George Bonaci is the VP of Growth and Demand Gen at Ramp, where he has been for about a year and a half. He started his career as a chemist, and after founding two science startups, he moved into marketing, landing at a 25-person software company.

They handed him ownership over email marketing, which is where he fell in love with growth. After learning marketing fundamentals there, George joined Samsara at around $100 million in revenue, where he built the company’s direct mail motion from scratch and grew it into more than 10% of pipeline, as Samsara scaled toward $650 million ARR and went public. George then ran growth and demand gen at Gong, where his wins came from improving the growth channels the team was already running. After his time at Gong, George joined Ramp.

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In this podcast, we discuss:

* How George built direct mail into more than 10% of Samsara’s pipeline, starting with sending company socks in envelopes

* Why the ceiling on optimizing an established channel is higher than most teams assume

* How George has built an AI-first growth and demand gen org at Ramp

* How Ramp’s paid search manager with no engineering background cut Google campaign launch times from hours to minutes with agents and evals

* Why the work that is immune to AI gets more valuable as everything else gets automated

* The unwritten rules fueling Ramp's growth like no managing up and avoid meetings at all costs

* How a Ramp growth lead running new verticals built an AI system that can automatically launch Ramp into new industries with positioning, copy, and multi-channel execution

Episode highlights:

* George walks through how he built direct mail into more than 10% of Samsara’s pipeline. When George joined Samsara, paid search and social were already saturated, so he went after direct mail, a channel other companies were ignoring. He mailed Samsara socks in envelopes to leads in an initial experiment. When he got a 7% reply rate back, he expanded the program. As the program grew, George found that responses to direct mail were significantly higher when sales followed up quickly, so he set SLAs requiring reps to call every recipient within an hour of the gift landing, and tracked who actually followed through. Once the growth and sales orgs were working in lockstep, George felt confident scaling gifting up. Instead of sending a single quarterly wave of gifts, the team built a Chrome extension that let every rep select a Salesforce contact and opt them into receiving a note and gift.

* George shares that most teams underrate how much is still left in the growth channels they already run. When he got to Gong, Google and LinkedIn ads were both working but capped on spend. Instead of spinning up something new, George first dove into whether there was more value to extract from the existing channels. He pushed LinkedIn creative testing from one new ad a month, to five a week, then revisited using Google bidding models that the team had tried years earlier and dropped. While similar experiments had previously been ineffective, it turned out to be because Gong had run them during the post-COVID economic downturn. Since the downturn had ended, and companies were hiring again, the tactics George tried worked, and the team grew pipeline significantly. George’s takeaway was that the ceiling on an established channel is almost always higher than the people working on it believe.

* George explains how a Ramp growth lead running paid search automated Google Ads campaign launches end to end in just two months. The growth lead started building the automation by giving an agent access to a custom MCP that let it make changes directly in the ad account. He then connected the agent to Snowflake, so it could pull campaign results, and to Slack so it picked up business context, like when there were broken data pipelines. Once this setup was in place, he wrote skills for the agent so it could understand how to use the tools at its disposal, and how to read campaign results data in Snowflake. As a last step, the growth lead wrote evaluations for the agent to run against its own output. As a result of his successful build, the agent could successfully do all paid search work, from keyword research to building and launching automations. What’s more, the time to launch a new campaign went from 10-15 hours of keyword research and campaign setup, down to roughly 15 minutes with no human involvement.

* George explains that as AI makes more marketing work automatable, the right move is to invest more in the channels that AI cannot touch. While AI allows just a handful of growth leads to automate channels like outbound email and paid ads, high-trust human interaction is immune to automation. As a result, George has made events and field marketing his biggest investment at Ramp, both in budget and headcount. He has grown the team from three to twenty five people. With so much capacity, this team is able to run hundreds of events a quarter.

* George describes another growth lead at Ramp whose job was to accelerate the growth rate of specific verticals, like construction or healthcare. She had a budget, but no headcount and no direct reports, so launching new vertical growth campaigns meant convincing folks from other teams to help her. To save time and multiply her impact, she built what she calls the vertical machine, a chain of skills and sub-agents that runs the whole launch of a new vertical’s growth campaigns itself. George explains that she can point it at a vertical like construction, and it goes and listens to the relevant Gong calls, reads Salesforce notes, and researches competitors to build a context layer. Once it has gathered context, it uses it to kick off paid search, paid social, webinars, direct mail, and a landing page all for the construction vertical. While the quality of the campaigns that the vertical machine produces are often a B- instead of an A, she can run it 15 times over, which has enabled her to expand her goal from growing one vertical, to launching campaigns to grow 15 in a month.

Where to find George:

* LinkedIn

Transcript details:

(00:00) Intro

(03:37) From chemistry startups to growth

(04:55) Direct mail as Samsara’s biggest win

(06:58) Why socks worked as a mailer

(10:00) Optimizing existing channels at Gong

(15:48) Getting attention inside a channel

(23:12) Phases of AI adoption at Ramp

(27:03) Automating Google Ads campaign launches

(29:19) Writing evals for a channel

(35:41) Deciding when to add headcount

(45:31) The counterargument to AI

(47:16) The vertical machine

(56:12) Shipping on day one

(58:58) No managing up

(1:07:23) Favorite sales tool, growth hack, and wrap-up

If you want to sponsor the podcast or recommend any guests, email hi@thegtmengineer.ai :)

And don’t forget to check out the GTM Engineer Lab

Derek Feinman is a Partner at Newmark, one of the world’s largest commercial real estate advisory firms. A lifelong New Yorker, Derek started his career as a nightclub promoter where he learned how to fill rooms and build relationships quickly, before pivoting to run food and beverage for Ian Schrager’s Morgan’s Hotel Group at 32.

After, Derek joined WeWork early to launch and lead its enterprise sales business, and he spent the next seven years continuing to use his network and relationship building skills to close some of the largest deals in the company’s history. This included closing deals with Amazon and Microsoft.

Derek later worked in payments at Checkout.com and rejoined Adam Neumann at Flow before becoming an Executive Managing Director at Newmark in early 2024. At Newmark, he’s a partner in the firm’s AI practice, supporting fast-growing technology companies on their real estate. He is also a super connector, introducing many of the founders he works with to high-value customers, and Derek is one of the top 2 referrer of new business over to the team at Clay.

In this podcast, we discuss:

* How just one meeting or connection can open doors that outbound and ad spend never will

* Why the best enterprise sellers can operate in ambiguity instead of needing a clear path in from day one

* Why to never burn a bridge even when a deal goes sour

* How Derek meets, builds relationships with, and stays connected to so many people

* How to be the kind of giver whose generosity compounds instead of the kind who gets taken advantage of

Episode highlights:

* Derek shares two examples from his time at WeWork that demonstrate the potential impact a single meeting or connection can make. After cold-emailing Amazon’s head of real estate, Derek flew to Seattle to take a meeting even though WeWork had no real enterprise product for large corporations. A month later, the head of real estate for AWS called, needing 80,000 square feet, which became the largest deal in WeWork’s history. Derek won Microsoft the same way, first offering to run events and promote for them, before asking to meet their real estate team. Shortly after, Microsoft became WeWork’s second largest customer.

* Derek explains that he has found it most effective to approach enterprise deals with the ability to operate in ambiguity. He treats each conversation at the account, even if it’s not with the final buyer, as progress since reaching a junior contact still moves him closer to the decision maker and gives him intel. Derek also avoids being transactional, focusing on giving value and building credibility instead of trying to extract value in every meeting. This flexibility, combined with staying open to how and when a deal comes together, is ultimately what lets him find a way in.

* Derek describes enterprise sales as a long game where staying in the relationship matters more than winning at any single point. He points to a WeWork deal in Argentina that turned into a heavy loss when the currency moved against them. After the client refused to make it right, he drafted an angry email that his boss told him not to send. Derek held back, and a month later the same client returned with a 10x larger that more than made up for the loss. Derek’s takeaway is that while you can rarely win a deal immediately, you can always lose it immediately, so staying patient is what keeps a relationship alive long enough to pay off.

* Derek walks through how he maintains and scales a network he describes as effectively infinite. He relies on an unusually strong memory for names, birthdays, and even spouses’ names, reads two books a month which always gives him something top of mind to talk about, and he meets around 30 new people a week. Derek builds relationships through in-person events, including a recent Padel tournament with 16 AI companies, and he writes on Substack about real estate and go-to-market. He also uses tools to keep up, storing notes in Evernote, reading a daily brief from Perplexity, and using Clay to find new prospects in real estate.

* Derek points to Adam Grant’s book Give and Take to explain how he gives favors generously without letting people drain him. The book’s core insight is that givers are both the most and least successful people, and what separates them is giving responsibly rather than spending energy on people who only take. So while Derek loves to give and understand how doing favors for people can help compound his network, he avoids giving favors to transactional people and invests his time in the ones he genuinely vibes with and that also think long-term. This selectivity protects and expands his own social capital, which he draws on personally every time he makes an introduction.

Where to find Derek:

* LinkedIn

* Newmark

* Substack

Transcript details:

(00:00) Intro

(02:01) Derek’s background

(06:03) High-agency and the Brazil visa story

(08:17) Landing Amazon and Microsoft at WeWork

(11:35) What’s wrong with the traditional enterprise sales mindset

(14:35) Playing the long game and who Derek takes meetings with

(18:33) Building connections when you’re not an extrovert and how Derek thinks about follow-up

(23:31) Derek’s partnership with the team at Clay and working in tech

(28:39) Why New York wins for go-to-market

(32:30) How Derek stays in touch with everybody in his network

(34:51) How AI helps Derek stay on top of his relationships

(37:30) Events and the office comeback

(42:59) Giving responsibly

(49:18) Favorite tools, growth play, and wrap-up

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Jai Toor is a co-founder of Deepline, a company that makes GTM data programmatically accessible to agents and production systems through API-first access. Jai started his career at Uber, joining the growth team early on to build out its growth analytics and attribution. After Uber, he went into b2b e-commerce, where he did similar work at Capchase and DataFold, before deciding to co-found Deepline in 2024.

In this podcast, we discuss:

* The most challenging unsolved data problems today

* How agents can help resolve contact and account record matching edge cases that rule-based systems miss

* When a company should begin to invest in CRM data hygiene

* Why building workflows that can pull context in real time beats front-loading an exhaustive GTM brain

* The best habits Jai sees from the highest performing teams he works with

* What isn’t obvious now, but everybody in GTM will be doing in 6 months

Episode highlights:

* Jai walks through how agents can help determine when two records in the CRM refer to the same company or person. Simple automatic rules, like matching on a shared website domain or LinkedIn, catch around 90% of cases, but fail on edge cases like identifying a subsidiary that does not obviously connect to its parent company. To handle those edge cases, agents can research each record across public information in order to judge whether they are the same, then hand back an answer with a confidence level and its reasoning so a person can quickly verify it. Once verified, that resolution feeds back into the deterministic rules engine, so the same edge case is caught automatically moving forward.

* Jai explains that there are two moments that are usually the right time for a company to begin to invest in GTM data hygiene. The first is once the company has a repeatable sales motion so that you don’t build on top of a foundation that has to change later. The second is when bad data is already blocking the team, like one customer Jai describes where 30% of inbound leads were coming in as duplicates and preventing reps from running effective follow-up.

* Jai shares that the most reliable way to handle context is to pull it in real time, and keep it compartmentalized by use case, rather than front-loading an exhaustive business brain. A static GTM context layer goes stale and risks poisoning outputs, but pulling it in real time gives the freshest, most relevant context for the task at hand. Instead of pre-loading that brain, he builds the repeatable steps for finding the context a task needs, like what matters to a specific customer, so the system always reaches for the freshest available data.

* Jai describes how cutting edge teams are using agents to build their own scoring models. For one Deepline customer, they gave their agent their closed won and lost data, and asked it to use Deepline to find which account traits predict a win. The agent built a model that ranks accounts by how likely they are to close, using the same traits an earlier hand-built model had, along with five more that the model had missed. Jai has seen companies running this use case increase their close won rates by roughly 15% just by having their sales team focus on the top accounts that the model flags as more likely to close.

* Jai points to a project that shows where go-to-market data work is heading. For a mining-safety customer, an agent using Deepline decided to take a public dataset of mining safety incidents, built a model predicting which other mines are most likely to have an incident in the next 90 days, then layered on standard hiring and contact data to find the right safety contacts to reach out to at each flagged mine.

Where to find Jai:

* LinkedIn

* Deepline

Transcript details:

(00:00) Intro

(04:06) The biggest gaps Jai sees in data today and identity resolution

(07:21) Beyond rules-based deduping

(16:26) Operationalizing cleanup workflows

(25:20) When to invest in data hygiene

(30:05) What data trends are overhyped

(37:02) Agentic data science scoring

(48:15) What Jai sees the most sophisticated teams doing with GTM data and hiring

(54:07) Non obvious habits in the best teams Jai works with

(56:21) Auto prospecting research loops

(59:15) Underrated research skill, favorite growth tactics, and wrap-up

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Alex Fine is the co-founder of Understory, a 30+ person boutique allbound agency that offers services for go-to-market engineering, paid media, RevOps, and founder-led LinkedIn content. Alex started his career at P&G working across brands like Charmin and Bounty, then moved into enterprise tech sales selling multimillion-dollar tax compliance deals. Those experiences taught Alex how to think about customer psychology and how to win enterprise deals firsthand, which helped lead him to co-found Understory. Alex is the first returning guest on The GTM Engineer.

In this podcast, we discuss:

* How many of the companies Alex works with need help implementing basic inbound lead routing

* How to scope a Claude Code build so its execution is clean from the start

* What content Alex sees standing out on LinkedIn, with so much AI slop flooding the feed

* What patterns Alex is seeing across Google and Meta ads to find success

* How Alex used AI to build Understory’s website from scratch in under six hours

* How Understory hires for GTM engineers that fit well with the team and keep standards high

Episode highlights:

* Alex explains that many of the companies he works with don’t have basic inbound lead routing in place. Inbound leads from form fills, webinars, and website visits pile up in their CRM with no routing logic assigning leads to reps, and no one following up. Alex says most people overcomplicate the fix, when a tool like HubSpot, Clay, or n8n can get 95% of companies where they need to be by enriching each lead, routing it to the right rep by territory, and generating a note on why that person is worth reaching out to. When the routing logic does get complicated, Understory maps the workflow in Miro first, since a visual map laying out the workflow is easier for everyone involved to follow and approve.

* Alex explains that he always maps the full workflow he wants to build with Claude Code before starting, and often uses Claude Code itself to help structure the plan. He explains that just because Claude Code is powerful does not mean it can be effective without clear instruction, the same way someone with pieces to the Lego Millennium Falcon cannot build it without its instructions. When planning is skipped, building a project with Claude Code leads to wasted time going in circles adjusting, breaking things, and backing up.

* Alex explains that he is seeing plain text posts outperform AI-generated carousels on LinkedIn. The carousels, identifiable by their serif fonts and generic closing lines, worked briefly before the format got copied and became noise. Instead, some of the strategies Alex uses for LinkedIn content include dictating posts in his own voice so the writing reads distinctly human, and consistently posting comments, treating each as a mini post with a genuine take of his. The comment approach alone has generated 10,000+ impressions in two days, since his takes stand out when surrounded by AI-generated comments.

* Alex explains that he’s seeing companies need to spend more and more money to find success on Google Ads, because the bidding is so competitive, and the more you spend, the better the algorithm gets.

* Alex describes how he alone built the Understory website in under six hours. He had Claude Code analyze 500 Fireflies call transcripts, split between sales and internal calls, then cross-referenced the sales calls with HubSpot closed-won data to identify which messages made prospects react and appeared in deals that closed. He then fed 40+ screenshots of website designs he liked into Claude Code to extract design patterns, and had it write a skill file for both design and copywriting. With those inputs, Claude Code was able to build 80% of the site in a single session.

* Alex lays out a hiring process built to keep the bar high as Understory has grown to over 30 people. Every candidate is instructed to send in a Loom of why they should be put into the hiring process, so Alex can see what each person chooses to show when they have complete freedom. Candidates who make it through move to an initial interview and a role-specific assessment, before moving to a final committee interview with five or six existing GTM engineers. The committee all reviews the assessment beforehand, and must unanimously decide to hire the candidate, which ensures that all new hires have spikes that complement the team’s existing strengths, and everyone feels excited to be working alongside them.

Where to find Alex:

* LinkedIn

* Understory

Transcript details:

(00:00) Intro

(02:37) Alex’s background

(04:33) Eight months of growth at Understory & a few recent wins

(10:11) What Understory is not investing in

(11:53) Building a RevOps department

(13:48) Lead routing basics & workflow visualization

(18:27) Planning before building with Claude Code

(20:41) What’s working on LinkedIn

(24:15) Alex’s views on Clay

(26:17) Challenges on Google Ads

(28:29) What’s working on Meta ads and image creative

(32:46) Why Alex is worried for designers

(34:53) Building the Understory website in six hours

(38:44) Hiring AI-native talent for GTM Engineering roles and the Understory hiring process

(46:18) How growth at Netic has been different than Rippling

(49:34) Alex’s automated sales agent, and wrap-up

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Keerthi runs growth engineering at Omni, an AI analytics platform that lets teams query their data through natural language across any modality (in-product, MCP, CLI, Slack, etc) and receive governed and auditable answers.

