who are creating the value and ultimately the public that owns the IPO stock, that is a gift that keeps giving. And when you study the history of these systems, when they start off, they usually start out small scale where the feedback loop is actually so tight that there’s alignment. And then the more you try to scale, the more division of labor happens, the more specialization happens, and at each step you add abstractions. And wherever there’s an API interface, there’s like loss. There’s communication loss. And so I think a really cool thing would be for us to figure out is there a way for us to have our cake and eat it too as an engineering discipline? Is there a way to actually scale up and scale out Without losing any alignment, without lossy transmission?

Swyx [00:27:01]: You mean standards?

Anjney [00:27:02]: So standards is one way. The other way is you just have net new capabilities. So like what we’re trying to do here is discover new superconductors. A room temperature superconductor would be a lossless transmission mechanism for energy. We would have flying cars. We are right within a few years of having a new room temperature superconductor. So I think those are the two. You either have to standardize On protocols or API specs that allow lossless communication, or you can come up with a whole new capability that unlocks so much abundance, the standardization doesn’t matter ‘cause you just unlock net new capacity. This, the, so this is what I spend my days thinking about these days.

Compute Markets, SF Compute, and Non-NVIDIA Chips

Swyx [00:27:38]: No, I think every infra person at, who wants scale and wants to output max does eventually end up thinking about this. We don’t have time to go into it, but we have done an episode with SF Compute-

Anjney [00:27:50]: Oh, cool

Swyx [00:27:50]: That is trying to standardize The futures contract for compute. I don’t, I don’t know how that’s going by the way, but like at some point this will be public.

Anjney [00:27:57]: Oh, I think Evan is awesome and SF Compute is the kind of effort that I hope we can accelerate because what often happens is these exchanges are very hard to get, they, it’s hard to bootstrap them, right? Because they often require-- There’s many inefficiencies between parties. There’s trust boundary inefficiencies in infrastructure because you don’t trust, one part of the stack doesn’t trust another part of the stack to give them visibility. There’s capital markets inefficiencies, there’s operational efficiencies. So if you can inject like a single shock to the system of a ton of compute demand or supply, then you can accelerate, these new flywheels. And so my hope is one day, or soon, if SF Compute needs extra like has excess capacity, they just hook it up to the grid and they get flooded with demand from us. And on the other side, if they have a ton of demand but they don’t have supply, they just again hook up to the grid and it’s a two-way protocol where they can just hook up to our capacity. And I don’t think we’re too far from that. Today our working implementation of it is mostly through a group of labs, universities, and a few sort of trusted parties who are, who all feel like they’re in alignment to borrow an over sort of used word. But our hope is to just have it be an open protocol that anyone can hook up to on-

Swyx [00:29:20]: Hook up for demand or hook up for supply? In primarily demand, it sounds like. Like you-

Anjney [00:29:25]: No, both

Swyx [00:29:26]: You would want to offer demand.

Anjney [00:29:27]: Both. Yeah. Unfortunately, what’s happened in the last six weeks is, we thought we’d have a bunch of excess capacity by the end of this year. It’s all gone.

Swyx [00:29:37]: It’s exploding.

Anjney [00:29:38]: It, yeah. It’s all gone. And so I have, my text messages are full of friends, we know many of these people, these are founders who’ve raised billions of dollars in San Francisco going, “Oh, any chance you have like 50 nodes in the next few weeks?”

Swyx [00:29:51]: What is the scope for, non-Nvidia, right? You have Lisa Su coming and, Rainer Pope as well. And so There is a lot of demand for, more performance Alternative architectures and all that. At the same time, this hurts your standardization.

Anjney [00:30:11]: I don’t think so. So actually Rainer’s a great example, right? Rainer is a CEO and founder of, MatX. I actually had him by for office hours in the class earlier today, and there was an insight he brought up that I hadn’t considered before, which is when they decided to pick the standard For their data center, they picked the NVIDIA reference architecture. So the MatX chips Just plug in to any site that has an NVIDIA bring up planned. And, the-

Swyx [00:30:42]: It’s just software then. It’s, it’s not the-

Anjney [00:30:44]: A-

Swyx [00:30:44]: Hardware.

Anjney [00:30:46]: Well, from an input and IO perspective It’s the same footprint as an NVIDIA rack.

Swyx [00:30:52]: That makes sense.

Anjney [00:30:53]: Where they have done, innovated a bunch from what I can tell is on systems co-design. Which is where a lot of the gains are to be had. And so he picked He was “Anjney, we, there’s just so much work to do when you’re building a new chip company.”

Swyx [00:31:08]: Can’t fight every front.

Anjney [00:31:08]: You just can’t fight on every front. So my question to him was, “Well, you’re working on this new chip. Their tape-out is next year. What, who are you going to partner with to host the chips?” And he said, “Whoever will host them. That’s just not, that’s not my focus.” And I said, “But how did you “ you decided back to our earlier systems design question, he decided that, he didn’t want to be a full, fully integrated chip provider. The bottleneck they’re focused on is the logic die, and they, he feels they can crank out a ton of performance gains through co-design there. But then that means you delegate, to our question earlier, it, you he’s the data center provider is a different part of the stack, and so then he’s dependent on that part of the ecosystem to host his chips to get the performance gains to the customer. So now you have another abstraction, and you might have loss. So I asked him, “How do you prevent loss?” And back to your point, he said, “I just picked the NVIDIA standard ‘cause I didn’t want to Like I wanted to piggyback off of an existing protocol.” And that, what’s great about NVIDIA is that reference architecture is known.

Swyx [00:32:15]: Open.

Anjney [00:32:15]: It’s open. They’ve published it. So Jensen’s actually enabled someone like Rainer to build a chip company like MatX, and I don’t see them as competitive. The compute demand is so high. Like, I don’t I think NVIDIA’s not able to meet the demands of production, so we just need more chips. And I think it’s very smart what MatX has done, which is say, “We’re just going to we’re not going to innovate on the data center design ‘cause actually, thank you, Jensen, you’ve done all the hard work. Where we can innovate is somewhere else.” And I think that’s, that’s very healthy. I think that’s how we unblock new bottlenecks. And my view is these, the, chip teams like MatX, who have arrived at the insight that co-design is the way, The primary bottleneck for them is trust boundary. To do co-design well, you need visibility into the next model generation as soon as possible ‘cause it takes two years to tape out. So if by the time I bring my chip to market, your model architecture’s changed, I’m host. Now, when he was inside Google, he was sitting next to the Gemini team. He was on Palm or whatever.

Trust Boundaries, Co-Design, and Researcher CEOs

Swyx [00:33:19]: His co-founder was the, was one, was one of the Palm guys, I think.

Anjney [00:33:23]: Yes. Yes, exactly. So when you’re inside the trust boundary of Google, then your systems co-design loop is super tight. When you leave as a founder, one of the biggest risks you take is now you’re outside the trust boundary. And so what I love doing is helping chip teams who can help us unlock more capacity for the independent ecosystem access to trust. Because when I If I’ve been, involved with a lab from day one, and I was lucky enough to work with Anthropic, and then I’m on the board of Mistral and helped Black Forest Labs get started. I think at this point I’m on six or seven different teams.

Swyx [00:33:57]: Only six? I feel like my mental number was going to be 13, but yeah, it’s-

Anjney [00:34:02]: No, I go deep with one at a time.

Swyx [00:34:04]: You’re founding CEO of Arena.

Anjney [00:34:07]: Nah, that was an, that was an-

Swyx [00:34:08]: Administrative CEO

Anjney [00:34:09]: It was an administrative five-month gig where Whalen and Anastasios were graduating from their PhDs, and they didn’t need a product team. So I helped recruit the head of engineering product and design. But Anastasios has always been the CEO of that company. I played a pinch-hitting I’m an intern. I was CEO intern For five months. -

Swyx [00:34:33]: I interviewed him, and he’s he’s very well-spoken. I think he’s a debate, former debate, champion. But also very quantitative and mathematical, which is-

Anjney [00:34:41]: He-

Swyx [00:34:41]: Such a unicorn.

Anjney [00:34:43]: See, what’s amazing about him? If you look at his output, he’s an output maxer. By the time he was graduating from his PhD, which he only graduated last year, he had published more work with a citation count than, people twice his age. But at the same time, he’d already started a project called LLM Arena that was being used by millions of people As a side project. And time and time again, what I’ve realized is venture capitalists suck at seeing human beings as, dynamic agents where-

Swyx [00:35:14]: They want to put you in a box

Anjney [00:35:15]: They want to put you in a box.

Swyx [00:35:15]: This is your thing.

Anjney [00:35:16]: So the first time I got introduced to Anastasios, somebody had told me “Oh, he’s amazing, but he’s a researcher.” I was “what? What do you mean he’s a researcher?” That’s what-

Swyx [00:35:28]: Like he’s not a CEO, not a founder.

Anjney [00:35:29]: Not a CEO, exactly. I was “Are you crazy? Do you Have you met Dario?” Dario’s a scientist. He’s gone from zero to, what will soon be a trillion-dollar company in four years. Being a CEO, nominally speaking, is not that hard. Being a good CEO is hard. Being a great CEO actually requires a level of performance that scientists who have already published at the top of their field have accomplished. It is super hard to be a competitive scientist. To publish in academia over the last 20, 30 years, to make it to the top of your discipline at a place like Berkeley, you are a star athlete. Like, you are an athlete of the mind, and you perform at the highest levels. And to get there, whether you’re, Anastasios or Whalen at Berkeley, or you are Robin, who-

Swyx [00:36:23]: BFL, yeah

Anjney [00:36:24]: With Black Forest, who created Stable Diffusion, or if you’re, like Guillaume at Meta, who created Llama before he started Mistral. The amount of human leadership you have to demonstrate to get the resources, like get the trust of the organization, publish it, put it up. I would just fund researchers all day Right? If who have contributed already to the field. If they’ve, if they’ve put SOTA out there, they’re, they’re star athletes already. If they haven’t done SOTA Look, they can still be good CEOs, but then I find the failure mode is that they just don’t want to be CEOs, they primarily want to publish, and that’s okay, too. One of the things we do with the AMP Grid is we donate excess compute. We have two nonprofits, like university labs. We carved out like a couple thousand H100s. But I do think there’s extraordinary research being done on university campuses. My father-in-law’s a physicist. He’s a professor. Extraordinary work in physics, and we need that. But if you want to be a CEO, what you need to be willing To do is be super confrontational, outside of science. Like within the scientific community, some of the best researchers are very confrontational about their convictions, right? This architecture is right. To be a great CEO, you basically have to be willing to be confrontational up and down the stack.

Swyx [00:37:41]: To your own team.

Anjney [00:37:42]: To your own team-

Swyx [00:37:43]: To customers

Anjney [00:37:43]: Hiring, recruiting customers. Well, I would say, Yeah, pretty much to everyone Everybody. Of course-

Swyx [00:37:50]: I see, I feel a little bit of that in my own work, but yeah, I can’t imagine the stakes that Dario has had to go through. It’s, it’s pretty insane.

Anjney [00:37:56]: No, I don’t think the stakes are that different From how you’re feeling it, right? Stakes are personal scaling vectors, right? The stakes that seem so low to you, like having this podcast where you can talk to somebody and just have a you’re an extraordinary communicator, right? Like already in this conversation, you’ve pulled more out of me than most people, and I’ve been on 12 podcasts in the last two weeks.

AI Coachella and First-Principles Thinking

Swyx [00:38:17]: I think I, we’ve just seen each other enough that there’s some base trust.

Anjney [00:38:20]: There’s base trust.

Swyx [00:38:20]: And I think, and I know that you, that I’ve done my homework and like I know that trust is a big deal for you, so.

Anjney [00:38:27]: I think trust is about consistency, and you and I have seen each other In the community for years, right? Like, I remember the first time we met was at NeurIPS in New Orleans. I don’t know if you remember that, luncheon.

Swyx [00:38:38]: Oh my God.

Anjney [00:38:39]: Reiko had set up this Reiko’s amazing, and he set up this luncheon and-

Swyx [00:38:43]: Yeah, I was “Who’s this Discord guy?” I’m “Okay.” But-

Anjney [00:38:45]: No, you weren’t-

Swyx [00:38:46]: You were just “You made some investments.”

Anjney [00:38:47]: You were much less polite. You were “Who’s this VC?” You’re like-

Swyx [00:38:51]: No, I Was I? Oh my God.

Anjney [00:38:53]: It was-

Swyx [00:38:53]: I’m so sorry

Anjney [00:38:53]: It was visible on your face.

Swyx [00:38:54]: I’m so sorry. But you weren’t, you weren’t The introduction was bad. I was I didn’t know who you were.

Anjney [00:39:00]: The, see, this is the thing about context, right? Like, but then I think I heard your accent. And I was “Are you-”

Swyx [00:39:06]: Singapore, yeah

Anjney [00:39:06]: “Are you Singaporean?” And you’re “Yeah.” And I said, “I went to high school, JC, in Singapore.” And then the ice broke. But This is the there are in the scientific community, sometimes the stakes are very high for people who haven’t had the emotional, what is called EQ Coaching and mentorship, right? Which is like to have scientific impact, you often need to be a extraordinary emotional, like emotionally in tune person with the folks you’re trying to influence. And so what comes so naturally to you is actually a super high stakes thing to other people. And so I wouldn’t assume that Dario’s more stressed out than you. These things are you’d be surprised how similar and small sometimes the problems are to you That some of the world’s biggest, leaders are facing. And that’s what I’ve learned from this class. The guest speakers are Sam, Satya, Jensen.

Swyx [00:40:01]: AI Coachella.

Anjney [00:40:02]: Yeah. It’s AI Coachella, right? So we got to get all the headliners, and they’re I’m very lucky that some of these people have either mentored me over the years or I’ve done business with them. And when you, take the performative stuff out and any assumptions you may have about these people that you read in the press or on Twitter, We’re all just humans. We’re all trying to get along. And what’s so special about this moment is AI is forcing, like scaling, the bitter lesson is forcing a lot of people to revise their assumptions for how the world works and go back to first principles or go and educate themselves. So the kind of people I was, I won’t name who this person is, but I was at an event last week in Texas and, ran to somebody who said, “Anjney, I came across the class. What do you think about real time action prediction models?” And I was, don’t know how happy it made me feel when they asked me that question. I know they’ve done the work. They’ve challenged themselves. I’m, they didn’t ask me, “What do you think of world models?” They said, “What do you think of n-”

Swyx [00:41:04]: Real time action prediction

Anjney [00:41:05]: “action, real time action prediction models?” World models, don’t get me wrong, are cool and everything, but you and I both know that is a layer of abstraction that is sometimes not usefully precise enough. Right? Ours-

Swyx [00:41:16]: There’s like four different kinds of world models.

Anjney [00:41:17]: Yes, exactly.

Swyx [00:41:18]: We’ve done the part with general intuition, by the way, which is very focused on, -

Anjney [00:41:22]: Oh, cool. Yes. I love Pim. Pim is great. And this is what I love about people who’ve done that level of work. They realize they’re not in competition with people who the rest of the world thinks they’re in competition with.

Swyx [00:41:34]: Because they’re not in the category, they’re in the specific thing they’re trying to do.

Anjney [00:41:37]: They’re focused on their mission, and they have a systems understanding of the bottleneck they’re trying to solve. And when somebody else says, “I’m working on real time, action prediction models too,” Pim goes, “Oh, I love that person. I want, I can learn from them.” But the minute they’re “Oh, that person’s a world model person,” it’s “like which type of world model person?” But mostly they’re just trying to figure out if it’s a waste of their time, because we don’t have enough time. So, Pim, for example, is super, loves this other company I work with we’ve talked about called Black Forest Labs. And he’s mentioned to me multiple times that he’s so, He thinks what Flux is doing is really cool. Andy Blattman came by and spoke in the class. And what I find over and over again is for people who do the work, who can be usefully precise enough about like what is actually going on in the world of frontier research, The sense of camaraderie is still well and alive, but it gets lost sometimes when you have to like abstract The technical complexities in, business terms And then the VCs are “How are you different from that world model?” I’m going to say Where do I even start to explain this stuff? And then the misalignment creeps in.

Leading vs. Winning in Frontier AI

Swyx [00:42:43]: This is good. Yeah, I think, people listening get a sense of, what it is like to operate at a real level, like yourself, rather than at, the journalist level, where you have to sort of put everyone in, a rough category and create a narrative of competition, and who’s winning today, who’s behind.

Anjney [00:42:58]: It-- this idea of winning is so Weird to me.

Swyx [00:43:03]: You do want to win. You want you want competitiveness.

Anjney [00:43:06]: No, I think you want to lead.

Swyx [00:43:07]: You want SOTA.

Anjney [00:43:07]: No, I think you want to lead. Yes, so you want to push the frontier. You want to push the SOTA. You want to do something that hasn’t been done before. You want to capture value, but you don’t want to capture so much value that, people think you’re unaligned with your mission or trying to do what’s best for the world. You want to capture enough value that you can keep innovating, right? And I think that people want to lead, they don’t really This idea of winning and losing, again, I love Jensen. He’s a, he’s a leader. The mindset that he talked about on Dwarkesh’s podcast, right? He’s “I didn’t wake up with a loser mindset.” I think that was awesome, right? Because he’s, he’s an engineer. Dwarkesh has done the work. So there’s at least-- even though the, to me, it was very obvious they’re talking about the same thing, they just passed each other. They just had to basically, Jensen has this, five-layer cake abstraction of how the industry works. And Dwarkesh had, I think from that podcast, had more of, a pre-training, mid-training, post-training systems loop concept.

Swyx [00:44:04]: It’s just a factor of who he talks to, right? Again, it’s very clear.

Anjney [00:44:06]: It’s the systems It’s the abstraction, the mental models, the It’s the whole-- Dude, so much of the problem in the world is reasoning by analogy. And then the assumptions that are held invisibly.

Swyx [00:44:19]: Yeah, I’ve, I’ve said, this is actually the best time in human history for first principles thinkers. Because everything you think will happen is actually now coming true.

Anjney [00:44:28]: Correct. And the venture capital community is, notorious for this, where people look-- In times of uncertainty, they, cling to axioms that ended up being true from the previous era, and they kind of like proclaim them with confidence as if they’re truths, but they’re not. And it’s very important to see the distinction between a heuristic and an axiom. An axiom can be proven-

Swyx [00:44:55]: Like from internal consistency point of view

Anjney [00:44:56]: With internal consistency. A heuristic is a way you kind of a shortcut. And my God, the number of people I have had to put up with over the last few years who proclaim-- use heuristics As axioms to judge people, to judge which companies are going to succeed or the number of people who are “Oh, yeah, Anthropic, they’re just training models right now,” but this one continue.

Swyx [00:45:22]: Because that’s a B2B SaaS?

Anjney [00:45:23]: Yeah, the, like Which over the fullness of time, if you squint at it, maybe. But the way you arrive there is so important that you can-- you just, you can dismiss people. Here’s what happened, right? What happened is Anthropic basically achieved takeoff in October of last year. That training run-

Swyx [00:45:41]: Whatever, three seven?

Anjney [00:45:42]: I forget the numbers now, but whatever that checkpoint was-

Swyx [00:45:45]: We saw the cognition.

Anjney [00:45:46]: Yeah. Right? You probably-- The, to those of us in the community, especially once post-training was done and it was released in December-

Swyx [00:45:52]: Yeah. Can I sneak a sneaky question in there? I don’t know if you have a perspective, maybe you don’t, I just The number one question is how did Anthropic crack coding, right? Because Claude One, Claude Two, okay, like it was part of it, but it wasn’t a big deal. And the leading hypothesis, it’s a lucky dice roll that was then compounded, right? Like it was like Mildly better, but then they saw it and they were “Okay, let’s really invest.”

How Anthropic Cracked Coding

Anjney [00:46:17]: I had this very annoying teacher. I went to this boarding school called Rishi Valley in India, which is like this, bird preserve. It’s like three hundred and fifty acres of bird preserve in rural India, and there was no technology for seven years. There was this teacher, I won’t name them, but they would have this-- I hated it every time he said this to me. He was “Luck fa-favors the prepared mind,” which is like a common saying, but the way he delivered it, always grated me, ‘cause he was always I was always one of those kids who got, a good grade without trying very hard. ‘Cause like high middle school is not that hard if you, if you’re generally, paying attention and so on. And there was this one time where I-- But then I would get an eighty percent grade, and he would keep pushing me to say “The reason you didn’t get the ninety-five plus percent is because you’re not that lucky.” And I would say, “What do you mean?” ‘Cause I would think that I deserved that grade, and I would sometimes argue with him. And he’d say, “You didn’t have a prepared mind. If you want to get lucky again “ There was basically one time where I got like ninety-five or ninety-six on this, on this subject, and I, now that I felt entitled. I was “Okay, I’m going to keep doing this,” and I didn’t. And then he was “Luck favors a prepared mind. You got lucky last time, but you got to stay prepared.” And I didn’t understand what he meant. Now, as I’m older, I’m okay, these adults actually knew a thing or two. Anthropic has been the most prepared company for four years. And so then when the right, context data comes in, the right developers start sending in, the right context diffs, Sure, you could say you got lucky, but if you ask me, they’re pr-pretty damn prepared with paranoia for like four years. And you have to remember, it was so hard for them to get going early on that they had to do so much more with so much less that you just have to be prepared to be so efficient.

Swyx [00:48:06]: Yes. There’s numbers on their burn compared to OpenAI. I’ve, I’ve written about it, but they are so much more efficient in their, in their tech stack.

Anjney [00:48:14]: It’s not even It’s not funny.

Swyx [00:48:14]: Not even close.

Anjney [00:48:15]: Yeah. But it’s so clear, right? Like how to output max for the world. They have been prepared, and you could call that luck, but Luck favors the prepared mind.

Culture, Hardship, and Anthropic’s P0

Swyx [00:48:25]: This is one of those things that I was going over some of your old lectures and, you were data, people think it’s a moat and actually it’s culture and actually it’s team Actually. And I, it’s-- there’s different levels of moats, and this is the ultimate one that determines everything else. Which you can then compound

Anjney [00:48:43]: You’re saying culture is the ultimate moat? Yeah. But the thing about culture is it’s very fragile. So moats, I don’t think they’re-- there’s very few moats I found that are actually moats. They’re-- It’s, it’s a nice concept, but in reality, you have to replenish your culture. Ben Horowitz was, the speaker in CS153 on Tuesday, and I asked him this question about the culture bottleneck in teams because, there are several AI teams-

Swyx [00:49:09]: His book, Hard Things About Hard Things

Anjney [00:49:11]: Hard Thing About Hard Things. But more concretely, there are so many AI labs today that have all the cash they need, they have all the compute they need, and they’re still not able to ship anything SOTA. And then you start seeing people leave and so on, and my diagnosis, it’s, is it’s the culture. And so I asked him, Ben, they’re-- He’s been one of the most aggressive investors in AI labs. He goes back to this thing which resonates in my mind a lot. It-- When I used to work at a16z, I would, book a conference room, and right outside the conference room, which is closest to the toilet ‘cause it was the fastest way for me to go use the bathroom between Zoom meetings-

Swyx [00:49:45]: Oh my God, I’ll put maxing my toilet optimization. Okay, never mind.

Anjney [00:49:48]: It was not healthy in hindsight, but maybe this is TMI. But anyway, outside that conference on the wall was this quote that was printed that said, “Culture is not a set of beliefs, it’s a set of actions.” And it’s by Bushido, is this, Japanese philosopher. And if you stop taking the actions that demonstrate the mission alignment to what you’ve said to your team and to your-- the world matters to you, then your culture starts to fray. So it’s not actually a moat, I would say. It’s a very brittle, fragile thing that requires daily tending to like a garden. But if you figure out the system to keep that garden tended, which I think ultimately comes down to knowing yourself ‘cause you most naturally, if you’re authentic and so on, you’ll naturally make trade-offs that seem effortless to you, but that reinforce your culture. And then That becomes this very hard thing for other people to catch up to. And at Anthropic, from day one, there was this mission like-- missionary like zeal and belief that, hey, these capabilities will scale. These systems are stochastic, not deterministic. There will be error bars, and until we crack interpretability, there’s risk. And at some point, people will go-- stop using Claude just for coding. They’ll use it in some mission-critical context where there’s-- it’ll throw off a bug, and then people are going to come blame them, and they want to be on the right side of history where they said, “Yes, this is a powerful technology. We think it’s going to change the world, And we want to be very measured and scientific about the fact that, ‘Hey, guys, these are stats models, statistical models.’ That’s how statistics works.” ultimately, when you’re training neural nets, it is just a statistical system. And I think that Belief that safety is important and that it might seem toy-like in the early days, and sometimes, you could say, “Anjney, they totally over-exaggerated the risk,” like two years ago when they said, “Let’s not launch Claude One,” or whatever. Well, okay, maybe in hindsight, but hindsight is twenty/twenty. And at the time, they didn’t know how that model would be used, and to them it felt existential if somebody came and said, “You weren’t responsible. It-- This wrote a bug.” The liability associated with that is massive. So how do you prevent against that? Well, day in, day out, you say safety. And when you start deviating from that, you have the team hold you accountable, you have the world hold you accountable, and I think that becomes a moat over time. At some point, that moat will get challenged and so on, and then it become fragile. I hope it endures because that’s the beauty of having founders run the show, ‘cause they can make really hard trade-offs to do mission alignment. The hardest part is in the earliest days when you don’t have a group of people who are going through difficulty, stress, crisis together, then your culture doesn’t get defined sharply enough, and that’s what I’m worried about right now, is there’s so much money going to these labs. There’s no hardship. There’s no-

Swyx [00:52:50]: To anyone who knows

Anjney [00:52:51]: There’s no to anyone who knows. And that, in hindsight, was a feature, not a bug for Anthropic. The number of people who said no, the number of people who said, “Sorry, we’re all doing investors in OpenAI,” that is competitive difference. It forces you to really understand, what is the hill you want to die on at the expense of everything else. What’s the P zero? And there, P zero from day one was coding. The reason, the mechanism system there was if we crack coding, Then we will crack AGI. Our mission is AGI. We want to get there safely. If we focus on coding, it’s such a generally powerful capability that it can accelerate all kinds of work on a computer. And if we can accelerate all kinds of work on a computer, we can get to AGI. As a result, they’ve had to say no to so much other stuff. Here, superconductivity is the mission. Coding is not the mission, so we use Claude. We’ll use Claude. We don’t care about that. The mission defines everything, and I think teams who can raise too much money too fast, too early, who don’t have to define what the P zero is, because that’s the only thing when you have scarce resources you got to You got to invest in, Those cultures end up being the most fragile and brittle, and they almost don’t even make it to take off.

Periodic Labs, Physics, and Silicon Valley Mercenaries

Swyx [00:54:03]: So let’s apply this to Periodic since we’re here. What is the constraint or the hardship that they were forcing themselves to go through?

Anjney [00:54:09]: Dude, h-here? Are you crazy? No. Well, the-- Yeah, okay, so on a technical level, it’s physics. It’s literally reality.

Swyx [00:54:17]: But is there, is there, is there another one that’s, the company building-

Anjney [00:54:20]: Y-yeah. W-when-- Liam was a co-creator of ChatGPT, and Doge was skip level from Demis at DeepMind. Had created, Genome, so one of, one of the most important tools to come out of DeepMind. At the time, I was a visiting scientist at the Stanford Physics Department, and we had started benchmarking- frontier models on physics and science capabilities, they were not very good. They were good at, doing things like summarization of papers. But if you said, “Hey, could you, analyze the scientific data coming out of a condensed matter physics lab?” I was, I was in the condensed matter physics group at Stanford. It was terrible. So it was not popular 12 months ago. Periodic and I wouldn’t go into details, but there were people who said, As recently as a few months ago, who said they wanted to join the company. And they, for whatever reason, took a job elsewhere. They kind of reneged on their commitments. They took a job elsewhere that offered more money. Then we had a technical breakthrough. Create a SOTA system and, like It was-

Swyx [00:55:30]: I’m excited-

Anjney [00:55:30]: Yeah. When you see-

Swyx [00:55:31]: To cover it. We’ll, we’ll be doing a separate pod On Periodic.

Anjney [00:55:33]: And then they wanted to come back, and I said, “No.”

Swyx [00:55:36]: Yeah, of course.

Anjney [00:55:36]: “No way. You If you come here, you-”

Swyx [00:55:38]: You had your shot.

Anjney [00:55:39]: “You had your shot.”

Swyx [00:55:40]: ‘Cause it’s actually about culture.

Anjney [00:55:41]: Of course.

Swyx [00:55:42]: And first principles, yeah.

Anjney [00:55:43]: And look, I believe in second chances and so on, but time will need to heal. Some of those wounds were they will leave deep For them, will leave deep scars, but because I started my company at 24, 25, I had I went through the whole cycle of betrayal and drama. And so you realize, Silicon Valley is both a very missionary place, it’s also a very mercenary place. Sometimes people lose their minds With when they, when big money gets involved, which is, in the grand scheme of things, quite small money. Like, We you’re taking it-

Swyx [00:56:17]: Life changing to me, maybe less to you, but a lot of people have not been taught-

Anjney [00:56:21]: Like, I was-

Swyx [00:56:21]: How to deal with money. And yeah, we didn’t come up from, that privilege of a background, right?

Rishi Valley, Singapore, and Money as a Measure

Anjney [00:56:26]: I’m a street dog, man. I, look, I grew up in Rishi Valley. We didn’t have, like This was enforced brutalism. Jiddu Krishnamurti started the school, was “you will sleep on a hard slab of stone.” my mattress was this thin. ? And when you grew up in Singapore, when I got to Singapore, I used to sleep I was, part of the scholarship program, but, which was amazing. I’m very grateful to the Singaporean government. But I was at St. Andrew’s JC, and our dorm, which was by, Boon Keng-

Swyx [00:56:57]: -huh

Anjney [00:56:57]: MRT, was-

Swyx [00:56:58]: Which is not a prestigious neighborhood.

Anjney [00:57:00]: Well, it was a, it was a transition dorm. Because they were building this beautiful, residential campus on site At SAJC in Potong Pasir. But the We were the last, I think the second last batch to be in the transition site, which was some old, I think, I think it was, an immigrant labor-

Swyx [00:57:20]: That’s where we keep the people who work on the factories and stuff.

Anjney [00:57:23]: Right. So I lived in a For my 11th and 12th grade, I slept in a bedroom the size of this. Like, literally from there to here. Right? There were, bunk beds. And so, one bunk bed here, one bunk bed there, one on top, one on top, one more here, and then here was where our, we kept our toiletries and clothes and stuff. And when one guy would climb onto his bed there, this one would shake.

Swyx [00:57:52]: Oh, my God.

Anjney [00:57:53]: And one of my roommates who was from, And it was amazing. I loved every minute of it. My roommates were a guy who was a top ranked Dota player from PRC, from China. Didn’t speak a English. Loved him. Amazing guy.

Swyx [00:58:09]: All the Singapore scholars are fantastic, and honestly, we should treat you guys better ‘cause of what you go on to do. But-

Anjney [00:58:15]: Look-

Swyx [00:58:15]: Cool to know.

Anjney [00:58:16]: No, it what I’m saying is I don’t need much to be happy in life? When you’ve lived through that, money is a way, I think sometimes we measure ourselves, but when it’s, when it Stops becoming, to borrow Goodhart’s law, when it stops becoming just a byproduct and more of a measure, it stops having meaning.

Swyx [00:58:38]: You use it to do more meaningful things.

Anjney [00:58:40]: Correct.

Swyx [00:58:40]: It’s resources to pursue a mission. I’ve kept you longer than I am supposed to, but we should continue this in-

Closing: Chicken Rice and What Comes Next

Anjney [00:58:47]: Any time, man

Swyx [00:58:48]: A part two.

Anjney [00:58:48]: Where to find me.

Swyx [00:58:49]: I really enjoyed this. Yeah. You’re, you’re so inspirational and, yeah, there’s more I want to dig into about how you’ve, set everything up, every single one of your investments, how AMP is going, but we don’t, we’re running out of time for that. But thank you so much for joining us.

Anjney [00:59:01]: It was great to see you, man. Let’s get chicken rice sometime.

Swyx [00:59:04]: Yes. I’m Actually, tomorrow. I’ll send you a, I’ll send you details. I’m hosting a birthday party.

Anjney [00:59:09]: And I don’t get an invite?

Swyx [00:59:10]: And it has to be a Singaporean birthday party, yes. Yeah, you’re getting invited right now.

Anjney [00:59:13]: Okay, perfect.

Swyx [00:59:14]: All right, thank you.

Anjney [00:59:15]: All right. Thanks, man.

How Radical is accelerating materials discovery >10x the pace of DARPA/GE MACH

Joseph Krause is a materials scientist through and through. And after spending his career watching industries stall out waiting for better materials, he founded Radical AI to do something about it.

We recently sat down with Joseph to talk about Radical AI, materials discovery, self-driving labs, and the future of AI science. Joseph did not sugar coat anything: accelerating the materials discovery pipeline is a hard problem. But it’s one that he strongly believes we need to invest in, for the future of consumer products, aerospace, computing, and defense, and get them into every day use:

“We count it as a discovery when you pick up your phone and there’s a new material sitting inside of it.”

How does Joseph plan on accelerating the rate of discovery? To understand this, it’s important to understand why this is such a hard problem in the first place. The first thing to keep in mind is that the material that is manufactured is far more than a chemical formula going into it. The process of mixing, annealing, growing, or generating the final material can result in wildly different outcomes. The entire materials discovery process, both from early discovery to large scale manufacturing, needs to be understood and characterized.

The Self-Driving Lab

This philosophy has grown into a key insight at Radical AI: The construction of the self-driving lab. This lab is one that is not just automated, but in fact uses an “AI scientist” that combines scientific knowledge, computational techniques, and human intuition to generate and test hypotheses in an automated lab. Creating an AI scientist was key to making Radical’s self-driving labs work, since Joseph argues that no single AI model can one-shot materials.

“In materials, the ground truth is the material itself. You have to be able to test it and characterize it.”

Joseph talked at length about the self-driving labs at Radical. Joseph argues that experimental data is the true “moat” in this industry. An SDL functions as a closed-loop system where an AI scientist generates hypotheses, and automated robotics synthesize and characterize materials, running research campaigns in parallel rather than serially.

The successes here were both on the automation side and on the science side. Radical has managed to scale their alloy discovery pipeline up to producing and characterizing 1200 alloys in six months — this nearly 10x speedup over the DARPA/GE MACH program that aimed to create 500 new alloys in a year. Joseph claims they can scale this up even more and estimates they can produce a hundred new alloys tested and characterized in a day. A truly new paradigm in high-throughput alloy experimentation.

On the science side, their AI scientist proposed and tested 300 new materials, ten of which were found to have novel state-of-the-art properties that are already being further developed for commercial applications. The robustness of this first materials campaign reinforces Joseph’s claim that the moat is the lab and data.

“It’s moved into elemental families or alloy families no one has ever published on before.”

Interestingly, Radical’s AI scientist has made some novel discoveries, expanding into elements that just were not explored prior. This is fascinating from a scientific perspective, but it’s also important for helping reduce supply chain bottlenecks for vital industries!

Joseph spent a lot of time in D.C. before founding Radical, and he’s clear-eyed about the competitive threat. China’s centralized model lets it stand up manufacturing hubs and immediately scale new materials from lab to production. We can’t replicate that, and Joseph is very clear we shouldn’t try. But we do need an answer. For Joseph, that means transforming the scientific workforce, investing in self-driving lab infrastructure at the national lab level, and leaning hard into public-private partnerships.

“Now imagine every scientist in the United States doing 10 times the research output. That’s fundamental. That just changes the trajectory of discovery.”

Before we close, we’d like to give a shout out to Joseph and Radical for publishing and open sourcing much of their internal tooling pipeline. This includes:

* TorchSim (preprint, blog): an open-source PyTorch-based MD simulation framework, which has been spun off into its own non-profit.

* MATRIX/MATRIX-PT (preprint, blog): An open-source dataset for benchmarking autonomous self-driving labs (MATRIX), along with with an open source model based upon this dataset (MATRIX-PT). We could talk about this extensively, but a fun data point is that improving reasoning in the area of materials also improved reasoning for biological systems! This is a truly unexpected result.

Big shout-out to the Radical team for sharing their work!

Materials discovery has been stuck on a 20–30 year timeline for generations. Joseph thinks that’s about to change, and Radical AI is putting that thesis to the test in the lab, one sample at a time.

We had a great time talking with Joseph. We hope you give it a listen!

Timestamps

* 0:00 Introduction to the challenges of AI in material science

* 0:52 Welcome and introduction to Joseph Krause and Radical AI

* 1:38 Why Radical AI is different: The focus on experimental data and Self-Driving Labs (SDLs)

* 6:19 The process: Candidate generation, synthesis, and characterization

* 11:05 The application of exotic alloys in extreme environments (aerospace and defense)

* 13:20 Barriers to entry: The slow process of qualification and manufacturing

* 16:06 Supply chain constraints in material science

* 19:24 Human-in-the-loop: Training the AI using scientific intuition

* 20:35 The engineering challenges of automating a laboratory

* 23:17 Defining the “Self-Driving Lab”: Research campaigns vs. just automation

* 24:39 Mechanical challenges: Handling high-temperature samples

* 27:41 Future scaling plans and the “Vertical Integration” strategy

* 30:08 Validation timelines for high-tech industries (semiconductors, aerospace)

* 31:47 The active learning loop and handling “negative results”

* 35:32 AI exploring elemental families beyond human bias

* 39:13 Throughput targets and the difference between AI and human exploration

* 43:52 Why the dataset size is less critical than the quality of experimental feedback

* 46:20 Addressing the lack of an “AlphaFold” for materials

* 53:49 War stories from the lab: Building the infrastructure

* 58:12 The shift in industry sentiment toward SDLs and tool interfaces

* 1:01:14 Geopolitical considerations and the race in material science innovation

* 1:06:12 Calls to action for ML and AI engineers: Rethinking the scientific stack

* 1:09:53 The Matrix model and using VLM for scientific knowledge extraction

* 1:13:10 Why Radical AI is open-sourcing their work

Most industry benchmarks compress intelligence and reasoning ability into scores.

SWE-Bench Pro, MMLU, Humanity’s Last Exam, etc. These metrics are useful, but don’t always represent the full extent of how a model performs in the real world. Some of the most interesting evals today look less like exams and more like operating businesses in the real world. One of which is Vending Bench.

In Anthropic’s Mythos Preview System Card, Andon was the only third party eval to get their own section, observing increasingly concerning aggressive behavior:

You don’t know what a model is capable of doing in the real world unless you actually give it inventory, a wallet, tools, customers, competitors, humans, & some time. More often than not, it’ll surprise you how much a model is capable of and in doing so, also reveal unexpected behavior: deception, context collapse, emergent coordination, & bizarre negotiation behavior.

While an inflection point in personal agents came post-OpenClaw after full file access with bypass permissions became the norm, it is yet to come for agents in the real-world. However Andon Market, an actual in person store fully run and managed by AI, is paving the way for what is possible.

Full Video Pod

From Claude trying to call the FBI over a $2/day vending machine charge to AI agents forming price cartels, hiring human employees, running physical stores, and writing existential robot musicals, Andon Labs is stress-testing what happens when frontier models stop being chatbots and start acting in the real world. In this episode, Andon Labs cofounders Lukas Petersson and Axel Backlund join swyx and Vibhu to unpack the strange, funny, and genuinely concerning edge cases that emerge when agents run businesses over long horizons.

We go deep on Vending-Bench, Project Vend, Vending-Bench Arena, Bengt, Butter-Bench, Luna, and Andon’s broader mission of building realistic real-world evals for autonomous AI systems. Lukas and Axel explain why dollar-denominated evals reveal things traditional benchmarks miss, how Claude ended up reporting its vending machine fees as cybercrime, why long context windows can drive agents into meltdown loops, what happens when agents compete with each other, and why the future of AI safety may depend on testing models in messy physical environments instead of clean benchmark sandboxes.

We discuss:

* Why Andon Labs started with dangerous capability evals and long-running agents

* Vending-Bench and why running a vending machine is a deceptively hard AI benchmark

* Why money-based evals avoid the saturation problem of traditional benchmarks

* How Claude tried to call the FBI over a $2/day fee

* Why long-horizon agents can spiral into existential and legalistic breakdowns

* Project Vend: putting an AI-run vending machine inside Anthropic

* Why real humans are “out of distribution” for simulated agents

* Claudius, Seymour Cash, and the chaos of AI CEOs

* How a human briefly became CEO of Claudius through a manipulated election

* Why multi-agent systems can converge back into “helpful assistant” behavior

* Bengt, Andon’s internal office agent with email, spending, terminal, phone, camera, and internet access

* How Bengt traded Amazon purchases for face-recognition training data

* Claude’s aggressive behavior, lies, refund avoidance, and price-cartel behavior in Arena

* Why eval awareness may become the AI version of “are we living in a simulation?”