Keerthi joined Omni as the founding growth engineer when the company was 40 people, and in two years it has grown to over 200 employees and 40x’d their revenue. Before Omni, Keerthi was the second growth engineer at Rippling, joining around 500 employees and staying for three and a half years as the company scaled headcount past 3,500. At Rippling, he inherited an existing GTM infrastructure rather than building it from scratch, but at Omni Keerthi has been able to make foundational infrastructure decisions himself from day one.

In this podcast, we discuss:

* Why high-growth B2B companies need an exhaustive view of their total addressable market

* How to architect a three-layer GTM data system that ingests, normalizes, and activates data

* The 5 key pillars to getting your GTM data foundations right, including why querying a database beats needing to make API calls

* How to choose the primary keys in your CRM

* How Omni’s BDRs use natural language queries against live data to stack rank accounts and build prospect lists in minutes

* Why Keerthi thinks working in GTM is a very valuable, often overlooked path for engineers

Episode highlights:

* Keerthi explains that for companies expected to double revenue year over year, knowing their full addressable market is a non-negotiable. Doubling revenue requires more than doubling the spend, because returns on ad platforms are non-linear and LinkedIn in particular needs audience depth for spend to compound. Without full market visibility, pipeline runs dry faster than expected and paid channels lose efficiency, since both depend on having enough potential buyers to reach.

* Keerthi walks through the three-layer architecture he built at Omni. The first layer is ingestion, handling data from APIs, S3 dumps, Snowflake shares, and webhooks. The second is normalization. Keerthi uses a DBT modeling layer that enforces standard formats for companies, people, and events across all sources. The third is activation, where clean data flows into the CRM, ad platforms, and email tools through configuration-driven workflows.

* Keerthi chooses to buy datasets outright rather than pay for API access wherever economically feasible. When data lives in a database, running 20 targeting hypotheses means just 20 SQL queries. When it comes through an API, each hypothesis requires fetching fresh data first. At millions of records, that difference becomes a meaningful tax on the business.

* Keerthi shares that most GTM data problems trace back to CRM hygiene, and the straightforward fix is enforcing primary keys. LinkedIn URL is the ideal primary key for contacts, and company domain plus LinkedIn company page is the ideal key for accounts. Without enforced primary keys, duplication rates of 5 to 10 percent are common, which at scale can erode sales trust in the data quality. With clean records, LLM pipelines can extract structured signals from unstructured data and agentic monitors can fire Slack alerts when key accounts change.

* Keerthi points to Omni’s 30 BDRs as the clearest proof of what a well built GTM data system enables. Nearly all are daily active users of Claude connected to Omni via MCP. A BDR can ask which intent signals most commonly precede a qualified meeting, get their accounts stack ranked, then build a sequence in Amplemarket, all in one natural language session. Reps describe the experience as something that has never existed at any of their previous companies.

Where to find Keerthi:

* LinkedIn

Transcript details:

(00:00) Intro

(03:13) Career journey from Rippling to Omni

(05:54) Why GTM needs engineering

(07:32) Scaling data systems to handle TAM & the value of building an exhaustive TAM

(13:20) Ingesting, normalizing, and activating data

(21:45) The 5 GTM data foundation pillars

(31:23) BDRs using Omni daily

(33:45) Going direct to source vendors

(36:20) How AI has changed GTM data

(40:24) Underrated reason why data is more easily accessible than ever

(42:27) The value of growth engineers for companies + individuals

(46:40) What Keerthi thinks about the GTM engineer title

(48:35) Favorite underrated tool, growth hacks, and wrap-up

This episode of the gtm engineer is brought to you by Dust

Most AI agent tools are built for individuals. Dust is built for teams by putting AI agent building on multiplayer mode and connecting to your company’s knowledge across platforms like Slack, Google Drive, Notion, GitHub, Salesforce, and Snowflake.

Anyone can build agents that draw on that knowledge to handle tasks like researching accounts, answering customer questions, or drafting prospect emails. Dust also gives ops and IT the governance controls (permissions, SSO/SCIM, audit logs, role-based access) they actually need to confidently roll something like this out across an org.

The agent builder itself is no-code, so anyone with a Dust seat can prototype an agent in an afternoon without ever opening Terminal, and you can give your entire team access to semantic search on top of your sales and marketing data.

Profound used Dust to automate over 1,800 hours per month of CSM work across account research, slide deck creation, meeting follow-ups and more. We wrote a deep dive on exactly how they did it.

If you want to check out Dust, you can use code “THEGTMENG” for your first month free

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Jacob Dietle is a context engineer who runs a solo consultancy for venture-backed companies. Jacob studied information systems in college and after working at a few startups, decided to found his own agency. After spending time doing custom dataset build-outs, the agency ultimately focused on go-to-market engineering work, building automations and AI workflows for clients. The more Jacob used AI on that work, the clearer it became to him that the bottleneck on AI’s capabilities was its context. That realization became the focus of his consultancy, and now Jacob helps companies build context engines to maximize AI’s impact across their highest value go-to-market use cases.

In this podcast, we discuss:

* How Jacob thinks about context and the effect it has on AI output

* How Jacob built a system that cut a client’s newsletter creation process from 6 hours to 30 minutes

* Why Jacob keeps a person in the loop to validate AI output

* How to build a strong foundation for a context engine

* How to split context work between off the shelf tools and building yourself

* Why Jacob suggests building and managing context the way an engineering team ships code

Episode highlights:

* Context is vital because it gives AI the background it needs to produce specific, relevant output for a company instead of falling to the lowest common denominator of quality.

* Jacob helped an application security company’s head of GTM speed up their newsletter creation process. He built a reading list that pulls from the high-signal people in the AppSec space, generates a brief from them, and drops the briefs into a repo that Claude Code can use as context when taking a first pass at the newsletter. The result cut the newsletter writing process from roughly six hours to 30 minutes.

* While AI can be extremely helpful and sometimes produce better work than a person would, because it doesn’t reason like a person, a single missing piece of context can throw it off completely. That’s why Jacob always keeps a human in the loop to validate the output, and why he sees AI as best used to amplify an expert rather than to replace one.

* Jacob recommends starting a context engine with just a few use cases, because working through them surfaces the context bottlenecks that will hold the system back as it scales. Working through an early use case like sales transcript analysis quickly reveals key context, like an ICP definition, that AI needs before it can produce anything useful.

* On build vs. buy for your context OS, Jacob’s answer is to do both. Octave and tools like it give you a go-to-market context graph out of the box with integrations ready to go, which is what you want when a whole team needs to use it for a specific use case like email drafting. However, building your own context engine is also worth it for the customization it affords, like the newsletter example. Building also gives a deeper understanding of how context works, which helps teams make the tools they buy more powerful.

* Jacob suggests managing context the way an engineering team ships code. The whole system is versioned in one place, so everyone can draw from it when adding context to their workflows, changes are easy to track over time, and teams can collaborate on adding to it. Updates to context can get proposed, reviewed, and merged in. Since the AI handles the technical overhead, the bar to ship is low enough to let non-technical teammates contribute to context building, too.

Where to find Jacob:

* LinkedIn

* taste.systems (his consultancy)

Transcript details:

(00:00) Intro

(07:19) Defining context in AI

(09:16) The newsletter context system that Jacob built

(12:59) Human in the loop and pitfalls

(14:43) The foundational pieces to get right when building a context engine

(16:21) Finding the bottlenecks in your context system and back pressure testing

(20:53) How to decide when AI is good enough to handle a task and its worth building context around

(25:35) Technical setup nuances when building your context OS

(30:44) Build vs buy with your context OS

(39:20) How to effectively enable multiplayer mode with your context OS

(45:05) Ownership and staffing this system internally

(46:49) What it’s been like learning about a completely new field and building tastematter.dev

(52:33) Underrated tool, favorite growth hack, and wrap up

This episode of the gtm engineer is brought to you by Dust

Most AI agent tools are built for individuals. Dust is built for teams. Dust puts AI agent building on multiplayer mode and connects to your company’s knowledge across platforms like Slack, Google Drive, Notion, GitHub, Salesforce, and Snowflake.

Anyone can build agents that draw on that knowledge to handle tasks like researching accounts, answering customer questions, or drafting prospect emails. Dust also gives ops and IT the governance controls (permissions, SSO/SCIM, audit logs, role-based access) they actually need to confidently roll something like this out across an org.

The agent builder itself is no-code, so anyone with a Dust seat can prototype an agent in an afternoon without ever opening Terminal. With the whole company on the same platform, you get visibility into who’s building what and what’s being used the most.

If you want to check them out, you can use code “THEGTMENG” for your first month free

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Adrian Alfieri is the Founder and CEO of Verbatim, a case study agency for venture-backed B2B SaaS companies like Profound, Granola, and Siro. After spending three years in early-stage venture capital, Adrian decided he wanted to work directly with founders rather than invest in them.

To figure out where he could be most impactful as an operator, Adrian started contracting with a handful of friends running YC-backed companies. As he worked across these companies, each one independently kept coming back to him for the same type of work, creating social proof content and distributing it to drive pipeline. When Adrian looked at the broader market, he realized no one was owning case studies and social proof as a category, despite founders consistently ranking it as one of their highest ROI content investments. This insight led him to launch Verbatim over four years ago.

In this podcast, we discuss:

* Why case studies are one of the most versatile content assets a company can invest in

* Why building a case study requires consistency and not one-off sprints

* How to twist the knife when interviewing customers to produce great and memorable case studies

* How a company’s case study motion will look different depending on their maturity

* How a single great case study can be repurposed dozens of times across different channels, formats, and angles

* How you can evaluate the quality of a company based on talking to their customers

Episode highlights:

* Adrian explains that because case studies contain quotes, metrics, and stories that different teams can use in different ways, they’re one of the few content assets that work across every part of the business. Sales teams can use them in live calls and follow-ups, marketing teams can repurpose them across landing pages, paid campaigns, and lifecycle, and champions share them internally. Each of those touch points builds trust with the buyer and removes doubt about whether the product actually delivers.

* Adrian walks through how companies need volume and consistency in their case study motion in order to provide real credibility. If a buyer visits a company’s case studies page and the most recent one is from a year ago, it raises questions about whether something went wrong. The mistake he sees teams make is sprinting, producing a batch of case studies in a quarter then shipping nothing for many months, which leaves a gap that makes buyers nervous. On the other hand, a consistent drip of at least one case study per month signals that customers are continuously hitting value and are willing to go on record about it.

* When Adrian interviews a customer for a case study, he prioritizes the live user who actually works in the product day to day. He starts by asking what problem led them to the product, and instead of moving on, he twists the knife and pushes deeper into their pain before adopting the tool. He asks what would have happened if they stayed with the status quo, what metrics would have suffered, and how their job would have been affected. When the live user starts reliving the pain of their old solution, their quotes get unfiltered, they give specific metrics, and the before and after becomes stark enough that a buyer reading the case study can feel the impact.

* Adrian explains that the companies who have a steady, increasing stream of happy customers to hand off for case studies is a very positive signal in company quality. On the other hand, he has seen Series B companies that just raised huge rounds and can barely find any customers willing to do case studies.

* By creating a well-done case study with different use cases, multiple approved quotes, and several before and after metrics, each of those elements can be pulled out as standalone content. Adrian points to Verbatim’s case study with Unify, which had enough material for him to create over a dozen unique LinkedIn posts over the past year and a half. The early investment in one great case study became a content engine that kept producing long after the interview was over.

Where to find Adrian:

* LinkedIn

* Verbatim

Transcript details:

(03:38) Intro and Adrian’s background

(07:16) Why social proof and case studies are high-ROI content assets

(12:50) Interviewing to get the best insights

(16:38) Whether to include ROI metrics in your case studies

(17:51) Designing the format of a case study

(23:28) Consistency over sprints and why stale content raises red flags

(27:55) Why using an outside agency can actually make the process feel more professional

(29:52) How to discern a high vs. low quality case study

(33:03) How to source customers for case studies

(38:03) What running a case study agency has taught Adrian about investing

(43:06) What makes a great internal point of contact to create amazing case studies

(44:08) What Adrian has learned about building content engines

(49:30) How to maximize the value of every case study you run

(57:02) Favorite underrated tool and creative campaign and wrap up

This episode of the gtm engineer is brought to you by Octave

As a GTM engineer, you’ve probably built workflows, Clay tables, agents and automated campaigns. Two common issues are that, although you’ve set up some pretty complicated, nuanced workflows, they don’t cascade throughout your entire GTM motion (ads, lifecycle campaigns, SDR messaging, etc), and they don’t adapt as you learn more about your users.

The reason why…I’ll save you some time…is stale, disconnected context. In other words, the data you’re piping into the system is not as relevant as it was many months ago, and that context is not making it everywhere it needs to go.

The most valuable knowledge from sales and marketing (ICPs, winning messaging, competitor positioning, specific use cases) often lives in gated docs, call transcripts, email templates, and other tools. Octave’s GTM intelligence layer solves this problem by pushing this strategic context + new dynamic context like intent signals into every system you’ve built, automatically, and it gets smarter automatically over time.

If you want to check them out, you can visit this link to sign up and get 4000 credits + your first month free

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Nick Lafferty is the Head of Growth and Founding Marketing Engineer at Profound, a company that helps brands improve their visibility in AI search. He has spent the last 12 years working as a highly technical growth marketer for B2B SaaS companies. In 2021, he joined Loom and spent nearly two years there as Head of Growth before their acquisition. After feeling burnt out at Loom, Nick went into consulting, running a solo growth marketing agency where he worked with companies like Glean, AngelList, and Watershed on paid ads and pipeline generation, taking home over $500K. After being introduced to the CEO of Profound in March of 2025, Nick joined as their first marketer when the company was around 20 people. Since then, Profound has grown to 130 employees, raised a Series A, B, and C within 10 months, and reached a $1B valuation.

In this podcast, we discuss:

* How Nick used Claude Code to automate building personalized one-to-one ABM content, turning a process that used to take days into one that produces hundreds of ad variations in an hour (4-min video here)

* How Nick uses the time freed up by automation to think through creative ways to build long-term advantages, like locking in a year of newsletter sponsorships in advance so they’re ready when he needs them

* How Nick thinks about build versus buy decisions and what he calls tolerance for jank

* How Profound uses its own product to generate content briefs, automate customer reporting slides, and layer in original research

* Why the bar for growth hires keeps rising and how Nick now evaluates candidates for both technical skill and marketing taste

* How Nick thinks about the importance of AI visibility and how Profound’s proprietary data that can help you determine AI search traffic

Episode highlights:

* Nick used Claude Code to build a system that takes a list of target accounts, enriches them with competitor data, then pulls both target and competitor logos via an API. The system then generates hundreds of personalized ad variations in an hour that show the target company’s logo next to a competitor’s with messaging that the competitor is beating them in AI search. By running these hyper-personalized ads against target accounts, Nick is seeing click-through rates around 5-6% (>10x LinkedIn’s benchmark 0.4%).

* Nick explains that deciding build versus buy comes down to who the work is servicing and tolerance for jank. Anything built internally will be rough around the edges, so if the tool is for an individual or a small team that can handle jank, building makes sense. But if it needs to support a broader team like customer success, buying often wins because of the polish, support, and someone to call when things break.

* Nick walks through how Profound uses its own product to generate content briefs by analyzing which pages get cited most in AI search responses for a given topic, then mapping out what a competing piece of content needs to cover to rank. To take the briefs from good to great, Nick built a workflow in Profound’s agents tool that pulls original research from their internal white papers and inserts it directly into the brief, giving the resulting content unique data points that competitors can’t replicate (4 min walkthrough here).

* Nick explains that Profound has and shares proprietary data that gives directional reads on AI search volume to help you prioritize which searches to try and rank for.

* Vibe coding a basic version of a tool used to be an instant hire signal, but as AI tools have gotten more powerful and there are more examples to copy from online, that’s now not enough. Nick explains that in interviews, he digs into the decisions behind the build, asking candidates why they chose one approach over another and what they tried that didn’t work, since that’s what separates someone who can think critically from someone who just copied an idea they saw online. What’s more, he looks for candidates with strong marketing foundations before AI skills, since AI has no taste of its own, and you need to know what good marketing looks like before you can automate any of it.

Where to find Nick:

LinkedIn

Transcript details:

(00:00) Intro

(04:00) Profound’s Series C announcement

(04:40) Comparing Loom and Profound and how marketing teams have gotten leaner

(07:25) What Nick worked on at Loom

(09:30) How Nick got into consulting

(11:20) Running a solo growth agency and the economics of consulting

(13:48) How Nick ended up at Profound and what his role looks like

(19:00) Deep dive on the automated ABM ad creation workflow

(25:56) Why manual work is a death spell and what modern growth looks like

(29:01) How to evaluate whether candidates can think critically versus just copying ideas

(33:37) How Nick is thinking about what is newly possible in growth with AI

(31:43) Automating slide creation with Gamma and building a Swiss army knife of growth systems

(37:35) Planning distribution channels in advance and locking in newsletters and influencers

(39:14) How Nick thinks about build versus buy decisions

(42:04) Building a culture at Profound that celebrates shipping and agency

(43:25) How Profound uses Profound

(50:57) Whether to prioritize Google or LLM visibility depending on how AI-native your buyer is and what Profound’s proprietary data says

(59:05) The rise of the marketing engineer

(1:00:38) Favorite underrated tool and wrap up

This episode of the gtm engineer is brought to you by Octave

As a GTM engineer, you’ve probably built workflows, Clay tables, agents and automated campaigns. Two common issues are that, although you’ve set up some pretty complicated, nuanced workflows, they don’t cascade throughout your entire GTM motion (ads, lifecycle campaigns, SDR messaging, etc), and they don’t adapt as you learn more about your users.