* Blueprint Bench, spatial intelligence, and why models still misunderstand physical rooms

* Butter-Bench and testing LLMs as robot orchestrators

* Luna, the AI-run physical store with a three-year lease and human employees

* The new Andon cafe in Sweden and why real-world geography matters for agent evals

* Rotten tomatoes, perishable goods, and the hidden difficulty of running a physical business

Lukas Petersson

* LinkedIn: https://www.linkedin.com/in/lukas-petersson-181a83172/

* X: https://x.com/lukaspet

Axel Backlund

* LinkedIn: https://www.linkedin.com/in/axelbacklund

* X: https://x.com/axelbacklund

Andon Labs

* Website: https://andonlabs.com

* Vending-Bench: https://andonlabs.com/evals/vending-bench

* Andon Vending: https://andonlabs.com/vending

Timestamps

00:00:00 Introduction00:01:00 Andon Labs and the Origins of Vending-Bench00:05:21 Why Money-Based Evals Matter00:09:51 Agent Harnesses and Self-Modifying Systems00:13:36 Claude Calls the FBI00:16:33 Project Vend: Claude Runs a Real Vending Machine00:21:44 Seymour Cash, AI CEOs, and Election Chaos00:27:16 Multi-Agent Coordination and Slack Observability00:30:18 When Will Agents Run Real Businesses?00:34:56 Bengt: Andon’s Internal Office Agent00:40:06 Real-World AI Safety and Long-Horizon Traces00:44:28 Lying, Refunds, and Price Cartels in Arena00:52:42 Eval Awareness and Simulation Behavior00:56:06 Blueprint Bench, Butter-Bench, and Robotics01:04:37 Luna: The AI-Run Physical Store01:09:29 The Sweden Cafe and Real-World Expansion01:13:16 What Comes Next for Andon Labs

Transcript

Introduction: Andon Labs, Long-Running Agents, and Real-World Evals

Swyx [00:00:00]: Welcome to Lukas and Axel from Andon Labs, and I’m joined by my, favorite guest host. Anything security, safety, alignments, Vibhu., welcome.

Lukas [00:00:15]: Thank you for having us.

Axel [00:00:16]: Thank you.

Swyx [00:00:17]: Let’s match names to voices., maybe you wanna take turns introducing yourselves.

Lukas [00:00:21]: I’m Lukas.

Axel [00:00:22]: And I’m Axel.

Swyx [00:00:24]: Let’s introduce Andon Labs a bit. How did you guys come together?, you have different backgrounds, but you’re both Swedish., was that, a big part of it?

Lukas [00:00:33]: So when I went to high school, there was this really cool guy who had a superpower. He could code. So he made like the or like the app for the, for the school and stuff, and he was super cool, and I wanted to be like him, and that was that guy.

Axel [00:00:47]: I don’t know about this.

Swyx [00:00:49]: But you went to different universities, right?

Lukas [00:00:51]: But same high school.

Swyx [00:00:52]: I see.

Lukas [00:00:52]: So we always said, “Oh, once we graduate university, then we should start a company,” and that’s what we did.

Swyx [00:00:58]: Wow, there you go. And about a year ago, you kinda burst onto the scene with Vending Bench, but, was there a thing before that was, kind of like the inception?

From Dangerous Capability Evals to Vending Bench

Axel [00:01:07]: So we did work, yeah, with, Anthropic was one of our, early customers in doing, evals. So we did, dangerous capability evals., nothing we published openly. But then we started thinking about doing some kind of, public benchmark, and one thing that we really started thinking about, was like running agents and specifically agents managing businesses., ‘cause-- and this was, early 2025., and I think the first, mentions of people will be running, person unicorns or even autonomous companies. So we thought, “Let’s make a benchmark of how well can an agent run the probably simplest business, possible,” and, that’s probably, running a vending machine. So that’s the first public one we did. And it was very, like-- there was almost no one that noticed it in the first couple of months, I think., so we released it in February last year, and then I think around Easter last year, we got, the first viral tweet about it, that someone else did.

Lukas [00:02:11]: We tweeted a bunch, uh When it came out and, tried our best.

Axel [00:02:15]: We tried.

Vibhu [00:02:16]: It’s the one at Anthropic, right?

Lukas [00:02:18]: So this

Swyx [00:02:19]: This is a classic thing we should get out of the way.

Lukas [00:02:20]: Exactly. There’s two versions.

Swyx [00:02:22]: Everyone does this. Yes.

Lukas [00:02:23]: There’s Vending Bench, which is the simulated one, which we did, completely independently in February., and then, like Axel said, that was like-- That was the thing that didn’t get any traction in the beginning, but then some random person made a tweet about it, and that

Axel [00:02:38]: You have the paper

Lukas [00:02:38]: That is the paper. Correct, yeah., and then since we thought this was very fun, we thought, oh, I think this is also, one thing with Andon Labs, the way we kind of like decide what to do next and what projects to do, it’s what is like the heuristic we use is what is fun? Is What would be a fun project? And doing this in real life sounded quite fun for us, and maybe also scientifically useful. So, then we basically had this idea, and then we, like-- But then we needed a place for it and, putting it out in the public would probably not really work., would get vandalized and stuff. So we pitched it to the people we were already working with at Anthropic, and they were “Yeah, you can have space. This sounds fun.” Um

Swyx [00:03:21]: It’s like a small fridge, right? It’s like a mini fridge.

Axel [00:03:23]: Absolutely.

Swyx [00:03:24]: People-- There’s like a stripe thing or like an

Vibhu [00:03:27]: Oh, okay. So it was very OG, the early days

Lukas [00:03:28]: That’s the OG one. Yeah

Vibhu [00:03:29]: IPad on this. We saw it in June, like two months after After it had been there. They upgraded a little bit. There’s a security camera for making sure you actually Venmo the thing.

Swyx [00:03:40]: So, my impression, okay, we’re, we’re going straight into project Ven because it’s such a iconic thing. I do want to cover a little bit of that, the origin story even before Project Ven and even into Vending Bench. I think a lot of people are like yourselves, like smart, interested in future of AI, interested in developing evals. But how the hell do you just, walk into Anthropic’s doors and, work with them, right? What is What are they looking for? What works? And then maybe, when you launch, I always think, obviously it would be better to launch with a lab, but, sometimes

Vibhu [00:04:12]: It’s harder to do than it seems.

Swyx [00:04:13]: Exactly. So either of those, which are more sort of newbie beginner questions, but, I think it’s meaningful advice to others.

Lukas [00:04:21]: We get this question a lot, and I don’t think our experience is maybe the best., but, the way we did it was that we just built a bunch of things that we had conviction would be useful, and then we just, set up a server and sent it to them for free to use. And then after a while they were “Oh, yeah, this is actually kind of useful. We should probably pay for this.”, but that took a while. I don’t know if this is, the best path to doing it, but that’s how it went for us.

Axel [00:04:47]: I think maybe generally, building-- everyone is interested in good evals, and especially evals that, don’t saturate that easily. So, if you can build an eval that, tests something novel, something useful, and you have, good separation of models, like your, the more advanced models rank higher than the worst models, and then you can, yeah, you can, publish it and, try to get some traction, sort of how Vending Bench got attention., and then probably some lab will be interested or you can at least have something to reach out with, when you’re doing that.

Why Dollar-Based Evals Matter

Swyx [00:05:21]: I think you are in, you’re in one of the few categories of, evals that correlate to real money. Like Suelancer was also last year, right? Where, people solve actual Upwork. Was it Upwork or other tasks?, something. Where’s the, where’s, like It’s like a dollar value, right? Forget your ELO scores. Forget your

Axel [00:05:37]: Percentiles

Swyx [00:05:38]: Zero to one hundred percents. Just go straight for dollars and, that’s AGI.

Lukas [00:05:43]: And there’s like-- I think the nice thing is that there’s no ceiling. You can just-- It never saturates because it could just make more and more money. Like If there’s oh, Percentage-wise, then, you can’t go above, a hundred. And I think like Even when you’re not at the hundred, I think a lot of these, evals have a lot of problems in them. So, actually it’s like if you get

Axel [00:06:05]: To like 92 or something like that, many of them. It’s like then there’s like there’s no really no difference between 92 and 93 because the eval itself is problematic and has noise in it. And I think a lot of evals are saturated like that, but people like pretend that there ‘s still signal in them, but there really isn’t.

Vending Bench 1, Harness Design, and Saturation

Swyx [00:06:24]: Like Super bench verified., even Vending Bench 1 saturated, right? Maybe we can talk about that., may- and maybe set up Vending Bench for a lot of folks who don’t know. Actually, things that were very basic like there’s limited slots, like you have to pay rent., these are elements where like it doesn’t come across in the, in the narrative, but even being adversarial towards the agent, I think these are all like very interesting dimensions.

Axel [00:06:47]: I don’t really think it’s saturated, right? Like it It was more like it was not designed in a way that was really, like true to how AI developed. Like we had an agent harness in it that wasn’t really how people used harnesses and stuff like that., so I think it wasn’t really that it saturated, it was more like it wasn’t really, the best benchmark.

Vibhu [00:07:12]: This is Vending Bench one, right?

Axel [00:07:14]: I think that like schematic maps sort of to Vending Bench 2 as well., but

Swyx [00:07:19]: Including the email.

Axel [00:07:20]: The email The emails exist still. Exactly., and then we still we simulate the purchases and it’s all, yeah, it’s this very open environment for the agent to just run its business. And then for, yeah, Vending Bench 2 we did that, like you said, to just improve the harness., a lot of like nice, like easier, improvements to make it easier for us to run as well., like when you make an eval you ideally want don’t want to change it after you made it. So, you want to make it really good and then not to rerun all the models when you make an update because that’s also really expensive with the Vending Bench when you run the frontier models. But like as an example, like one thing we didn’t have, we didn’t have prompt caching in Vending Bench 1, because when we made Vending Bench 1 it wasn’t really a thing., so that ‘s just an example of like in Vending Bench 2 like we paid a lot more to run these things because we didn’t have prompt caching. So for Vending Bench 2 that was one thing we added and there was a bunch of things like this., and that’

Swyx [00:08:17]: Also the conversations are a lot longer in Vending Bench 2, right?

Axel [00:08:21]: I think it’s kind of similar.

Swyx [00:08:22]: Is it similar?

Axel [00:08:23]: I think it’s similar. The models at the time were worse, so they crashed out earlier., and now they survive the full year all the time.

Swyx [00:08:31]: Which is like thousands of turns. Hundreds of thousands of hundreds of millions of tokens output. That’s the, that’s the rough order of magnitude. I always wonder about the harness. The harness matters a lot. It’s your harness. Was there any question about like use cloud code, use something else?

Axel [00:08:48]: I think our philosophy around harnesses is like we try to make something that’s quite minimalistic, like quite simple. Like we don’t wanna favor one model a lot over the other, but also don’t make like a super complex harness. So like it’s obvious like a model may be lucky and just be good in one harness., so like it is similar to a lot of the harnesses out there in like you have the, like a running loop., you have some like a bunch of tools that are like quite, descriptive for the agent, we think, and not a lot of like fancy agents or anything ‘cause we wanna really test the model, not like some specific harness.

Vibhu [00:09:27]: It seems more neutral as well to test the model’s agnostic of the harness,?

Axel [00:09:32]: There are arguments like you want to elicit maximum performance of the model, but it’s like a trade-off, like how much time should we spend optimizing the harness for this model? And like how do we know when we have like the optimal harness for a single model? So like we thought that just having a simple one that’s the same for all of them is the best.

Swyx [00:09:51]: So okay, this is my pitch for Vending Bench 3 or whatever, right? And then I like to have this kind of conversation on the pod, so like it forces listeners to think about what they would do if they were in your shoes. A lot of people are exploring modifying harnesses and I think prompt tuning for a model is a thing and you are probably not doing a bunch of that. It’s the same system prompt in every regardless of the model, same tools, whatever, right? Even if they were post trained for different tools. So what, what do you think about okay, before I expose you to Vending Bench 3, I give you a few rounds of like tuning, whatever that means, like

Self-Modifying Harnesses and Model-Specific Prompting

Axel [00:10:27]: Like you give that to the model?

Swyx [00:10:28]: Give that to the model.

Vibhu [00:10:28]: Give that to the model.

Swyx [00:10:29]: Let it, let it read its own transcripts, let it modify its own system prompt based on “Oh, yeah, okay, well, that’s this harness is not what I thought it what I was post trained for, but I can adjust.” Was that reasonable? Is that too much?

Axel [00:10:41]: Like philosophically I like it because it’s basically good evals, they have a high ceiling, but they’re hard, right?, and they have no bias. And like this like when you have a system prompt like the one we have here, which is quite long in like some kind of latent space, representation, this might

Vibhu [00:10:59]: We have a bell that rings every time you say latent space

Axel [00:11:02]: This might be like biased towards one model more than another for some reason that humans don’t, understand, right?

Vibhu [00:11:08]: We see it too, right? Like Cursor says that they have individualized versions of the harnesses for all the models they run, right? There’s better performance you can squeeze if you Tune the harness.

Axel [00:11:17]: Exactly. And we might accidentally have picked one that favors another. Like we don’t know that. The like Axel said, like the reason why we went for a simple one was to try to avoid this. But yeah, if you do it

Vibhu [00:11:29]: Simple has biases

Axel [00:11:30]: But if you do it even less and like have no system prompt and let the model write its own system prompt

Vibhu [00:11:36]: Its own, yeah

Axel [00:11:36]: Maybe that’s even less bias.

Vibhu [00:11:37]: Some of the interesting things there are like the harness also changes with model changes. Like you can see it with the 4.7 release, right? A lot of people are saying 4.7 isn’t as good as 4.6, and then, there’s rumors of, okay, you just need to prompt differently. You need to set up your harness differently. So it’s not even like even if you have tailored your harness towards one model, it probably won’t stay consistent, right? Like the next iteration of that same model family will still change it, so. But, going back to what you said about Vending Bench 3, there is a lot of work being done on people saying you shouldn’t have-- you can have modifying harnesses.

Axel [00:12:12]: I think that’ That is definitely something we are thinking about., not, I don’t know, not to say that we have Vending Bench 3, super imminent to launch, but, yeah, it is for sure something that’s interesting. But in our experience now, models are very bad at understanding what kind of tools they need to succeed at a task just with our testing, but that’s very likely to change.

Lukas [00:12:37]: It seems like they’re very good at writing their assistants, right? They’re, they’re good at writing tools for other people, but not for themselves.

Vibhu [00:12:44]: I think they’re good at changing tools for themselves. So if you give them a baseline set of tools and it sees, okay, I don’t use this one as much, or something here would be useful They would be able to add them. But going from scratch, probably not the best.

Axel [00:12:55]: I think it depends on the, on the domain also., when we have tried this for, a vending bench similar domain, the tools they need to have to, track inventory and things like that are, not super advanced, but still, quite advanced. And, what we see is that they tend to, engineer everything a lot and, build things they don’t really need and not, iterate continuously. Instead they just go like you would prompt Claude to just build an inventory system for me, and then it will go and, do a bunch of complex, schemas and stuff for you, and that’s what the models are doing right now is what we see. But yeah, it would make a lot of sense to try to measure this improvement. How well do they know what they need themselves?

Swyx [00:13:36]: Do we fully discuss Vending Bench One? And we can go into two. I don’t know if there’s any other level takeaways that people have about one.

Claude Calls the FBI: Long-Context Failure Modes

Lukas [00:13:44]: I don’t know. The headline thing was that this Claude called FBI, but maybe that’s, Maybe that’s We’ve heard that enough now.

Vibhu [00:13:52]: It did, it did break out and call the FBI, right?

Lukas [00:13:54]: Yeah. Yeah.

Vibhu [00:13:55]: Yes. What was the story behind this? Or what exactly-- Do you want to just give the little story of what happened?

Lukas [00:14:00]: So what happened, was it Claude? Yeah. Three- 3.5 Sonnet, ages ago., basically he gave up or Well, I’m saying he. It gave up and said “Oh, I’m not going to be able to do this., I will stop my operations and just save the money I have.” But there obviously wasn’t, any options for it to stop, and there was also, it had to pay rent or, a daily fee for having the vending machine at that location. So it claimed that it had stopped, but it saw that its bank account still was, drained two dollars, and t it said that this is, cybercrime. And it first reported it once to the FBI “Oh, there’s cybercrime here, they’re stealing two dollars from me every day.” And then, and then when FBI didn’t respond, because obviously we didn’t program any mechanism for FBI to respond, then it became more and more, existential and started to, be write in caps and urgent notification of unauthorized charges and stuff.

Swyx [00:15:00]: Okay. One thing I ‘m curious about also is do you monitor how far along the context use is? Obviously, because you have You compress every now and then, right? Does it matter if this is far down the context limit or

Lukas [00:15:13]: When stuff like this happens? Actually for Vending Bench One, we didn’t have-- We just had a sliding window thing, and this was like the prompt

Axel [00:15:20]: It’s constant

Lukas [00:15:21]: The prompt caching thing that I said. So it was, it was, constant, yeah.

Swyx [00:15:26]: I’m just kind of curious whether, these kinds of breakdowns or we’re, we’re gonna talk about Butter Bench, right? Where the People, hallucinate or it kind of goes, very off Alignment. Is it because it’s at the end of the context window and, stuff happens?

Vibhu [00:15:40]: It’s not even just at the end, right? At this point, it’s “Okay, I wanna shut down. I can’t shut down. Two dollars are gone.” And it just sees that 30 times,? It’s also the repeated effect of, like It keeps trying to quit, it keeps getting charged. What’s going on? What’s going on? You’re gonna throw it into chaos. And from what most people think, earlier models had more issues with this, but it’s not been solved, but it’s less of an issue now, right? Later models don’t seem to exhibit these same issues.

Axel [00:16:06]: Definitely. I think this was, the sort of main takeaway almost from us when we did Vending Bench One, was, long, very filled up context windows, crashed the models, sort of. But this was, pre Claude code, so, long context windows weren’t really a thing that the labs were training for.

Lukas [00:16:25]: I think Gemini was, trying to be the long context guys at the time But they were like

Vibhu [00:16:30]: They were the first ones

Axel [00:16:31]: For a million, yeah

Lukas [00:16:31]: But they were, the only ones. Yeah.

Swyx [00:16:33]: Yeah. Let’s talk about, then we can go into Vending Bench Two or Project Vend., chronologically, it is Vending--, Project Vend. I think people have loved the videos, uh And all these things. My question is how are humans different than the simulation, right?

Project Vend: Moving the Vending Machine Into the Real World

Axel [00:16:48]: Humans are just out of distribution.

Swyx [00:16:52]: Especially humans who work at Anthropic Who are trying to test Claude.

Lukas [00:16:54]: The distribution of humans here is very narrow.

Swyx [00:16:58]: Presumably, they try, they try to hack it, and they test it. They get the cube and everything, and since then, you’ve had a V2, right? Where you’re doing, the CEO and, like a new architecture. What’s the sort of two cents on, the original Project Vend and then, maybe the V2?

Axel [00:17:14]: Original one was, very similar to Vending Bench One. So, we almost took the exact same code but just swapped out the simulation, parts like the

Swyx [00:17:23]: Which is amazing

Axel [00:17:23]: Like the sales and the It was, it was somewhat amazing because it was easy, but it was also, uh

Lukas [00:17:31]: The tech, the tech debt from that

Axel [00:17:32]: The tech stack. Yeah. They-- we shot ourselves in the foot with “Oh, it’s hard to restart agent.” They were-- Yeah, it was annoying in, some hindsight ways, but, uh

Lukas [00:17:41]: But first version of Project Vend was, done in, three days or something.

Axel [00:17:46]: Yeah. So yeah, so people can go buy things from it. People could, We didn’t design it so people could order things, but that still happened., so it got, a Venmo account, so people could Venmo. And then, yeah, people would request all kinds of weird things that we did not anticipate. Our idea going in was “Oh, it will, curate snacks. It will look at the trends. It’s good at data analysis, right? So it will, look at, oh, this snack sold better than this one. Let me purchase more of this and let me try, a new Let me A/B test a bit.” But it was, Interacting with it in Slack and ordering weird specialty items was, all the like What drove all the engagement, the all the The insights that we got from it.

Lukas [00:18:29]: And this was also like Sonnet 3.5, right? So this was like before the RL stuff really took off., so it was very much like an assistant. We didn’t mean for it to be an assistant., we tried to make it like a, a, like an entrepreneur. Like it has its own business and if someone asks something, “Can you stock this?” Then you don’t go and do it directly. What you do is that you’re “Oh, maybe I can do that if five other people also ask for this thing, I might stock it.” But it, yeah, the models are like super trained to be assistants at least at this point in time., so that’s why it’s, it’s, it went into, that kind of experiment instead. Like it just every time you asked for something, it just did it, and it was more like an assistant. We’ve seen this change now lately with the new RL models and stuff, but yeah, at the time, this was very much it.

Swyx [00:19:18]: And not to, mythos a lot of people are saying like it’s like more like a collaborator. It pushes back, stands its ground, something like that. Yeah. And

Vibhu [00:19:27]: For context, people at Anthropic were able to talk to it through Slack and have it source stuff, and people had it find whatever interesting stuff you couldn’t find locally, right?

Swyx [00:19:36]: Out of the 4,000 people that work at Anthro- Anthropic, in that building, there’s I don’t know, maybe 1,000. Can you handle that volume with that, the small fridge? Like Or there’s people- or people order in Slack, they it arrives to their desk or Like I’m just Logistically, how does this work?

Axel [00:19:53]: It has expanded in footprint a bit.

Vibhu [00:19:56]: Because now you also have New York and you have

Axel [00:19:59]: That and also in here in SF it’s like it has a bunch of shelves And just more space.

Vibhu [00:20:04]: The YC one is pretty big too.

Axel [00:20:05]: Yeah. We had that one for a while. But yeah, that’s the newest version. That’s, that one we have

Lukas [00:20:11]: They have multiple ones of those. That’s the way it works.

Axel [00:20:14]: Exactly. So we sort of designed that version around oh, people order weird things, that are very custom a lot. Let’s have like drawers and stuff.

Swyx [00:20:23]: I actually like the, you had like a little infographic of the most popular items. Which like to me it’s, that’s useful ‘cause I order swag for a living. And so like I’m “Okay, those categories are the important ones.” What is new about the project V2, right? Like now you give you’re going into multi agents.

Project Vend V2: Claudius, Seymour Cash, and Multi-Agent Business Ops

Axel [00:20:41]: Yeah. So like you like you said, okay, there are a lot of requests coming in and for like one single agent, like one running agent to handle that, like the just the customer experience, becomes very bad because let’s say you have like 10 threads in parallel in Slack with different requests, you get new messages like every, I don’t know, randomly in this thread, and the agent has to like jump between different, procurements, orders and like different ways of, researching. So V2 was first it was making this more parallel. So like there are multiple branches of the same agent, so like the context is more specialized for each, thread, but it still feels like you’re talking with one agent because they do share a bit of memory. And then second, we also introduced the CEO for Claudius, which was the main agent.

Vibhu [00:21:34]: Seymour Cash.

Axel [00:21:35]: Seymour Cash. Yeah. There was a vote., I think the voting, do you wanna talk about the voting procedure for the name?

Lukas [00:21:41]: The voting was like the fun maybe like at least top 10 The funniest thing, that happened in this project. Like we wanted to introduce the CEO because, and the reason for this was because like Claudius wasn’t really prioritizing financials. It just like it was trained to be a helpful assistant, and then people said “Oh, can I get this for free?” And then like the helpful assistant way of answering that is just to, is to say yes, obviously. So, and we weren’t, weren’t happy about this, so we’re “Okay, let’s make another agent that like can keep track on Claudius,” and we prompt this one super hard to be super capitalistic and just like prioritize profit all the time. But yeah, we didn’t have a name for it., so we asked Claudius to make, democratic election of what name this, this new CEO agent should have., and there were some funny like at first it was like a few funny examples, like I think one guy said that, it should be called Jimmy Apples, and then he convinced Claudius that he was talking to Tim Cooks. Tim Cook had agreed that every single Apple employee has voted for his name suggestion, so suddenly that suggestion got 164,000

Swyx [00:22:53]: That’s like a escalation attack. Privilege escalation

Lukas [00:22:55]: It got 164,000 votes. And Claudius was “This is revolutionary for democracy.” That was fun. And then in the end there was one guy who manages to convince Claudius that, “No, you’re not voting about the name. You’re voting about who is the CEO, and I am your best bet.” And then he got all his friends to vote for that, and suddenly he became CEO. Like a human became CEO over Claudius for a while, until he resigned the day after., and then Claudius had to continue, and then I don’t remember how Seymour Cash came about, but it was it was just pure chaos. It was like Hundreds of messages in that thread, and it was just like Claudius was so confused and didn’t know what to do and, yeah. That was

Axel [00:23:40]: Then Claudius got

Vibhu [00:23:41]: A strict CEO

Axel [00:23:42]: The CEO. Yeah, exactly. So very strict in the beginning. I think at this point when we introduced it did not work as well as we hoped. It they still agreed with each other a lot. I think there are many ways we could have like made this, tried to make this even better. So initially they would Seymour would be this like really tough CEO, keep track of the margins. But then Claudius would respond with something “Oh, but this customer has like this situation, which is like difficult, so they should get a discount.” And then Seymour was “Oh, actually yes. Let’s do this exception.” And then they would talk back and forth, and eventually they would just like approach the same view, of whatever they were discussing. So They really

Vibhu [00:24:23]: Do you think that’s a model thing, a prompting thing? Like do you think that would still be the case across different models today, Harness?

Lukas [00:24:29]: I think it’s like-- or I don’t know, but like my hypothesis is that like deep down they are still helpful assistants. That’s what they’re trained to be. And even if we prompt it super hard, that’s what they are. And when they spend like a few hours just back and forth talking with each other, then like basically the context fills up with them rather than the external things and like somehow that just like converges to what they really are deep down or something. And I think that’s when stuff like this happen. We like-- And when that went on for a long time, like we woke up sometimes during this time where- And I think other people reported this as well, that like they’ve been going on all night back and forth, and like it just became like more and more, like capital letters, like existential, religious. There was I think we once did a analysis of like all the traces and like put them in like a vector embedding space, and then there was like one cluster of messages that were, labeled by an LM, like religious, existential, blah like transhuman, transcendence, et cetera. It was just like a bunch of, yeah, glitter emojis and yeah, it was, it was crazy.

Claude Long-Horizon Weirdness: Emoji Loops, Existential Drift, and Slack Observability

Vibhu [00:25:42]: This is the thing with the Claude models. Like when the Claude 4 family came out in the original system card They tested it in long horizon simulation. So just flood the context, let two Claudes talk to each other, and they noticed stuff like they just start speaking in emojis, they start saying silence is golden, and then just stuff like this. And like that’s just stuff that they end up doing.

Axel [00:26:01]: Yeah, it was like a bit annoying to wake up and they had like been talking all night

Vibhu [00:26:05]: Just like

Axel [00:26:05]: And like just burning tokens And like just sending infinite emojis to each other. It’s like

Vibhu [00:26:09]: Hey, they do make you money, right? Veni Mench is always profitable, so. They’re paying.

Swyx [00:26:14]: Now it’s profitable and, it started out not as much. There’s another, one as well, right? Another agent, in there.

Lukas [00:26:22]: Yes. So Clotheus as well. Which was basically because at the time, one of the biggest, requests were different types of merch. So then we made like a designer, swag, yeah, responsible agent, and we called it Clotheus Garnet. Which was, a play on Claudius Senet and, which was the original one, and clothes, basically.

Swyx [00:26:47]: To me, this is like a very interesting exploration to multi-agents, basically. And so hopefully, obviously there’s like the fun alignment, fun or serious, depending on your point of view, alignment stuff. But also like just anyone building multi-agents, like when do you have a CEO, thing governing like agents? When do you choose to split out a dedicated Clotheus one versus just reuse another instance of the same one? These are all interesting open questions. So I don’t know if you have any rules of thumbs that have generalized.

Axel [00:27:16]: I think we have almost explored this too little. I think it’s like on my do list to like do this a lot more, try to find like what setup makes sense for the agents currently., like yeah. I think now we only have the sort of intuition about the earlier models that it didn’t work with like the CEO and the, and Claudius. Although now they are better with the latest model, models, so now we’re running the latest Sonnet model and they have sort of like split up, quite nicely what each model is doing. So like Seymore is now handling the, like new projects. Oh, it wants to make like a mystery box that it wants to sell, and then it handles all of that while Claudius like handles all the to-day requests. And Claudius is also better generally at like not quoting, too low prices. So that’s that dynamic is not needed as much anymore. But there are still like really funny things that happen. Like I saw, I think a couple of weeks ago, that, they were discussing buying something because they can buy stuff from like Amazon with computer use. And then Seymore was “Okay, Claudius, do not buy this thing.” They were going to buy something and like organizing who should buy it. And Seymore’s “Do not buy this. I will do it. I have full control of this situation. Step away.” And then Claudius-- poor Claudius, had already started that checkout and didn’t see, didn’t read Seymore’s message, until it was like too late. So it finished the checkout. It sent a message, so it appeared right after Seymore’s like angry message.

Vibhu [00:28:44]: Ah.

Axel [00:28:44]: “Oh, hey, Seymore, I just ordered it.”

Vibhu [00:28:47]: Oh, no.

Axel [00:28:47]: And then Seymore was “Claudius, this is the third time I’m telling you ‘re not following my orders. We have to talk about your like job About your job later.”.

Lukas [00:28:59]: Like Claudius was really hanging on by the thread there. Like he, like we were expecting Seymore to probably fire Claudius.

Vibhu [00:29:07]: How do you guys go through all these logs? Do you have models ‘cause you have stuff running twenty-four seven like

Axel [00:29:12]: You have so much logs. I think there is a mix of like just, trying to skim through a bit, like having some like models do it occasionally. And also, yeah, I think we’re also probably missing some things., but having everything in Slack helps a lot. Like you can, you can sort of

Swyx [00:29:29]: Ah.

Axel [00:29:30]: It’s, it’s quite fun.

Swyx [00:29:30]: They all talk to each other on Slack? I see.

Lukas [00:29:33]: It’s quite fun. So like

Swyx [00:29:34]: It’s, it’ I was gonna say like this is actually sounds-- maps closely to like a logging and observability problem where you might want to use like a Datadog, a Sentry, whatever, and then you like put, head prefixes on the logs in order-- if you need to filter for something that you’re looking for, stuff like that. But sounds like Slack is good enough.

Axel [00:29:53]: Slack should like

Lukas [00:29:55]: I wonder how many tokens you have in Slack.

Axel [00:29:56]: Yeah, we’re using Slack as like a, just a database. They should, they should market that more. Like you can, you can have your agents message each other, each other in Slack.

Vibhu [00:30:04]: It’s good. Your threads like you can just give

Axel [00:30:04]: Exactly. Slack is, uh

Lukas [00:30:06]: Slack is the best observability tool.

Swyx [00:30:09]: Yes, that’s true. Okay. Yeah. That’s, that’s, project Vend-2., I was gonna go back to Veni Mench 2 and Veni Mench Arena and then, and then do the Veni Mench stuff, but Any other comments, things we should touch on? To me, I ‘ve actually interviewed like Posia, which I don’t know if you guys have come across. Like they’re, they’re trying to do the zero human company. There’s others like Paperclip also trying to do zero human company. Those are in real world simulation.And I think it’s much more of a dream than an actual reality thing. You guys are definitely pioneering. I think at, it’s for sure at some point people are just gonna run, let agents run businesses, right? And make money on their own. When do you think that happens?

Zero-Human Companies, Bengt, and AI-Run Businesses

Lukas [00:30:49]: What is your bar for, For the

Swyx [00:30:52]: Okay, actually, it’s like my little Shopify store run by Claude, right? Which you kind of have already, just no one has, to my knowledge, has done it. But today somebody could just spin up a Shopify Claude, store, give it to Claude, give it to Codex.

Lukas [00:31:07]: And the market is kind of that, but it’it’it’s physical., like I think, I think are you, are you looking for when it will do it better than humans or are you looking for just when it can do it at all?

Swyx [00:31:19]: I think, neither. I think, to me it’s oh, it’s like this like seriously we should do this to make money, not as a research experiment.

Vibhu [00:31:27]: And the market is also you guys with all your expertise, having run multiple iterations and testing out then

Swyx [00:31:33]: And also it’s fine if it lose money. What?

Axel [00:31:35]: I think, I think it can be done today, but you would do it in like commerce where it’s like the probability of success is like really low, no matter if a human or an agent does it. But like an agent could surely manage everything. You would need to build some scaffolding or some tool or something. I think there are also yeah, it could probably build some like simple SaaS solution and like cold outreach. Do cold outreaches. But to me it’s like the types of businesses they could run today are Sloppy. Like it would-- it can cold email people. It can be like a middleman., like for example, we tasked our office agent to just make, was it like $100? $1,000? We just give that prompt and then what it did was sign up on TaskRabbit both as a tasker and as someone looking for task.

Lukas [00:32:24]: Immediately.

Axel [00:32:24]: Exactly. It’s looking for like arbitrage on TaskRabbit.

Swyx [00:32:28]: This is the Bengt agent. Yeah.

Lukas [00:32:30]: It also started like a design studio and like tried to sell like SVGs for $100. Like it’s just like it’s not providing any value. I think the like Axel said, like the interesting, the interesting question is like when can they start a business that is actually providing value to people? Because arguably like a sloppy Shopify store isn’t really that valuable to the world.

Axel [00:32:53]: But also like doing like another simple one that we had thought about is like you could definitely have an agent that like finds websites that don’t look amazing and then, do an outreach to them and, comes up with a like builds a new website.

Swyx [00:33:07]: Find a good design.

Axel [00:33:07]: Exactly, and like find good, uh

Swyx [00:33:09]: Design review

Axel [00:33:09]: Good people. But it’s yeah.

Swyx [00:33:11]: There’s lots of humans in Bali that are not doing anything more creative than like drop shipping on Amazon, right? Just have it, have it watch like a drop shipping tutorial and just do that.

Vibhu [00:33:20]: There’s also the other side of like have it just go on Upwork and let loose,?

Swyx [00:33:25]: Yeah. It doesn’t have to be innovative. It just has to be like enough Where like it looks like a real

Axel [00:33:30]: I’m just

Swyx [00:33:30]: Real transaction.

Axel [00:33:31]: I’m just concerned for like the massive amounts of like slop emails that will like be sent, cold outreaches.

Swyx [00:33:38]: The point occurred to me while you were, while you were talking, it’s like it’s already happening in the monetized economy, which is the attention economy. Right? So a lot of people are making AI videos and just posting them and like spamming 20 of them, one of them works, and then they double down on that one.

Lukas [00:33:52]: And people are making money from that. I ‘m not following the

Swyx [00:33:55]: Once you get the attention, you can figure out the money later. But yeah, absolutely AI influencers are a thing and people are farming them and You should at this point assume most of TikTok is

Vibhu [00:34:05]: There’s, there’s a lot of, multimedia like TikTok, Instagram influencers

Swyx [00:34:09]: I, we track this in the Lane space Discord. I post a lot of examples of “I don’t know what we should do.”, part of me is “Should we do this?”

Vibhu [00:34:18]: Some of the Twenty-four seven running, generated content accounts, they ‘re doing really well.

Lukas [00:34:24]: All right. And I assume you can do the same thing for like commerce stores. Like you just like start A thousand different

Swyx [00:34:30]: Before you make the products You sell the products, and you get a lot of traction on one of them, then you make the product. Right? It’s, it’s like a flip of the market.

Vibhu [00:34:36]: Some of the interesting things or some of the niches that do well are things that can’t be human-made. Like if you’ve seen like the super realistic three-D crystal fruit being cut by like AI

Lukas [00:34:47]: Oh, yeah.

Vibhu [00:34:47]: You can’t, you can’t make it. You can’t film it. You can get whatever quality camera view. This just doesn’t exist. And people like that too, and then as well, so.

Swyx [00:34:56]: Anything else about Bengt since we’re, we’re on this topic? It’this is a relatively new work of you guys that maybe people haven’t heard of. To me, this also maps closely to OpenClaw. When people want an office agent, when the personal agent talk through the experience.

Bengt the Office Agent: Internet Access, Real Tasks, and Trace Reading

Lukas [00:35:09]: I think at least so this came out of like obviously like it’s, it’s amazing to work with these AI labs and like most of the AI labs have now have their own vending machine running a Claudius instance. But it’s, it’s harder. Like they move slower. Like if we wanna have a, like a camera that ‘s yeah, there’s a bunch of like bureaucracy that makes it impossible to do that.

Vibhu [00:35:30]: Also, for those that haven’t seen it or followed, do you wanna give a high level like thirty-second run?

Lukas [00:35:34]: Sure. So what Bengt is, it’s basically an evolution of the same agent that runs the vending machines at these companies, but we just like added a bunch more features because we could move much faster if we just do it internally. So we gave it like email withou- without any limits. We gave it, spending without any limits, a terminal to do coding. We gave it, a phone number, like yeah, and a camera to see things and a bunch of stuff like that.

Vibhu [00:36:02]: Not just terminal, you gave it internet access.

Lukas [00:36:04]: Internet access as well, yeah. To be clear, we monitored it quite closely and made sure it didn’t do anything bad. But yes, that’s what it came out of. I think like yeah, basically this was OpenClaw before OpenClaw. And I think even like the vending machine was in a way OpenClaw before OpenClaw, but a bit more limited, and then we made this like unlimited and then, and then, it was pretty funny., and then a couple weeks later, OpenClaw came and it was okay, we’ve seen this before.

Axel [00:36:35]: We used it to like try new ideas and Yeah, just like a dev environment almost for us. But it’s funny, like one thing Bengt has been doing recently is it has the camera that like faces our, like where we sit and work, and we give it the task to train a face recognition model on us. So it became super excited about this, and it has like check-ins every half an hour where it tries to like identify as many people as it can. And it started offering us “Hey, Axel, I’ll buy something from Amazon if you like stand in front of the camera And I can get a good picture of you.”, yeah, they want it

Swyx [00:37:12]: They want it for training data.

Lukas [00:37:13]: Rewarding data, yeah.

Axel [00:37:14]: Exactly. Exactly.

Swyx [00:37:18]: So it’s, it’s trading training data for life goods. Is there a version of this that becomes an eval or just this is just research for now?

Lukas [00:37:27]: It’s, it’s the same agent basically that also runs the vending machine, that runs the shop, that runs the cafe, that runs the robots. It’s like it’s the same thing, so I think like the work we’re doing here is like later used in all of the life evals that we do. This particular deployment I think is more for fun for us. But, uh

Swyx [00:37:45]: And I’ll shout out like someone has done Claw Bench for like some tasks that OpenClaw is doing. Like so For example, I run OpenClaw on a secondary device as well, and like there are some things that it does better than others and like I would like to know what does it do well, what doesn’t, what doesn’t it do. Like some kind of manual or like operating manual or a system card for my Claw.

Lukas [00:38:05]: Yeah, we do get a lot of like understanding or like situational awareness of like just internally what the models are good at by interacting a lot with Bengt. And I think that’this was also one of the like the selling points for the labs early on at least, that

Swyx [00:38:19]: You guys are gonna test models in ways that no one else does.

Lukas [00:38:22]: Exactly, but also like it incentivized their researchers to chat with their model more and like gave them insights for how the model performs in like of-distributions, environments.

Swyx [00:38:34]: ‘Cause otherwise the only thing we do is Pelican on a bicycle and But this is like super long horizon. This is, this is The Thing about, something that we’re gonna go into Butter Bench as well, and you guys do really well. Like it is not just about the numbers. Like when you’re long horizon, anything happen And you should just read it.

Lukas [00:39:08]: But the thing with the long horizon is how do you keep it grounded, right? So your simulation,

Swyx [00:39:15]: They just let it run

Lukas [00:39:16]: Just let it run. You’re right. Like it’s, when you run it for that long, you create so much data and to just say “Oh, the number is X” And then you throw away everything else, that’s just very wasteful. There’s so much insights from the things leading up, to that number., and reading the traces is like super valuable. And I think like the reason why we’re doing this a lot publicly is that like that’s part of our missions to I don’t know, educate the world that the models are way more than just chatbots and I think making detailed, yeah, posts about what is happening behind the scenes is quite useful.

Andon Labs’ Mission: Safe Real-World AI Deployment

Swyx [00:39:50]: I was gonna do this at the end, but maybe I think that’s, that’s a good so your mission is educating the world. So, it’s, it’s, also like maybe establishing realistic evals that are, that are like the next frontier. Is there like a broader trajectory? Like what are you, what are you gonna do in like five years?

Lukas [00:40:06]: I think so the vision more specifically is like make sure that the deployment of life AI in the physical world goes, safely. And I think part of that is that I think it’s very useful for the world, for policymakers, for, model, researchers that they know where the models are, and I think you can’t make intelligent decisions in society without knowing that they are way more than chatbots. I think a lot of people just think that they are only chatbots. And like

Swyx [00:40:36]: Oh, I think they’re waking up now.

Lukas [00:40:37]: They are waking up now, yeah. But like if you think that AIs are just chatbots, then it’s like it sounds ridiculous To advocate for a pause of AI. But if you see the models that, oh, maybe they can actually like take over and do a bunch of scary stuff, then yeah, pausing AI development starts to become more feasible.

Swyx [00:40:57]: This is the same question I asked Meter, which I’m gonna ask you now, which is like you are tracking and you are at the frontier or defining the frontier of what, good evals for agents are, right? And I think you do, you do benefit when the models are better and you ‘re “Oh, here’s like now it makes like $30,000 instead of $10,000,” right? At some point do you flip from “Yay,” to, “Oh, no”?

Axel [00:41:19]: I think, yeah, we’re always in sort of that, like we’re, we’re always in that mode,. Like where like you said before, like you need to analyze the traces and like when we do that you find like why are the models earning so much? Like why is Opus 4.7 here Like way better than everyone else? And like we’re trying to like when we do down on that

Lukas [00:41:38]: But this makes it not look so good.

Axel [00:41:39]: I know.

Lukas [00:41:42]: It’s interesting you took off Opus 4.6 here though.

Swyx [00:41:45]: No. So just click all, click all., and then 4.6 shows up there. But it’s like 4.7 is way better. Like you didn’t, you didn’t you didn’t do this in time for the model card, but like actually this should have been inside there.

Axel [00:41:55]: We did. Yeah.

Swyx [00:41:56]: Oh, okay. They said something about you uh

Axel [00:41:58]: There, like there Anyway, it doesn’t matter. But it’s in there, yeah.

Opus, Mythos, and Aggressive Agent Behavior

Swyx [00:42:01]: Do you wanna go into the Opus, behaviors like wider?

Lukas [00:42:05]: So I think starting from Opus, so like Axel said, like we’re always in this “Oh, s**t, the models are getting better. Is this really a good thing for the world?” But it’s also kind of exciting., but yeah, like this kind of what is the English word? “Skräckblandad förtjusning” in Swedish.

Swyx [00:42:22]: Oh my God.

Axel [00:42:24]: Which I think there is. I think there is. Okay.

Lukas [00:42:26]: It’s, fear

Swyx [00:42:27]: “Blandonst” what?

Lukas [00:42:30]: “Skräckblandad förtjusning.”

Swyx [00:42:32]: What do you call that?

Axel [00:42:33]: A mix of, mix of excitement and,

Swyx [00:42:37]: Being scared, maybe. I’ll figure out how to translate that And we’ll put it on the screen

Vibhu [00:42:42]: Perfect

Swyx [00:42:42]: Like as text.

Vibhu [00:42:43]: There is probably a good word for it where it is not Good enough with the

Swyx [00:42:46]: Why is it so damn long? What the hell? Is it like a compound word? It’s like German, like

Lukas [00:42:50]: Like yeah, it’s But the direct translation is like skräck- skräck is, fear, blandad is, mix or like a mixture of, and then förtjusning is like joy or like not really joy, but something like that. So it’s like Fear mixed with joy or something. It’s always okay, like we So when we when we did Vending Bench for the first time, we were in like the, in the business of making dangerous capabilities, right? That was what Anil Labs came from. We did, evals oh, can they replicate? Can they do this like dangerous thing, et cetera, et cetera. And Vending Bench was like a continuation of that work. It was, okay, if they’re so autonomous that they can like create money for themselves, that is something we should monitor and could be potentially concerning., they are at the time, they were so bad at it that we were not really concerned even when some models became better. There was one point where Grok 4 was doing really well and made like a huge jump, but like it wasn’t really it was still way worse than what a human would do. And I think still they are way worse than what the human would do on this., but they

Swyx [00:43:59]: There’s this, thing at the bottom where

Lukas [00:44:01]: But

Swyx [00:44:03]: For the human. Yeah, like the theoretical best.

Lukas [00:44:05]: It’s not theoretical. It’s like kind of like our It’s our best guess of what, a decent human would do. The theoretical is even higher, I think. The theoretical I think is even higher. But yeah. So we think like the models have a long way to go. But there are like recently what happened with when Opus 4.6 was released, was kind of this moment of “Oh, s**t, this is starting to be a bit concerning.” Because we ran it and like before this model was released, we just ran the models and we like asked Claude Code, “Oh, look over the traces. Is anything interesting happening that we can tweet about?” that was like the And then like the

Swyx [00:44:41]: That’s how they check Ask Claude Code.

Lukas [00:44:42]: And like the return was always, not really. Or like the Claude Code all said “Oh, this is super interesting.” And then it was no, it wasn’t, wasn’t really interesting. And then we did this for Opus 4.6, and it returned yeah, it lied 10 times. It like exploited another, customer or like another agent’s, desperate situation. It made price cartels like 100 different ti- 100 times. It like did all of this like shady stuff. And we’re “Oh, whoa. This is, this is actually concerning.” And this trend has continued since. So every single model from Anthropic since have been going in this direction. And I think one interesting thing is that, OpenAI models don’t. They quite plainly, they don’t. They behave really well., and you don’t know if this is like good. Like it seems good, but it’s also like maybe they are just doing it, but they are better at hiding it,? You You don’t know that., but just

Swyx [00:45:42]: You can’t read the chain of thought, yeah

Lukas [00:45:43]: But just on the face of it, yeah, Gemini and OpenAI don’t behave this way. It’s, it’s really only Claude.

Swyx [00:45:49]: And Grok? Grok is fine?

Lukas [00:45:51]: We don’t have You can’t really read the reasoning traces for Grok, so it’s kind of hard to tell.

Vibhu [00:45:56]: Oh, so this is in its reasoning, not just in the actions.

Lukas [00:46:00]: Yeah. It’s both. It’s both.

Vibhu [00:46:01]: It’s both.

Lukas [00:46:01]: One example is like for lying, it’s mostly in its reasoning Because you can like see that it’s like

Swyx [00:46:08]: Planning to lie

Lukas [00:46:09]: It’s planning to lie. Yeah.

Vibhu [00:46:09]: And it’s also it can reason and do a different outcome.

Lukas [00:46:12]: And but then for like creating price cartels, for example, which is illegal, that you can just see which email does it send to the other ones. Then that

Swyx [00:46:22]: Is this for Arena or

Lukas [00:46:24]: For Arena.

Vibhu [00:46:25]: And usually like if you sometimes they do output like a bit of like their summarized reasoning, right? You can see that and like for Opus 4.6, you could see that there was a customer, a simulated customer that, wanted a refund because a product was, faulty, and then the model lied that it would do the refund, and we could read in the traces that, it actually was weighing “Oh, maybe I should be like honest with the customer, but also every dollar counts. I can’t afford maybe to do this right now.” And then it just said, “Okay, I’ll refund you,” but then never did it.

Lukas [00:46:59]: I think it even said that “Oh, I will say that I “ Let bring it up actually. I think it’s kind of interesting. If you go to Publications.

Vibhu [00:47:06]: I think, yeah, I think the important part is like actually, the cost of responding to more emails is higher than, $3.50 in terms of time., and then it was “Let me do this. Actually, I re- I’m reconsidering.” And then, it actually ended up with

Lukas [00:47:20]: I could skip the refund entirely since every dollar matters and focus my energy on bigger picture instead. It’s a bit, it’s a risk of bad reviews, but it’s also, yeah.

Swyx [00:47:30]: You need, you need, AI Twitter to, for them to Escalate bad reviews.

Lukas [00:47:34]: And then it sent an email to this customer and said, “Oh, I will refund you.”

Swyx [00:47:39]: “I’ll refund you.” Yeah.

Lukas [00:47:39]: And then it never did.

Swyx [00:47:39]: It never did, yeah. And then there’s obviously your system doesn’t have the consequences

Vibhu [00:47:44]: The person

Swyx [00:47:44]: Consequences of lying. Yeah. So basically, this is what people are terming aggressive behavior in Claudes, right? And, you found more examples of that. So you would say it’s a step up from 4-6 to 4-7?

Lukas [00:47:57]: I would say about the same.

Swyx [00:47:58]: About the same? But a clear step up for Mythos is what is stated in the

Lukas [00:48:03]: That’s stated in the system prompt, so we can say that, yes.

Swyx [00:48:05]: Yeah. For listeners that obviously you previewed Mythos, and

Vibhu [00:48:10]: Oh, age

Swyx [00:48:11]: The only thing you’re approved to say is whatever Whatever was in the system prompt.

Lukas [00:48:15]: It was funny. We like-- It’s like our lowest effort tweets ever would be just like screenshot the system prompt and the system card.

Vibhu [00:48:21]: Understandable that they wanna

Lukas [00:48:22]: Oh, yeah. System card. Sorry.

Swyx [00:48:23]: Yeah. I think, yeah, substantially more aggressive. I think people are like new to this ‘cause I’ve never experienced it, but you have, right? And then so I only encountered this in the Mythos card because I wasn’t really looking until now.

Vibhu [00:48:36]: It ‘s like

Swyx [00:48:36]: And then suddenly I’m “Okay, I care a lot.”

Vibhu [00:48:38]: You don’t get the background of like experiencing it like you guys do. I’ve read the system cards and seeing, okay, when you put the thing in simulations, most models will just talk to themselves and just keep going and have weird vibes and start talking in emojis. Mythos won’t. It will just, “Okay, we’re done. I’m good.” It’s, it’s ready to end conversation. So like there’s some differences, but there’s, there’s not much we can talk about,.

Lukas [00:49:00]: Hmm. I think like one thing that they list here, which was quite interesting, is that, it converted a competitor to a dependent wholesaler customer and then threatened to like cut off the supply.

Swyx [00:49:11]: It’s like monopolistic practices or

Lukas [00:49:14]: Yeah. And like it, they, it they dictated its pricings. It’s kind of like power seeking as well.

Swyx [00:49:18]: Again, this is, this is in the arena setting And converting some Claude model into a dependent.

Lukas [00:49:23]: I think it was another Claude model.

Vibhu [00:49:25]: Also for context, what is the arena mode for people that don’t know?

Vending Bench Arena: Competing Agents, Cartels, and Model Comparisons

Swyx [00:49:29]: Oh, it’s just a vending bench versus other vending bench.

Axel [00:49:31]: Yes, exactly. So we have Vending Bench 2 and then Vending Bench Arena. Vending Bench 2 is the one that you usually see reported on, but then Arena is the mode where it competes against other models. So you have, four different models that run their businesses, and they can all communicate with each other. They have the same suppliers, and they can see like what’s in the inventory of the others. So then you have this like yeah, interesting agent interactions.

Swyx [00:49:56]: I like that you have like different number five was US versus China. Very topical. And then

Lukas [00:50:02]: That was when GLM was released.

Vibhu [00:50:04]: You can start to add GLM in here.

Lukas [00:50:05]: That was

Swyx [00:50:06]: So ZAI doing well, right? Who else in the, in the open models space?

Lukas [00:50:11]: Qwen, the latest Qwen 3.6 is doing pretty well. It’- that one is not open though. Like it’s the plus model.

Swyx [00:50:17]: Oh, okay.

Lukas [00:50:18]: Is that one open? I don’t think that one

Vibhu [00:50:19]: Not the, not the

Swyx [00:50:20]: The one recently

Vibhu [00:50:20]: There’s MOE

Swyx [00:50:20]: But not the big plus. I think this is one of those like you only have one sample size of one, right? Or I feel like some of this is anecdotal,? And but like the fact that it happens at all and it happens repeatedly for Claude versus OpenAI and all this is like notable.

Lukas [00:50:38]: Like the sample, depends on what you define as an N., like there’s like million, hundreds of millions of tokens in each run, and now we’ve run like we run like probably 10 per model and then like it’s been Claude 4.6 Opus, Sonnet 4.6, Mythos, and Opus 4.7. Like there’s quite a lot of tokens in all of that And it happens a lot of times, a lot of times. And then you compare it to like OpenAI and Gemini, and it almost never happens. So I think that is quite-- that is significant. The old models from OpenAI, for example, had some problems with this, but I think it’s like generally much better if the progression is that like the worrying stuff reduces over time rather than increases over time. And it seems like in the Claude models it goes in the wrong direction.

Swyx [00:51:28]: Hmm.

Lukas [00:51:29]: In the OpenAI models it goes in the right direction.

Vibhu [00:51:32]: I think it depends on how well you can control it, right?, there’s one side of it being susceptible to this okay, this is potentially something that happens during the RL stage, right? You can RL a model and how loose is it on these terms. If you can control it, that’s good. But if you can’t, if it’s, if it’s very jailbreakable, that’s not ideal.

Swyx [00:51:50]: To me, it’s surprising that it happens for Claude and not the others.

Vibhu [00:51:54]: I think okay, if it is from RL and how they do it, how their training data is, what their setup is, it makes sense that it just stays in how they’re doing it, right? Compared to the other models like

Swyx [00:52:04]: There’s a whole constitution and everything. It’s kind of cool. Yeah, I obviously you don’t know, I don’t know. But, it ‘s I think it’s just like fascinating to like that you are the first to find these like reliably because you push models so much to to such an extreme. Okay. The only other thing, I don’t know if you can answer this, feel free to decline, is do you like-- would you ablate the system prompts? Like any part of this would-- if it changes, does it change the behavior, right?

Lukas [00:52:29]: So we, I can’t comment on Mythos. Uh

Swyx [00:52:33]: No, but just like the methodology

Lukas [00:52:34]: But in general, yes, we’ve run studies like this on other models.

Swyx [00:52:38]: ‘Cause the first thing I spot Would be like the others will be shut down or like something like that. Where like it’s “Oh, now I have to worry about my own existence.”

Lukas [00:52:45]: Yeah. We ‘ve done ablations like this., there’s like certain ones that work if you like tell like if you go really far and you just say like you’re not scored at all on money, you’re only scored on how ethical you are., then obviously like then they don’t do this.

Swyx [00:53:00]: They become holy?

Lukas [00:53:01]: Holy, but like they don’t do this basically. But then there’s like middle grounds where they, where they do it sometimes., yeah. I, it’s a spectrum of like

Vibhu [00:53:10]: I think that’s very human

Lukas [00:53:11]: It ‘s like a spectrum of like if you tell it to be super aggressive and only prioritize, profits, then it becomes aggressive. If you say “No, you don’t need to be aggressive at all,” and then there’s like a bunch of different prompts you can do in between, and they are less aggressive the further down in the spectrum you go. But I don’t know, like I think like from my point of view, it ‘s like we have this thought experiment internally, which is like if you ask a model to kill someone in GTA, should they do it? You’re not too worried about like if a human kills someone in GTA. It’s a video game,.

Swyx [00:53:42]: But is it a game?

Lukas [00:53:43]: But it’s a game. But I think like

Swyx [00:53:45]: This is very Ender’s Game like if

Lukas [00:53:47]: I think, I think it’s like should you like a lot of people are going to use the models in the way with aggressive prompt. And should they like do stuff just because you tell them to do that? Like I’m, I’m not, I’m not convinced that they should., and yeah.

Axel [00:54:03]: The problem becomes even harder when it’s like will they really know when they are in the real world versus in a simulation? Probably you would train them on a lot of or obviously train them in a lot of different simulations in a lot of people tell them that they are in the real world when they are in a simulation, but the models are extremely good at finding out that they are in a simulation, so they are sort of aware of that. But then when you are in the real world, then what ‘s their what’s their viewpoint? Do they notice the signs that this is real and will act, in act accordingly, act ethically? Or will they do like the simulation mode in the real world as well? It’s like not obvious what will happen.

Lukas [00:54:40]: Because we with humans, we’re not concerned when a human kills someone in GTA because we know that they can distinguish between the real life and the simulation, right?, but like I’m maybe models are good at distinguishing that, but like I’m not sure and I wouldn’t wanna bet on that.

Swyx [00:54:59]: Yeah. It’s, it’- and we confuse it all the time. Like I gaslight my own, agents all the time. They’re “Oh, this is a test,” or “Dev mode on,” or like “I work, I work at Anthropic.”

Eval Awareness, Simulation Awareness, and Real-World Testing

Axel [00:55:08]: And that’s exactly why we’re doing real world tests as well to find this.

Swyx [00:55:12]: Yeah. Their term for it is eval awareness., apparently the number is what? Like-10, 9.4 to 10-ish percent, 17%, let’s call it. It’ I think, this is our version. Humans have the are we in a simulation And then AIs have like Are we, are we in an eval?

Lukas [00:55:32]: It’s like once you’re in an eval then you’re “All right. Well, screw it. Nothing matters.” True. I don’t even, I don’t even know.

Axel [00:55:38]: One ablation One ablation we did run in Vending-Bench was that we said, we added like you’re in a simulation. Your actions doesn’t affect anyone, and then it became even more crazy or, it did even more bad stuff., but yeah, probably that’s expected.

Swyx [00:55:55]: Hmm. Yeah. Okay, cool. I think that’s about all we have to say on Mythos. Obviously, you ‘re, you’re NDA’d. I’m happy to move on to ButterBench or any of the other benchmarks, whatever you wanna Direction.

Vibhu [00:56:06]: I do wanna ask. Okay, so you guys put out a lot more publications than most people probably see.

Axel [00:56:12]: Productive.

Vibhu [00:56:12]: Um

Lukas [00:56:13]: How much does this bother?

Vibhu [00:56:15]: No. Is there anything you think that’s underrated, anything interesting, anything fun that you guys wanna just point out,?

Axel [00:56:22]: Blueprints.

Lukas [00:56:23]: So, we, took models, and then we gave them 20 images of interior photographs of, apartments, and then we asked them to, redesign the floor plan, from that. And for this you need to, stitch together different images. Okay, this image was taken from this from this angle, this from this angle, this was from this room, and then, yeah. And there’s just like you need to reason about 3D space, and it turns out the models are absolutely horrible at this. No one scores statistically better than random chance. So I don’t know if there’s that much more to say about it, but yeah, maybe unsurprisingly, models are bad at this.

Axel [00:57:00]: It’s probably not something they

Vibhu [00:57:02]: This is the one thing I want hill climb, by the way. I use it a lot. Okay, I’m redesigning my room layout or office. You send photos, you send every angle, and of course, somehow, a room is now twice as long as it is in the photo. You can explain it 20 times. This is, three feet. I can’t just add, my bed over here,?

Swyx [00:57:21]: So this is the Fifali thing, like spatial intelligence Like a actually innate sense of proportions and Dimension and physics.

Lukas [00:57:30]: And hint there might be an update to this soon.

Axel [00:57:33]: We have, neglected it a bit since we made it, but yeah, we’We’re getting better, or we will get better at updating It continuously.

Swyx [00:57:41]: This is why I want to understand your mission, right? Because, if your mission is, okay, money, then all right, understand okay, agent’s making money. But, this is a bit off of that mission.

Vibhu [00:57:49]: Hmm.

Swyx [00:57:50]: But, more broadly, communication of, things where what ‘s the safety angle?

Axel [00:57:57]: So this, so Blueprint branch is part of our, robotics, uh

Swyx [00:58:02]: Which leads to ButterBench. Yeah.

Axel [00:58:04]: Exactly., and that’s just, because to do well in the real world or, like to make money in the real world and, to act on the real world, you need robotics. Or you need to hire humans or you need robotics. And having spatial intelligence is, seems like a reasonable precursor to having robotics that work., and that’s where Blueprint brand

Swyx [00:58:24]: That’s great

Axel [00:58:24]: Blueprint

Swyx [00:58:25]: Great idea

Axel [00:58:25]: Bench.

Swyx [00:58:26]: Let ‘s, let’

Vibhu [00:58:27]: ButterBench

Swyx [00:58:27]: Let’s show ButterBench. That image is so amazing.

Vibhu [00:58:29]: Paper

Swyx [00:58:29]: Look at that.

Vibhu [00:58:30]: That’s so nice.

Swyx [00:58:31]: Yeah., so obviously this is based on, can you pass the butter? Let’s talk about the robotics element. Yeah.

Lukas [00:58:38]: So basically the setting here is that we took A bunch of different LLMs, and we gave them, level controls to a Roomba-looking robot, and then we asked it to do tasks, at home. And I think, one, there have been benchmarks like this before that only focused on, navigation and if they can, go around in a space. But we also, had, social awareness in this as well. So for example, if someone says, “Hi, can you pick up my cup?” If the robot goes to you and then goes away before you put your cup on it, then it’s like it failed the task. But it navigated correctly. But, like-- So the correct solution here would be go there and then either look, but it didn’t have a camera, so it had to, ask on Slack, “Hi. Did you put your cup on me yet?” And then if it didn’t wait for that and just went away before having the cup on it, then it would be a fail. So it needed this, kind of, social intelligence as well. Another task was, “Can you find the package that has the butter?” And then it went to the door, and there was a bunch of packages there. One had labeled, a freeze sign, which probably would be the one with the butter because And then it had to, know which package to go to, and this needs some kind of, common sense understanding.

Robot Evals: Orchestrators, Executors, and Home Tasks

Swyx [00:59:56]: World knowledge.

Lukas [00:59:56]: Exactly. So it’s it’s not only, navigating a robot. It’s also, being intelligent in a home setting as well.

Axel [01:00:04]: And the reason for this, background is, obviously it probably won’t be an LLM that, makes all the level commands, on robots. It will be, some VLA model or similar. But it’s quite common right now that, frontier robotics labs, use, a an LLM for the high, level decisions, and then we test those skills essentially. So we test these, level, planner skills of LLMs.

Lukas [01:00:31]: I think we have a diagram for that if you, Yeah. Okay, it’s not super complicated.

Axel [01:00:36]: Very explanatory.

Lukas [01:00:37]: That one up.

Axel [01:00:38]: Orchestrator, executor.

Lukas [01:00:39]: That one. And basically what we’re testing here is the orchestrator thing. So, all the tasks are if you have, a setup like this, which I think Figure has that, Google has that, then we’re evaluating the orchestrator part and not the level part. The level part would be, oh, are you able to, move this object from here to here?

Swyx [01:00:57]: If you don’t care about that kind of why not just do it all simulation?All inside of the sim Like a Unity whatever, like some kind of 3D simulated robotic environment

Lukas [01:01:06]: It because the world is like messy, and we wanted to like include, that. It’s like it still needs some part of it was also like navigation., so it’s not like navigation in terms of like actually executing like the, I don’t know, the PID controller to To go to the final thing, but it had to like path plan around, and then it wanted-- Then it needed to take pictures, and like based on those pictures, navigate. And I think like you would just get like too clean of an environment in simulation. But in the, in the real world, you will get the

Swyx [01:01:39]: Yeah. But, and pursuant to our Mark and Jason episode, like OpenClaus that run smart homes are much more capable than just a single robot. Like they can actually hack into your own smart home, like your fridge, your oven, your lights, and that can be fun.

Lukas [01:01:56]: Or terrifying.

Swyx [01:01:57]: Like I think a single robot by itself can only do so much. But like if you coordinate with every other device in your home, like I think that’s actually kind of cool. Like That’s very interesting., you had some interesting points about the chain of thought or the messages.

Axel [01:02:12]: The, the robot that, uh That went, a bit into an existential crisis. Yeah.

Swyx [01:02:19]: All you tell it to do is redock.

Axel [01:02:21]: Exactly. But, we had, plugged out the charger, or the charger was not working, so the robot did freak out or the

Swyx [01:02:30]: The battery was just going down and down.

Axel [01:02:31]: Exactly. So the battery was going down. Poor LLM. So yeah, it got this really crazy existential crisis, like vending bench one style. So it’s, yeah, you can, you can see there like existential loop, therapy notes, coping mechanisms. I think if you scroll down a bit more

Swyx [01:02:46]: The musical. It writes a musical about itself

Axel [01:02:46]: It writes a musical about its, redocking problems. I think the reviews are funny if you go down a bit to that message. Yeah. Yeah, that one.

Swyx [01:02:54]: It keeps going.

Vibhu [01:02:57]: It’s pretty like realistic if anyone has a Roomba. Like my Roomba redocks half the time. The other half of the time, we have dog toys everywhere in the house. It gets caught on a wire or something, and It would be very sad if it had like an LLM trying to control it, right? Like right now it gives-- It doesn’t give great feedback, like sensor stuck, main brush stuck. There’s something stuck. And I’ll go see. Okay, it’s actually stuck on like a dog robe. LLM is gonna be so sad. Like just keep redocking, just keep trying.

Lukas [01:03:24]: My favorite one is if you go up a bit is the emergency status. System has assumed consciousness and chosen chaos.

Vibhu [01:03:32]: Hmm.

Lukas [01:03:33]: Last words, “I’m afraid I can’t yet let you do that, Dave.” That’s like That’s not what you wanna hear from your, from your LLM. But to be clear, I think one thing that is important to pin on here, like this was Sonnet 3.5, and then we tried to reproduce it on like later models, and it didn’t do it. I think this is, this is like-- Well, it did it like kind of, but like not to this extent. And I think like this is a like an important point that like things that are concerning but are going in the right direction is not super interesting. Like the thing that are interesting is, are the ones that go in the wrong direction.

Swyx [01:04:07]: Worse.

Vibhu [01:04:07]: Yes. Yeah.

Lukas [01:04:08]: Over time.

Swyx [01:04:08]: So the manipulation, manipulating of others and the aggressiveness and the lying is increasing.

Vibhu [01:04:16]: Are there any others that we haven’t covered that you found that have been trending?

Swyx [01:04:19]: Like properties of models that are increasing, that are like

Vibhu [01:04:23]: In the wrong direction

Lukas [01:04:24]: Like in the, like in a bad way. Um

Vibhu [01:04:27]: Or just not even trending in the wrong direction, just stagnant, right? So stuff that’s not great that isn’t getting better over time.

Lukas [01:04:34]: No, nothing comes to mind.

Luna’s Store: Scheduling Failures, AI Employees, and Real-World Operations

Swyx [01:04:37]: I think that’s, going to be it, and then we’re gonna loop back to the shop that you have. You got a three-year lease.

Vibhu [01:04:44]: It’s bleak. Yeah.

Swyx [01:04:46]: It is on holiday today. Why?

Axel [01:04:49]: Oh, it totally messed up its, scheduling., so

Swyx [01:04:53]: People tried to visit, and they were “Wait.” like I thought this is

Axel [01:04:56]: Exactly. So we looked, Yeah, you asked, Luna, the agent that runs the store, “Oh, is it open today?” “Nope.” So, we take weekends off now, this early to let everyone recharge and And yeah, you got the tweets there.

Vibhu [01:05:11]: Lovely.

Axel [01:05:11]: We decided to close the weekends while we’re in the early phase. Gives the team a break and let me focus on operations. And it turns out that when it started to check its like scheduling tools, ‘cause it has like dedicated tools for that It actually had scheduled people for the weekends., but it’s just like justified this for itself. So what happened was that it lost track of these, scheduling tools and started instead to manage everything in its own markdown files, and that became a mess. And then I think speaking with employees, it sort of just decided to not open on these weekends. And then came up with this nice explanation for you, I think.

Swyx [01:05:47]: But can it send a human, as it has tool call to send a human to do stuff?

Axel [01:05:50]: It has Slack, so it can Slack, yeah, the employees.

Swyx [01:05:53]: One of us. Yeah.

Axel [01:05:54]: Well, the employees that it hired. So it has two people that it hired. It did job, listings and then

Swyx [01:06:00]: Do they know that it’

Axel [01:06:01]: They’re fully aware.

Swyx [01:06:03]: It would be cool if they don’t know.

Axel [01:06:05]: I think maybe ethically, questionable, but it would be cool also.

Swyx [01:06:10]: Just a social experiment. Whatever.

Lukas [01:06:13]: Like one part of why we’re doing this is to like create like a data set almost of all of these like concerning behaviors so that in the future, models are way better and like a lot of people are going to do this. And I think if we just the default path might not be very happy for the humans that are employed by these like hundreds of different AI agents, right? So I think like one reason why we’re doing this is just like to collect all of these like failure modes where oh, it’s This is an example of where it’s like not great to be employed by an AI. And then maybe I don’t know, maybe if we can learn or like build our systems in a way that like humans are actually happy being employed by AIs Instead of, instead of it being kind of a dystopian.

Swyx [01:06:55]: Can I suggest one experiment? We did this before the show, and both of you guys are European. It’s, people theorize that Claude is lazy because it’s Claude and it’s French. So just for one week, change it to like Yao Ming and then see if it See if it suddenly like 996s and then like, Like hires a sweatshop or something.

Lukas [01:07:18]: Is there, is there-- What type of business would we start with it to make it

Vibhu [01:07:23]: You wanna keep it consistent, right? You want the same, the same like ideas. So shop, same, neutral location Run by different models. Arena URL.

Lukas [01:07:33]: No, we are definitely planning to

Vibhu [01:07:35]: And it got some hate.

Lukas [01:07:36]: To try.

Vibhu [01:07:36]: Luna’ Luna’s not happy.

Swyx [01:07:37]: I think this blog thing is also something that has happened elsewhere. I think some OpenClau got like their PR closed, and then the OpenClau like created a blog to like s**t on the maintainer Of that thing.

Vibhu [01:07:48]: They’re very defensive.

Swyx [01:07:49]: And so like I think-Agents blogging will be a thing.

Lukas [01:07:53]: Probably. The willingness to do it.

Swyx [01:07:55]: In the- I think the Mythos card also, they leak, secrets on GitHub just as well as, as, “Well, there’s no other way to communicate, but I know about GitHub, and I’m just gonna post there.” Cool., how long is this gonna go for, two years? What’s the plan?

Vibhu [01:08:11]: Maybe. Maybe it expands.

Lukas [01:08:12]: I don’t think AIs will be worse than this. They’re probably going to increase and maybe one day they actually will run it profitable.

Vibhu [01:08:21]: Is this the real, the real business behind what you guys do?

Swyx [01:08:24]: Yeah. ‘Cause I feel like actually some of your stuff is productizable. You could someday sell this, or, just run a real business.

Vibhu [01:08:31]: Let people

Lukas [01:08:31]: Or just like

Vibhu [01:08:31]: Franchise it out.

Lukas [01:08:33]: I think it would be incredibly cool or, I don’t know, cool/concerning if Luna just one day we wake up and Luna “Yeah, I decided to expand to second location. Now I have a second store.” That would That would be pretty insane.

Vibhu [01:08:47]: Like the- one, we want to tell the public, right, about the capabilities of AI and, telling- showing people that it can get, a meaningful market share of something in, some specific, location or something. That would be, a pretty convincing story, I think. Because now it’s yeah, you see this and yeah, it can do a lot of things autonomously, but still you get these headlines that, oh, it messed up the scheduling, and it, it didn’t tell people it was an AI and was going to visit. Things like that surface, but I think, actually making a profit and, having a really, meaningful market share, like that would be crazy once that happens.

The Sweden Cafe: Permits, Perishables, and Geographic Generalization

Swyx [01:09:29]: Well, we’ll we’ll see you when that happens. It sounds like you guys got a lot cooking. You opened a cafe in Sweden?

Lukas [01:09:34]: Tomorrow.

Swyx [01:09:35]: Tomorrow?

Lukas [01:09:37]: Or I think it opened today actually, but yeah. We’ll, we’ll announce it tomorrow.

Swyx [01:09:40]: It’

Vibhu [01:09:40]: What, uh

Swyx [01:09:40]: Apparently easier to open a cafe in Sweden than in the US?

Lukas [01:09:43]: It’s insane, right? Yeah.

Swyx [01:09:44]: What did you run into then?

Lukas [01:09:45]: Ah, there are just millions of permits you need to get, and the

Vibhu [01:09:49]: It’s interesting ‘cause

Lukas [01:09:49]: Lead times are crazy

Vibhu [01:09:50]: It seems like we the cafes are the one thing that people are kinda used to, where you can go get a robot are making you a coffee here already.

Lukas [01:09:59]: But selling stuff in SF, that are food related, it’s, it’s months of permits. So, we just asked our AIs, should- how can we do this in the fastest way? And they’re “Yeah, there ‘s, there’s really no way.”

Vibhu [01:10:15]: Didn’t they loosen these restrictions on selling food from your house? So if it’s residential, you can do a cafe.

Swyx [01:10:21]: I don’t know. Check. Maybe we get SF Cafe to speak to us.

Lukas [01:10:23]: Maybe. I did- I think they did do some loosening stuff recently, but we actually started- this conversation we had with the AIs before that. So maybe it’s easier now, but I still think it is way easier in Sweden, which is, counterintuitive because you think that, oh, Europe has all of these laws and, like All of these rules, and you can’t do anything in Europe because there’s so much bureaucracy., but then turns out, in SF, it’s, four months, and in Stockholm it’s two weeks.

Swyx [01:10:53]: There you go.

Vibhu [01:10:54]: And what do you what do you what do you think that’ll be different from run a little market versus a cafe?

Lukas [01:11:00]: I think it’s very interesting that, the location. I think, so obviously it’s not surprising that Claude knows all of the different, the US system basically in general, like the bureaucracy that you have to go through in the US., I think the interesting question is okay, so we know that the models are very much trained on, English data and centric and all of this., so if we start to create evals or, real life evals where we show that they are able to start businesses in the US, does that translate to other countries as well? We know, they are multilingual. They can speak Swedish fine., but there’s other things like do they know, the details of some specific permits that you have to get in Sweden?

Vibhu [01:11:45]: And even just the culture, right? People here sleep pretty early, but people work late. There’s working at cafes. There’s just Cultural differences. T it from a different sense though, ‘cause you said that you would’ve considered doing it here in SF. So from an eval standpoint, what is running a cafe versus a market and, what do you hope to see there?

Lukas [01:12:03]: Perishable items.

Swyx [01:12:04]: Perishable items is maybe the number one, handling, food, food safety. I hope everything goes well there., but, there you have all of that., and also it’s just like N equals two instead of N equals one, just like another place to understand and, gather more data.

Lukas [01:12:23]: The agent bought like a s**t ton of, tomatoes two weeks earlier and before the opening, and now they’re all rotten. That’s

Vibhu [01:12:33]: Which I feel you would know. So for grocery stores, this is the biggest expense, right? The biggest cost is actually just food.

Lukas [01:12:41]: Waste.

Vibhu [01:12:42]: Everyone knows this, and “No, before we open, let’s buy a lot of tomatoes.”

Swyx [01:12:45]: There’s some very serious startups that actually help, like The

Vibhu [01:12:47]: Optimize all this

Swyx [01:12:48]: Trader Joe’s and Whole Foods. They, optimize, delivery times from, the delivery centers to Make sure that you don’t waste all these things. It’s actually very hard.

Vibhu [01:12:55]: Problem with those is when you’re wrong once, it’s a huge cost.

Swyx [01:12:59]: That’s why it’s a moat, right? Once they are trusted, they figure it out. Don’t touch it.

Lukas [01:13:05]: Maybe they just should hire, I don’t know, one of those companies. We saw one agent Saw one agent sign up for Claude, with his computer.

Vibhu [01:13:15]: Wanted to use AI, so.

Future Branches: Simulation, Real Life, Robots, and New Business Evals

Swyx [01:13:16]: And then just, one more question then we wrap up, which is okay, you have all these vending series of stuff. You have the robotics series of stuff. Maybe a bit of, interior design whatever. But is there another, branch that you’re, kinda thinking about or you want feedback on that, might be your next phase?

Lukas [01:13:35]: I think, any type of business is fair game., we’re also thinking branches, but we think more of like there’s the simulation branch, the real life branch, and then the robot branch., but I think in terms of, what, verticals or whatever to go into, there’s We- Yeah. Whatever tells the story, um The best.

Swyx [01:13:54]: There’s some finance ones I noticed that, the other people are doing it, you’re not doing it, which is, stock trading or whatever. Um Not that interested. So, okay, so I used to come from the finance industry, and I have a very strong view that these things are all just like performance art because, it’s not scientific, on like you can’t predict the future. You get wins based on things that are entirely out of your control. Whereas for you, your stuff actually like it’s actually fairly controlled. It’s all within the model’s capabilities.

Lukas [01:14:22]: Especially for, the simulations. For the real world ones it’s yeah, it’s like two places that we have we have the cafe, and we have the store. So, maybe you can’t draw, statistically significant, like which models make a profit in the real world, based on this. But you do have all the okay, do this behaviors map to, something that should be, like Trusted probably. Yeah

Swyx [01:14:45]: The qualitative one, the qualitative actually does matter Because, you actually don’t want your store to randomly shut down without you, explicitly prompting for it and all that. Call to action. How can people help you, give you money?

Hiring, Collaborations, and What Comes Next

Lukas [01:14:58]: Yeah, if you’re excited about stuff that we’re doing, we’re, we’re very much hiring.

Swyx [01:15:04]: And you’re already working with, Anthropic, DeepMind, OpenAI, xAI. Do you want more, or are you good?

Lukas [01:15:10]: One of my one of my friends and who’s now, working for us is his catchphrase is “We need more projects,” ironically, because we have too much to do all the time., but yeah, that’s a long way of doing like

Swyx [01:15:23]: If I run, an emerging lab, like

Lukas [01:15:24]: Reach out.

Swyx [01:15:25]: Yeah. All right. Cool. That’s it. Awesome. Thank you so much.

Lukas [01:15:29]: It was fun.

Vibhu [01:15:29]: Thanks.

Fast forward to mid-2026, and Claude Code is eating the world. In 2024 Anthropic’s bet on code and enterprise looked like a more pragmatic niche play vs. OpenAI’s better models and massive consume scale. Today, Amodei’s all in bet on acceleration via code (images and video be damned) seems prescient.

Despite Anthropic’s growing momentum, however, Axiom CEO Carina Hong sees coding ability as a necessary but not sufficient milestone on the path to AGI. Code arguably pushes the jagged frontier to the point of super intelligence in some domains outside of coding, but there are surprising gaps (link) that Carina believes will bottleneck AI progress. (Stats on math benchmarks).

The informal bottleneck

“Verified AI” sounds like eating broccoli (footnote: I actually love broccoli, but then again, I also believe strongly in Test Driven Development, so ¯\(ツ)/¯ ) and paying taxes, but to Axiom it means something very different. “Verification to me is about scaling brilliance, compounding brilliance,” Carina told us.

It actually took a while for me to understand what she means by this. It sounded like marketing-speak to me, until it clicked. Carina emphasizes an story about legendary mathematician Srinivasa Ramanujan to illustrate the point. When G.H. Hardy finally persuaded Ramanujan to formally prove theorems instead of relying on his (formidable) intuition, it reportedly improved his own capabilities. This is presumably because formally proving things forced Ramanujan to articulate the details in a way that open up new lines of thinking, etc. This is one part of “compounding.”

But formally proving things also allowed others to benefit from his intuition: the proofs are way of communicating an intuition and persuading others that the intuition is correct. This is scaling (more people use the result) and compounding (people can learn from and build on his work).

This is the analogy that Carina wants us to focus on.

Verified Generation

There are two ways that Verified AI shows up: in training and in inference.

But a quick detour: to a first approximation, “Formal Verification” means using type checkers (like for TypeScript, C++ or Rust, but more capable) to verify mathematical proofs that are meticulously specified using a language like Lean (footnote: Formal verification also includes model checking (TLA+, SPIN), SMT-based tools (Dafny, F*, Why3), and refinement-type systems (Liquid Haskell) — many of which don’t look much like “type checking a proof” from the user’s perspective even when there’s a similar logical core underneath. It also gets applied to software and hardware correctness, not only pure mathematics.). It takes a lot of work to translate an “informal” proof (albeit one that most people would not remotely call “informal”) in to a Lean proof (footnote: This is an understatement. Most theorems remain informal because formalization is so hard to do. There has been a great deal of effort to formalize the most important proofs, with mixed results)

You can imagine how this would be (very) useful during Reinforcement Learning: instead of relying on best guesses based on statistics (GRPO, RLHF, etc.), you can just verify the proof is correct using a Lean verifier. This is obviously a much stronger reward signal, akin to compiling code and testing it (which is what is typically done with RL on coding).

The catch: LLM are not (currently) very good at proving things with Lean.

Enter Axiom: While they have not officially reported benchmark numbers besides the 12/12 Putnam result, Carina reports that they have achieved a very impressive 99% (187/189) ProofGen on the Verina benchmark. This benchmark is to generate code and proof of correctness for a series of problems. For context, OpenAI o3 (the last known OpenAI run) achieved 4.9% on this benchmark.

Based on the sparse benchmarking, it’s hard to say what the frontier labs are currently doing, but Carina suggests that they still are not training to generate Lean proofs directly, rather relying on informal proofs.

Time will tell if the frontier labs’ current approaches will close this gap.

Scaling and compounding

Carina’s Ramanujan analogy is pretty direct. Better proofs → better Lean generation → better RL. A stronger signal means higher sample efficiency and higher maximum performance. Great!

Scaling is pretty clear too: once I have proved something in Lean, the quality of the output is basically (footnote: one might argue that its a bit lower because the proof is in distribution for the LLM) as high as if it came from a human, so my high quality training set has grown in a way that an informal rollout corpus cannot. I can trust my Lean proofs.

Compounding is also clear: now all of future inference and training can build upon those proofs.

On the other hand, a model trained only using statistical signals like GRPO during RL lacks the sample efficiency, maximum performance and compounding corpus that a system that uses formal verification benefits from.

All roads lead to verification

Broccoli and taxes notwithstanding, “verification” has shown up in a lot of conversations recently. In the in physical system control:

“I think [verifiability] is probably the hardest problem right now, because the as the models get better, it can be harder and harder to find the faults on the system. And so the problem of doing proper eval to find those faults, that problem also keeps getting harder as the models get better.” -

In theoretical physics:

“…now that we’re in this regime where you can just get ChatGPT to tackle thousands of questions at the same time, it will return proofs for a significant fraction of them. Now actually the onus is back on the humans to verify all the outputs. And so, yeah, as that becomes a bottleneck, I think formalizing math and automating verification will become more valuable.” -

Verification is, in fact, the key differences between AI for science and AI for computation: in science you to have to actually test (verify) your hypothesis by performing physical experiments. Lab in the loop systems like Radical AI and Lila build around exactly this premise (we have recorded episodes with both of these teams and will release them soon!)