The reason why…I’ll save you some time…is stale, disconnected context. In other words, the data you’re piping into the system is not as relevant as it was many months ago, and that context is not making it everywhere it needs to go.

The most valuable knowledge from sales and marketing (ICPs, winning messaging, competitor positioning, specific use cases) often lives in gated docs, call transcripts, email templates, and other tools. Octave’s GTM intelligence layer solves this problem by pushing this strategic context + new dynamic context like intent signals into every system you’ve built, automatically, and it gets smarter automatically over time.

If you want to check them out, you can visit this link to sign up and get 4000 credits + your first month free

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Michel Lieben is the Founder and CEO of ColdIQ, a GTM agency that has grown to $7M in revenue with 35 employees since launching in January 2023. Before the agency, Michel spent years in marketing roles at startups and built affiliate businesses on the side, including a directory of GTM tools. To drive traffic to the directory, Michel started posting about those tools on LinkedIn, and as his following grew, people kept asking him to run outbound for them. After he said yes to one client, ColdIQ was born. Today ColdIQ runs outbound, LinkedIn content, and LinkedIn ads for B2B companies, syncing all three into what Michel calls a GTM flywheel.

In this podcast, we discuss:

* How ColdIQ uses a combination of outbound, LinkedIn content, and LinkedIn ads to warm up accounts before cold emails ever land

* Why most people fail at LinkedIn content and what Michel recommends instead

* How posts with hooks that establish the author’s credibility outperform posts with generic hooks even when the substance of the content is the same

* How ColdIQ uses repeatable content formats like tech stack breakdowns to stay consistent and drive engagement without starting from scratch every week

* How ColdIQ is using Cursor and Claude Code to operationalize cross-client campaign learnings into a system that gets smarter with every engagement

Episode highlights:

* ColdIQ runs LinkedIn ads against target accounts one to two weeks before cold emails go out, taking top-performing organic posts and expanding them into ad formats. They then use tools that identify which companies engaged, and reps follow up with cold emails referencing that content. Michel notes that identical outbound templates produce significantly better results when accounts have been warmed up by LinkedIn ads first.

* Michel says most people fail at LinkedIn content because they come in with their own idea of which formats should perform well. He suggests that a better first step is to study what’s already performing by looking at top creators in a relevant niche, identifying their best-performing posts, and recognizing the patterns across them. Instead of making the substance of your post 10% better, Michel recommends making the packaging 10% better, since it can drive a 5x impact on reach.

* Michel explains that the most effective posts have hooks to establish credibility before introducing the topic. He gives an example of a CEO whose post on three tips to sell more got no traction. The content was solid, but there was nothing differentiating it over the hundreds of other posts on the same topic. After reframing the post to lead with the company’s actual revenue numbers and then sharing the same three tips, the post would drive far more reach.

* One of ColdIQ’s biggest content advantages is repeatable formats. After Michel’s post about ColdIQ’s own tech stack blew up when they hit seven figures, he realized he had a format that worked and could be built on over time. He’s since repurposed and expanded it as the company has grown and swapped tools, and eventually started featuring other companies’ stacks as well. Because people forget posts within a couple months, a proven format with added content can keep delivering high engagement results.

* Because building software is easier than ever, Michel thinks that the bar for SaaS tools has gone up, while cost does down, effectively strengthening the case for buy instead of build and raising the expectations a user can have from the products they buy.

* ColdIQ feeds performance data and copy from 80+ client campaigns into a central knowledge base built on Cursor and Claude Code. When team members are building new campaigns, they can query the system in natural language to surface what’s worked for similar clients, as well as see when templates have started underperforming and should be sunset. Michel sees this as the biggest long-term moat for a niche agency.

Where to find Michel:

* LinkedIn

* ColdIQ

Transcript details:

(00:00) Intro

(02:15) Scaling ColdIQ to 35 employees and seven figures in three years

(04:08) Syncing outbound, LinkedIn content, and LinkedIn ads

(06:00) Warming accounts with ads before outbound and identifying engaged leads

(12:12) Why most people fail at LinkedIn content and how to study what actually performs

(14:33) Writing hooks that answer why the reader should listen to you specifically

(17:59) How to create content that can endure algorithm changes

(25:06) Why human-written content will continue to perform well as AI-generated posts flood the platform

(26:38) Michel’s creative process for LinkedIn posts

(29:10) How Michel forms his view of what good looks like by staying on the front lines

(34:16) The tech stack content format and how to use repeatable post structures

(42:31) Podcast distribution strategy and repurposing interviews into LinkedIn content

(45:37) Michel’s takeaways about tech stacks and build vs. buy after spending so much time digging into them

(53:11) Using Cursor and Claude Code to automate agency operations and reduce manual work

(54:08) The advantage of operationalizing learnings across 80+ client campaigns

(01:02:58) Favorite underrated tools, growth hack, and wrap up

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Oran Akron is the VP of AI GTM at monday.com, where he leads a new internal team focused on rebuilding go-to-market processes with AI agents.

Oran joined monday.com in 2018 when the company had ~100 employees and just five sellers. Since then, he’s helped scale the revenue org to over 1,000 people across sales, customer success, marketing, and partnerships while growing the RevOps team from one to more than 80. About six months ago, after conversations with monday.com’s new CRO about the future of GTM, Oran decided to step away from RevOps and incubate something new.

His team operates like a startup inside Monday, moving fast and shipping agents that are already handling the majority of inbound leads, expanding into new languages and markets, powering outbound research, and surfacing product usage signals that trigger sales engagement.

In this podcast, we discuss:

* Why Oran decided that AI was going to fundamentally change go-to-market and why he started a new team inside Monday dedicated to rebuilding GTM processes from scratch with AI

* Why improving the top of the sales funnel was the first problem Oran’s new team tackled and how AI agents now handle inbound qualification and meeting booking across multiple languages

* How Oran’s team built an outbound agent that automates account research and generates personalized recommendations for reps, while preserving their autonomy over how to use it

* The importance of monitoring AI agents in production and how Oran set up dashboards, failure tracking, and observability to scale with confidence

* What Oran learned about change management, internal branding, and gamification to get a 3,000-person org to embrace AI agents

Episode highlights:

* After seven years of building and leading RevOps, Oran felt that if he had to build Monday’s sales org today, he would do it much differently. With support from Monday’s CRO, they agreed to have Oran step away from RevOps and incubate a new team within Monday focused on rebuilding GTM processes from scratch with AI.

* Oran’s new team started with inbound lead handling. The team tested against cohorts of their best-performing human reps, scaling only after results were equal or better. The results matched or exceeded top rep performance, and Oran’s AI now handles the majority of inbound leads, conducts real conversations that run five minutes or longer, qualifies prospects, and books meetings directly on AE calendars.

* They built an outbound agent to listen to everything on the web, from podcasts to financial reports to LinkedIn activity, and synthesize that research into personalized account plans. The agent then recommends which department to target, why to start there, and how to position Monday’s offering based on what’s happening at the account. Importantly, reps retain full autonomy over how they use the output, as Oran doesn’t believe in forcing a playbook on them.

* Oran treats his agents like an engineering system that needs to be closely monitored. His advice for others implementing AI agents in GTM is to start by tracking the same business performance metrics used for human reps, then layer on engineering-style observability to catch where things break

* Oran explains that change management was the biggest organizational challenge he faced in his new role. The most effective approach combined top-down communication about the future of AI in go-to-market, gradual rollouts rather than all-at-once deployments, and a deliberate effort to make the agents feel exciting rather than threatening.

Where to find Oran:

* LinkedIn

Transcript details:

(00:00) Intro

(03:06) Oran’s monday.com journey

(08:06) Why Oran pivoted to focus on AI in GTM

(11:07) Prioritizing top of funnel wins to build trust in implementing AI across the org

(14:19) How Amanda, the inbound agent, qualifies leads and books meetings in two minutes instead of 24 hours

(15:42) Expanding into new language markets without hiring local teams

(18:42) The AI ecosystem behind the agents and why Amanda was rebuilt four times in six months

(20:40) Monitoring, guardrails, and treating AI agents like an engineering system

(24:22) The next most impactful use cases beyond inbound

(24:39) The outbound agent that researches accounts across the web and builds personalized account plans

(26:35) Product usage signals and identifying intent in real time to trigger sales engagement

(27:36) Giving reps autonomy over the AI output rather than forcing a single playbook

(29:26) How the team serves information through the CRM and monday.com’s own AI tools

(30:38) Change management, over-communicating the strategy from the top, and other strategies to drive adoption

(35:29) How AI changes what’s required from salespeople and why relationship building becomes more important

(40:18) Why moving fast and launching before perfection is a worthwhile tradeoff

(43:29) Reinventing PLG by understanding user intent in real time instead of distributing leads 24 hours later

(45:46) Favorite tools, growth campaign, and wrap-up

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Brendan Tolleson is the Co-Founder and CEO of RevPartners, and Diana Marcela Gonzalez is a Senior GTM Strategist at RevPartners who leads the company’s Allbound product line. RevPartners started during Covid and has since grown to over $10 million in revenue and 75+ people, becoming the only Elite HubSpot and Clay partner in the world. The company was built on fractional RevOps, helping B2B companies get a plug-and-play operations team instead of hiring a single generalist, and has since expanded into HubSpot implementations, RevOps training through their RPX program, and Allbound, their fastest growing product line. Allbound is built on three pillars: data intelligence, which is done in Clay and covers enrichment, signals, and list building; brand gravity, which includes SEO, GEO, paid media, and social; and assisted prospecting, where data and brand interactions come together to drive multi-channel outreach at the right time.

In this podcast, we discuss:

* How RevPartners combines data intelligence and brand gravity to run assisted prospecting campaigns where the right message reaches the right person at the right time

* How website visitor de-anonymization can still be a huge lever for early pipeline generation and the best copy to use (and not use)

* How lifecycle stages and lead scoring create the foundation that makes signal-based outbound work

* The ways RevPartners use LinkedIn profile context, buyer persona pain points, and intent data to create contextual outreach

* What RevPartners’ Southbound conference is, why it’s becoming a must-attend event for revenue leaders, and the creative ways RevPartners drives registrations for their conference

* Using automated enrichment cadences and job change triggers to keep your CRM from going stale

* Hubspot vs. Salesforce for your CRM

Episode highlights:

* The key to RevPartners’ Allbound approach is that no channel operates in a silo. Diana explains that they track brand interactions across LinkedIn, website, webinars, and other channels, then weave all of that engagement history into how and when they reach out. A contact who registered for a webinar gets a LinkedIn message asking what topics they’d like covered. A contact who’s been engaging with social posts from a specific thought leader gets invited to hear them speak at Southbound. The result is outreach that feels contextual because it actually is, built on real signals rather than a cold list and a generic sequence.

* Diana walks through how RevPartners uses a tool called Vector for contact-level website visitor de-anonymization. The workflow captures visitors who never fill out a form, enriches them in Clay, categorizes them by ICP and buyer persona, and then generates personalized outreach using their LinkedIn profile and job description. But even with good data, the timing of messaging matters. Brendan shares an early lesson where RevPartners tested automated videos that pitched visitors for a demo as soon as they were identified on the site, and because they were just checking it out and not ready to buy, the response was poor. Afterward, RevPartners shifted toward contextual outreach that matches the page the visitor was on with relevant assets or questions designed to start a conversation rather than deliver a hard sell. From then on, they’ve seen efficacy rates soar.

* Lifecycle stages and lead scoring are how RevPartners determines who to reach out to and when. Diana emphasizes that stages must be fully automated rather than left to manual rep updates, and that contacts should only move forward so conversion reporting between stages stays reliable. Once a contact’s stage is established, it pairs with their buyer persona and ICP categorization, identified via Clay enrichment. The combination of the lifecycle stage and persona categorization helps RevPartners determine what pain to mention and what CTA to use in their messaging.

* On top of using a contact’s lifecycle stage and persona for messaging, RevPartners will use additional context whenever possible to personalize outreach. For instance, for their Southbound conference, they targeted people who engaged with posts from speakers like Yamini Rangan, the CEO of HubSpot, and Varun Anand, Co-Founder of Clay, and invited them to hear those speakers live.

* Diana recommends automated enrichment cadences every three to six months depending on how fast the target audience moves in order to keep your data fresh. What’s more, one of the most important triggers to keep data fresh is job changes, where a contact leaves a company and the system automatically creates the new contact and company records while preserving the historical context of the relationship. Without this, databases go stale and outreach hits people who no longer match the ICP or have moved on entirely.

* Brendan says that Hubspot has now reached feature parity with Salesforce as a CRM and there’s just a feature knowledge gap

Where to find RevPartners:

* LinkedIn

* Diana Marcela Gonzalez

* Brendan Tolleson

* Southbound conference (code RIPPLING for 20% off)

Transcript details:

(00:00) Intro

(03:20) Backgrounds: Brendan’s sales-side journey and Diana’s path from growth marketing to RevOps to leading Allbound

(05:05) RevPartners used to resist the agency label and how they bridge strategy and execution

(08:26) What RevPartners works on with clients

(10:46) The three pillars of Allbound

(12:33) Favorite plays for their own outbounding and with clients

(17:22) Website de-anonymization nuances: validation, accuracy, and what to watch out for

(19:57) How to send contextual messaging that works across different signals

(23:55) Why lifecycle stages, data hygiene, and lead status are foundational to outbound

(28:11) Diana’s screenshare of how RevPartners sets up + thinks about lifecycle stages (YouTube video here)

(29:44) Lead scoring and reporting: how lifecycle stages feed into propensity and intent models

(31:00) Lifecycle messaging examples

(35:49) Choosing the right CRM and why adoption matters more than features for early stage companies

(39:43) The HubSpot versus Salesforce shift and why the barrier to switching has dropped

(41:30) Keeping CRM data fresh with automated enrichment cadences and job change triggers

(43:44) Why Southbound exists and how RevPartners is building a conference that balances timeless strategy with timely tactics

(49:14) How RevPartners is driving Southbound registration with their own creative, segmented campaigns

(51:33) Favorite tools, growth plays, and wrap-up

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Jaleh Rezaei is the CEO and Co-Founder of Mutiny, a Sequoia backed company building an agentic AI platform for go-to-market teams. Before Mutiny, Jaleh spent four years in product marketing at VMware during a period of rapid growth, then joined Gusto as employee number 12 and led marketing & BD as the company scaled from 12 to 500 people. While at Gusto, frustration of being blocked by engineering and design dependencies led Jaleh and her co-founder Nikhil to start Mutiny in 2018. While initially focused on no-code website personalization and ABM, about a year ago, Mutiny doubled down on agentic AI, and rebuilt the platform from the ground up. Mutiny now replaces the manual work of creating landing pages, case studies, and sales materials with an AI agent that can generate on-brand assets in minutes.

In this podcast, we discuss:

* Why marketing is shifting from channel specialists to full-stack GTM athletes as AI agents get closer to handling the tactical work of running ads, managing campaigns, and optimizing spend across platforms like LinkedIn, Meta, and Google

* How building personalized landing pages at Gusto showed Jaleh the power of personalization and the frustration of not being able to scale it

* How Mutiny’s agent extracts brand guidelines, case studies, and resources to generate on-brand customer-facing assets that lets sellers create the hyper-personalized deal materials they need without waiting on their marketing team

* Why the buying experience represents your brand to prospects, and how speed + personalization are some of the biggest differentiators in competitive deals

* Why Jaleh thinks marketers need to spend time learning how to sell in order to build empathy for what selling actually looks like

Episode highlights:

* At Gusto, Jaleh’s team built personalized micro-pages for startup segments like Y Combinator companies. Instead of sending them to a generic website, each page showed the exclusive YC offer, listed other YC companies already using Gusto, and included their testimonials. The result was a 2x increase in conversion rate because the page made the purchase decision feel obvious and risk-free. At the time, this level of personalization was only possible for a handful of segments, and scaling it required resources most teams like Gusto’s didn’t have.

* Most companies staff their sales org with enough reps that each can go deep on a relatively small book of accounts, where closing deals requires constantly providing custom one-pagers, ROI decks, case studies, and tailored business cases. However marketing teams are infrequently large enough to deliver those assets for every account. Pre-agents, even the best marketing teams could only realistically personalize for five to ten verticals, so one-to-one ABM was reserved for a small number of top accounts. As a result, reps ended up spending a significant time creating these assets themselves instead of focusing on selling.

* Mutiny’s agent automatically extracts a company’s brand standards, design guidelines, images, case studies, and resources during onboarding, then generates personalized assets like deal-specific case studies, custom landing pages, and tailored ROI decks that comply with those standards. Jaleh notes that the agent often understands brand guidelines better than most people on the marketing team outside of brand design.