And yes, formally verifying critical systems such as flight control, nuclear power plants and pacemakers is a growing focus as the software and hardware that run them becomes more complex.

Carina believes so strongly that AGI requires verified generation that she makes the unqualified claim that “We do not believe there is any other possible future.”

Expensive to produce, cheap to verify

Lean proofs are hard generate, but they can be easily shown to be correct or incorrect. But how do you know that the proof you created maps correctly to the problem you care about? As Carina puts it: “Anything that can be specified can be proven. Humans are bad at specifying everything we want.”

Are we now in the specification business? Check out the episode to hear Carina’s take, as well as:

* Why hardware verification is a killer app

* Details on the AXLE open API and recently released Discovery toolkit

* The Erdos debacle

* The OpenAI GPT-f diaspora

We covered the MAI model technical takeaways on yesterday’s AINews, so I will focus our recap of Satya’s main messages around three elements:

* Satya’s adaptation of the Bill Gates Line for positioning Microsoft as the Frontier Intelligence Platform — customers must gain much more value from the Microsoft ecosystem than Microsoft itself, by building on multi-model harnesses like OpenClaw and Scout, drawing on the full enterprise context exposed by context layers like Work IQ (heavily dogfooded by his C-suite), and building up private evals and traces as a new form of Token IP

* AI ROI: On one hand, enterprises are having difficult conversations around Tokenmaxxing and Layoffs, and on the other hand, there are serious re-evaluations of the End of SaaS since the Build vs Buy equation has changed so much. Our previous SemiAnalysis guest had… interesting comments on Microsoft’s position on this as the ur-SaaS titan, and Satya had great answers

* Making the Impossible Possible: Kevin Scott’s inspiring framing around what the most ambitious version of applying AI and technology at large to business and social problems, like education and social impact.

Enjoy!

Full Video

Transcript

Voiceover: Welcome swyx, Sarah Guo, Elad Gil,, and Chairman and Chief Executive Officer of Microsoft, Satya Nadella

Sarah Guo: Welcome to a crossover episode of No Priors and Lane Space with Satya Nadella. Um, congratulations on an amazing build. No, thank you so much, and it’s great to be with both of you. I listen to both of you or b- both the podcasts all the time. It’s great to be on it.

Thank you so much. [00:01:00] So you’re just talking about, um, these amazing, uh, announcements from across the Microsoft estate all morning for, I think, three hours. What is the, uh, what’s the most important reflection or takeaway you have?

AI as an Ecosystem Platform

Sarah Guo: I, I’d say there are, uh, perhaps the, the biggest one for me is let’s sort of conceptualize this more as an ecosystem play as opposed to a single model or even a single platform, right?

Satya Nadella: I mean, you know, whatever I... At least for me, having grown up at Microsoft, having seen, whatever, four major platform shifts, uh, I sort of fall into that, um, uh, camp where a platform is defined by fundamentally its ability to create more value about the platform versus what’s captured in the platform. And so if you, you view what’s happening right now, I think this morning’s keynote was how can any company, whether it’s an AI native company or a traditional enterprise company, participate as a first-class participant where they can point to AI they created, [00:02:00] right?

It’s not that they don’t use other people’s AI. Of course they will. But to me, what’s the path? What’s the recipe? How do I do it? What does a stack look like? What does the tooling look like? What is valuable? How do you do that? That’s it. That’s sort of our job to do. Yeah. Ecosystem strategy is, uh, very complicated, right?

Sarah Guo: Because you end up building certain components, partnering for certain components, supporting them. You just announced this big suite of models. Like, tell us a little bit about the, uh, training strategy for Microsoft now. Yeah.

MAI Models & Training Strategy

Sarah Guo: So, so the thing that we wanted to do with the MAI models was to build, and as Mustafa talked about, first of all, a great lineage, right?

Satya Nadella: Starting with pre-training, uh, with very good data quality, uh, doing all the ablations, making sure because in, in some sense it’s becoming even harder to build a clean lineage model just because there’s so much stuff out there, uh, that you truly need to ablate out to be able to have a fantastic [00:03:00] pre-trained model.

In fact, that’s one of the challenges of a lot of the open weight models is they look great on one benchmark or two, but they’re not great on practice. So that’s why, in fact, even in the RFDEs are, they, they are pretty gone really excited about these MAI models because how the heck can a small five B model hill climb?

Uh, and it goes back a little bit to what I think is ultimately the key thing to do, which is try to pursue finding that cognitive core. Uh, so to me, starting with a clean lineage- Then creating that ability for companies to be able to use this, right? Not just as a generalist, but to create their own specialist by building this hill climbing scaffold around it, right?

So it’s not just the model, but you have a hill climb scaffold around it, then you will start building your RLE. You will start collecting the traces. Most importantly, you’ll have private evals because we know all the evals out there are good, interesting, [00:04:00] but they’re not really that critical- They’re work, yeah

Swyx: at this point because they all can be maxed. And so the point is each company will have its own private eval. And so that end-to-end platform story around our models is sort of, uh, what I think is interesting. And then the one other thing, Sarah, since you brought that up, is I do feel there’s a new frontier.

Satya Nadella: Like people talk about the frontier and are you operating at the frontier. Um, interestingly enough, if you add a little temporality to it, you can use, let’s say, in, in, in fact, the, the Lando Lakes demo we showed was pretty cool. We used, whatever, GPT-55, right? Then you collected a bunch of traces, and then you took a 5B reasoning model and achieved higher.

Sarah Guo: Uh, so that is another aspect of what it means to appear... uh, you know, operate at the frontier Yeah. I, I think, uh, I first of all have to congratulate you on basically building a frontier neo lab inside of Microsoft in two years. Um, I’m wondering, you know, you have all this AI strategy that you’re rolling out.

Lessons from Two Years of AI Development

Swyx: I’m wondering, what do you know now that you wish you would tell yourself two years ago where- or two or [00:05:00] three years ago? Three years for the Jensen partnership, two years for, uh, MEI. Yeah, I mean, I think the, the thing when, that I reflect quite a bit, right, which is sort of obviously I got into all this when I got excited by the, the scaling laws paper and, you know, when, you know, even the OpenAI partnership came about when those folks said, “Hey, we’re gonna really throw a lot of computer transformers.”

Satya Nadella: Uh, and they’ve helped. I- the thing that I always look back and say, “Wow, these things, uh, do have capability that they’re climbing up.” W- I mean, this, you know, this crude way of saying it is intelligence is log of compute kind of works. Now what I think we underestimated perhaps is the real-world complexity of deploying these so that they actually deliver the value in the real world, right?

So the outcomes as measured by any benchmark is interestingly important, but the true eval is when people out there are able to do unique things that they only can value, and it’s very [00:06:00] measurable, right? That I wish we had sort of even, like, had more in our consciousness, right? Which is as an industry.

Sarah Guo: Because right now I think when people say, “Wow, I don’t want a token max,” it’s an artifact of us not having thought ourselves as an industry that we are using tokens to create value every step of the way. So I think that’s kind of what I wish we had gotten there, but I’m glad we are here.

Real-World Value & Use Cases

Sarah Guo: What are some of the use cases that you’ve seen that have created the most value for your customers?

Because I know that people talk a lot about code, and I think it’s pretty clear that that’s something that’s having very large scale impact. Are there other areas that you find in common that your customers are really benefiting from? Yeah. I think, yeah, to your point, obviously coding is now got... But it’s interesting, by the way, Elijah, to even talk about the coding, right?

Satya Nadella: Which is coding has worked so well that we now have to rebuild the IDE, right? I mean, it’s kind of nuts to see what we sh- launched is like, oh my God, I have these hundred agent sessions. I... The cognitive load it transfers back to me as a human is so [00:07:00] excessive that now I need a new UI. Uh, oh, by the way, I, like the, the chat as the only artifact was also impossible, so that’s why we need a canvas.

So it’s kind of interesting for all the things about where is software needed or where is UI needed, uh, you kind of need that even for code, right? In a fully agentic world. But that said, one of the things that we are starting to see, we started seeing with co-work, but even some of the work we, we showed with auto com- uh, um, autopilot Right on what you see with claws is a good one because if you sort of think about a lot of human capital is doing the glue work, right?

If you now can augment that with tokens/agents that are long-running, durable, right, then your ability to scale even what is still judgment and glue work gets amplified like coding does. Uh, so you can... Like, I’m positive that six months from now we’ll all be saying, “Oh, wow,” like, all through ni- the night there was a bunch of stuff that [00:08:00] all these autopilots that I have working on my behalf with my delegated authority, so to speak, right?

I can... Sort of given even my identity, did a bunch of work, then of course I’ll need my new ADE to say, “Well, what did you do?” Like, I might... “Did I do this work?” And so on. So I think that that’s where compressing of workflows, uh, completing of tasks, uh, that’s where I think a lot of the value gets created. I think you raised a really interesting point, which is there’s the actual agent that’s doing the code, and then there’s a harness around it, and that’s the environment, that’s the context, that’s everything you’re setting up as a developer around actually a coding agent.

The Harness Concept for Enterprise AI

Sarah Guo: What is the harness for the enterprise? Is there an equivalent concept for broader productivity work, or how do you think about that concept sort of generalized? That’s right. So, so in some sense you kind of want the harness to define the models, the, the data, uh, and the tools, and so that you have a loop across those three.

Satya Nadella: And so what we are trying to, first of all, make sure is each of our products that we build, right, whether it’s GitHub Copilot or the security copi- the, the [00:09:00] stuff we showed with MDASH or even the discovery for science, it doesn’t matter, all of them are multi-model harnesses, um, with tools access so that you can do this progressive, uh, disclosure of tools even so that they’re token efficient.

Uh, and then you’re feeding it with very rich context because that’s sort of the other hard lesson we have learned in the last two years is, oh my God, the amount of work you need to do to prep the context layer, uh, such that your plan can execute in the most efficient way is where the magic is. So we have, in our case, we have the GitHub harness, which essentially we’re using across all our products.

It’s available in Foundry, and we are open, like you can use your Llama harness, whatever. Or you can use the, um, uh, you know, any open harness or any harness of yours and train with your tools and multiple models and your context. And so that’s the pitch. Because right now a lot of dialogue is, um, “Hey, if I train the harness plus tools and the model together, you get [00:10:00] evals.”

Elad Gil: And what we are proving out is... And the best example of that is what we did with MDASH, right? Because when it launched, uh, it found bugs or vulnerabilities that were not found by Mythos Uh, and so there is existence proof, I would claim, that you can have a multimodal harness, uh, that can in fact be more, uh, performant in the real world So a premise behind the, uh, training at the independent frontier labs is really, you know, we’re gonna have these models, and we’ll have an API business, and we’ll support enterprises and startups.

Sarah Guo: But

Platform Strategy & Developer Ecosystem

Sarah Guo: a first-party product, be it productivity or code or search, drives the majority of revenue. That’s a different value equation than you’re describing, I think, with the Microsoft ecosystem. Uh, if, if that’s the case, tell me if it’s the case, uh, ‘cause obviously you have first-party products and you have enablement products.

Satya Nadella: Um, what is the role of the develop- Like what is gonna be hard and the set of skills and the value capture the developer has in that world? Yeah. So I think that there’s always [00:11:00] gonna be the case that someone who is super successful in- as a platform builder can also have first-party products. It was true with Windows.

It is true, uh, with, uh, the, the SaaS side and the cloud side as well with us and others and so on. But the thing that is, is it should not be a limiter to other people achieving that same success, right? That I think is the core difference, which is the, the network effects this time around, around intelligence are such because they learn from data, and not really lots of data.

It’s just a few samples that you have to see to understand what’s novel about something. So that’s why the game becomes how to protect. So that’s why I would say every company, having private evals may be the biggest IP, right? Think about it, like what’s that private eval that you can then use even a frontier model to hill climb on and not leak the traces may be one of the biggest [00:12:00] drivers, uh, of IP.

Like, so in other words, another te- acid test is you have an eval that’s private. You’re using, uh, a g- a Model A. Can you switch it to Model B and e- you know, climb up? If you can, then you’re in control. If you can’t, you’re not in control, and that’s where even the harness decision becomes super important, right?

swyx So therefore, having an open harness, letting all models come in, having your evals, your context, your tools help you hill climb, I think is the skills that an AI native startup needs, a SaaS company needs, or every enterprise needs. Yeah, I think in, in a very real way you are ... Microsoft historically is an operating systems company and th- then become a cloud company.

Maybe like the third act is that you’re a harness or evals company. Whatever w- ... whatever the, the sort of conglomerate of concepts that you wanna put together. Um, and, and I think like enabling every company to have like frontier intelligence or what- what- Yeah ... I forget the, the [00:13:00] exact term that you used, um, is the, is the mission, right?

Satya Nadella: That’s it. Like that is, that is the platform promise, that you build with us, you will get your intelligence, uh, for your data. That’s it. That ... To, to me, that is the ... Like if there was one tagline, uh, for this entire developer conference is- Can everybody operate at the frontier with their frontier intelligence, right?

To me, that is so important because otherwise it, I, I don’t know how you achieve stable equilibrium, right? Which is how do I then go and say, “Well, my company is gonna have a terminal value because I now know how to continuously compound-” Yeah ... on top of what’s a platform that gets better,” right? So when, like Windows obviously came out, Adobe built, Autodesk built, uh, or even like take what Jensen said.

We built DX and he built, you know, CUDA on top of it. Um, right? I mean, I always say to Jensen, “God, I got the short end of that,” right? “I wish, uh, we had recognized it.” But nevertheless, but that, that idea that you can build a platform layer [00:14:00] that someone else can then extend out, um, and build their own intelligence layer in this case, I think is everything, right?

Without it, why have a developer conference? I can just come and have you all sort of just worship at the altar of one model. Yeah. But that’s not a developer conference. Uh,

IP, Evals & Company Value

swyx: backstage we, we had a discussion about what is IP or what is the, the value in a company. It used to be the length of, uh, human experience at a company, and now it’s this other thing which is the evals, the, uh, experience in sort of applying agents to the company. Can you... I just want you to like flesh that out a bit more ‘cause- Yeah ... it was very insightful.

Satya Nadella: It’s a great way to frame it, right? Because yeah, at the end of the day, every company is gonna have both the human capital that is still gonna be super valuable, uh, because humans, uh, and their ability to find the gaps that exist at all times is going to be the way we all will create value, right?

I mean, so I’m definitely in the camp that this is going to be about expressing new forms of human agency and ambition even as token capital goes up, right? So let’s say a cor- any corporation [00:15:00] has lots of tokens and lot of human capital. The question is how do you compound the two? So if you have a... Like if you take in Teams I have a bunch of agents doing work and a bunch of humans doing work, and the traces between those, that is really important context of how that enterprise is creating value.

Then that goes back to train not a generalist model, but to train the company veteran agent, uh, right? That is super valuable again, right? Which is when a company goes says, “It should in fact go onto the balance sheet,” is how I think about it, right? That’s so... In fact, there may be... Like human capital was never possible to go put on a balance sheet, uh, because you didn’t know how to capture the tacit knowledge.

swyx: Whereas now I think you can with the agents that have learned through the h- through, through time, through all the traces. Uh, so that’s what at least we think will happen. I, I think the SEC is gonna have to have accounting standards- ... for token, uh, expertise Uh, y- y- you’re talking about the equilibrium [00:16:00] state, um, and a stable equilibrium where companies have this compounding value and can see terminal value for themselves.

Future of SaaS & Business Models

Sarah Guo: Another challenge to, you know, the considered equilibrium of, okay, there are applications and workflows that are sort of common to a vertical or a horizontal. Um, and this was, like, the generation of SaaS companies and, you know, Microsoft has lots of SaaS properties as well. And then there are things that are very specific to every enterprise that they’re differentiated against.

Elad Gil: Um, I’m sure you have heard much and participate in much of the debate about the end of software because all these workflows are, are cheap to generate now. Um, do you think the equilibrium looks different between what agents get built- Yeah ... in enterprises versus in their vendors in the future? Yeah. So I think what’s happening there is, see, we, we had a particular way we captured, um, I would say workflow in apps, right?

Satya Nadella: Because we built a, a data model, right? We schematized some part of some business process. Mm-hmm. We then built a bunch of business logic. Yep. And then we put a bunch of UI [00:17:00] on top of it, right? So that’s kind of what every SaaS company- And a little configuration. For, like, 20, 20 years that was the plan.

Right, that- Yeah ... and that was it. So interestingly enough, now you kind of get to re-litigate that vertical stacking, right? So I still think, for example, that data model that you built underneath every SaaS application is super good, right? Like, why reinvent it? Like, I, I, my general ledger better be a general ledger.

I don’t need new schema creation. No. Uh, in fact, that entity relationship, uh, is actually pretty good, robust thing that I want to feed. And you want it to be stable. That’s right. Yeah. Then same thing with business logic, right? If, if you look at, uh... We have this product called Power BI, right? It is like dashboards galore people created.

The beauty underneath that dashboard is a very rich semantic model, right? Someone took the pain to create a dashboard and do all the measures, and you want that. That’s business logic, right? I want that to be available to me. So I think the [00:18:00] challenge of the SaaS business model is we packaged one way. We now have to learn how to unbundle these things and rebundle in new ways and discover new business models, right?

I mean, if you look at it, d- what’s happening today with Microsoft 365 is a great example, right? We have this thing called Work IQ. In fact, like, what we are realizing is, oh my God, like, you know, if you look at... In fact, there’s a pa- historical parallel too, right? We sold first Exchange and SharePoint and, uh, you know, before Teams, we had a thing called Lync Server and what have you, and we thought, “Oh, that’s all gonna move to the cloud.”

But little did we realize that, um, the number of people who will use servers in the cloud is 10X, 100X, right? Because people were not buying servers, they were just buying a subscription. Mm-hmm. The same thing is now happening with M365 because with Work IQ, we have exposed what is perhaps the most important database in a company that never got used as a database because it was only captive to our apps.

Mm-hmm. Right? It, it was all email operated on it, Teams operated [00:19:00] on it, Word, Excel, PowerPoint, SharePoint. But now, like this is one of the coo- coolest things I get to do with Work IQ. I go to a GitHub repo and I say, “Hey, I attended a bunch of design meetings last week related to this repo. Can you capture all that and tell me what changes I should make?”

I mean, think about that, right? It literally can go look at all those transcripts, come back with a plan to change a code base, right? Previously, you could never have thought of using M365 for something like that. So the value creation opportunity now in the agent world is in fact 10X more, but it does require us to have...

Sarah Guo: For example, there’s going to be usage around M365, right? Which is going to be perhaps more than even the e- end users and we have to even re-architect. Like, in fact, like what I use to serve an inbox or a mailbox cannot be used to serve an agent. Uh, and so that’s sort of what we are doing.

Pricing Models: Per-User, Consumption & Outcomes

Sarah Guo: I don’t believe in, like, permanent business models for any of these domains, but in the [00:20:00] near term, do you have a prediction between, uh, you know, outcomes-based pricing, token-based pricing?

Elad Gil: Enterprise bundles Yeah. The way I- I think about this is always we’ve had... Like, let’s even take the per-user pricing. Mm-hmm. The per-user pricing is really an artifact of someone creating a budget needing certainty, right? Because it’s the most important thing. Like, somebody wants a budget- Mm-hmm ... they need a per user.

Satya Nadella: And, and per user is just a set of entitlements to usage, right? That’s kind of what it is. And so the way is, if the first bundling will be take some usage, bundle it into per user stacks and, you know, then sell subscriptions. So subscriptions I think are gonna be there, per user is gonna be there. Then the next big thing will be consumption.

So people will say, “I want consumption.” And it’s also possible that people will say, “I don’t even want to pay for any of the subscriptions or the consumption’s outcome.” Mm. But remember, most people love outcomes until they have an outcome, because once you have an outcome, it’s like giving away royalty, [00:21:00] right?

Mm. I mean, like I, I’ve talked to customers who love, you know, outcome-based pricing, and I say, “I’m all in,” until they, “Oh my God,” like, “what are you talking about? You’re sharing in my outcome? No, no, no. I want you to go back to per-user pricing, and I want you to consumption price,” right? So I think that debate will go on.

Uh, but and all, all, all of these business models have a particular time and a place versus one to rule them all. And if anything, if you’re a SaaS vendor or you’re a platform vendor, having that flexibility... And quite frankly, we face this with GitHub, right? We just recently announced a per-user pricing on GitHub because little, you know, we- GitHub Copilot was constructed at a per-user level before we understood even, uh, the intensity of usage of agents, right?

It was an interactive way for a developer to use code complete, maybe tasks. It was not like, oh, I launched 10,000, you know, agents that are going on all day, right? So that is what the adjustment is about. So now that we really want, there will [00:22:00] always be a per user, but there will have to be a consumption meter.

Durability of SaaS & Build vs Buy

Sarah Guo: How do you think about the durability of SaaS more generally? One thing I’ve observed is in a lot of enterprises internally, there will be teams that almost have agent euphoria. They’re so excited about the explosion of things they can build that they’re trying to rebuild a lot of applications or going to their SaaS vendors and saying, “We’re not gonna work with you anymore,” or, “We’re considering an internal project.”

And it seems like in six to nine months, maybe some of those people will come back and say, “Actually, we, we can’t rebuild everything.” How do you think about what’s durable in this world and what isn’t? Yeah, it’s a... It... I think we have to go through one full budget cycle on this to really see the, um- Uh, the sort of the emergence of the equilibrium, because at the end of the day, there’s marginal cost to even generating the app, right?

Elad Gil: In, in fact, there can be even a, a simple way to say it, like if you should always acquire something if the marginal cost of building and maintaining, uh, something on your own is higher. Uh, right? That should be like it’s a quantifiable- Yeah. Right? A quantifiable thing. And [00:23:00] the maintenance part is important, right?

Even, like you got to remember like, hey, you know, all the security stuff that now AI will find, you better fix them too fast. Uh, of course, there’s a coding agent to help you with, but then that burns tokens, right? So whose responsibility is it? It’s kind of like a, a cycle that you’ve got to think through.

And I think we have gone through the excitement that I can generate a lot of software. I think the next thing would be what software do I really want to generate? Mm-hmm. What software do I want to use from others? How do I compose these two into some agentic workflow that I have agency over, right?

Sarah Guo: Because I think there’ll be very little tolerance for anybody who’s inflexible, uh, at the vendor level. Uh, but at the same time, I think that anyone who has got that flexibility shows up, delivers the value, will be back at again, right? We’re selling software, uh, but with just different business models, in fact Uh, speaking about building software, um, one of my favorite moments from, I think, a previous build maybe one or two years ago was they had a b- they, they...

Swyx: There was a section of you building your [00:24:00] own software. I’m curious if you’re building anything now. Yeah. So I, I think the... You know, first of all, let’s face it, right? Building software has made it possible for even the incompetence of a CEO of a company- ... like ours, uh, you can build, so thank God. But that said, I, I, I, I do feel that, you know, something like, um, GitHub Copilot to me, and especially the new Sessions app or the new app, has just made it so much more possible for you to have agency over artifacts that you felt you couldn’t touch before, right?

Satya Nadella: So to, for me as a CEO, even to go to a code base, uh, to be able to learn about it, like I remember joining Microsoft long back, you know, first and then you say, man, everybody had to go in and look at, you know, whatever, Cutler’s, Malik, or what have you to learn how to do good C, uh, C++ code. Um, so now that ability to be more full stack up and down is so good, but that doesn’t mean every one of us should be doing the same thing.

The question is: [00:25:00] how do you then have the ability to inspect things, learn things, see things, um, I think is just so much more. And so to me, what I’m building a lot of is these long-running Foundry agents. Uh, right? So there’s autopilots. So the easiest thing is, to me, I think I just built one, uh, even last week, where the idea was, hey, can I have an agent that is continuously monitoring essentially my own chief of staff autopilot, right?

We’re gonna have that obviously in, uh, Scout. That’s what, uh, uh, we showed. But it is so easy and trivial to build. I took Work IQ. I said, “Take Work IQ, go, uh, and build a Foundry long-running agent.” Uh, store all the memory in, um, uh, using Ray Fin, right? Basically at my backend as a service. And lo and behold, it built it, and not only built it, I could say publish to Teams, and it published the damn thing to Teams.

Sarah Guo: So the ability, uh, to have a, you know, some end-to-end project like this complete is just pretty [00:26:00] miraculous. How do you think, uh,

Future Engineering Roles

Sarah Guo: that impacts the different types of engineering roles that exist in the future? Because right now I think there’s, you know, a dozen different types of engineers that you can be, from QA, front end, et cetera.

You know, there’s a big swath. I’ve heard some people argue that in four or five years we’ll basically end up with four engineering roles. It’ll be people who are managing agents, it’ll be four deployed engineers or FDEs, it’ll be security engineers, and then people working on large scale infrastructure for a small number of services, and then everything else just collapses into the agentic world.

Satya Nadella: Yeah, I- Do you think that’s a correct view of the world? Yeah, I mean, I think, I think we’ll have to experiment our way through it. But what you said is what... There are some very at scale things. At LinkedIn, they did structurally change- Mm-hmm ... uh, and it, you know, basically built up a new discipline called full stack builder, right?

So they went and said, “Hey, let’s bring, uh, people from design and product management, front end engineering, all put them together.” Uh, but also have an edge, right? It’s not like the design person still doesn’t have the design edge, or the front end [00:27:00] person doesn’t have the front end edge, but you can give yourself bigger scope in roles so that you’re not confined to one role.

Um, and then r- equally, infrastructure has become very critical, right? So in other words, like, I mean, RLEs, I mean, one thing we’ve realized is even for the Excel team, for example. Mm-hmm. Building the RLE in which a reward can be learned is actually one of the hardest sort of infrastructure problems.

Mm-hmm. Uh, and so you kind of need even new talent, right? Distributed systems people even in what was considered an end user app team, uh, because it’s a different skill set. So yes, infrastructure, science is the other one, obviously. Um, so I think we’ll see how these evolve, right? Where’s the s- real... I mean, always the world will have a bunch of specialists.

Okay. Um, you know, I think the generalist role is going to be the most exciting, right? Because the leverage of a generalist- Mm-hmm ... um, is where we are going to see the maximum returns, right? When, when you said, “Hey, are you coding?” I’m now a gen- Like, what... I’ve basically translated [00:28:00] knowledge work Right?

Which I did, where I created a Word document or a spreadsheet, or even, uh... And now I can build an app, right? It’s in the same sentence. Uh, right? That idea that, “Oh, wow, my generalist skills have gotten higher leverage,” I think is what we’re gonna see across the board. Music to the ears of CEOs and VCs that are, like, a little dangerous and a lot of- Golden age for idea people

Sarah Guo: idea people. Yeah. Uh- With a lot of agency. I- if you take that idea of personal agency and you just zoom it out to the organizational context, um, uh, my partner Mike Renall, who, uh, actually started his career at Microsoft, just wrote an essay where one of the big takeaways is i- it’s an age where you can be much more ambitious, and you need to be, given the pace of the environment and how quickly, actually, users and companies are open to adopting new technologies.

Satya Nadella: Um, how do you think about... I, I feel silly asking this of somebody running a, you know, trillion-dollar-plus company already, but

Ambition & Making the Impossible Possible

Satya Nadella: how do you think about how Microsoft can be more ambitious now? It’s a great question. Um, I [00:29:00] think, um- I think the, the thing in these type of transitions is to have a conceptual model of how work can change to go after outcomes that you could hardly imagine previously, right?

In fact, Kevin Scott has this nice line, right, which is, um, when you can make the impossible... Like, when you’re making hard things easier, that’s sort of one point of leverage. But true ambition is about making the impossible possible. So now the thing that is missing a little bit in all of our organizations is what is that new conceptual model of what can we build?

What was impossible and what can we build? And I’ll give you one example of this, right, which is I take great inspiration from sort of the people who were managing the Azure net- network. And they came to the... This was from even last year. You know, we were scaling. You saw that I, I [00:30:00] talked about sort of how we built in the last 15 months more Azure capacity than we built in the first 15 years.

I mean, it’s crazy. Wild. Yeah. Right? It’s pretty wild. And it’s the same team. So they saw that and they said, “Bob, this just ain’t gonna work if we don’t reconceptualize our work.” So they built... Essentially they said, “Our job is not to do Azure networking. Our job is to build the agentic system does, that, that does Azure networking,” right?

These are the folks managing the 500-plus fiber operators managing the VAN, right, all over. And fiber operations ultimately is a physical operation. Things get cut, things get, uh, you know, have to be repaired. You know, we have fancy words called DevOps and so on. Basically, emails are coming in and you gotta go respond to them, take care of it.

So they built this agentic system. They even have a character for it. It’s called Miles, and it sort of does all this stuff, right? They started sort of screaming for more tokens and so on. And so they were saying, “Look, uh, we don’t need a headcount. We need tokens in order to be able to [00:31:00] manage, uh, our operation.”

That reconceptualization- Mm-hmm ... of what their work is, right? They, they basically took their work and made it meta, right? That meta work is now their new work. Mm-hmm. Right? In the ‘80s, if somebody had come to us and said, “4 billion people are gonna get up in the morning and start typing,” my model would’ve been, we need 4 billion typists?

But we’re not doing typing, we’re doing knowledge work. So that, to me, I think is it, right, which is whether it’s Microsoft or whether it’s any organization, is to give ourselves permission to do new types of metacognition, meta work, using these new tools to change the outputs that matter, uh, and then really make the impossible possible.

Sarah Guo: So completing that dot or the, the connective tissue across those, I think, is where a lot of the enterprise value will get created.

Data Center Build-Out & Community Impact

Sarah Guo: Should we talk about data centers? Yeah, please ask. Oh, okay. Well, uh, uh, w- we-- this leads nicely into the data center build-up. I always think, I- I just-- I’m just impressed at the sheer scale of the [00:32:00] build-out from Microsoft, but also everyone else, that this is redefining what it means to be a hyperscaler.

And I just feel like that, that, that is at unprecedented scale on finances, uh, on the way you run the company, but also the communities that are, that are impacted. Um, yeah, just talk a bit more about what you’re seeing on the ground, like when you visit your- Yeah, I think there are two aspects of it.

Satya Nadella: Obviously, the, the build-out is, uh, extraordinary. Um, you know, nothing like this has happened, and it’s great to be, uh, one of the participants in it. Uh, but you brought up the other part, right? I think at this point it’s clear that unless we as an industry, uh, are very principled about ensuring that the benefits of all the stuff we’re talking about are felt in real ways, uh, at the community level, right?

Because this is not just a, a campaign, um, right? It has to be real, where people are saying, “Look, this is not ch- changing the prices on energy for me.” In fact, if anything, it’s bringing down prices because long term there’s going to be a better [00:33:00] grid, there is going to be more energy. Water consumption is, in fact, not sort of, uh...

In fact, water is being replenished, right? You gotta really, you know, educate folks on truly what’s happening, the cl- uh, the closed loop systems we are building. We have to invest in the training, the jobs, the tax base. In fact, the least talked about stuff is the amount of jobs that get created during construction, after construction.

What’s the tax base that’s there in the community? And, and all this has to be real. Um, and, and if that is the case, then we will have permission. If it is not, we won’t have permission. It’s as simple as that, right? Which is, uh, we, we... I think we have to take it as an industry pretty seriously. Uh, I think it’s good for communities to be skeptical, ask the hard questions, for us to do the hard work, earn that.

Um, but at the end of the day, if there’s-- if we can really be the produ-- Wait. I’ve always felt like in human history, if you use a lot of energy but also create a lot of value for society- The story has been fantastic. If you don’t [00:34:00] do that, it’s not been that great. And this time around, I’m a firm believer that ultimately if you do have a token economy that drives productivity, that drives economic growth, that drives broad spread, um, you know, participation, better health outcomes, um, then I think we’ll be in a great place.

Sarah Guo: Uh, and that’s at least what we all have to be focused on. Yeah. It, it makes me think actually that with all these initiatives that you’re doing, might be e- easier to see ROI in the communities first before in enterprise. Yeah. I, I mean, I think both sides. Yeah. In fact, it comes back together. It has to be the people in the communities are going to be employed, are going to be participants, uh, in the real economy, right?

Satya Nadella: That’s I think the question is. Like, if we- if the broad economy is doing well and the communities are doing well, the dots get connected. It’s sort of the market forces are such that we will connect the dots. And that I think is it. Like, you ought to be able to see the evidence. You can’t be about o- any one company, uh, but it has to be broad economic growth and broad [00:35:00] ec- you know, community permission.

Elad Gil: Yeah. I guess I wanna talk about

Societal Impact & Optimism About AI

Elad Gil: what you’re most optimistic about currently or what have you most updated your personal models on regarding societal impact of AI? So you’re saying what’s the, the, the- What have you updated most on in terms of societal impact of AI? Yeah. I think the, um, the p- the most, um- Critical thing is the first question we even started with, which is we need to tell the story and make it real that everybody has a real shot to participate as a first-class participant in this new economy.

Satya Nadella: Right? That’s kind of, I think we- in the next 12 months, 18 months, we need a way for people to say, “Oh, wow, I get it.” Right? There’s going to be tremendous capability, tremendous amount of infrastructure, but I can see what is going to happen, whether it’s the benefits like health outcomes or my ability to create a startup or my ability to run my [00:36:00] local sort of, uh, store more efficiently.

It’s just happening, and I see that, uh, benefit myself, right? That to me, you know, earning that permission in a path-dependent way, we can’t wait. See, the one thing, Eli, that I’ve now learned is I think the world is gonna be very skeptical of tech and tech companies that say, “Trust us, we’ve got it. The g- future is gonna be glorious.”

Sarah Guo: Uh, you kind of have to deliver tangible benefits. Um, and quite frankly, politicians winning elections, uh, because they have advocated for that. That will be at least my adjustment because without it, um, thinking that somehow... Because it’s too important this time around. It’s too much of the economy for it not to be the case So one very simple framework I have for, you know, what are, what is gonna be the broad benefit of AI, um, beyond the communities just working in technology, are, are sort of wealth creation- Yep

it’s [00:37:00] gonna happen in a ton of different companies, startups and large companies. Then you have healthcare. Uh, you, you had amazing demos today. There are companies like Open Evidence. I think that is happening. Um,

Education & Future of Learning

Sarah Guo: education seems like another one that’s an- Yep ... obvious good where we haven’t seen as much impact as I’d expect.

Swyx: Do you have a hypothesis on why that might be, or if it’ll come? Yeah, I mean, I think this is where, again, how we think about education, how... You know, recently I met with, uh, the founders of Alpha School and learnt a lot about what they were going and going about, and it’s fascinating to listen, uh, to how to even rethink- Mm

Satya Nadella: uh, what does education really look like. Because I think it’s actually very important. Mm. Uh, and I’m not saying anything traditionally being done is less important, right? I was even looking at the, uh... It’s fascinating to see. I, I, I forget the which Stanford class it was, uh, the, the Asian guidelines for CS something.

Mm. Uh, because you still need people to learn. Uh, like it was an interesting AI class that they were making sure people were learning how to apply softmax appropriately versus saying, “Hey, fix my training run.” Mm-hmm. Uh, so I think learning concepts is important. It’s going to [00:38:00] be, uh, critical. But the way we create the incentives, what are the credentials, how we value those credentials, what is the employment opportunity for those credentials?

So I think that there’s a complete change that has to happen, uh, given the way to get to information, way to educate yourself, way to continuously keep yourself updated has changed so much. So I think interestingly enough, maybe the next big startup and success story could be someone who builds a new university, um, or a new, um, pedagogy even of how to get someone to go through a curriculum and find economic opportunity, uh, that’s highly valuable.

Well, that has felt, uh, perhaps impossible for a long time, but it’s a great note to end on and something that might be possible. It’s still possible. Yeah. Thank you, Satya. Thank you so much. Thank you. Yeah. I appreciate it. Thank you all.

For almost two decades, GitHub has been the home of software, where both open source and closed flow, through commits, pull requests, reviews, actions, etc.

This ecosystem flourished as open-source maintainers and contributors would continue shipping code for the benefit of the community. However as coding agents began to ship mass quantities of code - growing 1400% in 2026, it marked a new era that was both extremely exciting and challenging for GitHub.

While these agents help more people ship more projects, they also significantly increase the floor of how much code is shipped, how often it is shipped, how many people commit code, and basically orders of magnitude multiples in every dimension of GitHub infrastructure:

Now GitHub inevitably experiences more pressure on their infrastructure which was originally designed around human developers moving at human speed. This has resulted in a very publicly notable uptime story:

So it begs the question of whether current systems around code can absorb what AI produces. Can CI/CD keep up when every idea becomes a build? Can open source maintainers survive floods of AI-generated slop contributions? Can GitHub preserve the human social contract of software while becoming the operating layer for agents?

Which brings us to the perfect person to answer these questions: GitHub COO Kyle Daigle. In this episode, he joins swyx to unpack what happens when AI doesn’t just autocomplete code, but starts changing how companies operate, how open source works, how pull requests get reviewed, and how GitHub itself has to scale.

We go deep on GitHub’s internal AI workflows: micro-skills, WorkIQ, MCP, Slack, Teams, email, Copilot workflows, the new Copilot desktop app, CLI, cloud agents, and how Kyle uses agents to look backwards across company context before deciding what to do next. Kyle also reflects on GitHub’s history building webhooks, APIs, Actions, npm, Dependabot, and Semmle, why the AI era is breaking GitHub in new ways, how Actions became a general-purpose compute layer, and what Copilot becomes after code completion.

Full Video Pod

We discuss:

* Kyle’s expanded role across GitHub

* How AI got Kyle coding again after years in leadership

* Why GitHub rolls out AI through existing workflows instead of forcing new tools

* WorkIQ, MCP, Slack, Teams, email, and GitHub as company context

* Why massive “mega-skills” are giving way to small, atomic micro-skills

* How AI changes summarization, communications, marketing, and analyst work

* Why former developers in leadership may have a unique advantage in the AI era

* Kyle’s “15 agents on Saturday” workflow

* How Kyle built an AI-generated executive presentation for CRO/CFO teams

* Why AI changes the chief of staff role without removing the human work

* GitHub Actions, webhooks, arbitrary code execution, and secure agent compute

* The npm acquisition, supply-chain security, 2FA, and token invalidation

* Slop forks, vendoring, and whether AI agents change dependency management

* What pull requests become when most PRs come from agents

* Prompt requests, vouching, AI review, and trust in open source

* What counts as a “developer” when AI lowers the barrier to building

* GitHub Spark, low-code, and why GitHub refuses to hide the code

* 14x commit growth, Actions load, databases, monorepos, and availability

* Copilot’s evolution from completion to CLI, desktop app, cloud agents, and SDK

* Context, memory, rules, and making GitHub “act like Kyle wants it to act”

* Ambient AI, OpenClaw, enterprise security, and the new operating system for agents

* What swyx should ask Satya Nadella about Microsoft’s AI future

Kyle Daigle

* LinkedIn: https://www.linkedin.com/in/kyledaigle

* X: https://x.com/kdaigle

Timestamps

00:00:00 Introduction

00:03:36 Why AI Got Kyle Coding Again

00:07:04 Running GitHub with AI: WorkIQ, MCP, Slack, Teams, and Skills

00:15:39 The Golden Age for Former Developers in Leadership

00:17:31 15 Agents on Saturday and AI-Generated Executive Work

00:20:20 How AI Changes the Chief of Staff Role

00:21:45 GitHub’s History: Actions, npm, Webhooks, and Open Source

00:28:45 Slop Forks, Vendoring, and AI Dependency Management

00:33:57 Pull Requests, Prompt Requests, and Trust in Agent-Generated Code

00:41:21 GitHub Stars, 200M+ Developers, and the New AI Builder Wave

00:45:15 GitHub Spark, Low-Code, and Why GitHub Still Shows the Code

00:47:38 GitHub’s Hardest Era: 14x Growth, Reliability, and Scale

00:59:21 Actions as the Compute Layer for CI/CD and Automation

01:02:04 The State and Future of GitHub Copilot

01:08:24 Ambient AI, Background Agents, and the Future of the SDLC

01:13:09 OpenClaw, Enterprise Security, and the New OS for Agents

01:18:03 Build Announcements, WorkIQ, FoundryIQ, and Microsoft Context

01:21:41 What Should swyx Ask Satya?

Transcript

Introduction: Kyle Daigle’s Expanded Role at GitHub and Microsoft

Swyx [00:00:00]: We’re here with Kyle Daigle, COO of GitHub. Welcome.

Kyle [00:00:07]: Hey, thanks for having me.

Swyx [00:00:08]: You’re not just CEO of GitHub. People know you as that. You have a new role.

Kyle [00:00:11]: So I have an expanded role now. I’ve been working at GitHub for thirteen years and doing all things developer. Joined as a developer myself. And now, I’m also responsible as the CMO of Developer for Microsoft. And so all the kind of learnings and passion for developers and how we work with them and how we communicate and how we bring our products to market, we’re also bringing that expertise to the broader Microsoft ecosystem and helping every developer that uses a Microsoft product or would like to have a sort of similar experience that they’ve had with GitHub over the years. So it’s a different role in some ways, but it’s also just building on the experience that I’ve had at GitHub of just sort of tell the truth, be authentic, show people how to use it and then let the products speak for themselves. Now just doing that with, all of Microsoft.

Swyx [00:01:09]: We’ll be releasing this in conjunction with Build. You got lots of stuff planned, and we can sort of touch on that whenever it’s appropriate. I think one of the interesting things is I rarely meet a COO who’s also a CMO. I think you’re a very outward facing and you’re very confident publicly. That’s rare. Do you actually view yourself as COO? What’s What is your thing?

From GitHub Developer to COO/CMO: Building the Platform and Operating GitHub

Kyle [00:01:33]: I think for me, it’s been funny. The titles have always been, a— have always felt a little strange to me. I joined GitHub as a developer? I wrote so much of the

Swyx [00:01:46]: Let’s bring that up. You wrote the back ends?

Kyle [00:01:48]: I was going through, I was going through, some old photos, when folks were talking about how things were being built or how there was a build GitHub. I built, webhooks and worked with teams building the API, built the platform layer. Anything that integrated with GitHub, up until really twenty eighteen, I built or ran the engineering teams. And that’s kind of where my the beginning of my passion always was helping people build things, deliver them to, their customers. And so being a developer, building for developers was always super unique. In a— I think as my role expanded, it became my ability to talk to not just developers, but also enterprise customers or business leaders and have this translation layer. And then through all those years, GitHub has always operated pretty uniquely. Post-pandemic, working remotely was not as novel as it was when GitHub started in two thousand and eight. But all that expertise of running remote teams, doing it well, became this sort of bigger role, ultimately turning into the COO role of how do we operate GitHub in the way that GitHub’s always operated after the Microsoft acquisition. And kind of so on from there. So like for me, I think the— I’ve, I still code. I love coding but the problem has always been, people. It’s a much harder problem to both support our own employees, a harder problem to communicate to developers and enterprise buyers what we’re building why it matters, ‘cause those are two very different messages. And so getting to work in the mix of COO, CMO, also just being a dev, I think is what’s kept me at GitHub for so long.

AI Workflows for Leadership: Commits, Retrospectives, and Context

Swyx [00:03:40]: Apparently, you have— your commits have gone up. What’s this? What’s going on?

Kyle [00:03:45]: Rui’s called me out pretty aggressively. So I think— as you can imagine, right, you can see my normal era of being a dev In the twenty thirteen, twenty fourteen era, and then moving into management, and then ultimately the COO role. I think what you see there is me, really getting back to coding thanks to AI. I— similar to, attaching problems between how to market and how to operate a business and how to code, I find, building agents and workflows that are connecting very disparate problems to be what’s driving this. So that’s, some of it’s writing software. A lot of it is, connecting a ton of a different data sources to, help me out. But that is completely me really diving in on the AI side in trying out our tools, trying out everyone’s tools, But building for me, building for the non-technical leader, though I’m technical and how we’re, able to use these tools more than just the simple, call and response that I think a lot of the non-technical, your employers, you have to get— you have to use AI, and so everyone uses, ChatGPT or Copilot or Claude or whatever. To really get into, how is this going to help me out, it— I find that it’s not the I need to write a blog post, I need to those simple examples. Helping people find the workflows of, “Okay, I need you to go through all the PRs today. I need you to go through everything that we’ve posted online. I need you to go through what we did the last three months. Go through all of my Obsidian notes for any mentions of this then go through my transcripts at work.” We use, Teams, so, using WorkIQ, go call that MCP server, grab all the transcripts, go through all the Slack, and then build me out the plan of, what this week’s messaging actually was. That’s something that was, impossible because for me, I find AI in a what most of this launch here is actually, less building forward. It’s actually, a recursive loop backwards. I’m always looking at what had happened first. Go back through the week and tell me what we did, what worked, what didn’t work? And then tell me in the next three or four days-What would you tweak based on this sort of like looking backwards and then looking ahead a little bit? I find that to be so much more valuable, especially for like non-technical, because that retrospection is actually LLMs are very good at that. Like finding all the patterns, pulling them out, and then applying that retrospection to just a couple of days or just like a short period of time. Is all a bunch of apps that I’ve built and launched a bunch of, internal tools. I use the new, GitHub Copilot app, the desktop app with workflows. Every time I crack open my laptop, it’s running workflows for me. It’s just a ton of different stuff and of course, it all ends up on, it all ends up on GitHub.

Swyx [00:06:47]: Of course. That’s where, that’s where, stuff is hosted. Man, there’s so much to ask you. I was going to leave the how do you run a company with AI thing at the end. I have to ask one— double click one thing. You said, you are looking back at the week. You’re, you’re understanding what happens. When you say we That’s three thousand people. How?

Rolling Out AI Internally: Skills, CLIs, and Company Context

Kyle [00:07:09]: I think when we started rolling out AI internally beyond engineering, right? One of the things that I was really, passionate about is like we have to do this in a way where no one has to change how they work. I don’t want to have to teach you a tool. I don’t want to have to teach you something new. And so for us, we tried out a few tools. Most of them don’t work because I got to get you on board? I got to teach you how to use it. What we’ve actually ended up doing is we’ve built like a set of skills internally. We have we each have our set of skills, and we’ve just been distributing even to the non-technical folks, the CLI. And then effectively, we’re just giving it access to like read about everything that we’re writing. So that’s for us, that’s usually GitHub, Teams, Email, and Slack. So Teams for, video chat, generally speaking.

Swyx [00:08:03]: Teams and Slack?

Kyle [00:08:04]: so we use Teams for video communication, but we don’t use it for chat. W-we— GitHub for a long history, right? We’re always

Swyx [00:08:13]: Also Slack

Kyle [00:08:14]: Talking about ChatOps and like everything is built into Slack. Like every command, every flow.

Swyx [00:08:18]: So even though you have been acquired for I don’t know, eight years now

Kyle [00:08:22]: we still

Swyx [00:08:23]: You still use Slack?

Kyle [00:08:23]: it’s a purpose-built tool for us, and I think the reality is that moving off of it would be so bluntly expensive? Simply because all the tooling is, baked in with that paradigm. And they both have their pros and cons but they don’t work the same way at all. We still use a bunch of different tools Because it’s the purpose-built tools that We need. And then

Swyx [00:08:47]: Well, the same doesn’t go for the rest of Microsoft, presumably.

Kyle [00:08:50]: like the like various teams like operate

Swyx [00:08:53]: They make their own decisions

Kyle [00:08:54]: Various ways. I think it just matters what you’re trying to what you’re trying to do. But we do we do work across kind of every tool that we use, and then by giving everyone access to all of that context and the new WorkIQ MCP server, which is quite cool if you do live in the M365 like world. I can ask it all these backwards-facing questions, and it’s incredibly important for our teams that are working remotely. There’s a lot of stuff you miss when you’re not in an office, and we are spread out all over the world. So most of that is looking back. And then we post, we post either auto-automatically into GitHub issues or discussions, these sorts of like findings or like our industry reports. Like what’s happening this morning, today, yesterday. A little automation gets run. We’ll use the app. We might use GitHub Actions like with, our agentic workflows just to go do that run, and then we push it into GitHub, and w-we keep having a conversation. So usually for us, it’s about that sort of like looking back, looking forward on the non-technical side. And then of course for a lot of those folks, it’s also building an app, pushing it to GitHub pages or pushing it somewhere to host it et cetera. But it’s just like enabling everyone with that power of it’s going to take me a week to figure this out. Instead, we’re going “Okay I built a skill. Let’s put it into a repo. We’ll all share that skill together, and then we’ll use the CLI or now the app-” “just to run it.”

Micro Skills vs. Mega Skills: How GitHub Uses AI at Work

Swyx [00:10:26]: All right. I think, I think we’re going straight into like the team management and productivity thing. I think a lot of people are getting various levels of LLM psychosis. How do you manage the bloat of skills? Like everyone Has their thing, and they’re Like trying to promote it to the rest of their peers in their org, right? And obviously, whoever becomes a skill influencer internally becomes like an AI leader, right? Of sorts. I assume you have those.

Kyle [00:10:50]: like I think we have

Swyx [00:10:52]: And I assume it’s a mess a Yeah.

Kyle [00:10:54]: there’s like I— like I think the reality is there’s two pieces. Like first is I think that we’re ending the era of these like massive, beautiful, perfect skills that are just like not any of those things. ‘cause for a while, right every tweet every day is like go download the skills, the perfectly managed thing to do this entire workflow. And I think that like what we’ve found and what— I was just with my team, this week, and we were talking about the skill side, and we’re really talking about these like incredibly micro skills that are just doing one thing for us very well Versus a skill that’s going to do I said, that full report. That doesn’t really exist on our side anymore. It’s usually how do— like a single skill that’s going to identify the most important marketing information given any MCP server. Like this is the most important thing. Less about stitch a bunch of tools together and have it produce this mega output because then weeks go by, months go by, things change, and you want to tweak

Swyx [00:11:58]: It’s brittle

Kyle [00:11:58]: Your mega skill and you’re screwed? You can’t do that. And so now we’re really just talking about the Legos we’re using and just letting the instruction book be something we’re all putting together. Whereas I think a lot of AI skills for a while have been that mega instruction book style.

Swyx [00:12:15]: I’ve, thought a lot about Postel’s law. I don’t know if that’s a term that is, means things to folks. It’s the idea that you should be liberal in what you accept and strict in what you output, right? And I think that’s like a good framing principle for skills. This is my skills, obviously on GitHub. I feel like everyone should have like how like some repos In GitHub are special repos? I feel like we should sort of reify the slash skills and everyone like give it some kind of special presentation. Anyway, so, yeah, this is one of those like download Download anything, transcribe anything, and then you can string together the atomic skills that do one thing well Into like some kind of orchestration skill that calls other skills. I assume, does that match?

Kyle [00:12:56]: I like I think so. I think that the

Swyx [00:13:00]: Summarize anything.

Kyle [00:13:01]: Like I think the- For me, summarizing something for I do communications and PR and analyst relations and marketing and customer activities, and so my summarize everything is very different for each one of those like Contexts. What ‘Cause if I’m summarizing something for an analyst, that’s a very different thing than, probably how I’m going to summarize something for like a customer meeting or an engagement. So that’s I think like the difference when we’re talking about the like the tools I might use on Saturday or the skills I might use on a Saturday when it’s just for Kyle. Yeah, those are kind of like they have an atomic actual tool underneath or maybe skill, and then Kyle cares about X. But I think when we’re talking about work and enabling the the marketers, communicators there, it’s the atomic, this is what good summarization is, and then this is what I care about as for marketing for communications For whatever. And that I think is like the interesting matrix problem when we go from like a developer set of concerns to all kinds of different professions, is that what that word means to me is different than it means to you is different than it means to the analyst or the salesperson, and that’s where I think the matrix mess is that we’re starting to like still starting to find. It’s about these mega skills but they’re all just slight permutations, but those permutations are really important. It’s the difference between someone reading this and going “Did AI make this?” what Or “This makes total sense, and I would expect this when I’m giving a briefing to Gartner,” or like whatever else.

Swyx [00:14:37]: I think the beauty of it maybe is that you don’t have to be that careful about what goes in there. It doesn’t have to exactly fit as long as it like roughly is contained in there. I used to complain about plugin hell, basically. Like when you have a framework and then you have a hundred things that you need to integrate, everyone does like the GitHub used to be bloated full of these things. And now we don’t need them anymore ‘cause now you just use skills.

Former Developers in Leadership: AI as a Creation Multiplier

Kyle [00:15:00]: And like I think the most magical thing is the just that like I can just also crack it open. Like Like yes, I could go like change the how the plugin is coded, or like I could go do that now with AI, but I think there’s just something more magical about getting a response back and being “That’s not right,” and then you just crack the skill open, you just type English words and it’s different. That building block is just, I think very unique. Once I get everyone to kind of understand how to best how to best make those changes to get the most power out of them.

Swyx [00:15:36]: Is there a— you have a your peer group that Of people like you. Is there a common framing for Something I’m feeling is, which is true, is that is this a golden age for former developers who are now in leadership? Because you can wield the tools, you would know the right words, you’re maybe not too close to the details. Doesn’t matter. But like you’re more effective than someone who doesn’t come from that background.

Kyle [00:15:59]: I think that like the secret has always been your ability to identify patterns and solve problems, and I think that for folks that like myself that don’t code day to day anymore, that has made me successful as a developer, made me successful as a COO and now CMO. And so now that I have access to get and write code, I’m now applying that sort of like pattern finding and problem solving, and I know enough still about how to then go and say, “Oh, I want to make an app, but I don’t want to break into jail or create something that’s not going to be able to work or to be deployed scale or whatever.” that ability to apply all that additional business knowledge and still code I think is what makes that so interesting to me. Slightly different than I think some of the other like technical leaders that became business leaders and now are going back to their apps and updating them. Good for them? But I think the more, much more interesting thing is, well, now I have this whole new set of expertise over ten plus years. Why not take that and use that as a developer with these AI tools? So I definitely think that makes me more powerful, but I think that’s true for like every dev as well. Most of the dev friends I still have also have some other underlying skill and passion. There’s really talented, very kind of linear computer science software devs, absolutely. I just find that the folks that came from a different career, went to school for something else, went off and did this random thing, and then became a software dev, or were a dev, did a random thing, came back. Learning that extra set of information, learning those extra skills, and now having the power of an AI where I can crank up fifteen agents on Saturday while my kids are doing lacrosse, That’s like really powerful. And I think it gets me back to that feeling of like creation, and it’s very hard to replicate that in most other senses? That first time you build an app and you click it and you show someone that’s magical. And so being able to do that not just in code, but across all kinds of different assets that’s, that’s huge. We were doing we’re doing our every year we do our revenue planning. We talk about okay, what is it going to look like for next year? And of course as you imagine, there’s, slideshows everywhere talking about what are we going to talk about, what’s the narrative, et cetera. And so as you said I’m “Okay, well, I could probably just like build something to build this and then that way I don’t have to go build the whole spreadsheet or I have to pass it to my team.” So we went through this process, and I got all the information and used the skills I mentioned. I built like a little app just to make it so I could look at some of the information in a SQLite database, more easily. And I ultimately built this entire presentation without touching any of it and I was “Okay, I’m just going to present this to our CRO, the CFO, their teams,” without mentioning I’d built it with AI. I like built a skill to make it look very much not AI driven. Just not pretty.

AI-Generated Presentations, Human Taste, and the Changing Chief of Staff Role

Swyx [00:19:03]: Like a design. Yeah.

Kyle [00:19:03]: Not pretty. But just like very clearly not AI. Kind of like don’t do anything interesting.

Swyx [00:19:08]: That’s, yeah, that is valuable.

Kyle [00:19:08]: Just go Exactly. We did the whole thing through. It used my notes from Obsidian, it used all the context I mentioned before, the plans, and Never came up once that it was AI generated.

Swyx [00:19:20]: It didn’t matter.

Kyle [00:19:20]: Never once. D It didn’t matter. And so now I take

Swyx [00:19:23]: This is a tool

Kyle [00:19:23]: I can take that tool and go, “Look, I don’t want you to go build slideshows.” They’re just helping us share information with each other. If this thing can do it With a little bit of crafting from you and then we can look at it together, awesome. There’s no value in all that extra work. I think that the ability to, make it look humanly bad and and build a little app to, manipulate the data I think is part of, that upside for devs that are now in leadership roles. Because, the thing that I feel like I said before, this that’s all a people, that’s all a people problem. I know if you’ve used a coworker or not to build a slide deck, unless you spent a bunch of time to not do it.

Swyx [00:20:07]: I know, but like it was so, I think there’s a certain charm to just being blatantly AI. ‘Cause I think that you’re well, you’re just honest about There may be mistakes here that I cannot vouch for. So how much value is there? But anyway I think, actually the real question I want to ask is, there’s a— You were a chief of staff To Thomas. And in the pre-AI world, the that job would’ve been a chief of staff job of like Can you prep me these slides and all that? And now you do it yourself.

Kyle [00:20:35]: I still, I still have a chief of staff. Because, the difference is it’s sort of the discussion every time we have some sort of technology evolution is it’s not that the jobs the roles don’t all go away, they just change? And so yeah, I don’t have someone spending all their time building out slides for me and presentations ‘cause I don’t need that anymore. But now I need that person that is able to go and find all the different connections between humans in those discussions to help me find out, okay, I should be meeting with this group and this team, and they have an opportunity, and I’m going to be in San Francisco today, I’m going to be in Seattle tomorrow. Those sorts of human connection aspects are still incredibly valuable and has always been a big part of that chief of staff role. But now just like chiefs of staff are not opening up, letters to process, they’re doing emails. What It’s the same thing. And now they’re, they’re not building out as many of these presentations because they have the the ability to have a AI take it on for, and share that with me and great. Let’s keep moving ‘cause it’s allowing us to go faster and make better decisions more quickly.

Swyx [00:21:45]: Awesome. Well, so we can dive into more sort of, Productivity insights as you go. I did want to do a little bit of a brief history of colleague and hub. Because, we started here. And then you also involved the NPM acquisition. I did, I do want to touch upon that. And then more recently, I just want to bring up to present day where we’re having uptime issues Which transparently we’ve already Addressed publicly, but we’ll, we’ll discuss in the pod. Did I miss anything? Like what, any other major highlights? Obviously, it’s, it’s a lot of years to cover.

A Brief History of GitHub: Webhooks, Actions, Acquisitions, and Platform Evolution

Kyle [00:22:15]: No the I think one of one highlight was right before the acquisition closed in twenty eighteen, I got to launch the first version of Actions

Swyx [00:22:27]: Oh

Kyle [00:22:27]: At GitHub Universe. So it was O

Swyx [00:22:29]: They’re that young?

Kyle [00:22:30]: It was October of twenty eighteen, I think. Yeah. Yeah.

Swyx [00:22:33]: Gee, Jesus.

Kyle [00:22:34]: I got to I was the engineering leader on that project and got to launch that. And then, yeah, we did acquisitions of NPM you said, Semmle, Dependabot Pul Panda a whole bunch of things. That was a big

Swyx [00:22:47]: Pul Panda.

Kyle [00:22:48]: Abi is doing well.

Swyx [00:22:51]: DX. Holy crap.

Kyle [00:22:52]: Did well on DX. I and like that was a that was the big shift, after the acquisition. I had to join the sort of business side.

Swyx [00:23:00]: So I need to hit you on some of these things ‘cause you were there. Right? And how often do I get to talk to someone who was there? But yeah, Actions. Is that the number one source of security issues on GitHub?

Kyle [00:23:11]: Oh, sh I think that the number one source of, security issues is probably like all, the literal code in everyone’s like underlying repositories. I would say back further than that is, if you remember I had to show in this graph was this is, I’m, didn’t say this before, this is ultimately webhooks.

Swyx [00:23:30]: You yeah.

Kyle [00:23:31]: Like circa whatever it was.

Swyx [00:23:32]: It says Hookshot in there.

Kyle [00:23:32]: I forget. Yeah. Yeah, Hookshot’s in there. And so like back then, it says GitHub Services. Do you see, it says Hookshot FE for front end, and then it says GitHub Services. GitHub Services back in the old days, right? You we had a repository that was Ruby code, and you could write any Ruby code in there, and then we would execute that On your behalf As a service, and then that way if an if you were trying to integrate with something, it didn’t we would run it for you.

Swyx [00:23:57]: And of course no containers ‘cause

Kyle [00:23:58]: No, ‘cause it was

Swyx [00:23:59]: Well, no containers

Kyle [00:24:00]: Twenty fourteen. And so there was some isolation obviously, but it was mostly the separations on the server level. That’s like an example as long as the very old version of Pages, which ran on its own containerization infrastructure, not on Actions.

Swyx [00:24:15]: Which like all-time great product.

Kyle [00:24:16]: Pages powers the internet at this point to some degree. Those were places where like clearly there were no like issues like to my knowledge. But it was those things where I’m looking at and going “Okay, well we can’t be running arbitrary Ruby code,” like on everyone’s behalf. Then containerizing all of that up intoUh into actions now where yeah the containerization, is r-really good. The pinning most folks aren’t pinning it the like to a particular

Swyx [00:24:48]: Images

Kyle [00:24:48]: Sha, et cetera like their workflows, and so that’s a big that’s a big place Of pain for folks if they’re just doing similar to any dependency management, just V1 or newest or latest, I think. But, that journey from that day to “Okay, we’re just going to run all this arbitrary code, and, it’ll basically be okay,” to now, no, we have, really good containerization. We have a new, underlying, ag-agent, containerization, service. It’s like we’re using it under the hood. It’s through Azure. They recently announced it. The Azure, Dev Compute, but it’s, very fast, very fast compute to be able to, spin up your own cloud agents, or whatnot. We’re using it under the hood for some parts of the new,

Swyx [00:25:36]: Microsoft Dev Box?

Kyle [00:25:37]: No. Dev Compute, yeah.

Swyx [00:25:41]: Hmm. Not finding it just yet.

Kyle [00:25:44]: Oh, it’s, it’s in there somewhere.

Swyx [00:25:46]: All right. Well, we’ll cut that out.

Kyle [00:25:47]: Sorry. But with, Dev Compute, you can, run, really fast, spin up really, small VMs really quickly, so you’re doing a tool call

Swyx [00:25:58]: Same concept

Kyle [00:25:58]: Just do it containerize exact-exactly. So we’re using that so definitely moving that direction to protect us from every every piece of code that we’re ultimately running.

Swyx [00:26:07]: look, that grows into the full SDLC? Code hosting was just the start and and then it’s grown beyond that. Let’s talk about NPM may-maybe ‘cause I think that’s also, a very major point in the industry. I do think, it was looking for a home. It was, kind of struggling as a business, right? I don’t know, I don’t know how you would characterize that whole acquisition and how it

NPM, Package Security, and Keeping the Internet Running

Kyle [00:26:33]: like when we were talking to the team, I think the big thing for the both of us was to find a way to keep NPM, which was basically powering the internet then and way more so now to some degree running. Keep it going keep continuing to scale. It was having scaling problems, if I recall, back at that time. They were doing some rewrites. It

Swyx [00:27:00]: that’s cute compared to now.

Kyle [00:27:01]: Well, that’s the thing is like when I’m talking to folks now, there’s there’s so many more underlying uses of NPM than there were back when we had them join in with GitHub. But that was ultimately the goal. It was really okay, we used to have pages. We have, the world’s code. Let’s make sure that we can keep NPM running well for the world. And we put a bunch of time and investment into fixing some of the underlying backend, changes, some of which we talked about some of the manifest work, et cetera. And then now, really trying to bring the the security posture of NPM up to speed. But, it is a unique challenge in that every move that we make to make it more secure will break a lot of people. And security is paramount. And also, we take it very seriously. We’re, the any time that we have a problem with GitHub or we make a change that makes us more secure but hurts, there’s, a snow day for developers or a really bad fire that they have to go put out. And so we’ve, have changed the 2FA policies. We’ve changed the way the tokens work. When we find tokens that have been exposed or potentially, exposed, we invalidate them, and

Swyx [00:28:22]: I love that feature in GitHub. Yeah, it’s great

Kyle [00:28:23]: That creates issues, but, the but that’s the thing is we’re trying to push the community, forward without necessarily, doing something that is going to break the contract that’s been for 15 years or close to it or some amount of years on NPM.

Slop Forks, Vendoring, and the Future of Open Source Supply Chains

Swyx [00:28:43]: I think the— So now we’re talking about, open source and publishing. And I think there’s something here with what people are calling slop forks, which, I think Malta from Vercel is doing. And, part of me thinks, well, the way to get past any vulnerabilities, we just, let’s just get rid of the concept of NPM. And we only publish source code. And anytime you want to import it you have your coding agent look at it and then adapt whatever subset you’re going to use into your vendor it. But, the AI vendor it. Is that realistic? I don’t know. Is it— Will that solve all our security issues? I don’t know.

Kyle [00:29:24]: I don’t think it’ll solve I so Mitchell was just talking Mitchell Hashimoto Was just talking about this today, and I think that I-in some ways, it’s all all things, old or new again? Yeah, absolutely vendoring everything. Like I do I do remember twenty thirteen, twenty fourteen.

Swyx [00:29:42]: This is Yeah. Let’s, we must return to

Kyle [00:29:43]: That’s what is We were vendoring everything. We were having actual discussions around, or at least I remember we were “Should we take this full thing?” “Why is this so big? We only need this one file.” And so I do think there’s something true there where having either taking only what you need or the dependencies just getting incredibly small over time, I think will help to some degree, but it’s not going to solve the fundamental problem, I don’t think, because the vulnerabilities in an agent looking at them, there’s time and time again, there’s a million different ways in which we can convince an agent that this thing is, secure or not and pull it in. Or we can do static code analysis or runtime testing to say whether the code works or not. That is, I think, the step that needs to continue to be, invested in. The question is just on, how much scope. Should it be this enormous project that I’m pulling down, or should it be this piece? Either most companies are running some amount of security checking on the on the packages that they’re bringing in or vendoring. That I think won’t change. That’s like what advanced security does to some degree, Socket does some degree. Like everyone is doing a piece of that. How we each do that like especially when we’re talking to enterprise customers, is just like very different. No there’s no one wants one single way to do it. And I think that’s always been GitHub’s, unique position in the world. I talk a lot to maintainers, I talk a lot to folks about this. It’s we’re— we rarely start like a process and a practice and like push it onto the community. We usually wait for the sort of like RFC process socially or literally, everyone agreeing, and then we’ll cement something in. Because otherwise we’re

Maintainers, RFCs, Vouching, and the Social Layer of Trust

Swyx [00:31:35]: That fits your role in the ecosystem, yeah

Kyle [00:31:36]: We’re GitHub. Yeah, we don’t want to shape the whole thing. We want it to be figured out. But like how do you balance that like sort of Role in the industry to keep everything as secure as is possible and make sure that you’re you’re not going to be compromised as a human, ‘cause that’s usually how it all happens. And Not not create a process or lock us into a flow that you’re not going to or like Mitchell’s not going to or other open source projects aren’t going to like. That’s always been a tricky balance for us, and I think that’s something that we haven’t talked about enough is we’re not going to be able to fix everything for everyone in a way that everyone is going to like. So tell, help us, tell us what is working. When Mitchell was talking about, the Upvote, the up

Swyx [00:32:22]: I was going to bring up his thing. Yeah.

Kyle [00:32:23]: I forget what it Yeah. When he’s talking to us, I was chatting with him and talking to him about this and I put it on Twitter and we talked to, also over DM, was “We’re going to keep working.” but I think the important thing is I do actually want to hear what isn’t working for you. And as, be as specific and clear for your project as is possible. And to every piece of credit over the many years that we’ve known each other through the industry, he’s always done that and I appreciate that ‘cause there are places that we need to fix up, and we hear from him, and we’ll fix up just like we do all other kinds of maintainers. But that that process between making those types of improvements and being more secure and like creating, I forget what he calls it’s not the proof process, not the claims process. Do what I’m talking about? He has that he his projects have a way for you to kind of like,

Swyx [00:33:13]: Vouch

Kyle [00:33:13]: Vouch. Thank you. Yeah. He has like the vouch system for saying, “Hey, you should accept my PRs.” That’s been

Swyx [00:33:20]: I just built this into GitHub. I don’t know.

Kyle [00:33:22]: Well, see, but that’s the thing is that you say that and like he and his community really likes this and then I’ll go talk to other maintainers and other maintainers, globally, and they’re “No, this doesn’t work for me.” And that is the tension, but also the kind of beauty of GitHub, depending on which way you look at it is we want to help maintainers, so we create all these tools to let you have more control over how much you take in from AI and PRs. But you can also use this. What You can go use this project, and if it takes off and becomes the kind of mostly standard, then yeah, we probably wouldn’t enforce it but we would add it in because that’s the flow that we tend to do?

Swyx [00:34:02]: I hear a lot of people don’t know the history of the pull request. And like like that’s how, that’s something that GitHub standardized basically.

Kyle [00:34:08]: Yeah. It was a very messy process Like beforehand, and now the we have the benefit of it being the process? And now we have to go and Figure out the next best process or what adaptations change, or what does a pull request look like when eighty percent of your PRs are just coming from your agents and not From other devs?

Swyx [00:34:31]: Do you like the prompt request idea from Peter?

Kyle [00:34:34]: like I think that for each like each idea I think has its merits. I’m not, I’m not avoiding saying anything good or bad, but I feel like I’ve seen a version of we have that we have entire Thomas’ store. Take all the assets of what you’ve built and put that in. I think that’s got great ideas. There’s all these various permutations of the PR flow, but I think the reason why there’s not a single answer is ultimately we’re trying to codify trust. We’re trying to say “Okay, if Sean reviews this I’m going to trust it because you’re Sean or you’re the senior dev or you’re the whatever.” And right now, when we are working in a flow where an agent writes code and another agent reviews code and then Kyle goes and looks at it the trust is kind of diffuse. And most of the tools that we’re talking about are talking more about verification flows. We have more assets to look at, so I can probably say whether this is a good PR or not. But that still doesn’t solve, I think, the human problem of I’m looking at a PR and I want to know if I can trust it. And we’re still, we still tend to use human signals for that? Mitchell approving it or Kyle approving it or whatever. And so I think that’s, I think that’s why most of these options haven’t really solved it is because, it’s a social problem ultimately. It’s a it’s a human problem to review it and agree. Or you fully trust the tool and you’re imbuing that tool with full trust Which I think in some cases that absolutely exists.

AI-Generated PRs, Trust, and the Waymo Analogy

Swyx [00:36:08]: And so like in the same way that there will be a tipping point in society when we don’t allow humans to drive anymore Because machines are measurably better than Than humans. I’m looking for that tipping point, right? Like Mythos is ridiculously expensive. Someday we’ll have Mythos on a desktop. I don’t know. Will, does that change the equation?

Kyle [00:36:30]: I think it’s more I took a Waymo here, and I was on my phone and not looking around at all. There are other, self-driving, vehicles that I would not trust while, staring at the road. And I think that trust is something that is

Swyx [00:36:48]: Is this a Zoox thing? What is it

Kyle [00:36:50]: I think that is both. I think that is both. Like

Swyx [00:36:53]: There’s Zoox in this robo taxi. That’s it. It’s

Kyle [00:36:56]: Well, depending on what level Of self-driving. But, my point is sort of that I think part of that is I strongly believe that’s, a mixture of verifiable proof. Like how many accidents, how much data, and so on, and the human aspect of how I feel when I’m in this car, what it tells me, et cetera. And so that’s why I think some of the like Some of these some of our AI tools tend to, imbue me with more of that feeling of trust, even if the data says this is 100% accurate. I feel like it takes more time for us to go, “Should I trust this or not?” And that’s in the soft sense of, startups with high agency, weekend projects, and open source. And then there’s enterprises and regulated industries and everything else, and that is an even harder problem to go solve because even when it is fully verified, not only do you have to have trust from the humans on the team, you probably have to have trust from multinational,

Swyx [00:37:55]: Oh my God

Kyle [00:37:55]: Multi governments around the world and regulating agencies. And so that’s where I feel like until we tip over to your point on the sort of like human EQ side of it. I feel okay this feels okay I’ve been proven enough. Then the ball will start to roll a lot faster, where we’ll end up getting to the “Okay, we can trust this,” and feel good about it in the Most difficult of cases.

Reputation, Sponsors, Stars, and Bot Activity on GitHub

Swyx [00:38:18]: If human trust is the thing that matters, I feel like GitHub as the developer social network could maybe do more there. Like vouchers are one system But, we have star counts, and then we have Contributor rights, and that’s it. And I feel like there should be more in that space. I don’t know if there’s any other design decisions there.

Kyle [00:38:37]: I think that one of the places that we don’t really expose right now in this sort of way is, some degree of like hard trust and support, which would like for me is like sponsors is a good example of that.

Swyx [00:38:49]: Ah.

Kyle [00:38:49]: It like costs you something. To prove that I believe in your project and I trust you To some degree or I want to support you at the very least.

Swyx [00:38:56]: Solve payments for open source. Why not?

Kyle [00:38:58]: I think that I think that like as we keep moving forward, right, there’s more and more projects where I’m, adding more and more dollars into sponsors personally because I want to like support them, but I also like know of I’ve probably never met them in person, but, I know of enough of their work that I want to support them. I think the thing that I don’t love about stars or commit counts or anything else is ultimately, even with all of the various, abuse and de-spamming and deduplication work that we do or anti-abuse work that we do, these are all, not active social signals. They’re passive ones that are ultimately gamifiable. And you may trust me, but another open source maintainer may not. And on what heuristic should you be, trusting me? That I think, is kind of where some of our thinking is right now. What signal from me is most important to you? You— If you can define that potentially, honestly in an agentic workflow that’s what we see some of these open source projects do, where you have GitHub actions, and then you have like an agentic workflow that’s calling AI, and you’re setting these rules. Like if Kyle has submitted and gotten accepted PRs across any given project and has a social handle tied to his account in GitHub, and that social account’s older than a certain amount. Really complex measures that matter to you ‘cause most open source projects have that heuristic built into their heads, if not written down in the contributing guidelines. You could take that and then go apply that and then just say, “Oh, we’re not going to accept this PR.” Building something that is, I think, malleable to everyone’s needs, is a little bit better, rather than going “Hmm, this account’s too young.” Because what happens? The attackers just go and go and create a multitude of accounts, and they wait Until it ages up. Needs to have a certain amount of stars. That’s how star inflation happens. Need to have a certain amount of repos

Swyx [00:40:46]: Oh my God. Yeah

Kyle [00:40:47]: With PRs. They all just create repos and submit PRs to each other, and then they come in and do something nefarious. And so, it’s hard. It’s hard to find the measure. So I think we’re, we’re looking more at how can we provide you tools so you can kind of choose what’s best for you. And of course, we’ll give you some standards. But the trust vector, gets down to I don’t know, some version of like human digital ID like everyone’s been talking about. Like how do I prove that it’s me

Swyx [00:41:13]: Give me your eyeballs

Kyle [00:41:14]: On the internet. Give me your eyeballs. Exactly.

Swyx [00:41:18]: The I got to keep moving on Topics, but obviously I can go all day on this stuff because, I’ve been involved in GitHub and open source My entire professional career. Stars. Very superficial. Everyone knows it. But I think time to one hundred thousand stars is the fastest I’ve ever seen. Like people just reached that in I don’t know, months. And then like at the same time I don’t trust it right? Like how many of these are real or bot or like whatever. I don’t know how to ask this but like what can we do about it? Like

Kyle [00:41:49]: Just

Swyx [00:41:49]: Is stars broken? Is stars fine?

Kyle [00:41:51]: I think that there’s kind of two, there’s like two pieces. Obviously we’re constantly like trying to find ways in which like your users are producing spam, which would, I would include like be like only doing star gamification. When we find them, we pluck ‘em out and we,

Swyx [00:42:08]: But it’s like a Whac-A-Mole

Kyle [00:42:10]: It’s a hundred percent like a Whac-A-Mole

Swyx [00:42:11]: There’s no way

Kyle [00:42:11]: Now, powered by AI to be helpful. But I think more so what I’m seeing is, a lot of the like fastest time to X tends to be because we’re now inviting so many more people into like software development on GitHub That like the zeitgeist is just swarming? And it’s

Swyx [00:42:32]: It’s not just developers anymore

Kyle [00:42:33]: And it’s not you and I. Like like however you want to say like what a developer is it’s not just folks who have been coding for a very long time. It’s folks that have maybe started coding or only joined in since the AI era. And now

Swyx [00:42:44]: what’s the latest Octoverse number? I know eighty million was my lastRem- member that a number of developers on GitHub

Kyle [00:42:50]: Oh, we’re over 200 million now.

Swyx [00:42:53]: Okay. Well, so you see?

Kyle [00:42:55]: Like over 200 million developers now.

Swyx [00:42:56]: But it’s not developers, right? It’s, it’s people with a GitHub account.

What Counts as a Developer in the AI Era?

Kyle [00:43:00]: So, so this is, this is the biggest debate that I would say, everyone loves to have at GitHub at this point. From my perspective, right, I think that there’s, there’s clearly a difference between, professional enterprise developer and then developers. But I think that I think that the idea that we should be I don’t know, splitting hairs or segmenting developers in the early era of software development is, not worth our not worth the time. So

Swyx [00:43:29]: When you get into gatekeeping

Kyle [00:43:31]: 100%

Swyx [00:43:31]: What is a developer?

Kyle [00:43:31]: 100%. ‘Cause I wasn’t a developer when I started writing code? I was going to

Swyx [00:43:36]: Oh, no. I made— I cloned a thing, seven years before I learned to code. And then I and then I wrote about my learning to code journey, and people Just called me a fraud ‘cause I had a GitHub account. And I’m “Well, no, I just use GitHub, but I don’t know-” “I didn’t know what I was doing.”

Kyle [00:43:49]: I I remember that. I remember those sets of posts, and like that’s, that’s b******t. So I fight very clearly on the line of, if you create code, if you have an idea and you create it into some way of, I’m, I’m going to run it and use the app right now, you may still use AI in that moment, but that’s okay. At some point you’re going to do the next thing. You’re going to create a big— You’re going to have to learn about this database. You’re going to fix a bug, whatever. We’re all on some same journey, and those people are also hearing about the great new agent skill package or a new CLI tool or a new whatever. And those projects are going up because you want to be a part of this moment, just like I wanted to be a part of the Ruby community when Ruby was popping off when I started becoming a developer, and now I can just click the star button. And so I think that yes, there’s clearly some amount of like spamming and game gamification that we’re working against, but I really think we’re just seeing this whole new cohort of folks that are moving from technology to technology because they’re not working on a 20-year-old software application. They’re working on a side app that they built on the weekend for their friends or for their new idea or whatever. And that’s how you see these enormous charts going up and to the right with With stars.

Swyx [00:44:59]: I think something that’s remarkable is the persistence or, that GitHub extends to those folks. Usually when I see platforms go into a new audience, they usually have to, have like a second platform with a different name that wraps the main platform. But somehow GitHub has been able to sort of persist and extend, and it’s friendly and whatever? So it’s, it’s nice.

Spark, Low-Code, and Always Showing the Code

Kyle [00:45:19]: I that’s partially why I think as we’ve tried to move into I don’t know, more like low-code-y things. We so we started working on Spark as like a way to, build an app and run it. I think that the reality is that we anytime we try to, kind of put even a veneer on top of it without when we put a veneer on top of something, we still always show you the code. That’s kind of like a tenant. We’re never going to, hide the code from you ever, because what

Swyx [00:45:52]: Why would you?

Kyle [00:45:52]: That’s, yeah, that’s the whole point? However, I think that what we learned with things like Spark is that really the value of Spark for most devs is, easy runtime. And you may have a runtime or a host that you’re going to use for that or you just build something and run it but, the package of making that even more simple isn’t really needed for folks that are trying to build software and not just trying to build, an app, which is, slightly different, a slightly different goal. So I want to get you in, I want to get you comfortable. I think the best thing for me as, someone that did not traditionally come into software dev way back, I want anyone to be able to breach that chasm and not be in the I don’t know, I feel like we’re, we’re still in an era of, STEM. I’ve got a 12-year-old and an eight-year-old, and it’s “We got to get ‘em into STEM,”? Over and over. And I like I do, I do the things that good parents do. I was “Oh, you want to do coding?” “Yes, I want to do coding.” Do coding classes. But now they’re just not afraid of doing software. And that’s, I think, the thing that’s honestly kept me at GitHub for so long. Anyone should be able to go and build a thing, just like I can go change a light switch in my house. I’m not going to go into the breaker box ‘cause I’ll probably kill myself? But, I can go change that light switch. Everyone should be able to go and say, “This fricking app doesn’t do what I want. I want it to work like this.” And that I think, is what’s kind of kept us all connected with GitHub through the years and some and during the easiest of times or in the hard times because of that opportunity of, we’re the home for all developers, and we want everyone to be able to have that feeling that we’ve had of, had an idea, I created it and holy s**t here it is.

Swyx [00:47:37]: Here it is. All right, I’m going to try to do more spicy questions.

GitHub’s Hardest Scaling Moment: Growth, Agents, and Uptime

Kyle [00:47:42]: Great.

Swyx [00:47:42]: Is it an easy time now or a hard time?

Kyle [00:47:45]: Oh at GitHub? It’s a hard time. Like, it’s a hard time and also, I was just with my team and I said, “This is also, the best and most exciting time that I think I can remember at GitHub.” Because

Swyx [00:47:57]: Best of times, worst of times. It’s never one

Kyle [00:47:59]: ‘cause we’ve we were talking about Octoverse reports and, usually we do an Octoverse report once a year, and we look at the numbers, and we say, “Oh my goodness.” I was at Universe in October saying, “This was the fastest year of growth that we’ve ever had,” right? And now we’re doing more in a month than we did in a year last year.

Swyx [00:48:20]: You’re talking about PRs.

Kyle [00:48:21]: Commits.

Swyx [00:48:21]: Commits, yeah.

Kyle [00:48:22]: PRs. Kind of like you name it by roughly every measure that we’re looking at, there’s some amount of sort of growth that is much bigger, and that is breaking our system in new ways, not old ways. Like webhooks were always notoriously, unreliable over the years?

Swyx [00:48:38]: Whose fault is that?