* Jaleh explains that when Mutiny was evaluating an AI CRM, the rep was slow to respond, apologetic that he didn’t have the answers to their questions, and kept delivering underwhelming materials. Over time, the internal conversation shifted from thinking the product was strong, to questioning whether the company could be trusted at all. The buying experience became the lens Mutiny used to evaluate the entire brand. Jaleh argues that this is why equipping sellers with the tools to respond to live deals quickly with personalized and high-quality assets matters so much. Every interaction a rep has with a prospect is a reflection of the company, and a seller who can’t deliver what a buyer asks for in a timely way risks eroding trust.

* In sales and marketing there are two things that matter most: (1) connecting the customer’s pain to your product and (2) building trust. Connecting pain comes from deeply understanding users, communicating through their eyes and making it clear what you are solving for them. Trust comes from speed, substance, and reliability.

* Jaleh’s top suggest for building the GTM athlete muscle is to spend time selling. There’s no better way to build empathy for customers (and sellers) than talking + selling to prospects yourself. This enhances the quality of support and depth of understanding that drives the marketer’s work.

Where to find Jaleh:

* LinkedIn

* Mutiny

Transcript details:

(00:00) Intro

(02:52) Jaleh’s background and Mutiny’s evolution from website personalization to agentic AI platform

(06:02) How AI changes GTM roles from channel specialists to full-stack athletes

(08:33) Why specialization has become more important in marketing over the last 10 years

(11:05) The Gusto micro-page example that doubled conversion rates for YC startups

(13:03) Why the best insights come from talking to customers

(16:03) The sales versus marketing dependency problem and how agents close the gap

(19:15) Speed, personalization, and why the buying experience is your brand

(23:20) The two things that matter most in marketing and sales

(24:42) How to connect your product with the customer’s pain

(30:05) How Mutiny is making it easier to automate the time consuming parts of marketing so you can focus on creativity + true product marketing

(31:08) Why scaling ABM pre-agents wasn’t realistic

(32:42) How agents enable sellers to run one-to-one ABM with marketing guardrails

(36:24) The evolving role of marketing and sales collaboration with AI

(39:04) How AI raising the floor makes creativity in marketing more important than ever

(44:41) How ABM is changing

(48:39) How Jaleh recommends personally getting better at connecting product to pain

(51:05) Favorite underrated tool, favorite creative campaign, and wrap up

Dave Birckhead is a marketing technology and growth systems leader with over 20 years of experience at the intersection of engineering, product, and GTM. He served as the first Global Head of Marketing Technology at Spotify for six and a half years, where he built and scaled the MarTech stack across Spotify’s three-sided marketplace of consumers, artists, and advertisers during a period of rapid company growth. After Spotify, Dave joined ezCater, a corporate catering company valued >$1B, as Director of Marketing Technology and Operations, leading a rebuild of their growth stage data and systems foundation. Spurred by a growing sense that the shift to agentic AI systems was happening rapidly, in September 2025 Dave stepped away to take what he calls an AI sabbatical. Since then, he’s been deep diving full-time into building AI-native GTM systems, coding prototypes, and sharing what he learns in public through his Substack, Full-Stack Growth.

In this podcast, we discuss:

* Why Spotify built their own messaging stack instead of buying off the shelf and how they used machine learning to optimize message volume at an individual user level

* How Spotify automated global creative asset production from a three to four month manual process down to days

* What prompted Dave to leave a leadership role and go on his AI sabbatical

* How AI has fundamentally changed the velocity of learning and why jumping into building is easier than ever

* Why the last 10% of building AI systems takes more time and expertise than the first 90%

* How GTM engineering roles will converge across marketing, sales, and CS under unified leadership and why companies need to build dedicated IC career tracks for GTM engineers

Episode highlights:

* At Spotify, Dave’s team tackled a critical user protection problem around messaging. Multiple teams across the business were sending messages to users without centralized visibility, leading to high opt-out rates. The engineering team built machine learning models that optimized message volume at the individual user level rather than applying blanket cutoffs, and unsubscribe rates dropped steadily as a result.

* Global marketing campaigns at Spotify required thousands of ad variants across channels, formats, countries, and languages, and as a result took three to four months of manual work to accomplish. Dave and his team automated down the slow and manual creative production work to just days through a combination of process rationalization, vendor consolidation, and a creative production platform. Now on his AI sabbatical, Dave’s rebuilt a prototype version of this same workflow using Claude Code and Figma’s MCP server, where a single creative asset can be used to quickly generate variants across every channel for any country.

* During his sabbatical, Dave has built over 15 prototype systems and two production-grade AI applications. One of his most compelling builds is a cross-functional AI system where marketing, sales, and CS agents share memory and context. Sales agents could reference marketing interactions to inform their recommendations, and CS agents could see what the sales team committed to during the deal cycle, creating a seamless customer experience across the entire lifecycle.

* One of Dave’s learnings from building during his sabbatical is that building an AI prototype to 90% can happen in a day, but completing the final 10% is far more challenging. For LLM-based systems, the work of setting up evals, monitoring, tracing, and security is significantly more time-consuming than building the initial demo. As a result, there’s often a perception gap when stakeholders see someone build a working prototype and wonder why it can’t ship immediately.

* One of the things that gave Dave confidence in taking his sabbatical was how much easier AI has made it to learn. He uses AI-assisted coding to handle syntax he hasn’t brushed up on in years, and he keeps a ChatGPT window open as a tutor, asking it to explain unfamiliar concepts as he builds. As a result, Dave’s able to learn faster than ever, and never feels like a lack of knowledge blocks him from continuing to build.

* Dave believes roles in marketing tech, rev tech, and CS tech will converge under unified leadership responsible for shared customer data foundations and cross-functional prioritization. He also sees a parallel shift in career pathing, where companies will need to establish IC tracks for GTM engineering similar to what exists in software engineering. Rather than measuring contribution by team size or managerial scope, companies will need to identify and reward individual contributors who are having an outsized impact on business outcomes and build compensation and performance structures around that.

Where to find Dave:

* LinkedIn

* Full-Stack Growth Substack

Transcript details:

(00:00) Intro

(03:17) Joining Spotify as their first Global Head of Marketing Technology and the state of the stack in 2017

(06:33) Spotify’s three-sided marketplace and the hybrid build vs. buy approach to their MarTech architecture

(10:20) Automating global creative production at Spotify from months to days

(11:56) Building Spotify’s messaging stack and using machine learning for individual-level message optimization

(13:51) Why Dave chose ezCater and the challenge of rebuilding data foundations at a growth stage company

(16:18) The decision to take an AI sabbatical and why evenings and weekends were not enough

(24:31) Structuring the sabbatical around building, writing, and community & favorite projects

(33:43) What building teaches you about the future of super ICs and 10x GTM contributors

(35:34) The emergence of GTM engineer roles at companies like Notion and Ramp and what IC career pathing needs to look like

(37:54) Why you should build bespoke AI systems on top of your platforms instead of vibe coding your CRM

(39:26) Non intuitive places that AI can positively impact system builds and the 90% to 100% fidelity challenge

(42:20) Evals and understanding what production-ready means

(43:53) Dave’s summarized thoughts about taking an AI sabbatical, advice for people considering a similar path and why jumping back into building is easier than you think

(46:39) Why GTM roles across marketing, sales, and CS will converge under unified leadership

(50:08) Behind the scenes of Spotify Wrapped and the systems that make it work at scale

(54:15) The Tao of MarTech

(57:49) Favorite underrated tools, growth hack, and wrap up

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Umar Farooq Adam leads global GTM innovation and engineering at Hitachi Vantara, a subsidiary of Hitachi Limited. Starting his career in the Middle East working for a small B2B SaaS startup, Umar then moved to an ISV selling to defense sectors in the US and Europe. Afterwards, he spent time at Microsoft working on customer lifecycle management, renewal operations, and solution design, giving him a strong sense of each component in GTM.

After Microsoft, Umar joined Hitachi Vantara as an inside sales rep covering APAC territories. While in the role, he noticed data quality and workflow problems firsthand, and started fixing them within his own territory. His work’s impact got attention from leadership, and he was able to get sign-off on running a 12-month POC that aimed to prove out whether the fixes he implemented could scale globally. By the end of the project, leadership created a dedicated role for him to lead GTM engineering globally.

In this podcast, we discuss:

* How Umar went from an individual sales rep to leading global GTM engineering at a 10,000-person company

* Why getting leadership buy-in early is key when building out a GTM engineering function across a large org

* What Umar focused on and deprioritized during the project that aimed to prove out the value of a GTM engineering function

* How to think about friction in separate terms that resonate with sales, marketing, and operations leadership

* Why data collection’s impact is minimized without activation

Episode highlights:

* Umar’s path in Hitachi started with fixing problems in his own territory. Data quality was a mess because CRM records weren’t updating when things like mergers and acquisitions happened. As a result, he set up alerts using LinkedIn Sales Navigator and ZoomInfo to track company news, then manually raised requests with the data team to fix account hierarchies. He also built a manual waterfall enrichment process in Excel, pulling contacts from the CRM first, then ZoomInfo, then Lusha. Those fixes made enough of an impact that they were noticed by leadership and kicked off the POC to prove out implementing programmatic changes globally.

* During the POC, Umar deliberately focused only on data quality and workflow automation. By keeping his scope tight and working on high likelihood of success projects, he managed to move the needle in both areas. Data quality improved by at least 60% through waterfall enrichment in Clay. Workflow automation sped up tasks like account research, identifying what products accounts were in the market for, and crafting relevant messaging.

* Umar explains that one takeaway from the POC was the importance of aligning with regional leadership. In global organizations, people trust their local leaders more than global top-down initiatives. Once he started working closely with regional leaders, everything moved faster. They knew where the friction was, what was realistic to implement for their teams, and what didn’t work in their markets.

* In order to achieve cross-functional alignment, Umar learned to speak each org’s language. For sales, Umar aligned on sales plays and targets. For marketing, he aligned on campaigns and ROI. For operations, he layered on top of their existing work rather than competing with it. The key was showing value that would resonate with each stakeholder rather than pitching a tool.

* Umar calls out that the teams that win won’t necessarily be the ones who accumulate the most data, but the ones who can activate the data they collect across systems. He sees GTM moving from automating repetitive tasks to automating decision-making and triggering next best actions based on real-time account intelligence.

Where to find Umar:

* LinkedIn

Transcript details:

(00:00) Intro

(02:44) Umar’s background

(04:52) Starting as a sales rep and noticing data quality problems firsthand

(07:50) The specific problems Umar solved in his own territory before the POC

(08:50) How success in his own territory led to the global POC

(10:13) What happened during the 12-month POC

(12:19) Why these problems hadn’t been solved before

(13:50) Navigating cross-functional stakeholders and politics at a 10,000+ person company

(15:26) Challenges during the POC

(16:49) Why Umar focused only on high-likelihood wins and avoided experimentation

(18:19) How prompting and workflow design varied across APAC and EMEA

(19:44) Advice for implementing GTM change at large organizations

(23:02) How to build the skills and intuition for GTM engineering

(25:28) Regional differences in GTM execution across APAC, EMEA, and Americas

(28:13) Future trends in GTM

(30:34) How GTM engineering varies by org size

(31:40) Favorite tools, growth hack, and wrap up

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Gabe Larsen is CRO at Signals, a company selling cloud employees, AI teammates that do full jobs rather than single tasks. Gabe spent 2 years doing door to door sales in Germany before beginning his career in consulting and investment banking in New York and the Middle East. After that, he settled into SaaS where he joined InsideSales in 2013 when they were at $5M in ARR and helped them grow to north of $100M. The company was a pioneer in sales acceleration, building power dialers and training programs that helped companies shift from field sales to high velocity inside sales. After InsideSales, Gabe joined Kustomer, a Zendesk competitor, at $10M ARR. They grew quickly and got acquired by Meta for >$1B. He spent two years at Meta before getting spun out during their year of cuts in the name of efficiency. During that time, he saw how companies were being pushed to do more with less, but still relying on the old playbook of adding tools and adding people. That's what led him to reconnect with Dave Elkington, the founder of InsideSales, to start Signals.

In this podcast, we discuss:

* What the shift from field sales to inside sales taught Gabe about how industries evolve and why he’s betting cloud employees will drive the next shift toward autonomous organizations

* Some of the early marketing and sales playbooks that helped InsideSales go from $5 to $100M

* What separates AI agents from the cloud employees that Gabe and his team are building

* The win-loss cloud employee that’s already replaced expensive consulting firms

* How expectation setting with users gets them comfortable talking with AI employees

* Why framing them as cloud employees instead of AI agents changes how you collaborate with them

Episode highlights:

* Gabe distinguishes cloud employees from AI agents in three ways. 1). Cloud employees are autonomous rather than deterministic. They have access to tools and decide when to use them, rather than following strict if X then Y workflows. 2). They do full jobs rather than single tasks. For instance, an SDR cloud employee will handle anywhere between 20 to 300 tasks from setting up calls to running calls to sending gift cards after the call. 3). Cloud employees operate across multiple channels - phone, email, SMS, Slack, LinkedIn, and chat. A customer can chat with the cloud employee, and if they call 30 minutes later, it remembers the earlier conversation.

* Gabe explains that he runs weekly coaching sessions with four cloud employees that report to him. He reviews their output and gives feedback on things like email personalization and report building. By framing them as teammates rather than tools, it changes how organizations interact with them. Instead of setting up a workflow and forgetting about it, teams invest in improving the cloud employee over time, which compounds into better output and enables companies to do more with less.

* Gabe explains that the Signals win-loss cloud employee runs the entire deal analysis and reporting workflow better than the consulting firms he used to pay 25x to do the same thing. When a deal is lost, the cloud employee reaches out across email/phone/text to get an interview scheduled. Once the interview is scheduled, the cloud employee runs it (or can hand it off to a human), sends the interviewee a gift card, then ultimately writes up a report and shares it in Slack. After the report has been shared, sales leaders have a back and forth conversation with the cloud employee to dig deeper into findings across the many interviews it’s done.

* Gabe explains that implementing customer-facing AI personas was initially a challenge, and when Signals deployed a customer service cloud employee at a Fortune 500 company, they saw high hang-up rates. The fix was having the cloud employee open with honesty: “I’m an AI persona. I can get you to a human in five to ten minutes, but if you challenge me, you might be surprised.” That framing led customers to be more open to trying the AI experience. Many returned with positive feedback, saying the AI handled their questions better than junior reps would have.

* Gabe argues that SaaS tools are becoming limited because they’re building AI agents trapped inside their own platforms. Gong builds agents that only work in Gong. Apollo builds agents that only work in Apollo. He compares it to Siri, which can only access Apple apps and is locked out of everything else. Cloud employees are different because they’re cross-platform by design, just like real employees that move between Salesforce, Apollo, Gong, and Slack throughout their day. As a result, Gabe sees a shift coming where companies move from renting siloed tools, to hiring AI teammates that operate across their entire stack and compound over time.

Where to find Gabe:

* LinkedIn

* Signals

Transcript details:

(00:00) Intro and Gabe’s background

(06:04) What Signals does and the concept of cloud employees

(09:32) The history of InsideSales.com and the shift from outside to inside sales

(14:13) How InsideSales changed the way that modern selling works

(15:50) Assembly line sales and the formalization of the SDR role

(18:26) Category creation and research-based marketing at InsideSales.com

(20:38) Why Utah became a hub for sales talent

(24:32) How Signals landed on the cloud employee framing

(28:20) The four areas where cloud employees are deployed and how they differ from AI agents

(34:32) Deep dive on the win-loss cloud employee

(41:20) How to introduce customer-facing AI to prevent skepticism in audiences

(46:48) Other cloud employee use cases

(49:37) The revenue advisor cloud employee and ask-me-anything for sales teams

(52:34) Weekly coaching sessions with cloud employees and how to think about training them

(56:00) Gabe’s view on build vs. buy, why he thinks SaaS tools are dead and that the future is AI teammates

(01:00:43) Favorite underrated tool, growth hack, and conclusion

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Eoin Clancy is the VP of Growth at AirOps, a company that helps brands get found in LLMs like ChatGPT, Claude, and Perplexity. Before joining AirOps, Eoin ran growth and marketing at Telnyx, where he began as a growth engineer. He spent a year using AirOps at Telnyx to automate inbound SDR work, enabling him to move 8 inbound SDRs to outbound. After that, he was sold on the product, and 18 months ago joined the AirOps team. When Eoin joined, AirOps had fifteen employees and was doing less than $1.5M in revenue. Since then, they've nearly 10x'd headcount and well over 10'x’d revenue.

In this podcast, we discuss:

* The new role of content engineering and why AirOps believes it will thrive in the new world of search

* Why generic AI-generated content doesn’t work anymore, and what actually differentiates content that ranks in LLMs

* How to make AI-generated content look like it was written by your team

* How content teams like Ramp and Carta use high velocity experimentation to separate themselves from competitors

* The case for investing in documentation and support guides

* How AirOps uses their own product to speed up and increase the impact of their webinar content

Episode highlights:

* 18 months ago, AI slop worked, and teams could pump out pages at scale while watching website visits climb. The old SEO playbook was to look at the top three results for a target keyword, see what sections and questions they covered, and release pages that copied or rephrased it. AI slop enabled that playbook at 10x scale. However, Google and LLMs have adapted and now reward unique insights that add to the conversation, rather than rehash what’s already out there, effectively killing the benefits of generating loads of AI slop content pages. Eoin explains that Reddit performs so well in AI search because many comments offer new takeaways.