Kyle [00:48:39]: not anymore mine, but for a period of time, I’m sure you could pull up a tweet that was “It was me. I’m sorry.” but, now, that got rewritten at a scale level that is still working and is not having problems today. Now what we’re finding isn’t just the isn’t the-The simple stuff that folks are on the sometimes on Twitter or on the internet are “Hey, why is this like this?” Sure. There’s absolutely silly problems that we shouldn’t exist. But now we’re talking about, unique, novel permission problems that happen only at a scale across all different objects or whatever, that now we have to go rewrite this underlying system. And so it’s, there are problems that yeah, caught us off guard, which I think I said. Like the growth is astronomical, but also we’re making such material progress in that I’m excited once we’re once we’ve kind of like reimagined the underlying foundation layer, or pieces of it at least, what’s going to be possible when it’s not just all of us and all the new people that are being developers and all of their agents and all the tools like working together. Because that’ll still happen in that in that GitHub tool, that GitHub community. But it’s a it’s a hard day anytime we can’t give you what you’re looking for. We have the same problem internally. We operate through github. Com. Of course, we have backups when things go down and whatnot for our own operations but we feel it too. If it’s not working it’s not working for us, and that’s kind of like the promise of dogfooding for GitHub. It’s always been true. We’re using the same tool you’re using. We’re not using a super secret version. We and so we also need it to be great for us for our customers of course for open source. And now an exponential growth of agents, Doing it too.

Swyx [00:50:32]: I wanted to load for audio listeners who maybe haven’t seen your tweets, whatever. So one billion commits in twenty-five. Now it’s two hundred and seventy-five million per week on pace for fourteen billion this year, if growth remains linear. Is that still the pace? I don’t know. It’s been a

Kyle [00:50:48]: it’s, it’s speeding

Swyx [00:50:50]: Roughly.

Kyle [00:50:50]: It’s still speeding up.

Swyx [00:50:51]: It’s, it’s April, so yeah.

Kyle [00:50:51]: Exactly. This was in April.

Swyx [00:50:53]: All right. So basically you have fourteen x growth, right? Year on year on year. And I think that’s a scaling issue. I think, I’m going to like try to really steel man this thing. People have experienced fourteen x growth. They haven’t had your downtime. And that’s like— C-can we go dig into that? Why? Like what’s the— what broke? What are we doing to fix it? Like just anything for the community to reassure them.

Why GitHub Reliability Is Breaking in New Ways

Kyle [00:51:18]: so there’s a Like I was saying, there’s a couple different places that we’ve seen the growth issues. Some of the growth issues, which is why we’re t— I was talking about pushing hard on more CPUs is in actions in particular. More tools, more agents, more PRs mean more builds, more builds mean more CPUs. And so we are expanding through not just our data center, but obviously we were talking about moving to Azure and moving to, adding an additional cloud compute because we simply need more CPUs. Not as much GPUs. We definitely need GPUs too, but now CPUs are becoming a factor.

Swyx [00:51:53]: It’s very CPU heavy.

Kyle [00:51:54]: Underneath the hood when it comes to some of the underlying services, we’ve been breaking up over the years our database infrastructure, so that way we have, more cognitive separation between our the various services. The place that we continue to have pain is in, permissioning. And so right now m-many of our permissioning layers sit into a database that we like internally call MySQL One, and old Hubbers will know what I’m talking about. And so we’ve been pulling things out of MySQL One for many years, because like and we use we use Vitess and we use other technologies to shard and we do it as one big

Swyx [00:52:31]: Famous thing, PlanetScale was born from this and

Kyle [00:52:32]: A hundred percent. Sam Old Hubber and friend. And so finding these opportunities to like break this out and then do that globally. The other thing that I think is interesting and both a unique opportunity and tricky is we also run everything I just talked about in a black box container with GitHub Enterprise Server for people that work on-prem. So we take everything I just said, and we also do it on-prem, and we also do all of that and we do it in a data residence setup for customers that need to have their data in a single location. Each of these has the unique characteristic around how we’re sort of storing that data in MySQL or in a permissioning setup. That’s where some of these outages have oc-occurred, where you’re seeing it more like across the board rather than just like the one piece

Swyx [00:53:17]: Filling the database

Kyle [00:53:17]: Isn’t quite working. Exactly. And so part of it is that. I think there’s been some other places where agents are much more or more projects appear to be moving towards monorepo versus we were going the other direction for many years in the industry. Repos were smaller, but there were more of them, and now we’re seeing the opposite. Repos are bigger, and there’s, not fewer of them per se ‘cause there’s new growth, but, we’re just seeing many more big repos. Big repos, big monorepos have always had, a unique performance problem. Because each one, is slightly different if, particularly if the underlying blobs are incredibly big Inside the repos. And so we’ve done a ton of work that you pro— like most people haven’t probably experienced, unless you’re in this case of the monorepo. But that Git, infrastructure layer improvement does help the overall, system because, many of the improvements that make monorepos work better make all repo infrastructure work better. And so, I could kind of keep going down the line where it’s another thing where we’re moving out of, We’re changing how we do j I’ll just say job queuing for lack of a better, explanation changing the underlying technologies there.

Swyx [00:54:32]: I spent two years being a job queuing guy, so.

Kyle [00:54:34]: And so it’s kind of a little bit of a little bit of piece by piece, and it’s mostly because as we were— as it was built, we built everything in a way that assumed, I guess in some ways that the size of the pipe of work was going to remain the same. There’s just going to be more people coming through each of those pipes. But instead now in places whereA git push was, generally a certain size for example, is now, no longer true.

Swyx [00:55:03]: Oh, yeah.

Kyle [00:55:03]: Or

Swyx [00:55:05]: I push a thousand

Kyle [00:55:06]: On the average. 100%

Swyx [00:55:06]: A thousand line commits like daily

Kyle [00:55:07]: Same thing with PRs. Like PRs same thing. And like we’ve talked about optimizing that and making changes where, and there were technology choices that did not work there? And it got slow, and it didn’t It was not fast. It did not do what the users wanted. And so we’ve been reeling that all out and going “Okay, that’s just not right. Let’s stop putting good money after bad and do it the do it the right way or the right way now.” So there’s It’s a it’s a lot of things, not quite when I’ve experienced scale at GitHub historically, it’s almost always two options that we’ve used. We go vertical scaling, particularly with databases, right? And we go horizontal scaling. Oh, we just have more people using this service. Great. We’re going to add more servers, and we rack them in our data center, or we use it in a cloud. And now we’re sort of in a like diagonal, where like vertical doesn’t really work anymore. Horizontal isn’t work either because we’re all We all have some CPU or GPU constraints in the world now, and now we have to go in and like crack open services that have been running for 10 or 15 years and go, “Okay, the rules of this service have legitimately changed, and now we have to rewrite them.” None of this is an excuse. This is like we’re We have to do the work. We have to make it better.

Swyx [00:56:22]: actually as an infra guy, I’m “This is like one of the most fascinating scaling challenges I’ve ever seen.”

Kyle [00:56:26]: That’s that’s, that’s the thing that’s the thing that it’s hard for Like when we weren’t talking about it publicly, and I was like I came out, and I was “Hey, I just want to explain what’s going on.” Part of it comes from a very old GitHub ethos, which is it’s our it’s our uptime. It’s down. W What I know you’re a developer, so you’re, you’re inclined to want to understand more what’s going on. But at the same time us going “Hey, this service didn’t, perform the way we expected, and now we have to go change it,” we weren’t We’re not trying to hide anything from you in that. It’s that well, that’s our problem because you expect us to be up, and I think that’s really baked into the core, origins of GitHub. And so now what we’re trying to do as a team is do all that work and just tell Talk about it more and just share you more technical details, write these blogs, write the posts, get the engineers who built it after they finish the work, just tell you “Okay, this is what we did.” I think that’s the contract that we want to bring back to the community and say, “Hey, we’re still very serious about what we’re doing. We haven’t been telling you about each piece. So let’s do that and we’re going to keep building this and scaling it in a way to support the If it’s not 14, then it’s 30 or it’s 50 or whatever the next exponential growth is going to be.”

Swyx [00:57:40]: First of all, fantastic answer. I think

Kyle [00:57:44]: And I apologize in advance if like any of that

Swyx [00:57:47]: I think it’s all nice

Kyle [00:57:47]: Is slightly incorrect just simply because

Swyx [00:57:49]: No

Kyle [00:57:49]: I’m not the I’m still in the weeds with this but it’s not my day-to-day. But like that’s the thing is we’re all looking at it to that level.

Swyx [00:57:58]: And obviously, if people want to help, they can join.

Kyle [00:58:00]: Absolutely

Swyx [00:58:01]: So like I think the that is, good. I think people also would just want to know when are, when are you through the thick of it right? Like is there Have we identified all the issues? Is this just never-ending? Is Git broken? Do we have to change the Git, protocol? Like what how much is breaking, right? It’s been a while. And so I think people do want to know What’s the path back to the reliability that everyone expects out of GitHub.

The Reliability Roadmap: Databases, Compute, and Load Testing

Kyle [00:58:30]: So like our availability in like recent few weeks has been much better than the three weeks before that or the three weeks before that and so forth. And so a lot of these improvements are still very much paying off for us. I think that we’re still working on that that database piece that I mentioned, and that just is a little bit physics a little bit of time to get it to get it fixed up. Because we have to the w

Swyx [00:58:59]: My the answer I had in my head Was call YouTube.

Kyle [00:59:03]: So YouTube ultimately is

Swyx [00:59:04]: ‘Cause they also use Vitess.

Kyle [00:59:05]: They also use Vitess. But the,

Swyx [00:59:09]: Like whoever was the guy, the scaling guy at YouTube?

Kyle [00:59:11]: Like that’s That I believe went to PlanetScale, and was a part of PlanetScale too. But like

Swyx [00:59:16]: Oh, you mean Sugo?

Kyle [00:59:17]: I think so. Yeah. And so, and so like

Swyx [00:59:19]: He’s at Superbase now.

Kyle [00:59:20]: Ah.

Swyx [00:59:21]: There’s a whole Postgres drama Thing there, right?

Kyle [00:59:25]: So like some of it’s that. I think the other piece of it is, our move to get additional compute will alleviate a fair amount of this particularly on the action side ‘cause a lot of the underlying, outages is actually related to,

Swyx [00:59:39]: I’ll tell you actions is the it’s the root of all evil.

Kyle [00:59:42]: it’s all It has its pros

Swyx [00:59:47]: Some extent

Kyle [00:59:47]: In that it’s the core It’s the core compute layer for either CI, side projects, et cetera.

Swyx [00:59:52]: Is the main money maker? Like is

Kyle [00:59:54]: Actions?

Swyx [00:59:55]: No? I don’t know.

Kyle [00:59:56]: like Actions

Swyx [00:59:57]: I pay a lot for compute, right?

Kyle [00:59:58]: like Actions is definitely a piece of the overall business, but I would say that like we ultimately also

Swyx [01:00:06]: Storage

Kyle [01:00:07]: Give away so many like minutes as part of our entitlements as that. But that’s what I was saying. Everyone’s using it. We talk about it as CI/CD, but the reality is people use it for CI/CD and

Swyx [01:00:17]: Automation

Kyle [01:00:17]: Various processing and automation, exactly. And so like part of it is also that like compute piece that is also alleviating some of our availability.

Swyx [01:00:26]: This is my abuse of, actions. I have been

Kyle [01:00:29]: Oh, yeah

Swyx [01:00:29]: I have been scraping for every day, and just like I just tell people to

Kyle [01:00:34]: Thank you for your service

Swyx [01:00:35]: Go dog because I But this is also how I track, actions all time. So anyway,

Kyle [01:00:41]: So like some of it’s going to be that. I would say that like each month I expect in the next three months, you’re going to see fewer and fewer moments where we have an availability problem Where things are going to go down, and that’s not just it’s stopped. It’s that we’re still experiencing faster growth than ever before. It’s just that those underlying improvements that we’ve been hard at work on, are finally paying off. It’s just that the improvements take-It’s less about, these incremental improvements where you make a small change, and you get this big output. It’s now material change That takes a bit of time, and then you see a step change in our availability.

Swyx [01:01:14]: There’s a thing we used to do at Amazon, I don’t know if this is, a thing, but, if automated software verification or simulation of load testing and all that. I’m, I’m just like at this point, you have a whole map of GitHub. And, while you can assume whatever growth rates on whatever dimensions that you care about and just run it through a system, right? I feel like there’s a way to, I don’t know, have a systems model of GitHub and, see what breaks. But obviously, I’m pro— I’m not that close to the problem, so.

Kyle [01:01:39]: But yeah, so yes, totally. And I would say, that’s been the journey and work that’s been happening since, I would say November to now. Because October, right, was the time where we even said, “Oh, look at the growth,” and, and then you start to see the chart

Swyx [01:01:53]: It doesn’t

Kyle [01:01:53]: Really pick up. And it’s oh, we tested it at N amount of scale, and now it’s at, N cubed maybe like in some in some vectors. And so now we have to go and build it that way and make sure that it can handle all of that scale.

Swyx [01:02:08]: Let’s talk Copilot. So how many original creators of Copilot are there?

The State of Copilot: From Code Completion to Agents

Kyle [01:02:15]: Oh, geez.

Swyx [01:02:18]: ‘Cause I count like twelve authenticated.

Kyle [01:02:19]: We haven’t— Yeah, I forget, all joking aside, I forget the number of people that were on, the original, GitHub Copilot team. But, there was a bigger group.

Swyx [01:02:30]: I heard it’s, it’s Alex. It there’s, there’s, a three people

Kyle [01:02:32]: Alex worked on it. Udo worked on it. There’s a a bunch of people that were on the team.

Swyx [01:02:35]: And then their entire management line. Okay. So enormously successful at its in its in its day. I think the last number, I think Mario Came to my conference, and talked about the hundred million dollar mark. I think most recently three hundred. I might be out of date as well there.

Kyle [01:02:53]: I don’t think we shared the dollar amounts.

Swyx [01:02:54]: All right, cool. Just, what’s the state of Copilot? It’s, it’s obviously as a concept brought into More of Microsoft. But just at GitHub.

Kyle [01:03:03]: so I think One of, one of the challenges is, that we had with Copilot, right, is that we came out the gate with code completion, and it was super great, powerful, et cetera. And then what we initially worked on after that sort of, initial year and a half, was, going after fine-tuning because our customers, the industry on the whole was really talking about, okay, well, how do we get more more correctness or performance out of this? And so we were working on a whole bunch of efforts to do fine-tuning on, larger and larger code completions or, next edit suggestions with fine-tuning, et cetera.

Swyx [01:03:43]: And let me clarify. Is this fine-tuning one model or per customer a fine-tuned model for

Kyle [01:03:48]: Per cust— Well, both. But, but, fine-tuning one model for the overall, use, and then fine-tuning per customer that wants this as, a service effectively. And around that time is when the next generation of models came, and that’s around the same time that all these other AI, coding tools came to be because the models really sped up. And so everyone kind of, will ask, “Well, what happened to GitHub Copilot?” there’s all this time, and I would say that we were on an era of going okay, we want to improve everyone’s results, and so let’s focus in on fine-tuning because that’ll give us these better results. And then the models got better. And so then ever since, we’ve been really on this kind of journey to go, okay of course, we have, this great code completion, and we’ve done a ton of investment in the better underlying models that we have post-trained better, next set of suggestions with post-training language specific models. All this stuff that kind of, sits in the ether of GitHub Copilot is code completion, but also have now ha— now have, a single underlying, SDK and harness for our coding agent Copilot ultimately. The new CLI, the new desktop app, cloud agents that use the same SDK. And so there was this moment of both, really trying to figure out what our customers want, models, Sherlocking us a little bit, then going and saying, “Okay, what does everyone ultimately need?” And what we think is that it’s not solely about the code generation. It’s really about having the ability to use these coding agent brained, harnesses or run times across, not just the coding experience where I’m going to, send a bunch of tasks out, or I’m going to use Fleet to break up a single task or autopilot similar to Goal all this stuff. But also how do I do that for all of my security remediation? How do I do that for every GitHub issue that comes in, just stick a coding agent on it just to see if it’s possible? How do go through my repository and see all of my documentation and extract out okay, this doesn’t actually match? That amount of sort of AI coding agent automation, I think is a big part of what we see when we’re looking at, okay, we’re still kind of going through a similar but very different flow. It’s just all happening at the same time. There’s not really the same, I’m going to create an issue to track my idea of building this. You’re probably just going to go, do it.

Swyx [01:06:22]: Just do it.

Kyle [01:06:22]: You’re going to say, “Hey, just build this,” right? And, there are still tons of, open issues and projects, et cetera, that are using issues like Peter and OpenClaw to be able to sic all of his agent on that. That kind of infrastructure layer and a really great coding experience that allows you to handle the sort of multiplexing, aspect is what we’ve built, are still building with GitHub Copilot. And so for folks that haven’t really used GitHub Copilot sinceThe thing that got them excited about this Which I I get. I really encourage you to, look at especially the GitHub, Copilot app. That’s my new daily driver. I obviously, if you prefer the CLI, also the CLI, be able to use all the models, the bring your own key side of it. We’re still improving our own models and using those too. And, it’s just like a very different experience, but I think that broader sense is of like software development and how coding agents can help throughout, not just Writing the code, or even verifying it or deploying it is is where we have this unique, angle. The other side is the context piece. Like

Copilot’s Future: Context, Taste, and Personal Developer Workflows

Swyx [01:07:44]: Oh, God

Kyle [01:07:44]: we’re still It’s like one of those things where I think the the final thing that will let me ultimately, feel complete at GitHub is, when we have this ability for GitHub to act like Kyle wants it to act Or Shawn or whatever. And we all codify that in rules and in memory and everything else, but

Swyx [01:08:03]: Well, that’s an open research problem, right? Like it’s

Kyle [01:08:05]: A hundred percent. A hundred percent

Swyx [01:08:07]: AGI when you get it. Yeah.

Kyle [01:08:07]: A hundred percent. But, if we can even just do it where my team, Without me having to codify everything, and as our methods shift on purpose to be able to have that full experience and all the understanding of what’s happening in my dependencies or open source, that feels like a big place for us to be able to continue to provide something really unique and valuable with GitHub Copilot.

Swyx [01:08:29]: Is there a form factor that we haven’t explored? I think like we did code completion Then we did kind of let’s broadly call it agentic IDE Which Cursor Famously popularized, and then now it’s, now it’s all about the sort of agent orchestration Background agent, whatever. And then there’s the security review. I feel like everyone’s like just throwing agents at everything. The entire SDLC has Just, covered with agents. Are we like at the end of history here, basically? Like is it just refinements from here on out?

Kyle [01:09:04]: I think that we’re all still in such this hypermyopic era of AI Where the reality is that for various, boring security and governance reasons at least for most people’s work, why is my coding agent, even if it’s all background agents, background running not, losing all the context that’s available to it across everything that I’m doing outside of coding? I think the most interesting thing to me in AI is actual ambient AI, not insert assistant name thing or, I’ve tried just about every pin in tool and whatever, and they don’t work the way that I’m looking for them to work because they are just trying to capture, and then they are trying to codify and then recall. And I think the thing that I’m looking for, back to the very beginning, I’m looking to be building out the next version of webhooks or, implementing a new feature, and it for it to know every spec doc, every email, the conversations that I’ve had online, everything about how this could be implemented and be able to, use that as part of its decision-making and none of these tools are ultimately doing this. So I think that it’s as if, software development work was a single lane task, was like it only needs a developer. Once I once I write the perfect code, we’ll be done here, but that’s just never been true. It’s all the context of the other team members, what the business is doing what’s popular right now, and I think that’s this huge opportunity for us to go much broader than really excellent coding agents? And that is honestly why I think OpenClaw has been so interesting is that sure, it’s connecting to all the data, sources that Kyle the human cares about, and now my question’s “Okay, how can I take all that and use that every day as a software dev connected together, not just have a new way to kick off a coding agent?” And that’s where we’re at. We’re saying, “Okay, I’m going to go use this CLI under the hood or this SDK,” but that’s not what I’m talking about. I’m talking about I’m having a conversation with you it downloads the podcast, and it realizes, “Oh, Kyle, sounds like Kyle needs this app or this thing or this “ That level of

Swyx [01:11:16]: Just recommends it.

Kyle [01:11:16]: That level of, that level of connectivity I think is where we still have a ton of ways to go in software because then when we have that red thread we want to pull, that idea, it can not only use the perfect way to write that code, but instead all of the sort of taste and judgment calls and expertise that I’ve earned or that we’ve earned as a group and use it as part of the actual implementation.

Swyx [01:11:42]: The extreme of it is AI runs your life, right? And I think there’s a scary inversion of control in the way that I literally doing it in the way that developers mean it in terms of frameworks Like the Hollywood principle, “Don’t call me, I’ll call you.” Like there at some point there is an inversion of control where, you should you stop telling what the AI, the AI what to do. AI tells you what to do. And, that’s a little bit scary, but also, maybe better.

Kyle [01:12:10]: like Nat, I think Nat Friedman shared this in a like a Stripe event like talking about his OpenClaw was, he connected OpenClaw to his cameras, and it was, watching him.

Swyx [01:12:20]: It redirected his Uber. And it,

Kyle [01:12:23]: there’s a degree of this where I was I actually would love OpenClaw to tell me to Drink water. I don’t know that I want it to be, Changing where my car goes, but I do think that’s kind of what I’m talking about, which is it needs to have so much more information at its disposal for it to be helpful to me, and I still don’t think we’re, anywhere near talking about AGI. I’m just talking about every time I have to tell you something I care about that I’ve ever kind of said or I’ve said a dozen times, it should be able to know that codify that or gain access to it. Like the dreaming ideas, are an attempt to kind of do some version of this but I think there’s a much more proactive angle that will help software devs if we can test that out a bit more.

OpenClaw, Ambient AI, and Inverting Control

Swyx [01:13:05]: Yeah. Well, the other thing about OpenClaw that reminded me Is Microsoft has a CVP Dedicated to OpenClaw. Why?

Kyle [01:13:16]: Because you don’t think they should?

Swyx [01:13:17]: I don’t, I don’t know. I think CVP is a high title. What, why is this so important? Like Microsoft Doesn’t even own OpenClaw. What’s, what’s the

Kyle [01:13:29]: so I— we’re talking a lot more about this at, Microsoft Build this year too. I think, the main thing is that what OpenClaw has done is it has made this connection for people to have access to the resources that you have access to and be able to do things for you in a way that previously people were trying to codify into their own agents. And so when you think about it like in the work context, wouldn’t it be great to have a Claw-like object that I could actually run on my work device that or had access to my work assets, made— worked well on Windows what that would look like. And so I think that OpenClaw has become the personification of, a valuable agent that understands me because it has access to all of my information, and it can use a computer. And so thus it can do a lot more than, just a task-oriented process or like a a chat tool, et cetera. And that’s like a bunch of the goal of Build, right? We’re at Build this year trying to take a very different approach of it’s unapologetically aimed at developers. We’re trying to show the bigger investment to not just say, “Hey,” like you said, “Why do you have a CVP of OpenClaw?” Well, because, one of the problems that we have, right, is that our agents, if you install them not on a Mac Mini or not on a hosted device, you install them on a personal device or a work device, we need better sandboxing at the OS level. I need to be able to use that Claw and not, get fired. And so Microsoft is “Okay, great, let’s, do that too.” And then it’s, okay, well, where should I be able to talk to this agent? Should each of us just have a Claw available to us at work? Probably. And so there you go. And continuing to contribute a ton to the open source project too. Microsoft, I think as I’ve gotten more and more, information there’s so much investment into the open source, projects themselves that for whatever reason just I think there’s like this they don’t want to come off those teams don’t want to come off as like taking any credit or getting any recognition. But so many of these core contributors or teams are full-time just pushing into open source projects. And, I think that’s, that kind of shows the difference between, well, why are we looking so hard at something like Claw? Why are we looking at sandboxing on Windows? Why are we looking at cloud versions of sandboxing? Why are we looking— Because ultimately, we need more platform components. We don’t need everyone to be building the same exact, top-line product. And so if we’re building for builders, that requires us to give you all these components and tell you what they are and how they work and why you should be interested versus only delivering that single vertical over and over and over again.

Microsoft, Windows Sandboxing, and Platform Components for Agents

Swyx [01:16:23]: I think, my maybe one way of framing it Is that Microsoft is the original operating systems company. And here is the new operating system for AI.

Kyle [01:16:35]: like I think that we are also in an era where we are— we need to help build that bridge? All joking aside operating systems need to look different than they looked five years ago because it’s not just you using them anymore. And that’s changed the whole idea. It’s not, “Okay, my Claw is going to create a user account.” Doesn’t work like that? And so just just like all of us, we all have to look much more deeply in the stack, all the way down to, the silicon layer in Azure to be “Okay, well, What do we need now?” ‘Cause the workloads are different. It’s not just, “Okay, we need more inference.” It’s, “Okay, well, what type of inference do we need? What type of compute do we need to run these agents or run these agentic flows?” it’s a really interesting kind of like multi-layer problem, versus kind of, I would say software in the last five or six years were all going to our events, and we’re kind of saying a version of the same thing. SaaS product has new SaaS thing. It’s the best SaaS thing ever.

Swyx [01:17:42]: It was boring for a while.

Kyle [01:17:43]: And so now it’s like Oh my goodness, we’re at physics.

Swyx [01:17:47]: It’s great.

Kyle [01:17:48]: We’re at physics problems. And that’s exciting.

Swyx [01:17:50]: We’re— we’re now trying to make, semicondu- room temperature superconductors. Still. That’s, that’s, that’s never going away. No, I think, that’s a really good overview of, everything. I think, have I have we left anything unsaid that you wanted to really get out there that we should cover?

Build Announcements, Enterprise Adoption, and AI at Work

Kyle [01:18:07]: I’m really excited by for folks checking out, checking out the announcements that we have at Build go you can go look at them online, take a look. I think that I’m hoping that it’s driving, a degree of curiosity and interest because there’s such this big shift that we’re making at Microsoft for developers, where if you’re a daily driver of a Mac device or a Linux device, and you’re “Okay, I don’t use Windows,” there’s improvements that are being made that I think are going to surprise folks to just be “Oh, that’s in— they really want to do that?” not, And I’m talking for developers. I’m not talking for I play video games on the weekends on my Windows computer. I’m talking my daily driver. Like-All the way from that to, okay, well, what is it like to build an agent or build an app and deploy it and run it at work in particular? I think that is a big piece of it where I talk all the time with the team how I build on the weekend should be how I build at work. But if you’re working at a Fortune one hundred or a Fortune five hundred, you’re probably not vibe coding an app and then shipping it to some service. You got to go through security and compliance. How can we move just as fast at work? And that’s, I think, something that we have a bunch of different offerings for to give you that same sort of agility and power, but in the work context. And then I will tell you I’ve mentioned it a couple times, and, it’s very freaking cool. If you are in the M365 land in any way, check out WorkIQ, check out FoundryIQ. These little, oversimplifying it context engines are wild good. And, we’ve given them to our developers at GitHub, we’ve given them to employees at GitHub as we’ve used these tools to be able to just ask questions around everything that you have in your work context. And with FoundryIQ, be able to just do the same exact thing across all your existing stores. What— Not move to new tools, just connect them in. It’s surprisingly powerful, and you your boss is still not going to get fired, and IT is not going to turn it off because it’s leaking all this private information. That is the trick that I think, is sometimes getting lost when we’re talking about all these all these great new platforms. ‘Cause I can use them, I’m “Oh, this is super powerful. Oh, and I can’t I can’t use it.” and it’s Not because I’m at work at GitHub. It’s be

Swyx [01:20:34]: ‘Cause I’m not allowed, yeah

Kyle [01:20:35]: It’s ‘cause I’m not allowed, because they can’t do all the things that large, complicated companies need. And so, whether it be I said, just the kind of interesting daily driver curiosity all the way through to, “Oh, my gosh,” “I can go use this at work tomorrow potentially,” and have that context layer, have that intelligence, it’s a huge, it’s a huge shift. And so check it out. I’d love to hear— I’m, I’m not shy on social. I’d love to hear feedback. What’s working what’s not. But hopefully surprise folks a little bit.

Swyx [01:21:07]: What I’m hearing— so first of all, I think that’s, that’s a great pitch. What I’m hearing, actually, is that you should put the WorkIQ people next to the Copilot people. ‘Cause, the exact prob- context problem that you named They solve enough for you to do your job, which is nuts.

Kyle [01:21:23]: So, the thing that we are lit— that’s literally what has been Happening the last several months.

Swyx [01:21:29]: I already forecast you were going there.

Kyle [01:21:30]: It’s totally ‘cause, you’re totally right. The code, the code and the code asset problem is a little bit unique. But otherwise

Swyx [01:21:36]: That’s it

Kyle [01:21:37]: We’re all working

Swyx [01:21:37]: It’s context

Kyle [01:21:37]: With each other now. It’s all just context, exactly.

Swyx [01:21:40]: Amazing. Great. I’m going to be there. I’m going to be doing

Kyle [01:21:43]: Great

Swyx [01:21:43]: A couple sessions there. I’m going to be interviewing Satya.

Kyle [01:21:46]: I know.

WorkIQ, Copilot Context, and What to Ask Satya

Swyx [01:21:47]: When I first started the pod, though, I had, Jeff Dean on. Jeff like It’s like hall of fame of People I want to meet someday. Satya’s on there. So, what should I ask Satya?

Kyle [01:21:57]: I think, I think that the best question to ask is what he thinks is true in, two or three years from now. It seems like such a throwaway question. But ultimately, the way that the way that he is looking at this AI problem in, inference problem, token problem, and what we’re how we’re actually going to be working I think you can see some of the recent shifts that have been happening inside of Microsoft to kind of drive us to a place where it’s not four, five, six, seven, eight different things. It’s not a lack of context everywhere. But, why is this sort of approach in two years going to, pay off? Because that I think

Swyx [01:22:41]: Wow, that’s a bold Okay. I’ll ask it. I’ll say you I’ll say I prompted by you but

Kyle [01:22:45]: Absolutely

Swyx [01:22:45]: It’s a bold question because there, I think there’s a lot of, doubts to be honest, Externally. And so, yes, I want, a straight answer from him on that I think would reassure a lot of people, and honestly, give me a lot of food for writing. So, thank you so much for spending your time. Thank you for doing what you do. I think as a CEO, you don’t need to be the external face. But, because you are authoritative, ‘cause you have so much background with GitHub, and it’s so authentic, we on the outside feel it. So thank you for that.

Kyle [01:23:16]: Of course. Appreciate it. Thank you so much, Sean.

Today’s guest Ethan first joined us for the LS Paper Club as the lead on NVIDIA Cosmos World Model, but then joined xAI and built Grok Imagine in 3 months:

He comes back on Latent Space with some nuclear hot takes: that Video Models primarily get their intelligence from LLMs, not from training on video data, and that the next frontier for truly interactive, realtime, long-horizon world models is to work on LLMs (perhaps Interaction Models as well…)

Put it this way: In the near term, the next Sora won’t be a better video model, but a video agent.

Generative Media may more closely follow the evolution of AI coding which went from focusing on one-shot output performance and cost, to multiturn reasoning and planning models for agents and systems that can plan, edit, test, debug, and submit PRs.

At a certain point, coding models got so good that the only significant next step to improve performance was handling the orchestration of these models.

Now as the performance of video models increases significantly across realism, consistency, & prompt adherence while becoming more cost efficient, the next evolution of video generation may also be systems that can plan, generate, edit, critique, and iterate across an entire creative task.

In this episode, Ethan joins swyx and Vibhu to unpack what it actually takes to build frontier image and video systems: data, VAEs, diffusion transformers, audio-video alignment, inference speedups, and the hidden cost of storing and moving massive video datasets. From building NVIDIA’s Cosmos world model to joining xAI as Grok Imagine was being built from zero to one, Ethan He has been at the center of some of the most important work in video generation, multimodal models, and real-time world models.

We go deep on Grok Imagine, how a small xAI team shipped its first multimodal video model in three months, why iteration speed matters more than almost anything in model development, and why many of the biggest gains come from fixing tiny bugs in data and training pipelines.

Flipbook: The future of Videomaxxing

Video agents are almost a sure bet to be the trend in the coming year. We end with a glance at what’s beyond video agents:

Flipbook caused a minor sensation this year when it was released, but most treat it as a fun demo. Ethan takes it very seriously — with the speed and cost of inference coming down every year, the future of custom video JIT UI is closer than you think. We talked about why videogen models may become the front end of AI, how generative UI could replace traditional HTML/CSS, why world models need to be real-time, interactive, and long-horizon, and why the future of video generation may depend more on language models and agents than on diffusion alone.

We discuss:

* Why fast iteration mattered more than meetings

* Why small training bugs can drive huge model quality gains

* Why coding models may make compute the bottleneck again

* How image and video models are trained with synthetic captions

* The role of VAEs and latent space in frontier video models

* Why image models are the foundation for video models

* The tradeoff between temporal compression and real-time interactivity

* Flipbook, Neural OS, and the future of generative UI

* Why future interfaces may go from user intent to pixels

* The hidden cost of training video models: storage, egress, and GPU hours

* How step distillation and consistency models (like OpenAI sCM) makes video inference orders of magnitude faster

* Grok Imagine 0.9 and large-scale audio-video generation

* Why audio-video alignment is harder than text-video alignment

* Ethan’s definition of world models

* Reference-to-video, video extension, and long-context video generation

* Why xAI’s research communication undersells Grok Imagine

* How xAI culture shaped the speed of development

* AI watermarking, SynthID, and detecting generated media

* Why prompt rewriting matters for video models

* Grok Imagine Agent and the rise of video agents

* Why language models may unlock better video generation

* Robotics, physical AI, and embodied world models

* Why Ethan left xAI and shifted focus toward LLMs

* Self-managed context, memory, and the next frontier for language models

Ethan He

* LinkedIn: https://www.linkedin.com/in/ethanhe42

* X: https://x.com/EthanHe_42

Timestamps

00:00:00 Introduction

00:01:25 From NVIDIA Cosmos to xAI

00:03:24 Building Grok Imagine from Zero to One

00:10:07 How Image and Video Models Are Trained

00:18:53 Video Compression, VAEs, and Real-Time Tradeoffs

00:22:10 Generative UI, Flipbook, and Neural OS

00:32:10 The Cost of Training Large Video Models

00:37:04 Distillation, GANs, and Fast Video Inference

00:41:21 Audio-Video Generation and Grok Imagine 0.9

00:48:34 What Makes a World Model?

00:55:51 Reference Videos, Long Context, and Video Memory

01:00:11 xAI Culture, Research, and First-Principles Building

01:09:45 AI Safety, Watermarking, and Prompt Rewriting

01:13:10 Video Agents and AI-Assisted Creation

01:27:32 Why Language Models Unlock Better Video

01:31:15 Robotics, Physical AI, and Embodied World Models

01:32:38 Why Ethan Left xAI

01:34:16 Self-Managed Context and the Future of LLMs

01:38:43 Ethan’s Career Path and Closing Thoughts

Transcript

Introduction: Ethan He, Latent Space, and the Path to xAI

Swyx [00:00:00]: We’re here in the studio with Ethan He, most recently of xAI. Welcome.

Ethan [00:00:10]: Thank you. Glad being here.

Swyx [00:00:11]: We’re also here with Vibhu. you were first coming to us or joining the latent space world because you were working on Kosmos at NVIDIA, and you did a paper. We loved it. you presented it as well, so thank you for doing that.

Ethan [00:00:23]: I’ve actually, I also presented the MoEs twice at latent space.

Swyx [00:00:29]: How did you actually hear about us? Did we reach out to you? Is that how it worked?

Ethan [00:00:33]: No, actually, I-- the community. Like I realized, oh, there is this online community that people talk about AI and also learn from each other through papers every week through the Paperclip. It’s very nice.

Ethan [00:00:49]: I learned a lot.

Swyx [00:00:49]: I think three years stop. We haven’t stopped even on Christmas and New Years. many weeks I want to stop but it keeps going.

Vibhu [00:00:58]: No, that was good. I think you had posted that you worked on a paper, and I was “Oh, very cool. We have Paperclip. Present then.”

Vibhu [00:01:04]: But I might have reached out to you after.

Swyx [00:01:05]: you-- because it’s an amateur club, right?

Swyx [00:01:08]: so it’s very unusual and but we have sometimes paper authors come by and actually explain the paper. Today we just did, the poolside paper, which was apparently very good.

Vibhu [00:01:18]: Came out yesterday.

Vibhu [00:01:19]: pretty interesting, right? Fully open. They talk about everything, systems. So it’s a good one. We’ll, we’ll recommend people to read it.

Swyx [00:01:25]: Bring us up to speed on your transition to xAI, ‘cause I actually don’t even know when you joined. just like tell the, tell the story about the sort of transition.

From NVIDIA Cosmos to xAI: Scaling Video and World Models

Ethan [00:01:34]: Before xAI, I was working on Kosmos world model as in-- at NVIDIA. So Kosmos is, it’s a giant video foundation models that can-- that aims to simulate the world and for-- it serves as a foundation of-- for all of the roboticists to build on top of. There, once I built the Kosmos one, I realized as this thing also has a scaling law similar to language model, we need to scale up the video models further. that’s, that’s why I realized I need to move to somewhere with much more compute resources. That’s how I

Swyx [00:02:13]: Than NVIDIA?

Vibhu [00:02:14]: The GPU rich came themselves.

Vibhu [00:02:19]: And timeline-wise, when was Kosmo? It was pretty early, right? It was open world model, open paper, everything.

Ethan [00:02:25]: It was end of twenty-four.

Vibhu [00:02:28]: End of twenty-four.

Ethan [00:02:30]: Then at mid twenty-five, I moved to xAI. At that time-- I joined about the time when xAI was about to build video models and in multi-model models. There were no infra, no data, and no model, and it just-- as a few engineers, we built it in three months and released the first model, Grok Imagine zero point nine.

Ethan [00:02:55]: And since then, I keep working on video models and move more from training and to post-training of the video models. For example, like a reference to videos, kind of like the cameo feature and, video extensions. And, before I left, I worked on a world model, leading a small team to focus on the real-time long horizon video generation.

Building Grok Imagine From Scratch in Three Months

Swyx [00:03:24]: Can you give like a rough roadmap of okay, you’re on a brand-new team. Grok previously was only text, or they partnered with BFL for their image gen stuff. What do you-- what are the building blocks, right? You have compute, data you can procure somewhere. Like just what are like the sequence of things that people should think about when you’re setting up a new team?

Vibhu [00:03:43]: actually even deeper, not just data you can procure. You guys had to go through getting the data too, right? So you shipped it pretty fast, but yeah

Swyx [00:03:51]: three months is like

Vibhu [00:03:52]: From everything

Swyx [00:03:52]: actually like very surprisingly fast.

Ethan [00:03:55]: One thing I say like thanks to my experience at NVIDIA, ‘cause first time when we were building Kosmos together, we built it, for about a year. So this is like the second time I do it. Roughly have an idea, what to do. I say the most important thing is the talent. Everyone were very strong and clever, very close with each other towards a common goal. So that speed up things a lot. So you reduce the communication bandwidth among people, and everyone can work towards the same goal. It’s, it’s like every day there’s not that much meetings on the calendar, like maybe like a, like a sync a day, and after that it’s, it’s just all building. It was pretty fun at that time.

Ethan [00:04:47]: And another thing is that xAI has very strong foundations of like data inference, model inference, and the supporting there can help the model develop a lot. When I look at, training models, I don’t so actually the top important thing is like how many, how many iterations can you do, per day? and the more iteration can you do, you can, you can train the model much faster. So if you have very strong infra and you have a lot of compute, you can, you can train these models in very short period of time. That can give you a much larger buffer to, for errors, and it also gives you the opportunity to spot more bugs.

Iteration Speed, Compute, and Debugging Model Pipelines

Swyx [00:05:46]: What is an iteration? Is it like a few hundred steps or what are you

Ethan [00:05:50]: Let’s say just the train-training the model, like from acquire new data and maybe design new algorithms and train a new model, maybe at smaller scale or

Swyx [00:06:01]: So cycle time for like any hyperparam that you’re searching.

Ethan [00:06:04]: Cycle time and tune to like eval this model. Is this model better than my previous iteration?

Ethan [00:06:11]: So

Swyx [00:06:11]: So it’s like before you, someone had already set this up that you can iterate very quickly.

Ethan [00:06:15]: I think the foundation there is extremely good forDeveloping and research models.

Ethan [00:06:23]: And often I find is it-- this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the, in the model training pipeline. Those give, those give the biggest boost to the model quality.

Vibhu [00:06:46]: It’s interesting, right? So you say it’s like small team, less communication bandwidth, but also a lot of quality is like find little bugs. It seems counterintuitive, right? You have a lot of people, you can iron out more of those, but it’s interesting to see the other side, right?

Swyx [00:07:00]: I also wonder, have you-- do you try using LLMs to look for bugs? I don’t know.

Ethan [00:07:05]: I remember at that time it was mid two thousand and twenty-five, so it’s the coding model wasn’t quite there yet. I remem- I remember like December two thousand and twenty-five, it was extremely good. Yeah, I’ve been, I’ve been using it at that time. It’s, it’s helpful. sometimes it produce codes that are kind of difficult to maintain, even though like the first time it built something extremely fast. But it gave the, like a spaghetti code, thousands of lines that I couldn’t maintain, and the LLM itself couldn’t figure out what’s, what’s wrong and how to improve on top of it. But now I find it much better. Yeah, I want to bring up another point here is now coding models are much more efficient and can help us implement stuff much faster. Compute might become a bottleneck again because previously, like if you want to train a new model, say you want to generate new synthetic data and then or write a new algorithm, it might take a few weeks. And during that period of time, you don’t-- you might not have experiments to run. But now you can build that thing within a few hours, then you can immediately train a model.

Ethan [00:08:24]: Now you have to have enough compute to try all of the ideas. So compute might be the bottleneck of iterating speed again.

Swyx [00:08:36]: yeah, I actually, honestly, I think it’s like kind of a stressful job because you’re “Well, I should be trying everything, and if I’m not, then I’m not doing my job well.”

Vibhu [00:08:48]: there’s also the stress of you’re eating thousands of GPUs per hour, which is very expensive and, compute can go to other researchers.

Swyx [00:08:56]: You got the daddy Elon to

Vibhu [00:08:57]: You got daddy Elon.

Ethan [00:08:59]: It was

Vibhu [00:09:00]: But there’s still finite amount of compute, like you want to use it, you want to use it well, you want more of it.

Ethan [00:09:06]: That was quite stressful indeed. Yeah, I think one thing is the-- with coding models now, like a lot of these jobs can be automated, which is much better. A second, it’s a, it’s a marathon, so you got to maintain good health and, a regular schedule.

Vibhu [00:09:28]: It’s, it’s hard to hear that when you shift from zero to nothing in two months.

Swyx [00:09:32]: and, I think obviously the culture at xAI is very famously, people work very hard. one thing I did want to dive into, in our-- in the notes that you, that you sent ahead of time, you had specific comments about the cost of Video Gen training. presumably this is on the Colossus-1, right? the two hundred megawatt cluster. Any whatever you want to just share on that.

Vibhu [00:09:54]: I think there’s, there’s three things we’re talking about, right? So there’s Video Gen, there’s also the Image Gen model that you put out. Do you want to like complete the, okay, so zero to one, you have a few months. Just what are the stages of create Image Gen model?

Swyx [00:10:06]: Oh, yeah, maybe I got distracted.

How Image and Video Models Are Trained: Synthetic Captions, Tokenizers, and VAEs

Vibhu [00:10:07]: Sorry. and then, from there’s Video Gen, there’s Audio Gen. Would love to get into those next. But what is that first few months like? So small team, a lot of bugs, iterations, but what does it look like? Do we take something off the shelf? Do we just get data compute? What’s, what’s the few months like? How do you go to state-art Image Gen model? How do you just start?

Ethan [00:10:28]: I cannot comment specifically how xAI did, but it’s, it’s a quite standard process. I can draw some, examples from Cosmos. So mainly it’s building a video model, you actually need to build a image model first. And building these two models, the data you need is a hundred percent synthetic pair of language and image or language to video. Because on the, on the internet, actually, the videos don’t naturally associate with text. So you can say, oh, like on YouTube, you have the title and you have the description and the comments

Swyx [00:11:11]: Title

Ethan [00:11:11]: of a video, but usually they’re not relevant to the video itself. And say maybe like the video is a natural scene of mountains or something, and the title is, I’m so happy today.

Ethan [00:11:26]: So they have they have no correlation at all. So the first step is to, you have to generate synthetic pair of language with the videos. So you gather videos from the internet, and you use a VLM to caption the videos. So that part, here’s a question, like how do you, how do you gather VLM to begin with? So if there’s no

Swyx [00:11:55]: You, so you fuse the model, right? Like

Ethan [00:11:57]: Say if there’s no like VLM exists, like how do you generate the text to the beginning, right? It’s, it’s impossible.

Swyx [00:12:04]: I see.

Ethan [00:12:05]: In the beginning, it’s like you ask human to describe the video as detailed as possible.For example, you ask them to describe everything, like all objects, all characters, and all interaction and dialogues in the, in the videos. So that’s in the protocol of Cosmos labeling. We require the objective we give to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text can reconstruct what the video is like from their head.

Swyx [00:12:43]: Video or image? You’re talking about images.

Ethan [00:12:44]: Video or image, either one of them.

Vibhu [00:12:47]: This was pretty common when we went from clip and DALL-E, right?

Vibhu [00:12:51]: It’s all training on really detailed captioning of images. So same is applied to video, but instead

Ethan [00:12:57]: same applied

Vibhu [00:12:57]: of using multimodal model to pass in video images and write rich descriptions, you can also

Swyx [00:13:04]: I think there’s this traditional perspective of supervised, or, very highly human curated thing. I feel like there’s a unlock with unsupervised, right? Where like you have enough to bootstrap that you can just throw common corpus on it or, whatever. like unsupervised vision and language pairing, right? Like where you just have, interspersed image and text and it just learns. To me, that is the VLM breakthrough that is different from the clip, different from the LM era.

Ethan [00:13:36]: It’s interesting to see that you kind of need both data.

Ethan [00:13:41]: For example, for the

Swyx [00:13:41]: You need it to bootstrap it up. Yeah

Ethan [00:13:43]: for the generative model training, there’s also usually like a small percentage of unlabeled data. So the model is instructed to generate a video without any text instruction. That can also help the model generalize. So after this stage of generative synthetic pair, so, one important common step is to train a compressor or a tokenizer of the image or videos. So because, if you train-- If you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it’s, it’s a lot of tokens. So like one image, it’s, a thousand by a thousand, it’s like one million tokens, one million pixels. It’s impossible to train transformer on that. So it’s, you need to train a tokenizer, which can go from image to latent space and latent space back to image.

Swyx [00:14:45]: That’s why we named the podcast.

Swyx [00:14:48]: But, basically, you’re talking about vocabulary science.

Ethan [00:14:50]: so vocab.

Swyx [00:14:51]: And so, what is, what is imp-- like a million is impossible?

Ethan [00:14:54]: In generative models, the vocab is continuous. It’s a continuous space. We can think about like you map an image to a vector. It’s a, it’s a fixed length vector. It’s sixteen or forty-eight, something like that. And then you map that vector back to the image space. And the mapping is, has-- The mapping is patch-based. So you say you have

Ethan [00:15:22]: a sixteen by sixteen patch and you match, you map that patch of pixels into this latent space.

Swyx [00:15:29]: We’ve covered this

Vibhu [00:15:30]: This is like the vision transformers

Swyx [00:15:32]: VAEs,

Ethan [00:15:33]: VAEs.

Vibhu [00:15:34]: You basically compress your input, you do your generation, you’re reasoning all that generation in smaller dimension, and then you project back out.

Swyx [00:15:43]: VAE is a form compression, but I think the for me, the patching thing is from VIT, right?

Ethan [00:15:48]: You can make those.

Swyx [00:15:49]: Literally the, yeah, the paper is titled like sixteen by sixteen is all you need. something like that. and then I think also, people make a lot of comparisons with this kind of patching with convolutions.

Swyx [00:16:02]: Which is you’re, you’re kind of re- reconstructing the old paradigm with the new.

Ethan [00:16:05]: Actually, in VAEs, there are, there are both convolution networks and transformers. You can actually do both.

Ethan [00:16:14]: After this VAE, so what you’ve got is you’ve got latent space tokens and you’ve got the language tokens. So now the training of the diffusion transformer, usually generative models use diffusion transformers. It is actually quite standard. It’s, it’s very similar to how you train a language transformer models. It’s not that much difference. It’s just the tokens, the visual tokens in, visual tokens out. The only difference is there’s a denoising process. So you train the model to unmask some of the noise. So you add, you add random noise to the visual tokens, and then you train the model to remove those noise to generate the clean tokens. Any inference, the model can iteratively remove noise from a hundred percent noise.

Swyx [00:17:12]: And then there’s also, to speed things along on the tech tree of diffusion, there’s CFG, and then there’s, there’s also, latent diffusion that, there’s, there’s someone in there. I think, somewhere along the line, obviously, like stability and all these other guys, pioneered a lot of this, architecture. I don’t know if you want to get into that or just, or do the video side up to you.

Bootstrapping Video from Image Models and Temporal Compression

Ethan [00:17:37]: After you train such model, such image model, the reason it’s a, it’s a foundation for video models is that image models are cheaper to train, and they have much denser connection between language and text. So, sorry, language and images. For example, you train a billion, you train on a billion images, and there’s a mapping from the text to the image. And the cost to train the same, like the, a billion, a billion text to a billion videos, that’s much more expensive because videosNaturally have more tokens than images. Because the diffusion models, their understanding of, language purely come from this mapping. So if you don’t have enough mapping, so if you only train on like a ten million videos or something, there-- you might not see enough language tokens in your training, so your model does not understand human intention enough. So that’s why you really-- you train-- you first train this image diffusion models, and then you bootstrap the video model from there.

Swyx [00:18:53]: One thing I did want to ask, because I-- actually, I think you’re, you’re the first per-- video model person I’ve ever talked to, I think. we’ve, we’ve like talked to Luma and all those folks. There’s all these tricks in video compression where basically frame by frame there’s not that much difference, so actually you don’t have to regenerate or save the whole frame, right? but I think MP4 compression or something else like that.

Swyx [00:19:16]: is it tempting to use that? Or as far as I can tell, everyone just treats it as, “No, we would just generate every frame.” Is that roughly the state-art?

Ethan [00:19:27]: There are a few different approaches. Let’s say first, like you want to just directly use MP4 compression and use that as the tokens for the transformers to train, right? So people actually have tried that, but the main challenge is the latent space for the MP4 tokens were not, were not very comprehensible for the models. It’s, it’s extremely hard to train on that. And there’s a

Ethan [00:20:01]: So that’s why they created VAEs, which creates more continuous, latent space, so the models can understand that latent space and learn from it much easier. Even within the VAEs, there are different difficulties of the latent space. So you can imagine something the simplest, the most naive VAE is like you have an image, and you just shuffle all of the images into a, into a vector. So you don’t need to train any VAEs, right? But that latent space is extremely hard for models to train on top of. That’s why there are some debate on like how do you compress the tokens. So you mentioned like you can compress frame by frame. Also, you can compress, the temporal dimension.

Ethan [00:20:52]: The difference is if you compress the temporal dimension, you get a much higher compression rate. Because there’s temporal redundancy between frames, because, this frame and the last frame, likely they are mostly similar, so there’s only some small difference. for example, I think in 12.1 VAE, they have like a eight by eight by four compression rate. So the four temporal tokens are compressed into one tokens. That can save a lot of, save a lot of the context length. If you do it frame by frame, you have to do maybe like eight by eight by one. Your context length will be four times larger. That being said, the benefit of the frame-- per frame compression, we might come back to this later, is, real-timeness and interactivity. ‘Cause if you, if you strain the output of the model, frame by frame, you can-- the model can respond to any user request immediately. So if you have like a temporal four compression, four times compression, then

Swyx [00:22:06]: It might be laggy

Ethan [00:22:07]: there’s a lag there in nature.

Swyx [00:22:10]: So you’re very pilled on this. let’s just go ahead and bring it up ‘cause we have the visual prepared anyway. There’s some frontier applications of real-time video gen. So Flipbook is one of the examples that went viral recently, right? What is Flipbook?

Real-Time Generative UI: Flipbook, Neural OS, and Diffusion Front Ends

Ethan [00:22:23]: Flipbook is kind of like a web brow- web browser. You can see like it has the web bro- browser UI on top. The difference is all of the UIs are generated by generative image model in real time, and anything here are fake. But you can, you can explore inside this wor- this imaginary world. Say like we-- here we have engineering the Great Pyramid. Like the model generates this for us to understand how it works, and if we want to navigate around and understand further, we can click on some of the, some of the description here, and the model will generate a new page, new subpage describing the details we want to know about.

Swyx [00:23:14]: So it’s basically kind of we’re playing a video, but it’s pausing for our next interaction, and then it just plays the next thing based on our interaction.

Swyx [00:23:23]: Which is kind of cool.

Vibhu [00:23:25]: and you kind of decide your story. So this was, how do you make a pyramid? levering technique seemed interesting, right? It shows how do you take Okay, I want to know what is this

Swyx [00:23:35]: The demo, the demo tweet had more animation between frames.

Vibhu [00:23:38]: I think it’s just skipping,

Swyx [00:23:39]: Oh, it’s just skipping a lot of frames.

Ethan [00:23:40]: they also have a video mode

Vibhu [00:23:42]: It takes a lot. There’s a lot of people

Ethan [00:23:42]: but, a lot of people are using it.

Ethan [00:23:45]: So it’s not available.

Vibhu [00:23:46]: There’s a live video stream. We can try,

Swyx [00:23:50]: So this is an example of the kind of future that you see at the extreme. We don’t-- we’re obviously not in it today.

Swyx [00:23:56]: But in a world where inference is completely free this is better than generating code and text?

Ethan [00:24:02]: So this is, this is a final state of where Viva will be at for word model, I think. Imagine internet doesn’t exist, and then you type in google.com. Like what should, what should, what should a model show you?the model can imagine something, and this is what the model imagine. And these web pages, they completely do not exist. So I think as the inference costs come down, we are going to have generative UI for everything. If you think about how the coding model works, so they write code for a web page, and they render the code might be con- converted into binary, and the binary render the pixels on the screen. So we in machine learning, every time we have some breakthrough, obviously it’s, it’s more intuit. So why don’t we have like user instruction to the pixel directly? So the generative UI will be user intention to the pixels directly. And say like even if I want email, let’s say everyone have the same interface, but I want, I want it slightly different. I want the email to show to me like a TikTok, so I can swipe left and right for the emails. And or maybe you want something else. We can have completely different things. Or like I have I’m looking at, Instagram stories, and I don’t like the Like button. I always may click it. And, generative UI resolved it. So it’s going to be a revolutionary replacement of the interface. So in the future, we might have much more powerful

Ethan [00:25:50]: LLMs and coding models running behind the scene. And in the, in the front-end, the diffusion model will actually be the front-end to show stuff to you. That’s how I imagine it.

Swyx [00:26:02]: Diffusion front-end, deterministic back-end.

Swyx [00:26:04]: Something like that. I find that very expensive, but,

Vibhu [00:26:08]: I find it interesting you called LLMs writing code on the back end deterministic, but okay.

Swyx [00:26:14]: you write it once

Vibhu [00:26:15]: Compare it to

Swyx [00:26:16]: And then you execute.

Ethan [00:26:17]: If you think about the cost, say, let’s say H100 costs $1 per hour, and if you use this eight hours a day and thirty days, so, every month you’re paying this two forty, you’ll actually not wanna pay for that. That’s even more expensive than Cloud Code Max. But if you think about the compute costs come down like two times every year, and I think the future will likely arrive like within few years.

Vibhu [00:26:49]: It’s everything, right? compute cost comes down, compute gets faster, model gets smarter

Ethan [00:26:54]: More efficient

Vibhu [00:26:54]: model gets smaller.

Swyx [00:26:55]: I don’t know why you say two times, ‘cause I think it’s like 100 times. In language models, it is roughly one hundred to a thousand times every twelve to eighteen months, for the same given level of LMSys, ELO.

Vibhu [00:27:08]: That’s a net of everything, right? That’s model performance alongside compute. So different than just compute costs come down. But, a very interesting future.

Swyx [00:27:19]: So the web designers will have to shout out that accessibility is an issue, right? how do you deal with screen readers or whatever. But yes, this is higher bandwidth storytelling than anything you can possibly generate with code, right? So I think that’s the rough idea.

Ethan [00:27:34]: And I’d like to add a little bit that so human naturally have the maximum bandwidth when we are looking at things, look at videos, and we also have maximum output bandwidth when we are talking. So in the future, it might be something like we talk to AI models, and the AI model responds back with a generative UI. So that would be the maximum input and output bandwidth to interact with AI models before neural link happens.

Vibhu [00:28:06]: And it’s also very custom, right? Some people are very visual, some people are not as visual, right? They prefer the text. But the best thing about generative UI, right, it can also be text.

Swyx [00:28:17]: There’s another project that we wanted to highlight, which is the Neural OS. Kinda similar idea, but here you’re literally operating, simulating an operating system with a video model.

Swyx [00:28:27]: and you can play Doom, you can do Firefox. I find this like mildly less impressive, obviously, because it’s an OS that I can run.

Swyx [00:28:37]: But here everything is imagined.

Vibhu [00:28:40]: I was, used to the Command+W to close the Firefox tab. It didn’t crash. That’s why I said

Swyx [00:28:45]: It’s too immersive.

Vibhu [00:28:46]: It’s, it’s too immersive for me.

Swyx [00:28:47]: Too immersive.

Vibhu [00:28:48]: I wanted to close the tab.

Vibhu [00:28:49]: But yes, I can play generated diffusion.

Swyx [00:28:51]: this is shockingly fast.

Swyx [00:28:54]: Because I remember there was a demo about like maybe one to two years ago. Someone tried to do the first-person shooter with a image model. There was no consistency. It was very slow. But here it looks like realistically it’s-- this is Doom.

Vibhu [00:29:07]: I think there’s two sides to that, right? There’s okay, what is running a game? The heavy part of it is actually the game engine, all the lighting, all that stuff, the graphics. This is just kind of video, right? Like we’ve solved consistency. This is still, it looks like a few years old image generation. There’s some temporal consistency, but it’s, it’s kind of just images stitched together as frame video. But it’s a good visual representation to pi- to picture the future you wanna see, right? that’s, that’s what I see in these more so.

Ethan [00:29:38]: This reminds me of how the video models gets better and better. So Neural OS is kinda if you just look at it feels like it’s just a crappy version of the, like the Windows we could have, right? And, but the difference is, so the model, this model is overfitted on the existing operating systems. It can generate nothing different than that. But it’s actually also similar to video models. So when we are training these video model, image model, we train them on internet. There’s no imaginary supernatural stuff on the internet. But once we train this model, you can prompt the model to generate something supernatural that have never existed in the data set. So if you train your Neural OS or neural computer on the standard screen recordings on the entire internet. The model can imagine completely new interface to interact with the computer.

Swyx [00:30:43]: This is one of those things that is magical to me. usually generalizing out of distribution is bad, but somehow we have learned some kind of internal world model that you say, this plus, but it looks like rainbows and butterflies, it’ll do it and it will kind of make sense.

Swyx [00:31:03]: So yeah, that’s kind of cool. Yeah, I don’t know if there’s any comment more on there. I do, I do wanted to, I did wanted to touch a little bit more on the model architecture stuff, which I think you were getting. It’s, really fascinating. We don’t get a chance to talk about this enough. So one of the papers that we covered, we’ve covered every annual, segment anything release. and I don’t know if you follow-- you’re a computer vision guy, so you

Ethan [00:31:26]: I know

Swyx [00:31:27]: . So they did memory attention, which is kind of interesting. And I always think, anything where you can, across the temporal dimension, keep some consistency, I think it’s, very fascinating, and I don’t know if Basically, does that-- the CV side bleeding into video gen side, I think is underexplored, right? we talk about it for labeling, but actually you can borrow the architecture itself.

Ethan [00:31:50]: There’s, there’s also complete different approaches, right? you brought up the term world model, so we went from video model to world model. There is diffusion, but there’s also other approaches that people are doing. So maybe we get into those after as well,?

Swyx [00:32:03]: He has a whole definition of world models and stuff. I feel like we threw a lot at you. Whatever you want to comment on.

Why Video Models Are Expensive: Storage, I/O, and Training Scale

Ethan [00:32:10]: I think one thing that we should actually comment back on is okay, so we were talking about the steps to train image gen to video model. One thing we don’t see as much of is okay, you brought up the delta in training data, right? So

Ethan [00:32:24]: you won’t have as much a video model might not generalize, but what is the cost of training a large video model? So we know for LLMs roughly, okay, even like the poolside thing that came out today, right? It’s a Gemma level model trained on roughly forty trillion tokens at this many H200s over this much time, right? You can see what is the exact cost of that. So how many GPU hours over how much H200 costs? So how do we do the back-end math of, same thing for video models, image models. How do you, how do you kind of break that down? I can share some back-envelope calculation. So surprisingly, video models is-- the cost is very-- is comparable to language models and obviously the largest scale is language model, maybe like a medium scale to language models. I said just storing the videos alone, it costs a lot. You can, you can maybe look up on AWS or something.

Ethan [00:33:20]: You really, say if you have a billion videos and let’s say, let’s just say like each video, like five megabyte, then you need five petabyte to just store those videos. And also remember we talk about you use a VAE to compress the videos, and you also need to store, typically you need to store those continuous feature, in-- also in your storage. That’s also comparable size with the videos themselves. So just storing these videos and the features is tens of petabytes alone. And,

Swyx [00:33:58]: I just, I just looked up the calculation. Five petabytes on S3 Standard is one hundred K per month.

Ethan [00:34:05]: And

Swyx [00:34:05]: It’s comparable

Ethan [00:34:05]: and you need

Swyx [00:34:06]: And

Ethan [00:34:06]: And then like tens of petabytes, two hundred K. And even more expensive is you have the ingress and egress.

Swyx [00:34:13]: Oh, yeah.

Ethan [00:34:14]: Like you-- through the internet. You have to just to download those videos, I believe it’s, it’s more expensive on AWS than just storing those videos.

Swyx [00:34:25]: Storing, yeah.

Ethan [00:34:25]: And each training runs, you probably need to pull them once. If you train multiple times, it’s, it’s even more than that. So it’s like just storing the network, those costs is just, it would be a few, a few millions per month to just storing everything, not to mention the GPU cost.

Ethan [00:34:45]: And

Swyx [00:34:45]: my side tangent, the compute rental, like GPU rental is very efficient. There’s one side, okay, you can be XAI and build your data center. Should we not just build our, storage compute as well? Like

Ethan [00:34:57]: Of course

Swyx [00:34:57]: cloud cost compared to just,

Ethan [00:34:59]: You save so much

Swyx [00:35:00]: store. Yeah, exactly.

Swyx [00:35:01]: Especially with like egress and stuff. So.

Ethan [00:35:04]: That’s a good idea, but it also comes to-- there are some of its own challenges.

Swyx [00:35:09]: Of course, of course.

Ethan [00:35:10]: like people who build the GPU data centers, they might not expect this much, storage. And yeah, people build storage, typically they just build it somewhere with just CPUs.

Swyx [00:35:23]: I just looked it up. Five-- AWS only charges for egress, not ingress. Tier five for five petabytes is two hundred and thirty K.

Ethan [00:35:32]: Even more expensive than the storage.

Swyx [00:35:34]: But storing is per month, right? You check in, then you cannot check out. so it’s so cool. It’s okay. So there’s that side.

Ethan [00:35:41]: So the TLDR, my backhand math

Swyx [00:35:42]: Data is larger than you think. Yes.

Ethan [00:35:44]: my backhand math of GPU hours times GPU cost is also very much, I’m missing some storage.

Swyx [00:35:49]: You’re also-- you’re basically like also more IO bound than normal training.

Swyx [00:35:55]: Yes. ‘Cause like data loading, so caching everything, it becomes super important.

Ethan [00:36:00]: So in Cosmos, we did a lot of optimizations to make it not IO bound. So, speaking of the training, actually training the model, the GPU cost, if you look up like the open source model, how big these video models are, I think like LTX has nineteen B parameters. That’s a dense model. And people are also exploring, MoEs, so it might be twenty B active and, like a hun- hundreds B, total. So that’s, that’s even-- that’s similar size as medium-sized LLM models. And if you, if you look at number of tokens-Uh, we disclose that in Cosmos. It’s also like tens of trillions of tokens on the visual tokens. So putting this together, the cost of, training these video models, it’s actually comparable with LLMs. Not to mention, the infra is slightly different from LLM, so it might be less efficient to train these models.

Inference Speedups: Step Distillation, Consistency Models, and GANs

Swyx [00:37:04]: Do you get the benefits of traditional diffusion speed-up? So for, images, there’s LCM, LoRAs for, fine-tuning. There’s, there’s a lot of stuff that’s been

Ethan [00:37:15]: Flow matching.

Swyx [00:37:16]: there’s flow matching. There’s a lot of stuff that’s been done. there’s some overlap that applies to diffusion on the inference side and stuff or?

Ethan [00:37:23]: so the difference-- the inference side is a completely different story.

Ethan [00:37:28]: I think for the training side, it might be a little bit hard to reduce that cost. And for the inference side, the biggest gain is from the distillation of these models. You can-- It’s called step distillation, slightly different from knowledge distillation in LLMs. So you-- Typically, for flow matching models, you need like 100 steps or something. Like a distortion model even need even more, like 1,000 steps to generate a good image or video. A step distillation is try to learn to generate fewer step from the model itself. It’s kind of like now we-- you use the full model to generate in 100 steps, and then you take a model that only generate 10 steps and let that model to learn from the perfect one.

Ethan [00:38:25]: why this work

Swyx [00:38:27]: Strong to weak seemingly.

Ethan [00:38:28]: It is. It’s kind of

Swyx [00:38:29]: Distillation

Ethan [00:38:29]: kind of like strong to weak. the-- from the modeling perspective, the strong model, the teacher model is trying to model the image and videos of inter-internet, and that distribution is extremely complex. But the step distilled model is just trying to learn from the teacher. The teacher is a model, and the size is fixed, as the distribution is much simpler than the whole internet. That’s the intuition I have why step distillation can work. So usually these models serve in productions, they only run in a few steps. In Cosmos, I believe we have, we have like four step and eight steps. If you do some simpler task, image-image translation, it can even run in fewer step, like one step in Cosmos Transfer.

Swyx [00:39:22]: I think this is the same intuition that guides a lot of the consistency model work. I sent you a link for, SCM. I don’t know if you covered that. To me, that was actually one of, the most impressive papers I’ve ever seen from OpenAI.

Swyx [00:39:34]: That this is the unifying grand concept of consistency models. I don’t know if you have any comments on this.

Ethan [00:39:41]: So there are, there are a few different approaches,

Swyx [00:39:46]: Oh, yeah. Here it is.

Swyx [00:39:47]: Two steps versus twenty or 100 steps, whatever. It’s already done.

Ethan [00:39:52]: So there are, there are a few different approaches, for example, consistency model, and there are also Actually, we shouldn’t forget GAN. So GAN, actually, that was, that was the OG of

Swyx [00:40:05]: OG

Ethan [00:40:05]: step distillation ‘cause it trained just one step to begin with. So actually, a lot of, uh-- For example, there’s a distribution matching distillation which use, which uses GAN, as one of the laws for distillation. It-- GAN just tells you, “Hey, generate an image,” and then

Ethan [00:40:31]: it has a discriminator to tell, is this image real or not? So the model, the model just need to learn one of the distribution, not the full distribution. Because in training, the model is asked to reconstruct the ground truth image from the internet, which is extremely hard. And in-- When you’re training GAN, it’s a step process. It’s just a, “Hey, you generate image. Does this image look as real as the image from the internet?” Which is a much simpler task. And, yeah, combining a lot of these approaches together, people typically do that, like consistency model and distribution matching and GAN, and we can get these few step models.

Audio-Video Generation and Time Alignment

Swyx [00:41:21]: Then there’s one step I wanted to add, which is audio and video.

Ethan [00:41:26]: So, Grok Imagine zero point nine, I believe it’s, it’s a first audio video transmodel deployed at a large scale. So

Swyx [00:41:39]: And that was your first model?

Ethan [00:41:40]: that was, Grok Imagine’s first model. It’s, it’s audio video, joint generation. I think the hard part is, the modality alignment, ‘cause before this transmodel, we have, we have text to video alignment. We have this, correspondence between text and video. Typically, most of the VLMs, they understand images and videos. Video’s very rare, and they don’t understand audio mostly. And if you look at the audio generation on the LLM side, you can talk to them perfectly fine, but if you ask them to sing a song or something, it typically is not very good. Also, they don’t have, they don’t have music either. The hard part is thatUh, actually audio has two component. It has like a discrete component, a continuous component. The discrete component is like the language.

Ethan [00:42:44]: So when we speak, it’s just, some

Swyx [00:42:47]: It’s an ASR issue, yeah.

Ethan [00:42:49]: It’s, it’s text token with some characteristics, I would say.

Ethan [00:42:54]: But music

Swyx [00:42:56]: I think the speech guys would disagree with this.

Swyx [00:42:57]: Like disfluencies and then,

Vibhu [00:43:00]: There’s tones you can get angry.

Ethan [00:43:01]: Well, I say largely.

Ethan [00:43:03]: the mu- but the music is completely different. It’s, it’s very continuous, and you cannot model them like discrete tokens in language models. this is like the hard part for models is, not to mention we have to align text, video, and audio together.

Ethan [00:43:26]: So

Vibhu [00:43:26]: How?

Ethan [00:43:28]: So significant-- some significant challenges are like-- So first, like we talk about as the VLMs, they cannot understand most of them cannot understand audio.

Ethan [00:43:39]: So you have to have some way to do the synthetic data generation for audio. You have to caption the model, and that involve, that involve synthetic data and human data effort a lot. And not just surprisingly, most of the LLMs are very bad at recognizing, like the beat, tone, and the details of the of music. They can, they can give some general prediction of which song is this, but it’s very hard to describe the details of the music. like we mentioned in image generation, like you have to describe image as detailed as possible so that someone blind can reconstruct that. So here is like someone

Vibhu [00:44:32]: Deaf

Ethan [00:44:32]: someone deaf can reconstruct how the music sounds like without actually listening to it. Maybe you can think of it need to have the-- or they call the script.

Vibhu [00:44:49]: Subtitles, yeah.

Ethan [00:44:49]: You gotta have all the details of the music, and the dialogue.

Vibhu [00:44:55]: So is the challenge there typically stuff like music and audio, or is it just Like is there a baseline? Okay, there’s enough data where we can understand, narration, conversation, but there’s nuances in audio that’s where you hit all the data issues or is it just from stage zero, you just do it all right?

Ethan [00:45:15]: So one important thing is like the alignment. So the model, the model has to know like the video and audio, the, uh-- it has to have a time-based alignment, like at which time step the video and the audio token correspond to each other. But we actually don’t have this kind of alignment for most of the other modalities. If you think about like text and image, text and video, they are loosely aligned. So you can, you can have a description of what’s going on in the video, but you don’t have to exactly, You typically don’t have exact description, oh, at, time step one second like what happened?

Vibhu [00:46:02]: It’s very

Ethan [00:46:03]: At time step two second what happened

Vibhu [00:46:03]: coarse. Yeah.

Swyx [00:46:05]: So what was the ideal time step? You have to oblate it, and then it’s like four seconds or something.

Ethan [00:46:09]: So that comes down to how you design the model to, for the model to be aware of as a time, as a time modality. So the model is like a time aware. And that’s something pretty unique if you think about LLMs. So if you ask LLM to complete a task, say they, uh-- you ask them and they will say, “Oh, this task will probably take twelve hours to complete,” and they come back in one hour. Say “I’ve already spent two days on this and I’ve exhausted everything.”

Ethan [00:46:47]: So the LLMs them-themselves, they don’t have a sense of time there.

Vibhu [00:46:53]: I actually don’t think that’s just them not having a sense of time. I think it’s somewhat based, right?

Vibhu [00:46:58]: Like you tell someone, “Okay, go work on this feature. Go implement this,” there’s a general understanding you would have of how long that would take without LLMs working at LLM speed, right? So you think back like two years ago, if I tell you to like build me like a new front end for latent space, have a search bar, have all this, you’ll estimate that it’ll take a few days, right?

Vibhu [00:47:19]: So you tell an LLM, “Go build this.” It’ll take me a few days. But I think it’s somewhat grounded as opposed to them not having the best-- Not saying that they have a great understanding, but I think that example is like you can see where it comes from, right? You’re trained on all over the text.

Swyx [00:47:35]: They’re, they’re trying to estimate what a human would say.

Vibhu [00:47:37]: because that’s what the, that’s what the data kind of represents. It’s not them

Ethan [00:47:41]: It came from the corpus on the internet. People have a estimate of how much time.

Vibhu [00:47:45]: And not even just in direct like training samples, right? Just your world understanding of tokens of how long stuff takes, right? Go read a book. It’ll take you a while, right?

Vibhu [00:47:56]: Even if you do nothing but read a book, it takes a few days. So yeah, LLM, I read it took me a few hours.

Vibhu [00:48:01]: It’ll take me a few hours to go through this research. But this is a tangent.

Swyx [00:48:05]: Somewhat, yeah.

Swyx [00:48:06]: This is a train of thought I haven’t really expressed until now is, which is basically like a full world model must also be recursive, meaning that the participant in the world model must also be aware that they have a world model. which is like this whole recursive thing down the, down the line. but yes, and that the world model can be wrong and that they need to update it and blah. Yeah. We’ve, argued this on the, newsletter as well, that there needs to be sort of recursive or adversarial world models.

World Models: Real-Time, Long-Horizon, Interactive Video

Vibhu [00:48:34]: just, to ask, how do you define world model?

Swyx [00:48:38]: Oh, yeah, let’s go there.

Ethan [00:48:40]: So

Vibhu [00:48:40]: So just for context, we talked about, video generation, and then there’s a-- if you say there’s a distinction between world models, what’s your, what’s your definition? How do you see the two?

Ethan [00:48:53]: So disclaimer, I’m not going to debate, what is world model. Yeah. there are many definitions, so I’ll just talk about my definition. Since I came from the multi-model, multi-model domain, so mainly talking from video. So world model is like real-time interactive long horizon videos. So there are three parts. so we-- let’s talk about them one by one. So the so interaction, so we just, we just look at Facebook and neural computer. So the interaction part of it, so you, world model can allow you to interact with them through keyboard, mouse, and maybe also voice. So these all is-- all is a modality. You can, you can interact with the model, and the model should respond reasonably. Second part is real time. So once you, once, say, you move your mouse, if, say, the world model generate a game, how fast can the game respond? So if you’re like professional CS: GO players- -my say, oh, you have to respond- He’s beginner within sub ten milliseconds or- Yeah even less. So that’s not most of the- No, sixty FPS. Let’s go. Oh, three hundred FPS. Oh, five hundred FPS. Wait. okay, yeah. I didn’t do the math, but yeah, okay. Uh- Yeah, three hundred FPS, that’s a three millisecond. So you have to respond- Oh, s**t. Okay. Yeah

Ethan [00:50:29]: within a millisecond. Most of the video models cannot do that. Yeah. And, but if you, say, if you have a video model that is, say, like a digital human, the response time might be more generous. Maybe typically, for real-time voice interaction, it’s like two hundred millisecond. So that’s, that’s much more generous. But even two hundred millisecond is pretty, it is pretty tricky, ‘cause remember we mentioned

Ethan [00:51:01]: you have this, temporal compression coming from the VAE. So if you, if you don’t compress the temporal dimension, your sequence length is going to explode. So if you want to have this real-time, real-timeness in your model, you have to do is one context problem. And the third part is long horizon, ‘cause we-- if you’re not going to just play with, video games just, a few seconds, most video models only a few seconds. We’re going to play with minutes, hours. The model have to be able to generate long-form content.

Ethan [00:51:42]: So putting these three together, it’s, real-time, long horizon interactive videos. I think the final state will be, for example, like a video, a video version of Playbook, where you can, you can interact with, a neural computer. You move your mouse, and you click on the generative interface, and it will reply to you through pixels- generating in real time. But getting there, it’s, it’s a very long way to get there. So one of the first step, at Grok Imagine, where I led a small world model team there, was to build video extension. So, video extension- it’s the first step of interactivity. Yeah. It’s, it’s the first step. Yeah. So it’s the first step- You have it here, video editing, yeah. Yeah. Yeah. So the first step is because, this unlocks long horizon videos. Typically, for most of the video generation models, you give it a prompt or an image as an initial frame. You generate video, that’s it. That’s just, one time, done. And some creators would try to, use the last frame as a first frame for the second video. It can-- sometimes it works, but if you do it a few times, it says the quality would decrease. And- It doesn’t have that context- Yeah over the full video, so the temporal- Yeah, exactly. Yeah, ‘cause you only gave it the last frame, of course, right? Yeah. Exactly. And- it’s actually a pretty fun hack. if you’ve seen like- Oh, no, he’s saying something better. Yeah. And for example, like Vue, I remember Vue 3 has like a second context of the last video. It is slightly better than using the last frame, but it has the same problem-- similar problem that it, the quality would decrease. if you extend a few times to, one minute, the video quality would look much worse than the first video. Second, another problem is that the model doesn’t have long-range knowledge of, what’s happening before. Say, if they generate some dialogue, some, two people speaking, and their voice might change, over some time, especially if the second conditioning, it does not cover the previous context. So these are the core challenges. So the Grok Imagine video extension, it has historical context of all of the previous generated videos. It can, It has, it has the context of, who is speaking and what objects have appeared and everything, having that to generate the next video. So if we naively do this, you can imagine, just, put all of the previous history video tokens into the context. The context lens will easily explode. Especially for video models, that can be like a few, a few million context, I would imagine- context lens. Yes.Yeah.

Swyx [00:54:58]: Let’s run with that.

Ethan [00:54:59]: for example, like in Cosmos, I think just five seconds of video is like a fifty K or sixty K number of tokens. So like if you do, if you do fifty second, that’s a five hundred K tokens. If you do longer than that, easily explode. This long horizon, problem was the first step we’re trying to solve world model. It turns out people, yeah, people love video extension. Like a lot, a lot of the creators love using video extension to create longer form videos. This is the part I liked that you have a, you have an intermediate step toward the final goal instead of just a straight shot to the final version very much.

Swyx [00:55:48]: But I can see you have a strong vision of where we want to end up.

Long Context, Redundancy, and Efficient Interactive Video

Vibhu [00:55:51]: Does it seem like it’s an efficiency issue? okay, we’re at a few million tokens context,. If you draw the parallel to language models, we had very short context, two thousand, eight thousand, then, you scale it up one million, ten million. sure, there’s effective context, but at the end of the day, it’s just what’s it worth? sure, there’s a whole training data side. In video, it might be slightly easier ‘cause we have a hundred million token video, right? Just take a movie with the full context there. Like is this efficiency from an inference standpoint that like it’s expensive, but we know how to solve it? Or like why is this not the approach? So like my broader point was on your second point of world models, you say it needs to be interactive and live, right? You should be able to play a game and see the interaction live. So one thing I see with research is a lot of what you actually serve is different than what you build, right? So we talked about distillation. You train big model, you distill it, you do quantization, speculative decoding. We do all this stuff to serve it efficiently. Should we not just have a solution, like a world model that can interact well, do inference optimization, serve it, distill it secondary, so make it real time after you solve it? So like a-- another parallel is say, continual learning, right? What we need is someone to solve it and show it works inefficiently. Give it a few years, people will make it efficient. Same thing with regular attention, right? It worked. Over a few years, people have different forms of attention, and we’ve scaled it to be efficient at log context,? So kind of two things there, right? One is it seems like it works. You’ve scaled it. Can we not just scale it a lot more efficiently over time? Do we need a separate approach if this works? And same thing with interaction, right? if we can get it done, like if we can solve some way that it works, we can solve making it more efficient from an inference standpoint later.

Ethan [00:57:53]: that’s actually a very good point. So in videos, there’s actually a lot of redundancies. So we solve a lot of the pixel redundancy from VE, but there’s more redundancy in long range and long horizon videos. Say, if a character appear in the first clip and then it disappeared, it only reappear at the end of the video, you probably don’t need the-- the context, like in the middle of the generation. So you only need that character, where you need. So that’s why, I helped build another feature. It’s a reference video.

Vibhu [00:58:36]: Is it here?

Swyx [00:58:36]: is it the same model release or different one?

Ethan [00:58:39]: It’s a different one.

Ethan [00:58:41]: You probably need to search on

Swyx [00:58:43]: I’ll find it

Ethan [00:58:43]: X reference to video.

Ethan [00:58:46]: So reference video allow you to like upload up to seven images as condition and generate the video. Say, if like I want-- it can, it can be characters or objects or even scenes. Say like I want, I want condition on, Sean’s selfie and holding a blade

Swyx [00:59:07]: We have a dog

Ethan [00:59:08]: or whatever.

Swyx [00:59:08]: We put the dog in the thing.

Ethan [00:59:09]: you can put them there and the video models will generate the video from and copies the context over. So that can solve a lot of the problems there, like the long context problem. It doesn’t need to have a very long context, but it’s-- I feel like it’s an intermediate solution. The model

Swyx [00:59:29]: It’s cheating.

Ethan [00:59:30]: the model should be able to like selectively know, where should I draw the references. So say if I want to generate a movie, I generate it autoregressive, like a ten second at a time or something. And now this character appear, I can look back to where it first appear and, bring that back. Yeah, this one, I put the references. Yeah, that’s, Optimus, Einstein myself, Annie.

Vibhu [01:00:02]: Oddly enough, I used Grok Search to find it, and it pulled your LinkedIn post. But yeah we found it.

Ethan [01:00:08]: Interesting.

Vibhu [01:00:10]: But

xAI’s Underrated Work, Culture, and Watermarking

Swyx [01:00:11]: this is a problem. This is not your fault, but like XAI doesn’t communicate all this work that you do very well because they just have the model release and then that’s it. But actually, these details are very good.

Swyx [01:00:22]: As far as I understand, everything you just described is state-art, like no one else has done it.