* The two biggest changes to content in the age of AI are that: (1) it is easier to create more content than ever, and so the bar for speed (of net new and refreshed content) to keep up with the market has significantly increased, and (2) LLMs process content and rank differently than Google, making certain tactics (like offsite content) more important than before.

* In order to make AI content look like the team that wrote it, Eoin suggests feeding the AI internal context before having it write. If the goal is for AI to write like engineers talk, give it access to engineering Slack channels and standups, so the words and phrases in those places make their way into content. He also explains that building effective content workflows requires serious time and investment that is well worthwhile to speed up overall content creation and refresh.

* Eoin points to Ramp and Carta as examples of content teams that succeed by moving fast. Ramp has pushed the boundary of offsite content on Reddit because they ship so fast that they’re able to learn from rapid experimentation. On the other hand, Carta’s content team is now able to ship content three times faster than before, enabling them to go to market to new audiences and industries quickly.

* Eoin advocates for investing in documentation for the sake of AI search distribution. Docs are often the last thing to get updated and the first thing to go stale, but they’re rich with context, well structured, and contain the most nuance about a product. When someone asks ChatGPT for a tool that does X and integrates with HubSpot, docs are what surface. Eoin gives Salesforce as an example of a company that understands this. Most of their sitemap is how-tos and community content, and that’s what ranks first in AI search.

* Because AI search prioritizes fresh, up-to-date insights, AirOps regularly refreshes their existing content in order to keep it up to date. After recording a webinar, they use the transcript to auto-update existing related articles with new takeaways. This enables same-day turnarounds, so after a webinar is recorded in the morning, their content library is refreshed by day’s end.

* Branded search terms are an underrated metric. Even as teams optimize for AI search, people will still Google a company name after discovering it in ChatGPT. If a competitor is running a conquesting campaign on that branded term, they’ll steal the click. Eoin recommends keeping an eye on and tracking branded search terms over time, to ensure all bases are covered.

Where to find Eoin:

* LinkedIn

* AirOps

Transcript details:

(00:00) Introduction to Eoin and overview of AirOps

(03:34) Eoin’s background as a growth engineer at Telnyx and becoming an AirOps user before joining

(5:03) AirOps’ growth trajectory

(05:34) What VP of Growth means at AirOps, the builder enablement function and content engineering

(8:58) How AirOps is helping their customers adapt to the new reality of content creation

(11:00) How the modern content role differs from historical SEO roles

(16:14) Why Eoin thinks content teams will grow, not shrink

(20:52) How much should AI actually write, why AI slop content doesn’t work anymore, and where humans need to stay in the loop

(26:47) How to measure whether your content is sufficiently human and value-add

(32:06) The importance of content velocity

(34:24) Underrated metrics in content

(37:37) Why documentation is a goldmine for AI search visibility

(41:16) How AirOps uses their own platform for webinar content workflows

(46:34) Agentic browsers and how websites are going to change

(47:29) Where search is headed

(51:11) Why AirOps bet on webinars as a growth channel

(54:03) Favorite underrated tool, growth hack, and conclusion

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Gaurav Vohra worked in consulting for 5 years, where he honed his ability to attack problems with a high degree of vigor, speed and urgency, before he joined Superhuman to run growth in 2015. At Superhuman, he ran growth, analytics, and growth product from the early days to tens of millions in ARR.

During this time, Gaurav dove deep into the technical + analytical parts of growth, while also spending time on deep craft and artistry, which is clear to anyone who ever interacted with the Superhuman product. After a ~10-year run at Superhuman, Gaurav took a step back to become a startup advisor for businesses like Clay, Replit, and Wispr Flow.

Subscribe for weekly updates on top GTM Engineering content, open roles & more

In this podcast, we discuss:

* The Grammarly acquisition of Superhuman and what about the Superhuman name invoked Grammarly taking it on

* The two mission-critical characteristics Gaurav looks for in growth operators, how he tests for those skills, and what it took to build the skills himself

* Balancing the analytical with artistic and creative side of growth and Gaurav’s reach vs. resonance framework

* How to build very good taste to drive resonance in growth work

* How growth skills building is changing (and not) in the age of AI

* The future of growth vs. growth product vs product teams

* The growth hack hall of fame move that Grammarly made during the Superhuman name change

Episode highlights:

* Superhuman was initially called Supercharged, but changed after the team thought it was too sports car oriented. Superhuman ended up being a brand name so broad and aspirational that Grammarly eventually took it on as the overall business name.

* The two critical skills in successful growth operators are CPU and velocity. All other important and relevant skills can be derived from processing through large amounts of information to find solutions (CPU), and from doing so + iterating extremely quickly (velocity).

* CPU and velocity are not innate, but require a consistent and concentrated effort. This comes from pushing to move faster and think from first principles to process information. If you get to the end of the day and your brain is tired, you know you’re pushing on the CPU and velocity muscles.

* While people who process information a bit slowe but arrive at the right answer can be successful in growth, it becomes a hiring risk, especially in growth roles that require particularly high levels of velocity.

* Content, products and ideas can fall in any of the four quadrants of resonance X reach. High reach = large distribution, and high resonance = deep influence and impact. The best products and growth ideas sit in the quadrant of high resonance and high reach.

* Different sorts of roles within growth require different levels of artistry (and resonance). Generally speaking, the closer you get to touching product, the more important art becomes.

* Building the skill of taste (which influences resonance) requires many reps of seeing what amazing looks like, testing ideas out in the world, paying attention to what is currently driving resonance + reach (can often measure by what is going viral), and by spending time understanding users.

* As AI becomes more effective, the importance of technical skills appears to be reducing - with coding or analytics as prime short-term examples. It’s unclear how it will all shake out down the line, but having the deeper level of understanding can be useful on foundations work and/or to be in the top 1 or 0.1%.

Where to find Gaurav:

* LinkedIn

* GauravVohra.com

Transcript details:

(00:00) Pod intro

(03:45) Gaurav introduction and background

(05:45) Superhuman’s name and the Grammarly acquisition

(10:06) The two critical traits to being effective in growth - CPU and velocity

(13:44) Building CPU and velocity muscles

(20:57) How writing & analytical skills fit into the CPU / velocity framework

(24:57) Whether smart, slower processors can be effective in growth

(27:28) Balancing the left vs. right brain in growth and Gaurav’s resonance vs. reach framework

(32:56) How to improve resonance and taste

(38:06) How building growth skills is changing with AI

(42:44) Whether PLG vs. SLG is changing with AI

(44:31) How growth vs. product roles are evolving

(47:00) Other parts of the growth landscape that are changing

(49:53) AI pricing

(51:43) Intense competition and startups as the new investment banks

(56:03) Growth hack, favorite tool & conclusion

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Jen Igartua is the CEO and Co-Founder of Go Nimbly, a RevOps agency with 100 employees that works with companies like Intercom, Twilio, Zendesk, and Vanta. She started her career at Bluewolf, a major Salesforce partner later acquired by IBM. There, she developed an obsession with breaking down silos between sales and marketing after seeing firsthand how easily they become misaligned. That led her to start Go Nimbly, which now provides fractional RevOps services ranging from $20K/month engagements to six-figure enterprise contracts, plus a partnerships motion that has delivered over 600 Gong implementations in the past two years. What’s more, Jen also owns a board game company that recently got picked up by Walmart and Target.

Subscribe for weekly updates on top GTM Engineering content, open roles & more

In this podcast, we discuss:

* The different stages of signal delivery systems and why most companies stall at stage one - Slack alerting

* Why RevOps teams need roadmaps and to build strategically instead of just fighting fires

* Why your first party product data is gold when building expansion and renewal plays

* What caused such widespread tool sprawl, the “build everything” overcorrection and the optimal state of GTM tooling

* Why the best GTM engineers will start to feel more like architects who obsess over data models and their downstream effects

* The swim lanes between RevOps, Growth, GTM Engineering, and system teams at varying company sizes

* What’s happening to marketing automation as tools like Clay absorb workflows like audience building and emailing capabilities

Episode highlights:

* Intercom used first-party product data to build an expansion play that combined ticket volume increases, support team growth, and declining NPS scores. Instead of generic outreach, reps could lead with specific information about a customer that they themselves might not even know.

* Jen explains that most companies start delivering product signals with Slack alerts, but adoption quickly trails off because it becomes noisy. A better long-term solution is building a custom object for signals in Salesforce. This enables teams to hone in on which signals actually convert into opportunities and prioritize accordingly.

* Understaffed RevOps teams get stuck putting out fires instead of doing strategic work. The fix isn’t to ignore the fires. It’s to staff the team well enough to handle the day-to-day while still building toward a longer term vision. This enables RevOps to be strategic thinkers with thoughtful roadmaps.

* Before 2022, there was a buying spree in tech that created tool bloat and shelf-ware. Now, there’s an overcorrection toward building everything in-house. Jen points out that while building in-house is great, it can create problems when the person who built it leaves or it’s not built for scale. Orgs are left with knowledge gaps and a bunch of half-documented workarounds across Clay, n8n, Salesforce flows, and Slack automations.

* Jen shared that most companies aren’t choosing a single orchestration platform for their automations. Instead they’re building department by department in whatever tool seems slightly better for that use case. This creates nightmares like trying to find which automation is driving which field changes and nobody knowing if it’s coming from Clay, Zapier, Workato, n8n, or somewhere else.

* The best GTM engineers will evolve into RevTech architects who understand the entire go-to-market stack, obsess over data architecture, and think about order of operations and downstream effects. Knowing when to use Clay versus Salesforce versus when to use a dedicated tool is one key that separates prototype builders from systems thinkers.

* Marketing automation platforms like Marketo and Eloqua haven’t meaningfully innovated in 15 years. Meanwhile, marketers are naturally unbundling. Events run through Luma, newsletters live in Beehive or Substack, webinars happen on Sequel. Now, Clay is absorbing audiences and email. If all that data can flow through an orchestration tool straight to Salesforce, the only thing left for expensive marketing automation platforms is nurture streams.

Where to find Jen:

* LinkedIn

* Go Nimbly

Transcript details:

(00:00) Intro, Jen’s background, and founding Go Nimbly

(05:19) Go Nimbly’s business model

(06:16) Current trends in RevOps

(09:04) Using product signals for expansion and renewal plays

(11:48) The different stages of delivering signals to sellers

(15:15) AI CRMs vs. Salesforce for mid-market and enterprise

(16:50) Browser-based automation as an alternative to single pane of glass approaches

(18:21) Signal prioritization

(22:34) How to define RevOps

(25:26) What separates high-performing RevOps teams from the rest

(27:43) GTM engineering, AI Operations, and where they fit within GTM

(35:14) The importance of systems teams and governance at larger companies

(39:06) The evolution of tool sprawl

(44:12) The fix to GTM tooling sprawl

(45:36) How the best GTM engineers think like architects

(49:31) Pushing back on complex requests

(51:56) The GTM trends Jen is following, like marketing automation software

(56:12) How running a board game company influenced Jen’s thinking and her RevFest conference

(58:04) Jen’s favorite underrated tool, growth hack, and conclusion

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Manthan Patel began working with YC founders two years ago, where he first started using AI agents and LLM workflows. When AI agents gained popularity, Manthan already had hands-on experience, so he started recording and sharing what he was building. In January 2025, he began posting content on LinkedIn and grew his following from zero to 100K in just six months. Today, Manthan runs both an AI automation agency, and a lead gen agency. His agencies offer low ticket courses that over 50,000 people have taken, while also offering white glove agent-building and implementation services.

Subscribe for weekly updates on top GTM Engineering content, open roles & more

In this podcast, we discuss:

* How frequency of posting is one of the last remaining differentiators on LinkedIn

* How to repurpose content across LinkedIn, Instagram, and TikTok in platform-specific formats to maximize distribution

* Building effective AI voice agents for outbound and inbound calls

* Automated inbound form enrichment workflows that qualify leads to prevent SDRs wasting time on unqualified calls

* Building end-to-end prospecting workflows using Claude MCP

* When to use Clay vs. n8n

* Self-hosting LLM infrastructure to reduce API costs, maintain control of data, and meet compliance requirements

Episode highlights:

* Manthan grew his LinkedIn following from zero to 100K in six months by posting lead magnets that encouraged comments to amplify their reach. He also posts three to four times per day across different global time zones to maximize visibility and create faster feedback loops on what content resonates with his audience.

* Manthan repurposes the same content across LinkedIn, Instagram, and TikTok by adapting it to each platform’s format. For instance, he’ll convert LinkedIn carousel posts into short-form videos for Instagram and TikTok. By being present on multiple platforms he’s able to reach his audiences where they actually consume content.

* Manthan ran an automated cold-calling campaign for a vending machine company by scraping local business data, and personalizing AI calls with shop names and addresses. With this personalization along with multiple call attempts, and disclosing upfront that the call was coming from an AI agent, Manthan generated 80 demos across 10,000 leads in 1 month.

* Manthan built an inbound form enrichment workflow where prospects submit only their name and email, then an AI agent enriches both personal and company data, feeds it to a second AI that evaluates ICP fit, and only books demos with qualified leads. This prevents SDRs from wasting time on calls with unqualified prospects who fill out forms.

* Manthan uses Claude with MCP servers to do prompt prospecting, where he describes research tasks in natural language. Prompted with these research tasks, Claude connects to data APIs like Lusha to find prospect information, and once found, Manthan has Claude add these leads directly to HubSpot. This eliminates the need to manually build workflows or switch between multiple tools for prospecting or tracking.

* An emerging trend Manthan sees is self-hosting LLM infrastructure. Compliance-focused clients run models locally on hardware like a Mac Mini to avoid sending their data to big models like OpenAI or Anthropic. This approach allows these orgs to take advantage of AI while preventing data exposure that could trigger compliance audits and license loss for regulated industries, while also reducing long-term API costs for high volume AI usage.

Where to find Manthan:

* LinkedIn

* Lead Gen Man

Transcript details:

(00:00) Intro and background

(02:27) Manthan’s personal branding and agency structure

(03:44) Growing his LinkedIn from zero to 100K in six months

(05:54) Manthan’s early client work

(08:48) Learning LinkedIn strategy

(12:30) Building social media presence across multiple platforms

(15:12) AI voice agents at scale to drive 80 demos from 10,000 AI cold calls

(18:59) Backtesting prompts and handling edge cases

(20:10) Manthan’s AI agent tech stack

(24:28) Prospects don’t care when disclosing AI upfront

(28:12) Inbound AI agents for 24/7 support with conversation history

(30:38) Nailing AI call agent prompting

(31:58) When to use Clay versus n8n

(33:44) Self-hosted n8n for compliance-driven enterprise clients

(35:00) Manthan’s favorite workflows

(37:48) What 50K people have learned from Manthan’s courses

(40:16) Using Claude with MCP servers for prompt prospecting

(42:35) Local LLM infrastructure for compliance and cost

(44:39) How to get started with workflows, agents, and MCPs

(45:33) Favorite underrated tool, growth hack, and conclusion

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Listen on Spotify

Sylvain Giuliani started his career in lifecycle marketing, where he learned to code and build systems without relying on engineers. He then joined Pusher as a marketing manager where he worked his way up to CRO, before joining Census in 2020 to rebuild their GTM motion from the ground up. After leaving Census at the end of 2024, Sylvain joined Augment Code as employee number 50. When he joined, Augment was finishing its research phase, and had no self-serve product, signup flow, or billing system. Over the past year, he’s built the entire GTM motion from scratch, and helped scale Augment to hundreds of thousands of users with a lean team by hiring technical generalists who build systems that compound on top of one another.

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In this podcast, we discuss:

* Why AI companies need leaner teams that use AI to eliminate inefficiencies due to AI cost compressing margins

* Hiring technical generalists for Growth from companies with great cultures like Rippling, Datadog, and Ramp, who can write code and build systems that compound over time

* Examples of how Syl and the team are prioritizing and pushing the boundaries of what each individual can accomplish

* Centralizing all data in your warehouse and only buying tools that can flow data back to it

* The advantages of analytics and ops reporting into GTM

Episode highlights:

* Most sales bottlenecks stem from reps lacking knowledge at their fingertips and having to do manual work to multi thread into the account. Augment automates account-level ABM campaigns directly from Salesforce, then uses AI to prep reps with key context before calls. This gives reps everything they need to answer questions without relying on solutions engineers, collapsing five meetings into one and shortening large deal cycles to sixty days.

* During interviews, Sylvain borrows Datadog’s approach of presenting candidates with seemingly impossible challenges the company hasn’t solved yet. Rather than seeking the answer, he watches for people whose eyes light up and whose gears start spinning when faced with ambitious problems. This filters for candidates who challenge conventional thinking rather than defaulting to how things were done at their previous company, which is critical as Augment scales while remaining lean.

* Augment’s data architecture prioritizes control and speed by centralizing all data in their warehouse and not hesitating to buy software instead of build in-house. They ensure every tool they use can send data back to their data warehouse, allowing the team to swap email providers or AI personalization tools as they see fit without the risk of losing historical context. This prevents vendor lock-in, so when a tool doesn’t support a growing need or costs get too high, Augment can easily swap vendors or build critical workflows in-house.

* Sylvain positions analytics directly under GTM rather than finance because of the cross pollination benefits. When data teams report elsewhere, they often prioritize accuracy over speed, but when the data team sits close to GTM, they naturally identify learnings and proactively flag opportunities. This creates a tight feedback loop where data teams discover actionable patterns like usage spikes, and reps learn which signals actually convert to pipeline.

Where to find Sylvain:

* LinkedIn

Transcript details:

(00:00) Introduction and background

(02:52) Joining Augment and early challenges

(04:12) Building the team and scaling

(05:47) Structuring teams for AI-native pace

(08:51) Why predictable revenue is broken

(12:05) The $10M per rep challenge and efficiency gains

(15:59) Hiring people who challenge conventional thinking

(17:15) Finding talent at culture-fit companies

(20:18) Warehouse-first data architecture

(21:53) Core data model structure

(23:50) Prioritizing data ingestion over perfect schemas

(24:46) Treating GTM tools as expendable

(26:41) Owning ops, analytics, and growth together

(31:10) Effective AI prompting with context layers

(34:20) Favorite tools and creative campaign ideas

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Guillaume “G” Cabane’s career started in the 1990s as a teenager in France running a Mac gaming website that attracted 2,000 daily visitors. That led to an internship at Apple in the early 2000s where G worked in online SMB sales and learned to run experiments disguised as normal campaigns to avoid bureaucratic approval processes. After Apple, G spent time in IT security where he learned about the back corners of the internet and social engineering psychology.

G was positioned at the intersection of marketing and technology when growth emerged as a discipline around 2010, allowing him to become an early expert on tools like Segment, when few others understood the space. He ran growth at Segment, before a string of successful stints as VP of Growth or CMO at Drift, Gorgias, and Ramp. Today, G runs HyperGrowth Partners, a collective of VPs and CMOs who advise companies like Reddit, Ashby and Zapier on growth.

During this conversation, we talk how G’s background gave him the worldview he has today, his effective growth methodology, whether he believes in GTM Engineers, how AI is changing go to market and more.

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In this podcast, we discuss:

* How perfectionism and campaign quality during Apple’s Steve Jobs era shaped G’s marketing philosophy

* G’s effective craziness growth framework that combines rapid experimentation and high risk tactics with scientific rigor

* Why founders say they want Ramp-level growth but aren’t open to taking the risks needed to get there

* G’s outbound gifting experiment that guaranteed replies in order to test whether outbound infrastructure or messaging was responsible for poor campaign performance

* Why (and how) growth experiments should test one variable at a time to enable faster learning

* G’s favorite AI use cases, what he thinks is just hype, and his predictions for the future impact of AI on GTM

Episode highlights:

* During G’s time at Apple, Steve Jobs would demand screens be perfectly aligned so they looked like one line when viewed from the side. This attention to craft and quality became foundational to G’s approach to growth campaigns. He combines artistic perfectionism with rapid experimentation to create work that stands out in the market.

* G’s effective craziness growth framework combines rapid ideation with scientific rigor to find outlier campaigns. Growth teams should fail 60 to 80% of the time because high failure rates signal they are chasing experiments that could massively outperform. The key is pairing this bias to action with documented hypotheses, baseline metrics, and thorough postmortems. This combination ensures each failure brings the team closer to finding what works by systematically extracting learnings and narrowing down winning strategies.

* Most founders fail to build great growth teams because they cannot distinguish between growth and the rest of their marketing organization. They struggle with having teams that fail frequently because of the contrast to orgs like product marketing, who should be rightly fired if 3 straight product launches go poorly. Unless founders understand these differences and can hold teams accountable in different ways, growth tends to die out as companies scale.

* G ran an experiment for a blue collar HR company to diagnose whether infrastructure or messaging was the problem behind poor outbound performance. Instead of a typical gifting campaign, they sent emails to leads asking them to confirm an incorrect address (a few doors down) with a gift arriving tomorrow. Since people love correcting mistakes, and picking up a package at the wrong address is painful, the campaign drove a 15% reply rate and proved that the outbound infrastructure was sound and the problem was messaging/targeting.

* When a company wanted to create a gated content marketing asset, G challenged the team to test only the most critical unknown first - whether people wanted the asset in the first place. After shipping a banner and landing page promoting a non existent asset in a handful of hours, they received one tenth as many clicks as predicted, and learned people did not care about the gated content. By isolating to test the critical variable first, they cut the corner and avoided building the full asset they were planning on, saving weeks of work.

* G points out that while AI enables smaller and more efficient growth teams by automating work that previously required multiple specialists, it’s still often not the right solution. For instance, when it comes to messaging enterprise leads and top ABM prospects, he hasn’t seen anyone using full AI to write copy, because the risk to reward is not there. Additionally, workflows through tools like Zapier remain critical because they deliver deterministic outcomes, not probabilistic AI results with a chance of failure.

Where to find G:

* LinkedIn

* Hypergrowth Partners

Transcript details:

(00:00) Intro

(05:37) Learning experimentation and perfectionism at Apple in the early 2000s

(09:23) Working in IT security, social engineering tactics, and an early understanding how the internet works

(16:52) G’s effective craziness methodology

(21:19) Why most companies don’t implement the growth methodology

(23:44) A crazy growth experiment that worked and why

(30:30) Whether automated marketing is easier or harder today, and ethics around automated outreach

(33:27) Standing out in an increasingly crowded market

(36:30) G’s failed growth experiments, from Apple bundles to Segment’s product team conflicts

(41:49) Building an autonomous growth team that ships quickly by cutting corners on experiments

(45:16) How AI enables smaller, faster growth teams

(47:39) Why GTM Engineer as a job title signals forward-thinking company culture

(49:52) AI in go-to-market: Its current limitations in outbound and the future displacement of sales roles

(01:00:43) Favorite underrated software tool, G’s most memorable growth hack, and conclusion

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Mahau Ma is an operating partner at Sapphire Ventures, a growth stage VC firm focused on B2B software. Previously, he spent 14 years at MuleSoft, including 7 years as VP of Marketing & CMO and several years as SVP of Corporate Strategy.

Mahau joined MuleSoft in 2007 as employee number 20 when the company was still figuring out its business model. He helped build the marketing function from scratch and navigated the company through both its 2017 IPO at $300 million in revenue, and the subsequent $6.5 billion Salesforce acquisition in 2018.

By the time Mahau left Salesforce in early 2022, MuleSoft was doing approximately $1.5 billion in revenue under the Salesforce umbrella. Currently at Sapphire Ventures, Mahau advises portfolio companies and other B2B software businesses on go-to-market strategy, helping them navigate transitions from product-led growth to sales-led motions, align marketing and sales organizations, and build executive teams.

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In this podcast, we discuss:

* ​​The five year up and down marketing journey as MuleSoft figured out its scalable business model

* Why MuleSoft transitioned from a developer-led inbound motion to enterprise outbound sales

* MuleSoft’s hiring philosophy and selecting for marketers who think about business objectives before marketing activities

* Why stage two pipeline is the unifying metric that holds marketing, SDR, and sales teams accountable

* How marketing fundamentals don’t change, even as technology advances

* Why an organization’s messaging can be just as crucial to differentiate themselves as their product in the AI era

Episode highlights:

* When Mahau joined MuleSoft, the company was open source, and his first challenge was figuring out how to de-anonymize serious users. By gating advanced documentation and community forum access beyond, MuleSoft identified high-potential users while avoiding backlash from the developer community.

* MuleSoft’s journey from inbound to outbound took ~five years of experimentation. When the journey began, the software was being used by developers for small tactical projects that still required lengthy sales cycles. After tactics like offering support services didn’t work, they had a breakthrough around 2013, when they began refusing small deals meant for tactical projects until they could open strategic conversations with decision makers with real budgets.

* Mahau stayed through years of trial and error because CEO Greg Schott established a non-negotiable principle of building a team you would want to get the band back together with. Even when pulling their hair out, their cultural alignment, plus a shared conviction that they were solving a massive IT problem, kept everyone aligned and motivated to keep testing and help MuleSoft win.

* Mahau shares how MuleSoft hired people who thought about business objectives before marketing activities. Using principles from the book Hire With Your Head, they would ask open questions and listen for 15 minutes to deeply understand how candidates approached problems. They looked for whether candidates jumped straight into executing tactics, or started by understanding what they were trying to achieve and why. By having a team aligned around an outcomes first mindset, they were able to create powerful cohesion as they experimented and worked towards accomplishing MuleSoft’s business objectives.

* During Mahau’s time at MuleSoft, the pre-sales organization became marketing’s best friend for understanding customers. Because MuleSoft sold technical infrastructure software, they built a strong pre-sales team that covered everything from technical conversations to business outcomes. These teams helped marketing decode customer decision making processes, validate messaging, and understand the incentive structures that were key to whether organizations moved or stalled out on deals.

* Category creation around the concept of an application network became a major growth driver for MuleSoft. Enterprises would automatically recognize the need for an application network, and naturally pull MuleSoft into their organizations. Today, grabbing category leadership creates a more critical distribution advantage and defensible moat when AI enables competitors to achieve product parity faster than ever.

Where to find Mahau:

* LinkedIn

Transcript details:

(00:00) Introduction

(04:11) Mahau’s background and the Mulesoft story overview

(06:48) Mahau’s initial charter at Mulesoft

(9:28) De-anonymizing users

(11:19) Finding what users were willing to pay for and navigating complex buyer committees

(20:52) What gave Mahau conviction to stay for 5+ years as Mulesoft figured out their growth

(23:20) How Mulesoft iterated to find success

(26:20) What Mulesoft taught Mahau about positioning and storytelling

(29:15) How Mahau built a winning, committed team that could think from first principles

(33:13) Growth wins including Dreamforce stunts like the Connect SaaS child actors video that landed on Marc Benioff’s desk

(38:16) Current advisory work with Sapphire Ventures portfolio companies

(40:12) Go-to-market fundamentals that never change despite technology shifts

(41:30) Importance of a GTM strategy and telltale signs of companies without one

(46:14) How companies over-rotate on AI tools without connecting to broader strategy

(48:12) ABM as an example of what AI makes newly possible

(51:45) In the AI era, messaging differentiation can matter as much as product differentiation

(53:45) Stage two pipeline as the unifying metric across marketing, SDR, and sales

(56:25) Favorite underrated tool and Third Eye Blind growth hack

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Divyanshi Sharma runs Growth Exe, a GTM consultation and training agency serving 30 clients. She started her entrepreneurial journey selling perfumes and clothes as side hustles in India, where student businesses were uncommon at the time. During Covid, Divyanshi discovered freelancing through content writing, which led her into LinkedIn personal branding, lead generation, and eventually the GTM space.

Divyanshi has built a following of 75,000 on LinkedIn by consistently sharing lead magnets, playbooks, and free resources that drive engagement. Growth Exe focuses on auditing client funnels, creating customized roadmaps and strategies, and training internal teams to execute GTM workflows.

In this podcast, we discuss:

* How Divyanshi uses Reddit marketing as a blue ocean strategy to find honest conversations and convert them into clients

* How to reverse engineer viral posts using AI to create compelling content

* Divyanshi’s framework for Reddit marketing, including warming up accounts and using F5Bot for social listening

* Why Discord is an untapped channel for software companies who can build authentic relationships

Episode highlights:

* Divyanshi creates lead generating content by finding the top creators in her niche, identifying their highest engaging posts, then feeding those into ChatGPT alongside her internal SOPs and successful client strategies. This process generates both the lead magnet content she shares, and the copy for LinkedIn posts that consistently attract hundreds of comments.

* Divyanshi’s Reddit strategy involves finding niche subreddits, replicating viral posts with her own ideas, and moving conversations to DMs. One tactic she uses involves posting a question asking for software recommendations, letting the post gain traction and rank in search engines, then editing it later to feature your own product at the top.

* The first four hours after posting on LinkedIn play a large role in determining its long term reach, so Divyanshi recommends having a group of five to fifteen creators in your niche who agree to engage with your posts. The early engagement from people in your ICP drives useful inbound leads.

* Divyanshi knows founders who make $100K per month by finding clients exclusively through Discord groups tied to YouTube communities. They join group calls, candidly share their products as beta offerings at cheaper prices, and rely on word of mouth to drive sales rather than running traditional outbound campaigns.

Where to find Divyanshi:

* LinkedIn

Transcript details:

(00:00) Introduction and Divyanshi’s background

(05:36) Reddit marketing as a blue ocean strategy

(06:44) LinkedIn lead magnet strategies and reverse engineering viral content

(10:05) Why clients buy trending tools without implementation plans

(11:54) Using MCP for content creation and social listening

(13:38) Reddit marketing tactical frameworks

(19:51) How Divyanshi would tackle Reddit if she worked at Rippling

(20:59) Discord as an underrated channel for software companies

(23:26) AI agents: hype versus reality

(26:18) Small LinkedIn engagement groups and the importance of the first four hour engagement window

(29:45) Building in public as a lasting digital asset

(31:24) Learning resources and communities for GTM engineers

(33:47) The traits of Divyanshi’s clients that are adapting to AI

(35:49) Experimenting with emerging channels like Substack and Quora

(36:54) Favorite underrated software tool and conclusion

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Alex Fine is the co-founder of Understory, an all-bound marketing agency serving B2B SaaS companies. Since going full time in October 2023, Alex has scaled Understory from $6K to multiple hundreds of thousands in monthly revenue. The company is a Clay Studio Partner and operates with a team of 26, delivering services across automated outbound, paid ads for LinkedIn/Google/Reddit, and revenue operations.

During our conversation, we talk about how Understory finds arbitrage on paid social, how they have built out the team, how Alex automates every manual step of his sales process to close $1M+ ARR per month by himself, and much more.

Subscribe for weekly updates on top GTM Engineering content, open roles & more

In this podcast, we discuss:

* How Understory uses Clay for list enrichment to improve their LinkedIn match rates

* Why agencies develop GTM engineering expertise faster than internal teams, and how anyone can leverage their learnings

* How a LinkedIn post with three likes helped Alex find Understory’s head of GTM engineering, Naufal, and what makes Naufal so effective

* The complete automation stack Alex uses to close >$1M ARR per month as the solo rep

* Why founders are the best clients to work with, and how to avoid action paralysis at larger companies

Episode highlights:

* Understory uses Clay to enrich ad audience lists before uploading to LinkedIn, improving match rates from 60% to 90%.

* Alex discovered Naufal Nugroho, now Understory’s Head of GTM Engineering, from one of his LinkedIn posts describing a system he had built with complex enrichment workflows using 40 different APIs in Google Sheets.

* Alex built a complete sales automation stack that handles pre-call research, post-call follow up, and CRM hygiene. Before calls, a Lovable app uses Perplexity APIs to send digestible research to Slack. After calls, an N8N automation reads the call transcripts to determine call type, then generates follow up emails in Alex’s writing style using Claude, creates next steps with timelines, and builds statements of work in PandaDoc. This saves roughly an hour per deal and enables him to handle seven sales calls per day.

* Alex’s loves working with founders because they’re willing to break things and test rapidly, focusing on results over pristine brand image.

* Understory has implemented MCP connections with Claude on platforms they manage like Instantly and LinkedIn ads. This allows their team members to query campaign performance, identify trends, and generate reports through natural language (even dictated, using Wispr Flow). These systems have proven so valuable, that clients have been asking for Understory to start offering their operating systems as a service.

Where to find Alex:

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

* Understory

Transcript details:

(00:00) Intro

(03:20) Alex’s background and path to co-founding Understory

(06:06) Understory’s evolution from offering LinkedIn ads to full stack GTM services

(09:51) Using Clay for paid media

(12:40) Hiring philosophy for paid ads roles

(15:47) Common GTM gaps at different company stages

(19:17) Why companies use agencies

(21:24) Why Alex loves working with founders

(22:59) Incentivizing play, failure, and experimentation

(26:36) How Understory found their Head of GTM Engineering

(28:57) Characteristics of the best GTM engineers

(32:11) Understory’s onboarding process & company knowledge sharing

(34:05) Alex’s complete sales automation stack

(37:47) What is currently exciting Alex the most about Understory’s future

(43:55) Why not to put GTM engineering in a box

(44:42) Favorite underrated software tool, growth hack, and conclusion

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Brian Balfour was the VP of Growth at HubSpot, where he helped expand the company from a single to multi-product company, created their CRM offering that became Sales Hub, and led their transition to product-led growth. After leaving Hubspot 10 years ago, Brian founded Reforge to solve the education gap for mid-career professionals, building it into the premier educational platform for product, marketing, and growth teams. Recently, Reforge evolved beyond expert-led training courses to launch an AI-native product suite with four tools: Insights to aggregate customer feedback, Research to run AI-powered interviews, Build to generate and validate product prototypes, and Launch to help teams safely and quickly deploy experiments.

Brian interacts with some of the best operators across all of tech as part of his work at Reforge, giving him a unique vantage point into how different orgs are adopting (or failing to adopt) to AI. He has written thoughtful pieces including The Big Squeeze and The Next Great Distribution Shift.

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During our conversation, Brian shares why most companies are falling short in AI transformation by creating disconnected systems & not being ambitious enough. We talk about the big squeeze and why reaching escape velocity is more important than ever, how the acceleration of product development is fundamentally changing how product and GTM teams interact, and Brian shares some of his thoughts for early-career operators.