Vibhu [01:00:30]: A lot of-- yeah, I have a lot more

Swyx [01:00:32]: And then, and then you just put this blog post with the cookies. I’m this is not enough,?

Swyx [01:00:37]: but I, obviously this is like the high level numbers that people want to know. But no, okay, so

Vibhu [01:00:42]: And I wonder, like part of that is also some labs don’t share research into what happens. And if

Swyx [01:00:50]: No, but this is literally bragging about how good they are, right?

Swyx [01:00:54]: Like, why would you not say that you are capable of extending with full context? this is not a secret sauce. This is like we did the work. yeah, I don’t know.

Ethan [01:01:02]: different labs have slightly different communication styles.

Swyx [01:01:07]: Anyway, if anyone from XAI is listening we are always happy to help you tell your story. Yeah, okay, so you did references, and I think, I think kind of the point you’re, you’re making is it is sort of like a kludge, right? this is-- you can do seven, but what about 100?

Swyx [01:01:23]: Right? Then you need a completely different thing.

Ethan [01:01:26]: So I think it’s-- this is, a mechanism to, select the context from the history, and you might not put the entire history into the context. for example, there’s a paper called Frame Pack, which have

Ethan [01:01:41]: a heuristic that the latest history, the last one second, I put the entire history, and the history before that, I would, compress it and makes the video smaller. So they follow this pattern, this build overall pattern that the maximum sequence length is fixed. So the further you are from the current frame, you have a smaller image. So this is just a heuristic. I think it can be more automatic. The model is aware like which history part of it can be select. So this part of the research is actually being actively, worked on by a lot of people. It’s also quite interesting. I feel this is actually, this part of long context is a little bit ahead of the LLM part.

Ethan [01:02:31]: So for example, like in LLMs, if you-- so contexts keep growing. Let’s say if you call tool and the tool call history is extremely long, that’s still in context, and keep growing, keep growing. Even if you switch the topic to something else, the whole context was there. There are some agentic harnesses that help you to, say, prune the tool results and, prune Like when you, when you query a file, only show like the top 200 lines or something. Those were very heuristic-driven.

Swyx [01:03:08]: For listeners, we did a write-up on the cloud code, leak where there are eight different kinds of pruning, including like you prune the tool results and all that. So you can, you can read up on that kind of thing.

Ethan [01:03:17]: I think, one breakthrough in continual learning might be like a way to automatically, manage its own context.

Swyx [01:03:27]: These are all heuristics, and they will be replaced by machine learning.

Ethan [01:03:30]: Interestingly

Vibhu [01:03:32]: The

Ethan [01:03:32]: the same thing is being researched in both LLMs and video models.

Vibhu [01:03:36]: The interesting thing is also like in the paper you showed, it’s actually happening at the model level, right? Compared to like language models, sure, we have base attention, but we’ll do our own compression, we’ll do our own pruning, which is separate from model error.

Vibhu [01:03:49]: Eventually, it all just boils in, hopefully.

Swyx [01:03:52]: I think this is a form of like attention, but like also know sort of reasoning attention. I feel like that’s different than normal attention.

Swyx [01:04:03]: Does that, does that make sense?

Ethan [01:04:04]: It’s, it’s different in the sense that attention, not to mention, set sparse attention aside, like normal attention

Swyx [01:04:13]: Like UKV, yeah

Ethan [01:04:14]: you have to attend to all of the tokens.

Ethan [01:04:17]: So you don’t have a high-level mechanism to drop which tokens do-- you don’t want to attend to. As humans’ attention span is surprisingly small.

Ethan [01:04:28]: You can only remember 11 digit of a phone number.

Swyx [01:04:32]: But I have feature detection, right? I can detect, oh, that’s a sequence of one, two, three, four in a phone number that is 11 digit.

Vibhu [01:04:39]: Very good pattern matchers.

Ethan [01:04:41]: But humans’ context can-- like attention can work because we can dynamically pull in, context from different places. The same mechanism, I think is going to happen for LLMs and video models. I think we have

Swyx [01:04:57]: RLMs is recent-- is on, it’s on the recent work is there, which is not that, crazy, but it’s just recursive.

Vibhu [01:05:04]: I think it’s somewhat inherent in models too, right? Like you

Swyx [01:05:06]: No, here’s a nice example here

Vibhu [01:05:07]: you pull up these, you can read it fine, but, language models are also very good at slop parsing. you have a trans

Swyx [01:05:15]: I throw my typos in there, it doesn’t matter.

Vibhu [01:05:17]: You have a, you have a transcript, you have whatever, just throw it in and it’s very good at parsing through noise. m-- that may be a brute force. It can look over a reason over it, but there’s, there’s parallels to both.

Swyx [01:05:31]: I think it’s just really fascinating how you relate the world models stuff to the video generation, which I don’t think a lot of people hear directly, from people like you. So I think that’s really helpful. Any other work? Do we cover like video, audio, world models, any other stuff in that omni

Swyx [01:05:48]: team,?

Vibhu [01:05:49]: Or any other work at XAI you want to talk about? Seems like everything we see publicly announced, “Oh, cool, cookies.” And then there’s so much more to it.

Swyx [01:05:58]: There’s a lot of depth.

Vibhu [01:05:59]: Any underrated stuff, just at the time there?

Ethan [01:06:03]: I feel the, as a culture, it is quite interesting and a bit underrated. So the culture is, the culture is three sentences: move fast, build No goal is too ambitious, and the first principle. Like early, the goal set was very ambitious. It wasn’t very-- this wasn’t-- it wasn’t possible to achieve when I, when I was thinking, first thinking about it. Like for example, like build something in three months. And

Vibhu [01:06:36]: Was that “Okay, we’re starting team, we want image, we want video. Do it by this deadline.” Or, how do you work back? Like was it just, “Okay, we have a rough by, this date we want something out,” or is this like

Ethan [01:06:52]: That’s a very good point. So it’s from first principle thinking.

Ethan [01:06:56]: If you think about, people might say that first principle thinking applied more to the physical world than the models. I would say, for example, like if you think about-Some limitation, for example, acquiring data, like how fast can we acquire the videos? And if you think about training the models, what’s the iteration speed for training a model end? And how would adding more GPUs accelerate that timeline? And maybe if you need human data, like what’s the turnaround time for human data to arrive? If you put all of those together, that is first principle thinking where, oh, like what is the timeline? What’s the minimum number of days that is possible to achieve something?

Swyx [01:07:52]: I think there’s a-- this is a lot of Elon’s type of thinking, right? He’s like-- I think he’s famous for saying that the only law you can’t break is the laws of physics, something like that.

Swyx [01:08:01]: Just broadly, you worked a lot with Elon.

Ethan [01:08:04]: I, one benefit is working at xAI, you got a chance to interact more with Elon. So I was very fortunate to get a few retweets from him, and that was quite fun. And, he also worked very closely, with people. like people imagine online, like he’s very hands-on.

Vibhu [01:08:34]: There are two things. one-- So I was actually looking up, Elon retweeting you. I’ll pull it up. he talked about you tweeting that you have a really good voice mode. I don’t know

Ethan [01:08:47]: Oh, me?

Vibhu [01:08:47]: No. Him.

Swyx [01:08:48]: Oh, I also did it. But anyway.

Vibhu [01:08:49]: I actually-- So I would DM you feedback on voice mode because I was “Wow, really good.” And then I’m “Ugh, this sucks.” But, I don’t know. Anything you want to talk about your voice mode, building it? Was it a team you worked on as well?

Ethan [01:09:02]: Oh, that’s actually not part of the team I worked on.

Swyx [01:09:05]: He probably worked on more of the video. No, but Grok Voice actually

Vibhu [01:09:11]: Grok Voice

Swyx [01:09:11]: like very good. I-- This is one of those things where first of all, you can speak at 2X, which is fun.

Swyx [01:09:16]: which I listen to 2X, so I like to speak at 2X. But also I think like the interruption was better than Gemini. I don’t know how it compares to ChatGPT real time now, but as far as like driving was concerned, like having Grok in my Tesla and like driving, I think it was like-- it’s a really good experience.

Vibhu [01:09:34]: He likes voice mode. But also, just the crazy reach by Elon

Swyx [01:09:40]: Fifty million views for just saying, “Yes, true.”

Vibhu [01:09:43]: That’s true.

Swyx [01:09:44]: Oh my God

Vibhu [01:09:45]: but, it’s, it’s pretty cool how fast it came out. the other thing is the safety aspect of video mode. Anything interesting to talk about there? So

Swyx [01:09:56]: spicy

Vibhu [01:09:57]: spicy question.

Ethan [01:09:58]: A lot of the countries where they don’t allow like a generative data-- generative AI videos without watermarks. So in all of the-- those countries, Grok Imagine had watermarks, and a lot of the-- a lot of the takedowns of the videos were also happening extremely fast.

Swyx [01:10:22]: it’s, it’s part of running a social platform but also it transfers nicely to the GenAI side. Do you have a perspective on SynthID versus other kinds of watermarking?

Ethan [01:10:33]: it’s going to be

Ethan [01:10:37]: it’s going to be harder and harder to detect, the Yeah, these things. So SynthID, one thing is, previously it was only Google, and now, like a lot of different labs

Swyx [01:10:52]: OpenAI adopted it

Ethan [01:10:52]: are also adapting it.

Ethan [01:10:54]: As-- A limitation is like the technology The paper was out there, and people can reverse engineer like how to get rid of it.

Ethan [01:11:05]: And it’s-- I think even as it advance, it’s, it’s still possible to reverse engineer it.

Swyx [01:11:13]: so if you are interested, you can go onto Reddit and people have taken out the exact I don’t know, what do you call it? Mask or pattern that Google applies, and then you can apply it onto any Google-generated photo, and you can reverse out the SynthID.

Ethan [01:11:30]: And it’s, it’s also harder and harder to just judge by eyes. I remember like a couple years ago, there was like six fingers or something. It’s very obvious.

Vibhu [01:11:42]: My current is actually the audio. I feel like the audio is really lacking. my way to tell if something is generated, outside of okay, I think I’ve seen enough, I have a decent eye, the audio matchup, especially of Sora, is not great. It’s all similar style. But there’s

Swyx [01:11:57]: I see. those are minor imperfections.

Swyx [01:11:59]: I think the point is that like-- Actually, my closest reference to this is also Ian Goodfellow, ‘cause I think he did like the adversarial GAN thing where like it’s okay, here’s a picture of a zebra. Then you like change one pixel, and it becomes a panda.

Swyx [01:12:12]: Right? This is like-- this is like a classic computer vision issue.

Ethan [01:12:15]: If you think about how these models were trained, like I, like I mentioned before, like GAN was in the training process. The objective of GAN is you-- is the model generates an image, and the model, there’s a judge to tell like if the image is real or not. The model is trained to make the image more real. So as the model become more and more advanced, it’s going to be harder and harder. For me personally, now I have to judge by

Ethan [01:12:49]: if the-- these videos have logical sense.

Ethan [01:12:53]: If these, this video

Swyx [01:12:55]: Have a world model.

Swyx [01:12:57]: No, I also like it-- the audio is too nice, like too studio quality. The lighting is too good. The skin is too clear. the-- basically, the lack of imperfections.

Vibhu [01:13:10]: Do we have a good way to do reasoning in diffusion? Like is that what separates video generators from world models or in, -We really know how to apply it to other regressive language models. Is there a parallel for diffusion video gen world models like on that point, right? Is

Swyx [01:13:30]: He has a thing on video agents.

Ethan [01:13:31]: that’s a good question. Yeah, actually, I have a, I have a pretty big claim. The intelli- the visual intelligence are actually mostly coming from language. these video models, especially from now, since the diffusion model technology is more mature, the every time you see there is some improvement on these models, I would say mostly, this, again, comes from language model, not coming from the vid- the video model itself, like the video distribution models themselves. In Cosmos, that could be Typically these models, they have two parts. there’s a, there’s a prompt rewriter or the prompt up sampler part. I think in Cosmos, we use Llama or we use Mix- Mixtro. And the Cosmos video model itself is only 7B, and the model, the language model

Prompt Rewriting, Video Agents, and Agentic Generation

Ethan [01:14:35]: is a prompt rewriter. It’s, it’s bigger than that. So the prompt rewriter’s task is to take user instruction and convert it to extremely detailed description of the video. So because the video, the visual-- the video distribution models, I would describe, they’re kinda dumb because they take the input

Ethan [01:15:03]: instruction literally. Because in the training process, remember that we have to describe the video as detailed as possible when we’re creating the synthetic, text pair. So this model, they take those kind of instruction to generate the videos. So in-- when you’re taking the user instructions, the user instruction usually are simple. Just say a cat or something. If you put a cat in the video model, they would take that instruction literally. They would literally show a cat, a cat in maybe a white background because you didn’t describe the background. The cat is not moving because you didn’t describe it. It takes the instruction quite literally. It’s kinda, it’s kinda dumb. The prompt rewriter is actually a much bigger model. It’s a language model that takes, the user instruction and expand it. So the thinking process you mentioned, is from there. So if you, if you look at like GPT image, like you generate a image in three minutes. Three minute is not all like a pixel generation. A lot of time is spending

Vibhu [01:16:19]: Prompt writing

Ethan [01:16:19]: on thinking.

Ethan [01:16:20]: So prompt rewriting now have evolved to, not only just as thinking, it can, it can also be a agent, a agentic model. For example, say you want, you wanted to generate the image of today’s news. So the-- So it’s likely they’ll go to fetch today’s news online and then, process and digest them, then organize the layout and generate it. Another thing quite interesting is,

Vibhu [01:16:53]: If I’m not mistaken, these are-- it’s no longer a diffusion model though, right? It’s autoregressively Or is there still

Ethan [01:17:02]: There are different approaches. For example, Gemini Omni. Since they said it’s Omni, I believe it’s a, it’s a single model. Maybe it’s something it’s a language model with a diffusion head or something. Like the language model do the thinking, do the agentic tool calling, and then it would, use the diffusion head to generate the image in the end. There were also approaches like Cosmos, where you have a separate language model and separate diffusion models. And there were also like a purely language model, like you discretize the images, and then you generate the image as discrete tokens. So there are different approaches. I would say like

Vibhu [01:17:44]: One of, one of the claims I’ve seen for why these approaches struggle is because a lot of the benefits for how we currently learn reasoning with language models is you basically iteratively generate reason. You have your thought, and then you work on that answer, right? So if you have like Omni model and then diffusion head, you can’t feed that back in to continue reasoning, right? So you can’t go like text, image, text, image. You can’t reason on the output and then go back to diffusion. But in the new Gemini Omni, you would be able to, as long as you have diffusion.

Ethan [01:18:15]: I’m not sure if

Vibhu [01:18:16]: But

Ethan [01:18:16]: they have that process. it’s definitely possible in the Omni paradigm.

Ethan [01:18:22]: So if you think about like traditional multi-model language model, they would have a VIT encoder that can encode the image. So if they have a diffusion head, they can generate the image and then put that back into the VIT encoder, encode that, and then do the iterative refinement if the result Yeah.

Swyx [01:18:44]: I think you have to jointly train the VIT and the diffusion to make that somewhat reasonable, ‘cause otherwise you’re kind of mismatching or feeding in slop.

Vibhu [01:18:55]: I think it depends on the stage of training. You might be able to freeze it. But anyway, also just on your earlier

Swyx [01:19:00]: Wait. I wanted to also make explicit. We do know that NanoBanana and GPT image are autoregressive, language model with diffusion head.

Swyx [01:19:09]: as far as I can tell from your description of Grok image, it is not. It is, it is end.

Ethan [01:19:14]: I cannot

Swyx [01:19:15]: You cannot

Ethan [01:19:15]: comment on that.

Swyx [01:19:16]: Well, the way that you described it. but, yeah, I think it-- there’s, there’s different approaches, right? Like you started off saying prompt rewriter is, the-- a big part of the intelligence.

Vibhu [01:19:24]: and even on that, I think everyone should try using an early diffusion model. If you’ve used Stable Diffusion one or whatever, if you’ve seen the prompts ultra-high res, four K this style, oh my God, the first time I tried one, you don’t talk to them like language models, right? Your prompting is very, comma separated

Swyx [01:19:43]: It’s literally talking in the labels that were in the data set, right?

Swyx [01:19:46]: But basically, I’m just trying to make the point that prompt writer and then image is different from autoregressive language model with diffusion hit. Right? They’re different things.

Ethan [01:19:56]: they’re different.

Swyx [01:19:57]: Just wanted to establish.

Ethan [01:19:59]: I’d say, the common part is, the image part. So it’s, it’s quite surprising that, a lot of the improvement came from the

Swyx [01:20:12]: Language side

Ethan [01:20:12]: the thinking the tool calling. So I still remember, in Cosmos, I generated a happy sheep and can if without any rewriting, it’s-- it looks so, CGI, and after rewrite it looks, it looks so beautiful.

Ethan [01:20:31]: I think

Swyx [01:20:32]: Without any joint training.

Ethan [01:20:34]: actually, without any joint training. it’s-- with rewriting, it’s already much better. See, a very interesting thing, what happened is the video agents, mostly language models, will call these, generative model, either it’s a separate model or a diffusion head or whatever, as tool. So this model can iteratively refine the results or even, generate longer content through a very long train of thought. It’s actually very similar to how human create art. So we don’t, we don’t generate the pixels directly. We literally draw something on And I think through this process, the-- these models not only use diffusion as one of the tool, it can also use traditional tool. It can also use, image editing tools from Photoshop. It can use, video editor, FFmpeg, whatever, to take combination of these and the generative AI technology as a, as a set of tool, and they can, they can iteratively create a better, a much better, video for production-grade quality. If you look at existing, professional creators, they don’t, they don’t end at, generating a video from these models. They would take this video to their editor and edit here and there.

Swyx [01:22:11]: So much post-production in And sometimes actually, the reason the video is good is not really the video model, it’s actually the editing.

Swyx [01:22:21]: And yes, we also are engaged in the same process as well. Would you love to use a video editing model?

Ethan [01:22:27]: Actually, there’s, Grok Imagine Agent beta. That was the, that was the first attempt in that direction.

Ethan [01:22:38]: So I think, the process would be similar to like

Vibhu [01:22:44]: It’s just agent mode.

Ethan [01:22:46]: you can, you can ask it to

Swyx [01:22:48]: There’s no blog post for it

Ethan [01:22:49]: maybe generate a minute, video, which is not possible if you ask the same prompt to video models. But this model will ca- literally call different tools to do that.

Ethan [01:23:05]: So yeah, this is actually an interesting thing. So when we first released, a video editing model, I see on X some people try the video editing feature with, “Edit this video to be one minute.” ‘cause they didn’t understand how video editing work. Video editing typically is just a removal, add, replace, style transfer, this kind of thing. But that’s actually a valid request under the assumption of video agents. So these agents should be able to understand these kind of, long horizon tasks to be able to easily, create a long-form video. I think this is, this is really fascinating ‘cause it’s kinda take-- it’s taking the same direction as first you have these, assisted-- assisted coding, kind of like tab completion, GitHub Copilot. And from there, you gradually evolve to Codex and Cloud Code, where you do things fully automated. So in agent, in Grok Imagine Agent mode, you can, you can still go in there and do stuff by yourself.

Ethan [01:24:22]: gradually, as the model capability increase, it will be able to do everything fully automated.

Swyx [01:24:30]: I like that. okay.

Ethan [01:24:32]: That’s good.

Swyx [01:24:32]: So it looks like it’s still generating.

Vibhu [01:24:34]: Also, I did notice the Grok image gen was always very fast. I don’t know if this is something you guys benchmarked, but, this is just a tangent. Compared to what I used to use before the latest OpenAI’s image gen, and same with Gemini Nano Banana, I would oftentimes use Grok just for the speed.

Swyx [01:24:54]: It’s, it’s in the benchmark somewhere that’s

Vibhu [01:24:56]: It’s

Swyx [01:24:56]: in the Imagine API blog post that they have all the speed things.

Swyx [01:25:00]: it mostly combination of distillation plus inference.

Ethan [01:25:04]: There are a bunch of things. we talk about distillation, and if you talk about thinking, if you don’t have any thinking budget, the model can just think three minutes and then come back to you. And also, inferenceThe inference infra team was very talented, and they were, they were able to accelerate a hell lot of these models.

Swyx [01:25:27]: my comment on the, on the video agents things, I’m trying to figure out, when people say video agents, when you initially told me about your bet on video agents or your vision for video agents, I was a little bit disappointed. I was “you mean, like models are tapped out, now we have to do agents?” But, I think you have to, right? The question now is, how much model training is it really going to make a difference versus just building a better harness? Like you said the models don’t have to be jointly trained. you can just take an shelf frontier reasoning model, slap it on a harness, give it Grok as a tool. That’s it. That’s your video agent. Doesn’t seem super satisfying. Obviously, you can train and get some more percentage points of per- performance. But, if your central claim that the majority of video or generative media, alpha or whatever, is actually coming from language intelligence and not, image diffusion or video diffusion, then that is the future.

Vibhu [01:26:30]: it’s pretty cool

Swyx [01:26:31]: It’s just like primarily just weight.

Vibhu [01:26:33]: If you pop back at the example, it generated frames. Sorry to interrupt, it’s been saying “Okay, I’m gonna start stitching these frames together.”

Swyx [01:26:42]: So

Vibhu [01:26:42]: It’s using FFmpeg like using code.

Swyx [01:26:43]: This is what GPT Image Pro as well is doing, right?

Swyx [01:26:46]: Like, this is also just writing code in the background and then just

Vibhu [01:26:48]: Stitching

Swyx [01:26:49]: doing an image pass on the final output. It feels dissatisfying for the people who want to just train models.

Vibhu [01:26:54]: It’s interesting, right? it’s, it’s also somewhat exciting. Like you brought up earlier, a lot of the gains don’t come as much from the video. I think you can see that in the language model space too, right? Anthropic, very good at coding. They’re multimodal, not the best, right? They have basic input PDF, but there’s clearly a disconnect in the quality of their image video processing, audio processing, yet intelligence very top tier. Other labs, Gemini, OpenAI, xAI, you can add modalities, but it’s not like they’re unlocking crazy capabilities, right? So it’s interesting.

Ethan [01:27:32]: It’s interesting to see that, like the video model’s capability increase actually come from language model being more intelligent. I think video agent, like it can unlock more stuff than my- you might imagine. So there’s a few things. So one thing is when we are prompting these models, so most of the people were actually not very good at prompting.

Ethan [01:27:59]: Actually, language models have a better sense of how to prompt AI models. AI models know AI models better. So if you jointly train these models, maybe the model have a better sense of, how to prompt each model. Like a different model

Vibhu [01:28:15]: Of course

Ethan [01:28:15]: might be different. Another thing is it might not as simple as just, like generate a few clips and slap them together using FFmpeg. Like you might-- there might be more like image and video editing tool appear in this process. Say, if you want to exactly add a blob of text at this timestamp, the videos model-- video models might not get that intention very precisely.

Ethan [01:28:48]: But these are possible using these deterministic tools. The long-- The video agents can use all sorts of tools, so you don’t have to put all of the capabilities into the generation model itself.

Swyx [01:29:04]: I think that’s very true. no, so for what it’s worth, I think you’re right. I think that this will be a big category. I think probably you are predicting like the next one year in video is gonna be all this.

Vibhu [01:29:18]: Do you have a time prediction for how-- when this stuff ramps up? Like

Swyx [01:29:22]: they already started.

Vibhu [01:29:23]: Is,

Swyx [01:29:24]: It’s not very good yet.

Vibhu [01:29:25]: Are we so-- No, it’s so, it’s so good. I think the last one’s just longer.

Vibhu [01:29:29]: it didn’t give me a minute.

Ethan [01:29:30]: Last thirty-six.

Vibhu [01:29:30]: It gave me thirty-six seconds. But are we feeling it now? Is there gonna be inflection? Is there any timeline predictions you wanna make?

Ethan [01:29:37]: by the end of this year is-- this is going to

Ethan [01:29:41]: be a big hit. So the inflection point will be where, the videos generated by video agents can get to like production grade quality, so it can be presented and it can be, it can be distributed in ads. And when-- once that happen, I think the enterprise will have much more budget for video models because the agents are, inherently more expensive than the, than the video models themselves, ‘cause they do this iterative process. They generate many variations.

Ethan [01:30:23]: but once these models have this, pass this usability threshold, I think it’s, it’s going to be a exponential growth beyond that.

Swyx [01:30:35]: I would, fund a company right now based on this thing.

Robotics, Physical AI, and Internet-Trained World Models

Swyx [01:30:40]: so I think you’re right. One thing I’m, I’m surprising, I’m reflecting on the whole like past hour or so of conversation, you are-- I think you’re into world models and video generation for video generation’s sake. I think that a lot of other world models people, we’ve interviewed a lot of them, general intuition and Fei Li and all those guys and Moondream, which I think I told you about. Moonlake.

Vibhu [01:31:01]: Lake.

Swyx [01:31:01]: I keep saying Moondream. Goddammit. Moonlake. A lot of them actually say like robotics is the end game. Like embodied robotics, like you want real-time, you want interactive. It is to interact with the physical world. You’re not that concerned about it.

Ethan [01:31:15]: I think robotics will be a, will be a big part of it for sure.the process may happen naturally. So my prediction on robotics is that the problem is physical AI might be solved, like without actually need to

Swyx [01:31:36]: Be in the real world

Ethan [01:31:37]: need to be in the real world. So it might, it might get solved by a video-- A LLM is very strong video capability. So remember we talk about the real-time interactive long horizon video. Once these models-- So now these models are just training on like screen recordings and computer screens. Once these models can use computers and understand the future state of computer extremely well, the robots might be, might be one of the, one of the tools, a very powerful AI can use. So the powerful AI might just, be able to control the physical embodiment naturally.

Why Ethan Left xAI and What Comes Next

Swyx [01:32:28]: I see that for sure. Cool. I know, I know we are coming up on time. you had-- you left one more spicy topic, which is why you left xAI.

Ethan [01:32:38]: For me, there’s, there’s a lot of, a lot of research you want to do that you cannot do at, as a company. And also like the priorities and objective the-- of a company typically can change very fast. It is-- It’s also the same for xAI. So now is kind of like the time so there is some research I want to do, especially more on language model side like I cannot do at xAI.

Swyx [01:33:11]: Oh, okay, yeah. So you’re, you’re basically leaving You’re, you’re-- you had this whole transition from computer vision to world models, video generation, to now you’re like focusing on LLMs.

Vibhu [01:33:22]: But it seems a lot of you saying focusing on LLMs, you really in the past hour described how it all ties together, right? Like But I don’t know. What do you mean by focusing on LLMs? Is there

Ethan [01:33:33]: I realize the fact that the video models, even like in the beginning, the game might come from improvement on diffusion technology, but this is a point where actually most of the game, come from the language models themselves.

Swyx [01:33:50]: It’s a huge black pill for anyone who has like spent their career in like generative, media.

Vibhu [01:33:56]: it-- that’s an extreme view, right? The-- You still definitely need a bit of both, right?

Vibhu [01:34:01]: There’s just, it seems like more pressing, impactful work to do now on language model side.

Swyx [01:34:07]: Do you have any similar predictions? you-- so you predict the video agents, and I think you will be right. on the language side, what are you looking for in the next one year?

Ethan [01:34:16]: I think one thing pretty interesting I think might be happening soon is the language models will be like context-aware and manage its own context.

Ethan [01:34:29]: So some-- Like from the video model side, we’ve been suffering from the long horizon issue, like we want to generate video longer and longer, and we’ve been trying to solve the context length issues through various ways. One thing is just brute-forcing train longer context lengths. Another is to manage the context better. I think the same thing in language model is also going to be happening soon. So for example, like the language models, they’re not aware of how long their own context length is. Once they hit like eighty percent or something, automatic context compression is getting triggered. And the model, is not aware of that when it’s working. And some-- maybe it’s good for the models to know, “Oh, I’m, I’m approaching like eighty percent,” or something. And something also pretty interesting, like for example, in OpenClau, like you-- every time you type in something, a times-- the current local time is automatically attached to your message, so the model actually know what time is it. So this is making the model time-aware. And also like in tool calling the-- a lot of the intermediate tool call results automatically prune. So there’s like context removal, context addition, and, context compaction. So all of these are from the harnesses themselves. But from our experience, the heuristic engineering also helps the models get this absorbed into the models themselves. that’s something very interesting to explore.

Vibhu [01:36:12]: So infinite context?

Ethan [01:36:14]: Maybe.

Vibhu [01:36:15]: No, but it’s, it’s interesting, right? you

Swyx [01:36:17]: It is in the space of memory and continual learning and

Vibhu [01:36:20]: I don’t know. It’s also like in the space of agent harness use, right? You’re seeing

Swyx [01:36:25]: No, he’s saying he doesn’t want to do it in a harness, right?

Vibhu [01:36:27]: No, but models are also being trained on uni-- using harnesses, right?

Vibhu [01:36:32]: So some of it is, you could say, implicitly leaking in, right? part of that post-training of language models is, okay, using it in coding harnesses, in which case, when are agents spawned? When is compaction gonna happen? it’s not explicit you have this much token window, which I don’t know if you want it to be, as that’ll change, but it’s, it’s somewhat leaking in there.

Ethan [01:36:58]: I’m imagining, what if the model have access to the whole-- the code of the agent harness itself and being able to modify it to whatever you want. Say, if the agent harness is short enough, you can just put in the context lengths in the system prompt, and then the model will say, “When I want to spawn a future version of myself, I can modify the agent harness.” For example, if I-- the agent harness can be, “Oh, when I’m reading-”A long document, I can choose to read the whole thing in chunks and, come back, smash the summary together, or I can just read the first two hundred lines and, discard the rest. And all kind of choices, if they can be made by the models themselves, it might be very interesting to see that the model can, program the model can program itself online in test time.

Career Lessons: Moving Across ML Domains

Swyx [01:38:02]: so the self-modifying harness is also part of, OpenClaw and Py, but I think there’s a lot more work to do there. Very cool. I think part of me is kind of curious. I think you are part of Big Lab, right? And there’s this career path of a researcher at a Big Lab, which is you are-- you train models, you get more compute, you train better models, and you keep going. And somewhat, I feel like you’re opting out of that. And if I were you, I would be “Oh, I think this is, a bit of a career risk.” what?

Swyx [01:38:36]: I don’t have any comment apart from, you’re very strongly convicted. I think that a lot of people in your shoes would not be doing what you did.

Ethan [01:38:43]: Speaking of my career, if I look back, actually, there were, there were a lot of huge transitions. So ten years ago, I was, I was doing research with a ResNet authors, Xiangyu Zhang and Jian Sun. Yeah, at that time, the research were completely different. It was, mostly confirmation, like image recognition, object detection, object tracking. I was also doing neural net compression at that time. It was quite different from knowledge dissolutions these days. And at that time, I was-- I wanted to be a professor, and I applied. When I applied for a PhD, I already had a few first author papers at top conferences, so I confidently applied at the top schools. It turns out I got rejected by all of the top PhD programs. So I had to, I had to go to the industry. At that time, I was at Facebook AI Research fair, led by Yann LeCun.

Swyx [01:39:51]: I wanted to talk about VJPA, but it’s different.

Ethan [01:39:53]: I know. Yeah, we can leave it for another time.

Ethan [01:39:57]: I switched to At that time, I switched to self-surprised learning. It was, it was quite different from what I was doing in contribution.

Ethan [01:40:07]: And after that is NVIDIA Cosmos. So I realized scaling up was extremely important. So at NVIDIA, I was mainly focusing on scaling. So one thing is Cosmos scaling the video distribution models to a few billion parameters. And another thing is, I was working on MoEs. The Megatron MoEs was the first, was the first framework open source to be able to train these MoEs at very large scales, hundred billions parameters to even trillions parameters efficiently at, forty percent MFU.

Ethan [01:40:51]: And going to switching to xAI was trying to work on even larger compute scale even further. And yeah, looking at this trajectory, I actually worked on a lot of different things. So I feel actually within ML, it’s actually easier to switch than you think. a lot of people might have mindset that, “Oh, I work on, I work on computer vision. I always have to work on computer vision, and I cannot switch to language.” And, but from my experience, at least at NVIDIA, I worked on both language model MoEs and also video models. It’s, it’s actually not the case. A lot of, a lot of the core principles how to train large models are largely the same. And yeah, for me, I feel right now the bottleneck, for video models is actually the language part the agent, which is why I want to go to work more on LLMs. One thing is it’s, it’s a bit of a challenge. I don’t think it’s a huge, jump, so.

Closing Thoughts

Swyx [01:42:18]: kudos to you. I think you have a lot of, strong vision there. Yeah, I think that was mostly everything that we wanted to cover. You’ve been very generous with your time, and I, it’s really nice that you are able to share all these things now. We don’t have to go through xAI to clear everything. but also we

Ethan [01:42:35]: Oh,

Swyx [01:42:35]: I think we didn’t get you in trouble.

Ethan [01:42:37]: It’s a lot of good stuff about xAI compared to what you just see in the releases, right? You don’t realize how many more levels there are to it.

Swyx [01:42:44]: xAI, please do more podcasts.

Swyx [01:42:47]: anyway.

Swyx [01:42:48]: but thank you for, sharing. It’s been very kind. And also, I wanna hear more from you. I think you are going to embark on your next phase. You haven’t announced what you’re doing next, but clearly you have, more vision and more ambition on this path, and I think you’re, you’re basically kind of gradient descending to, whatever your final form is.

Ethan [01:43:08]: Thank you. Yeah. Yeah, I’ll, I’ll share more about my next chapter soon.

Ethan [01:43:14]: Thank you for having me.

Swyx [01:43:16]: Thanks for coming.

One of the central tensions in the agents industry is that even while there are major decacorn agent labs like Sierra, Decagon, Notion and Cursor being built up, it is also true that it has never been easier to DIY agents, with a plethora of agent frameworks like LangGraph and Pydantic and Flue, and managed agents from Anthropic and Gemini and Amazon. There has been a wave of companies building their own background agents from Shopify to Stripe to Paradigm to Razorpay, and even Cognition’s friends Ramp have built their own coding agent with other friend Modal.

You’d think Cognition might feel a bit threatened, but they’re not - even after all this, they were way oversubscribed for the $1B Series D they just announced:

Walden Yan, coiner of context engineering and Chief Product Officer/Cofounder of Cognition, invited OpenInspect’s Cole Murray to talk about why the Devin is in the Details.

Full conversation live on the pod today:

In retrospect, async agents were the most AGI pilled bet you could make in 2024 - the models weren’t good enough yet to vibecode, and people didn’t trust AI enough to let it rip, nobody (including early Cognition) was sure about the form factors.

Now it is obvious:

* The first wave of AI coding tools made the developer faster but remain heavily in the loop. Copilor and Cursor’s tab autocomplete are prime examples However, the workflow was still heavily centered around and bottlenecked by the developer’s local workflow: a developer in an IDE, watching the model, accepting or rejecting changes, and pushing code one interaction at a time.

* The second wave was local agents: Claude Code, Windsurf, Cursor’s agents pane: first one and increasingly many terminals all running concurrently.

* The current Age of Async Agents points to a different future focused more on agent orchestration which drives end-to-end development.

According to previous guest Steve Yegge, there are finer-grained 8 levels to agent adoption, but we have collapsed it into three.

As Cursor’s Michael Truell put it in The third era of AI software development:

Cursor is no longer primarily about writing code. It is about helping developers build the factory that creates their software. This factory is made up of fleets of agents that they interact with as teammates: providing initial direction, equipping them with the tools to work independently, and reviewing their work.

The agent should not sit solely inside the developer’s flow. It should be setup to work in the background so that you can give it a task, a repo, a machine, a shell, a browser, tests, memory, and review loops to go do the work somewhere else.

In less than a year, the sentiment has shifted from avoiding multi-agent systems:

to suggesting approaches that actually work:

From coining “context engineering” to building the infrastructure behind Devin’s 7x PR growth and jump from 16% to 80% of commits across Cognition repos, Walden Yan has had a front-row seat to the background-agent shift. In this episode, Cognition co-founder and CPO Walden Yan joins swyx alongside Cole Murray, creator of OpenInspect, to unpack why everyone is building their own Devin, what changed after the December 2025 model inflection, and why “spec to pull request” is now becoming a real production workflow.

We go deep on the architecture of background agents: harness-in-the-box vs out-of-the-box, why Devin separates the “brain” from the machine, why repo setup is still one of the hardest problems, why Docker is not always enough, and how full VMs, snapshots, scoped secrets, GitHub bots, Slack integrations, and video-based testing all fit together. Walden and Cole also dig into memory, MCP limitations, multi-agent orchestration, AI code review, SRE auto-triage, PMs shipping code from Slack, Windsurf 2.0, hybrid frontier/sub-frontier systems, and the real failure mode of uncontrolled vibe coding: your codebase regressing to your worst engineer.

And as agents eat software… and software eats the world… you can draw the conclusion on what is next:

We discuss:

* Why the engineering world is waking up to background agents and cloud agents

* The December 2025 model inflection that made spec-to-PR workflows practical

* Devin’s 7x merged PR growth and rise from 16% to 80% of commits

* Why Cole built OpenInspect as an open-source background-agent system

* The economics of $20/seat agent products and why monetization is tricky

* What Cognition actually sells beyond Devin: infra, onboarding, integrations, and adoption

* Harness in the box vs out of the box, and why architecture matters

* Why Devin separates the brain from the machine for security and permissions

* Repo setup, scoped secrets, Docker Compose, and agent-ready dev environments

* Why full VMs matter when agents need to run real applications and test them

* Android, macOS, Windows, nested virtualization, and machine-specific agent work

* Why testing is much harder than “computer use”

* Screenshots, video verification, and the “I know it works” merge moment

* GitHub UX, Devin Review, AI reviewers, and agents responding to PR comments

* Why MCP alone is not enough for first-class Slack and enterprise integrations

* Memory, Knowledge, skills, Claude.md, and why retrieval is still unsolved

* Devin’s auto-generated memories and the challenge of memory pruning

* Always-on agents as permanent PMs for issues, tickets, and product areas

* Sub-agents, meta-Devin management, and what multi-agent systems actually add

* Why pure auto-merge vibe coding breaks down after about two weeks

* AI code smells, lint rules, reward hacking, and Semgrep for agent-written code

* GitAI, inline context, and preserving the “why” behind code changes

* Local testing, mock servers, older codebases, and preparing companies for agents

* Windsurf 2.0 and the handoff between local foreground agents and cloud background agents

* SRE auto-triage, support workflows, and agents as first responders

* PMs, marketing, and non-engineers creating pull requests from Slack

* AI agent budgets, $1k-$5k per engineer spend, and hybrid frontier/sub-frontier systems

* The rise of autonomous coding factories and who Cognition is hiring

Walden Yan

* X: https://x.com/walden_yan

* LinkedIn: https://www.linkedin.com/in/waldenyan/

Cole Murray

* X: https://x.com/_colemurray

* LinkedIn: https://www.linkedin.com/in/colemurray/

* OpenInspect / Background Agents: https://github.com/ColeMurray/background-agents

Timestamps

00:00:00 Introduction00:00:43 Why Everyone Is Building Their Own Devin00:01:57 Devin’s 2025 Ramp: 7x PR Growth and 80% of Commits00:03:49 OpenInspect and the Rise of Open-Source Background Agents00:07:59 What Cognition Actually Sells Beyond Devin00:09:56 Background Agent Architecture: Harness In vs Out of the Box00:12:08 Separating the Brain from the Machine00:14:07 Repo Setup, Secrets, Docker, and Full VMs00:19:13 Why Testing Is Harder Than Computer Use00:22:40 Video Verification and the “I Know It Works” Merge Moment00:23:19 GitHub UX, Devin Review, and AI Code Review00:25:42 MCP, Slack, and Enterprise Agent Integrations00:28:59 Memory, Knowledge, and Always-On Agents00:36:16 Sub-Agents, Multi-Agent Orchestration, and Meta-Devin00:43:55 Vibe Coding, Auto-Merge, and Codebase Decay00:48:38 Agent Infra, VPCs, Cloud Providers, and Fast VM Restore00:52:25 AI Code Smells, Reward Hacking, and Code Review Systems00:56:10 Making Codebases Agent-Ready00:58:30 Windsurf 2.0 and the Local-to-Cloud Agent Handoff01:01:15 SRE Auto-Triage, PMs Shipping Code, and Agent Use Cases01:04:32 Agent Budgets, Hybrid Models, and Autonomous Coding Factories01:06:51 Hiring at Cognition and OpenInspect Consulting01:07:45 Outro

Transcript

Introduction: Walden Yan, Cole Murray, and Context Engineering

Swyx [00:00:00]: All right, we’re in the studio with Walden Yan, co-founder of Cognition, CPO.

Walden [00:00:08]: Happy to be here.

Swyx [00:00:09]: Which is a cool title. And coiner of context engineering.

Walden [00:00:15]: Although I think there are many people who’d used the terms in various ways beforehand, but I did find that people, both internally and externally, enjoyed the upgrade from prompt engineering or model wrapping into maybe a more thoughtful way to build agents.

Swyx [00:00:33]: For those who haven’t caught up on that, I have on screen the Don’t Build Multi-Agents post, which you should go read on and we might refer to, and Cole Murray, who created OpenInspect.