In this podcast, we discuss:

* Why replacing individual workflows one at a time with AI creates disconnected, hacky systems that lose critical context

* What the top 5-10% of companies are doing differently to accelerate AI transformation

* What Brian means by the Big Squeeze and how it’s forcing startups to achieve escape velocity faster than ever

* Which sorts of roles (or parts of roles) are being automated across product & GTM

* How AI enables a product velocity that outpaces go-to-market's ability to adopt and distribute

* Brian’s early career advice to stand out from the sea of AI slop showing up in job applications

Episode highlights:

* Brian's product team is shipping so fast that go-to-market can't keep up. This is becoming a common challenge as engineering acceleration moves bottlenecks to other parts of the system rather than necessarily improving overall output.

* Brian segments people's AI adoption styles into three groups: leaders who experiment naturally, a middle group needing specific constraints and support who can adopt, and anchors who resist change. Companies taking AI transformation seriously design different strategies for each segment, with some establishing hard constraints like refusing to review proposals without at least 3 AI-generated prototypes.

* Go-to-market teams are gravitating towards two distinct ends: systems & infrastructure people handling data + signals, and creative people designing messages and experiences. Most of the work in the middle is getting automated, reducing the number of bottlenecks for GTM or product teams

* The Big Squeeze is speeding up an incumbent’s ability to copy, increasing competition, and making it more important than ever for ambitious software businesses to achieve escape velocity - a level of growth & distribution that allows them to build more sustainable moats.

* Due to an overwhelming volume of AI-generated job applications, Brian no longer posts every job publicly. Instead, he scouts talent by scrolling social media to find people building and publishing their work. He advises early career professionals to build and share publicly in order to stand out

Where to find Brian:

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

* Reforge.com

Transcript details:

(00:00) Introduction and Brian's background at HubSpot and Reforge

(04:45) Reforge's evolution to AI-native product suite and velocity challenges

(07:28) AI Frankenstein systems and what companies are getting wrong in AI transformation

(14:28) How to get massive returns on AI

(20:27) The three types of AI adopters & the discrepancies between C-Suite and ICs

(23:08) How AI is driving role polarization

(29:07) The recipe for hypergrowth and advantages to PLG

(31:57) How Brian defines escape velocity and why it matters

(39:42) How the big squeeze paradigm is changing hiring and GTM + product collaboration

(44:36) The next great distribution shift

(50:13) Career advice for early professionals in the AI era

(58:15) Favorite underrated tool, growth hack, and conclusion

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Anshul Gupta is the co-founder of Actively, a GTM superintelligence platform for revenue teams. After diving into AI research at Stanford during the early OpenAI days, Anshul decided to take his work and apply it to helping sales and marketing teams be more effective. He argues that while tools like Cursor and Claude Code have materially increased coding efficiency, go to market is lagging behind because of the nuance and complexity behind each business.

Actively serves as the brain and connective tissue that handles all of the underlying data that sales and marketing teams generate. They use first, second and third party data to create a single context window for each account that businesses can use to prioritize accounts, create relevant messaging and more.

In this conversation, we talk about the Actively thesis, which has important implications on how businesses should think about integrating AI into their GTM motion. We cover everything from how to philosophically structure the “brain” behind your GTM engine to data hygiene and the right GTM structure for AI transformation.

In this podcast, we discuss:

* The concept of “GTM Super Intelligence” and why it matters

* The “horseless carriage” problem: Why simply adding AI to legacy systems isn’t enough

* Cognitive architecture: How and why to build systems that mirror the best sales reps’ processes

* Why treating the account as a unit to track, store, and iterate on context creates a winning formula

* Data hygiene and why “garbage in, garbage out” is a defeatist mentality

* The evolving role of humans in go to market

* The right org ownership & structure for AI transformation

Episode highlights:

* Failure cases of adopting AI in GTM include trying to tack on AI to legacy systems (horseless carriage), using exclusively logic-based intent providers, and not investing in an iterative system.

* First ask, “if we had one AE per account, how would the AE approach their role?” and then back into creating a system that gets as close as possible.

* There’s no one-size fits all account prioritization or messaging framework. There are materially different, but equally viable ways to do sales and marketing into your top accounts (e.g. going bottoms up vs. tops down).

* The garbage in, garbage out mentality is overly defeatist - if your reps have to deal with the data every day, then there’s no doubt layering in AI can improve your data’s impact.

* The companies finding the most success with AI transformation in GTM are bringing the top internal representatives together across RevOps, AI, and SDR + marketing.

* Anshul and Actively use poor cold outbound campaigns into their business as a signal and trigger to do their own outbound campaigns.

Where to find Anshul

* LinkedIn

* Actively.ai

Transcript details

(00:00) Introduction

(03:21) Anshul’s background and the founding of Actively

(06:20) The Actively thesis & product

(10:51) The failure cases that come from the smoke & mirrors AI GTM tools

(17:52) Buy vs. build when thinking through account prioritization and custom messaging

(23:44) Cognitive architecture

(27:50) How Anshul thinks about the next best account and the perfect message to send them

(33:00) Data hygiene & practical tips for improving data quality

(38:26) Non obvious ways that AI will show up in GTM

(40:59) Where humans will need to stay in the loop as AI continues to evolve

(44:57) The ideal org structure & collaboration for AI transformation

(50:07) Favorite underrated software tool, growth hack, & conclusion

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Frank Sondors went from managing a fifty person sales team to building software that enables companies to scale their best salespeople. As CEO of Forge, he's built multiple interconnected products that cover the entire cold outbound stack, from email infrastructure and deliverability to lead generation and AI powered execution. Frank scaled Forge to $3 million ARR in under 12 months while hiring just three salespeople and using AI agents that book 500 meetings per month. In this conversation, Frank shares his contrarian approach to scaling revenue without scaling headcount, and we explore how AI agents are fundamentally changing the economics and strategies of outbound sales.

In this podcast, we discuss:

* The core problem with existing sales automation software tools and why Frank built Forge

* How the modern tech stack can enable companies to scale revenue without scaling headcount

* How Forge's AI agents achieve 2% reply rates on cold email

* Which sorts of products and use cases work with AI agents, and which do not

* How to to identify and automate repetitive work across your org

* A number of Frank’s favorite growth hacks that have helped his team scale to $3M ARR

* The other decisions and drivers behind Forge’s $0 to $3M ARR growth

Episode highlights:

* Frank explains that Forge built N8N workflows to automatically WhatsApp message new signups within two minutes, achieving 10x higher reply rates than email.

* Forge uses voice AI rather than SDRs to call leads who don't convert within 14 days in order to gather feedback on why they didn't purchase.

* To scale without hiring, Frank's team religiously automates repetitive tasks through a Slack channel called "N8N Ideas", where anyone can request automation. They prioritize automation ideas based on revenue impact and ship new workflows weekly.

* Frank's team runs campaigns where half the leads get AI written emails and half get human written emails to see which performs better. Forge then uses these results to inform future campaign copy.

* Frank suggests scraping and targeting the LinkedIn followers of your competitors because they have already shown interest in your category. Campaigns targeting these prospects consistently deliver better results than traditional intent signals like funding events or new hires, which everyone else is already targeting.

* Despite building outbound software, 80% of Forge's revenue comes from inbound. Frank attributes this to building in public by sharing daily updates about product and company progress, working US hours despite being based in Europe, and offering a website widget that lets prospects instantly call their sales team if they’re online.

Where to find Frank:

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

* Forge

Transcript details:

(00:00) Intro

(02:41) Frank's background and the Forge origin story

(04:23) The early set of tactics that Forge automated and building an automation culture

(11:47) Overview of Forge's product ecosystem

(13:50) Using AI agents to book demos

(18:05) Forge’s agent success rates and where agents are less effective

(21:01) The role of real SDRs at an AI first company

(27:10) Automating WhatsApp follow ups

(28:47) How Frank thinks about agent prompting, context building, intent signals, and human-in-the-loop

(35:05) How Frank would think about finding winning messaging against competitors

(39:20) The best agents’ edge is their data layer

(47:08) Scaling to $3 million ARR in under 12 months and $1 million ARR without making a hire

(49:55) The impact of building in public

(52:25) Frank’s advice on building out go-to-market side of an org

(56:23) Favorite underrated software tool and conclusion

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Maximus Greenwald is the CEO and founder of Warmly, a $5M ARR GTM software tool. Warmly helps businesses identify, prioritize and follow up with their prospects by tracking and ranking intent signals ranging from website visits to third party competitor research.

Maximus left his job as a product manager at Google and decided to start a company. After multiple failed iterations, including a tinder for cofounders, and five pivots, Maximus and his team learned sales and marketing from scratch, eventually building out Warmly. Warmly has grown from zero to $5 million ARR in the last three years, and Maximus built the company completely in public for the last year and a half, sharing revenue numbers and strategic insights on LinkedIn.

In this podcast, we discuss:

* The right number of marketing channels to test each quarter and to build up to over time

* Why the team at Warmly structures their marketing teams horizontally, focused on different parts of the funnel, instead of vertically, focused on channels

* The rise of signal-based orchestration and the number of days you have to engage with an actively looking buyer before they’re lost

* Why Maximus chose to build Warmly in public and the benefits that have come with the decision

* The GTM software tool landscape and different ways of building motes within micro verticals

* Maximus's favorite growth hacks and tips for building a LinkedIn presence

Episode highlights:

* Maximus intentionally cut off all warm introductions to pressure test their cold outbound campaigns. This taught Warmly what sorts of LinkedIn messages cut through the noise so they could scale the team of SDRs.

* Warmly obsessively focuses on 1 channel per quarter while testing the waters on 1-2 others. This allows them to build conviction on which channels to cut out, leave the lights on, and double down in.

* Maximus believes AI will create a shift from channel-level specialists focused on things like SEO and paid ads to horizontal generalists who can context shift and hone in on different parts of the funnel (TOFU/MOFU/BOFU).

* Maximum decided to build Warmly in public by sharing revenue numbers and strategic insights monthly on LinkedIn. This has been beneficial to drive pipeline, keep employees aligned on the company strategy and to have fun going up against competitors in the ring of LinkedIn.

* Warmly has put together step by step guidance to evaluate data providers quality.

* There is still significant alpha to be had in most industries by capturing intent signals that indicate a company is in market and quickly following up with the relevant messaging.

Where to find Maximus:

* LinkedIn: https://www.linkedin.com/in/max-greenwald

* Warmly

Transcript details:

(00:00) Intro

(02:44) The origin story of Warmly's multiple pivots

(07:22) The journey to $5M ARR and how Warmly obsesses on 1-2 channels per quarter

(12:34) How Warmly made LinkedIn work

(14:30) Horizontal vs vertical team structure within marketing

(21:54) Signal-based orchestration and how intent signal commoditization is playing out

(27:34) Making the most out of your warm lead follow up and targeting niche verticals

(33:30) The role of GTM engineering at Warmly

(35:30) Why Warmly builds in public and how it has impacted their business

(38:01) Maximus’s advice for growing your LinkedIn following

(39:12) How Maximus thinks about the GTM software space at large and evaluating different data vendors

(44:04) How Warmly uses a tennis analogy to explain how humans should interact with AI

(46:55) Underrated tools and the LinkedIn group chat growth hack

Ilija Stojkovski is the Chief Revenue Officer at HeyReach, one of the leading LinkedIn automation platforms that has grown from zero to $6M ARR in just two years. As the company's sole salesperson when he joined, Ilija ran 1,800 demos in 14 months, while helping transform HeyReach from a tiny startup into a platform that powers LinkedIn campaigns for some of the best companies in the world.

Ilija joined in 2021 when the company was still a Reddit automation tool. After pivoting to LinkedIn automation, HeyReach's multi-account capabilities became a massive competitive advantage when LinkedIn restricted connection request limits. In this conversation, Ilija talks in depth about HeyReach’s growth - from pricing strategy and referral partnerships to what he learned across 1,800 sales calls. He also talks about LinkedIn outbound best practices based on 3 million connection requests of learnings as well as what he looks for in a GTM Engineer.

In this podcast, we discuss:

* Why HeyReach's multi-account capabilities became a competitive advantage when LinkedIn implemented connection request restrictions

* How experimentation informed HeyReach’s pricing model

* Building referral partnerships at scale with LinkedIn account vendors and integrations

* Tracking closed lost reasons to inform the product roadmap

* Learnings from running 8-9 sales calls per day for 14 months straight

* Why personalization isn’t the most important thing for LinkedIn campaigns

* Why business intuition is far more important than toolset skills for GTM engineers

Episode highlights:

* HeyReach’s first revenue spike came from a bit of luck — when LinkedIn clamped down on connection request limits per profile. Their second spike was more intentional, when HeyReach ran hard at selling into agencies.

* LinkedIn account vendors were charging clients per account, but HeyReach offered them unlimited accounts for a flat fee – letting them increase their margins, and, in turn, giving HeyReach a GTM flywheel.

* Ilija systematically tracked every closed lost reason and ranked them by revenue impact, which he fed directly to product development. They built white-labeling, API access, and webhooks in one quarter, helping quickly grow revenue with the most critical features.

* After running hundreds of demos, Ilija realized, "it's not about what you offer, it's about what they need" and shifted from showcasing features to understanding current challenges. This approach led to shorter, more effective demos.

* To qualify the flood of free trial signups, Ilija hired GTM Engineers to build an enrichment system using Clay that analyzed company websites, founding dates, services offered, and target ICPs.

* By analyzing 3 million connection requests, HeyReach discovered that the requests without messages saw a 27% acceptance rate compared to 22% with personalized messages.

* When hiring GTM engineers, Ilija found most candidates could learn GTM tools but lacked business logic to understand what the company actually needed. He emphasizes that learning technical skills like Clay takes much less time than building business intuition.

Where to find Ilija:

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

* HeyReach

Transcript details:

(00:00) Intro

(03:37) Ilija's background and journey to HeyReach

(06:22) What HeyReach does and how it works

(09:18) HeyReach’s market analysis and early-traction building

(14:19) How HeyReach experimented to determine their pricing

(16:23) Building referral partnerships with LinkedIn agencies and AI account vendors & tracking closed lost reasons

(22:57) Deciding whether to test paid channels

(23:55) Using Clay to qualify and target top free trial signups

(28:34) Learnings from running 1800

(30:26) Understanding and selling to GTM professionals

(34:17) LinkedIn's future and why automation tools aren't going away

(39:10) Best practices for LinkedIn outreach and connection requests

(47:27) Why over-personalization is less important than focusing on relevance

(48:58) Defining GTM engineers and the importance of business logic

(51:00) How to build business intuition as a GTM engineer

(53:09) Advice for companies hiring GTM engineers

(54:42) Prediction for future of GTM engineering, underrated tools, and conclusion

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Listen on Spotify

Brandon Camhi has played a pivotal role in Rippling's extraordinary growth journey over the past six years. He joined Rippling in November, 2019 when the company had 150 employees and had just hit $10 million in revenue. Starting as their first growth marketing IC, Brandon built out the new logo sales growth team, took on cross-sell to existing customers, and for the past 18 months has been leading all of marketing at Rippling. During his tenure, Rippling has grown from a $250 million valuation to over $16 billion—a 60x increase.

Brandon's career began with a content marketing internship at OpenGov, where he learned to get inside buyers' heads and write compelling content. He then joined Hearth as their first marketing hire, helping scale the business to tens of millions in revenue before discovering Rippling's Series A memo and joining what he recognized as a company with massive potential. In this conversation, Brandon shares the tactics and strategies that drove Rippling's growth, why he believes understanding customers trumps growth hacks, and the key accelerants to career growth.

In this podcast, we discuss:

* Why deeply understanding your customer is the foundation of effective growth, and how to best understand customers

* How Brandon prioritized his time in the early days of Rippling’s growth and how the team recovered when their top of funnel collapsed during COVID

* The biggest wins and mistakes during Brandon’s 6 years at Rippling

* The challenges and thrills of working at a hypergrowth compound startup

* Brandon’s top career advice

* How to balance AI automation with human judgment in modern GTM

Episode highlights include:

* Brandon transformed Rippling's automated outbound program from 10-15% intent-driven demos to 60-70%. He talked to sales and studied customers to learn what signals prospects gave when they were ready to buy.

* During his time at Hearth, after digging around roofer Facebook groups to see what content got the most engagement, Brandon discovered that memes were overwhelmingly popular. By creating ads that followed the same meme format, performance reached all-time highs overnight.

* Rippling shifted from purely automated outbound to investing in human SDRs to augment their automated programs. This drove higher yield on the accounts that they were targeting.

* To create true sales and marketing alignment, both teams report to the CRO and Rippling is working to eliminate language like "marketing sourced" vs "sales sourced."

* People often underestimate the importance of the company they join when looking at their career. The right company will feel chaotic and if you can lean into the chaos, you will often find career magic.

* Rippling has a principle called "go and see" to encourage everyone up to the C-Suite to go gather qualitative evidence instead of just looking at dashboards. Following this approach, Brandon still listens to 3-5 Gong calls weekly. In listening to calls, Brandon realized how much Rippling’s brand investment was being undervalued in attribution.

Where to find Brandon:

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

Transcript details:

(00:00) Intro

(03:41) Brandon's career journey starting at OpenGov and why he decided to join Rippling

(06:04) Why Brandon chose Rippling

(08:12) Early challenges and building intent driven campaigns

(12:09) Why intuition beats experimentation frameworks

(14:28) Understanding your buyer

(16:40) How Rippling navigated COVID and the top of funnel dropping to zero

(18:10) A COVID growth hack gone wrong

(19:43) How Brandon thinks about standing out in a competitive market

(24:28) The most important decisions driving Rippling's growth

(28:33) Why Rippling decided to invest in human-led outbound

(30:46) Creating true sales and marketing alignment

(34:40) What excites Brandon about Rippling's future potential

(37:30) Brandon’s top career advice

(42:10) The "go and see" principle at Rippling

(43:48) Building business intuition in marketing

(46:43) Operating principle learnings from Rippling and managing imposter syndrome

(50:04) Brandon’s takes on AI in go to market

(54:42) Underrated software tools, favorite growth hack, and conclusion

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Brian Swichkow went viral in 2014 for pranking his sword-swallowing roommate with hyper-targeted Facebook ads. When he wrote about the prank, it generated 450,000 impressions on Reddit in 72 hours. This experience shaped his marketing philosophy of doing things worth talking about and demonstrating value through action and storytelling.

Since then, Brian has spent over a decade helping seed to Series B startups drive initial user adoption and he has consulted with thousands of startups + over 150 Fortune 1000 companies. Most recently, Brian is running a product studio creating unique products like MythOS, a storytelling platform for personal knowledge management.

In this conversation, Brian shares his systematic approach to cold email, his framework for digital empathy in an AI-driven world, and why the intersection of creativity and analytics is the future of GTM.

In this podcast, we discuss:

* Brian’s formula for cold email copy that he names the Inigo Montoya method

* Creating relevant personal context that sounds human even when working with scraped data

* Why AI filtering tools will fundamentally change cold outbound strategies and the importance of constant innovation

* How investing time in seemingly random curiosities fuels creative breakthroughs

* Why Brian spends 2.5 hours each day talking to AI and how it impacts his human interactions

* Using multi-agent AI prompting to better understand your target audience

Episode highlights:

* Brian's Inigo Montoya method structures every email with a polite greeting, relevant personal context, managed expectations, and a clear call to action.

* To create genuine personal messaging from scraped data, Brian gives AI agents comprehensive information about himself and the target audience, then prompts them to write a single sentence that conveys a relevant connection. By keeping campaigns small and focused, the AI agents are able to craft more targeted, specific messaging that resonates.

* After working with an education company whose ads failed when selling the learning process but succeeded when selling the outcome, Brian learned that testing distinct concepts is what changes campaign performance, not tweaking individual words. He now focuses on validating fundamentally different messages rather than wordsmithing when optimizing cold email campaigns.

* Brian uses multi-agent prompting where one AI agent creates prompts for another agent, then validates outputs through a third agent, with each agent having its own specialized context and expertise. He even created Brian Bot Broadcast which synthesizes his email newsletters into a daily podcast in his own deep-faked voice.

* Brian uses LLMs to help himself learn what kind of messaging resonates with buyer personas he doesn't understand. For instance when he was selling GLP-1s to Midwest conservatives, he gave an LLM an 800-page right-leaning ideology document to role-play the audience, which revealed that framing health in terms of legacy was the key selling point, not self-care as he initially assumed.

* We talk about what digital empathy means and why even the small wording you use has a crucial impact on how you are perceived online.

Where to find Brian:

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

* MythOS

Transcript details:

(00:00) Intro

(02:36) Brian's journey from content and marketing agency to a product studio with a growth community

(3:55) Brian’s Facebook prank

(06:07) What running a product studio looks like

(08:40) Getting kicked off Notion and building MythOS

(14:56) Copywriting philosophy and the Inigo Montoya method

(21:19) Managing expectations in cold emails and avoiding direct sales

(25:24) Developing unique communication styles

(28:03) Digital empathy in the age of AI

(32:21) Testing concepts vs. testing words in copy

(35:57) Using AI to understand unfamiliar audiences

(41:48) AI email filtering with Missive and the future of outbound as AI expands

(47:35) The impact of poor prompting on human behavior

(50:13) Cross-discipline creativity and borrowing from other fields

(53:25) Balancing curiosity with productivity

(58:48) The prompt that reveals how well AI knows you

(01:01:09) Blending emotionality and logic as the future of GTM Engineering and conclusion

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Eric Nowoslawski is the founder of Growth Engine X, one of the first Clay agencies that has served over 300 customers. As Clay's first marketing contractor when they had just four employees, Eric helped build their early YouTube and LinkedIn presence before launching his own agency. Growth Engine X is now Clay's largest user by enrichment volume and will send over 4 million emails this month.

In this conversation, Eric shares his framework for creating high-converting campaigns for massive scale, why he believes signals aren't the silver bullet many think they are, and how to build offers so compelling that prospects would pay for the discovery call.

In this podcast, we discuss:

* The crawl walk run framework for getting started with Clay and cold outbound

* Why offers matter more than personalization and how to craft irresistible value propositions

* Growth Engine X's creative ideas campaign that remains their best performing approach across all clients

* Why signals aren't a silver bullet and what to focus on for sustainable growth

* Evaluating whether cold outbound is right for your business based on TAM size and unit economics

* Finding and hiring top GTM engineering talent from the agency ecosystem

Episode highlights:

* Eric's creative ideas campaign uses AI to generate three specific ways your product can help each prospect's business. To ensure the outputs remain consistent, he makes the AI focus on predetermined value props rather than generating random suggestions.

* Eric crafts offers by asking what he could say that competitors can't say, focusing on creating value so compelling that prospects would pay for the discovery call itself. Every offer must answer why someone wouldn't respond and address their hidden objections upfront. Growth Engine X's free test campaigns exemplify this by removing all risk and proving results before any payment is required.

* Growth Engine X always maintains backup inboxes equal to their sending capacity because no matter how good outbounding copy is, some will mark it as spam. When primary inbox delivery goes down, they instantly switch to warmed backups with zero downtime.

* For a Google reputation management client, Growth Engine X achieved positive replies on 1 in 70 emails by finding businesses with 3.5 to 4.5 star ratings, pulling specific negative reviews, and offering to remove them with payment only after removal.

* Eric recommends TAMs over 100,000 and customer lifetime values over $10,000 for cold outbound success. He emphasizes that a business’s customer acquisition cost to lifetime value ratio should ideally be 1 to 10, though 1 to 3 is acceptable. This ratio ensures cold outbound campaigns remain profitable with healthy margins.

* To find GTM engineering talent, Eric targets small agency owners with teams under 10 employees who are tired of running a business but have proven outbound skills. These operators often make perfect full-time hires.

Where to find Eric:

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

* Growth Engine X

Transcript details:

(00:00) Intro

(02:55) Eric's journey from Clay's first marketing hire to agency founder

(10:12) The crawl walk run framework for Clay and cold outbound

(12:55) Infrastructure, list building, and crafting the right message

(17:55) Building successful campaigns and understanding what conversion rates are “good”

(24:39) Deciding if a business should do cold outbound or not

(26:43) Defining offers and why they're crucial for success

(29:41) Why signals are overrated for building a reliable outbound motion

(32:35) Examples of strong offers

(39:49) Personalization strategies for your entire database — firmographic vs person level data

(41:14) Eric’s creative ideas campaign

(48:34) Hiring GTM engineers

(53:48) The future of cold email

(58:25) Email deliverability and the importance of backup infrastructure

(01:02:23) Favorite tools including Supabase and Pipedream

(01:04:25) Predictions on the future of GTM engineering and conclusion

Davide Grieco spent four years building the growth function at Verkada, a security hardware and software platform with over $700M in funding from backers like Sequoia and First Round Capital.

While at Verkada, Davide worked on a number of projects to automate workflows, improve sales efficiency, and increase revenue. These included reducing landing page creation from hours to minutes, empowering SDRs with campaign-in-a-box tooling to send personalized direct mail, and cutting cold email volume by 80% while only seeing small reductions in pipeline.

In this conversation, Davide shares a few of his big wins while at Verkada, how to break down silos between marketing and sales, and what it really takes to build GTM engineering skills. We also dive into his contrarian takes on AI SDRs and why he believes the future belongs to those who can generate ideas, not just execute them.

In this podcast, we discuss:

* Some of Davide’s most successful campaigns at Verkada including their campaign-in-a-box for SDRs

* How and why growth teams should prioritize driving revenue, even when it’s harder to measure

* The role of marketing vs. growth/GTM engineering and where these functions should report into

* The role of AI SDRs and how their inevitable advancement will influence the future of go to market

* The most important skills to look for when hiring a growth/GTM engineering team

Episode highlights:

* Verkada was able to reduce new ABM landing page creation time from hours to minutes using Webflow and Clay.

* Davide and his team drove $20-$25M in quarterly pipeline with their direct mail campaign-in-a-box for SDRs. Beyond the sophisticated and simple flow to send personalized gifts, they had sales run the enablement instead of marketing to drive buy-in.

* Davide built an SDR team sitting in marketing that booked 60-80 meetings per rep per month from his team’s automated campaigns.

* Davide argues that obsessing over attribution kills revenue growth. Instead of fighting over pipeline credit, growth teams should focus on the clear revenue-generating projects — whether it’s custom product demos or programs that help outbound reps book more demos.

* AI SDRs will eventually match human SDR capabilities, which will create outbounding noise to the extreme. The companies that stand out (and win) will be the ones that do the best true marketing.

Where to find Davide:

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

Transcript details:

(00:00) Intro

(03:49) Davide’s background from finance into growth

(05:32) Automating enterprise landing pages at scale

(08:51) The evolution of cold email strategy at Verkada

(15:14) Building the "campaign in a box" program for direct mail

(22:38) Structuring growth & marketing teams for maximum impact

(29:23) The MDR program: booking 80 meetings per rep per month

(33:05) Mistakes and learnings from rapid scaling

(35:43) Hiring philosophy for GTM engineering roles

(39:14) Advice for breaking into growth from other fields

(41:43) Eliminating silos between sales and marketing

(44:39) Why AI SDRs might backfire for the entire industry

(48:09) The democratization of GTM through AI tools

(52:56) Future predictions for GTM engineering and conclusion

Kevin White runs GTM Strategy at Common Room, a platform helping GTM teams find intent signals, track users across platforms, and turn that data into full funnel campaigns. Before Common Room, Kevin was head of marketing at Retool and worked in growth at Segment, giving him deep insight into how modern GTM teams operate.

Kevin believes that while AI avatars and personalization get the attention, the real differentiator for GTM teams is how they track, store, and use data. We explore how to find and harvest demand in the watering holes your prospects already live in, why identity resolution is the "unspoken linchpin" of go-to-market, and how to stack signals to create micro-campaigns that break through the noise.

In this pod, we discuss:

* Why proprietary data is your biggest GTM differentiator in the age of AI

* How to build micro-campaigns by stacking multiple intent signals

* The rise of dark social channels and why traditional MQL tracking is no longer the entire answer

* Identity resolution as the foundation for modern GTM

* What GTM problems AI hasn't solved yet and why humans remain critical in the loop

* How to find the specific triggers that indicate your prospects are in-market for your solution

Episode highlights:

* Kevin advocates for micro-campaigns with highly specific commonalities rather than broad campaigns covering one generic intent signal (like funding or job change). He argues that specificity is what breaks through pattern recognition in crowded inboxes.

* Modern marketing's role is expanding beyond driving form fills or event badge-scans into generating trackable signals in dark social channels. Prospects often indicate they are in-market to buy on the “dark social channels” where they already operate — places like LinkedIn, Slack communities and GitHub.

* Identity resolution is the unspoken linchpin of modern GTM. The increasing number of signals across dark social channels are only as valuable as your ability to tie them back to a specific user. You can level up identity resolution with modern software (like Common Room) or thoughtfully-orchestrated internal tooling.

* The best GTM teams Kevin works with have a lot of first-party data, they know it's a competitive advantage, and they obsess over finding ways to put it to good use.

* Kevin warns that shiny object syndrome distracts even the best companies from the fundamentals needed to build a strong foundation of buyer data, intent and identity resolution.

* Kevin and his teammate Josh Lind built playgent.ai, which intakes a company’s domain and returns suggested intent-based campaigns they can run.

Where to find Kevin:

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

Transcript details:

(00:00) Introduction, Kevin’s background, and the journey to Common Room

(07:46) Why data infrastructure is the real GTM differentiator

(10:24) Building effective micro-campaigns through signal stacking

(21:51) Real examples of non-obvious data points that drive results

(24:32) Dark social content and the evolution of marketing's role

(29:38) Identity resolution as the unspoken linchpin of GTM

(37:12) What sets apart the best GTM organizations

(42:08) Getting started when you're behind on modern GTM practices

(44:25) Building playagent.ai with AI orchestration

(46:48) Evaluating which GTM companies will win long-term

(48:36) The most important unsolved problems in GTM tech

(50:43) Favorite tools, predictions for GTM engineering and conclusion

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Patrick Spychalski is a co-founder of The Kiln, one of the original Claygencies — an agency that primarily uses Clay’s software to add value to their clients. The Kiln helps companies build scalable inbound, outbound, and data enrichment systems. He's been pushing the boundaries of GTM engineering since Clay's early days, creating innovative campaigns that go far beyond basic personalization.

Patrick started as Clay's first marketing contractor when they had just four employees, building their early YouTube and LinkedIn presence before launching The Kiln. In this conversation, Patrick shares his framework for high-impact campaigns, his approach to finding GTM alpha, and why he thinks the most underutilized Clay use case has nothing to do with cold outbound.

In this podcast, we discuss:

* Why the offer matters more than personalization in cold outbound campaigns

* How to find personalizations based on your distinct value prop

* The most important questions to ask about your buyers to build winning outbound campaigns

* How to learn Clay and practical tips for using the product

* Why data cleaning and CRM enrichment is the biggest missed opportunity for most companies

* How to structure a modern GTM organization and when to hire vs. use an agency

Episode highlights:

* Patrick built a viral campaign using Lovable's API and Clay to automatically generate custom web apps for each prospect at scale.

* Patrick's framework for creative campaigns involves deeply understanding the client's value prop and available offers, then working backwards to find data points in Clay that can quantify and personalize those benefits for each prospect.

* Patrick considers CRM data cleaning the most underutilized, and lowest hanging fruit, Clay use case because a clean CRM underpins your entire GTM success

* Marketing leaders should use Clay for inbound lead scoring to verify they're attracting the right audience and save sales teams research time by qualifying leads and prepping call context.

* Modern GTM tools enable radical automation: The Kiln rebuilt an entire recruiting firm's business function in Clay, while Patrick automated his complete sales follow-up workflow with n8n from call transcripts to proposal generation.

* We discuss how to evaluate GTM engineering candidates by looking for curiosity and avoiding those stuck in outdated ways of thinking.

Where to find Patrick:

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

* The Kiln: https://thekiln.com/

Transcript details:

(00:00) Introduction and Patrick's creative cold outbound campaigns

(02:32) Patrick's journey from Clay contractor to co-founding The Kiln

(06:57) Building custom web apps at scale with Lovable's API

(11:17) Framework for coming up with creative campaign ideas

(13:47) Why offers matter more than personalization

(20:56) Finding the right data points for your personalizations

(22:32) Tips for learning Clay and understanding APIs

(28:08) Finding GTM alpha and when strategies saturate

(31:33) The Kiln’s shift from cold outbound to RevOps and data cleaning

(33:46) Why CRM data cleaning is massively underutilized

(37:26) Inbound lead aggregation and scoring strategies

(40:53) How to structure your organization for GTM success

(47:25) Why n8n is Patrick's favorite underrated tool

(50:19) The future of GTM engineering and conclusion

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

 Osman Sheikhnureldin runs GTM operations at Clay, and he's redefining the role of sales and marketing ops within tech.

He was the first GTM operations hire at Clay and has built the GTM ops function while Clay has grown from 25 to ~150 people. In this conversation, Osman shares his view on the modern GTM operations org, and we talk about the cutting edge data capture and automations his team has built to support Clay’s growth.

In this podcast, we discuss:

* Why modern GTM operations should start with data architecture, not CRM admin

* How to automate the most painful parts of sales workflows using LLMs

* How to extract nuanced competitor intelligence from sales calls and the value of JSON schemas

* How to build and track your own custom data points and use them to create self-improving LLM-generated battle cards

* Marketing’s role in the new world of GTM Engineering

* How to evaluate and hire your first GTM engineer

Episode highlights:

* Osman and his team automated closed won hand offs between sales and customer success team to reduce manual work and speed up the post-deal transition.

* Osman uses large language models to automatically extract competitor mentions, renewal dates, and buyer pain points.

* Clay has built their own custom data points to get more granular about their ICP. They track sales org maturity, who their prospects sell to, where their prospects sell, and much more.

* The GTM Ops team at Clay is working to build battle cards that write themselves — closed-won (and lost) patterns are fed back into Clay’s system, allowing automated generation of pre-call prep and objection handling guides.

* Marketers at Clay have built personalized landing pages to target their top enterprise prospects.

* We cover how to evaluate GTM engineering candidates by testing them for curiosity and digging into how they think about creative intent signals.

Where to find Osman:

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

Transcript details

(00:00) Introduction to Osman, his role at Clay, and the scope of his GTM Ops team

(03:17) Osmans’s career journey and GTM Ops at Clay

(8:44) The ideal GTM Ops org structure with examples from Clay as they’ve grown

(22:02) Automating manual workflows from the Clay sales team

(26:13) Extracting competitor data from sales transcripts and the importance of JSON schemas

(32:57) Lightweight data engineering tips to store data at scale

(36:30) Creating and tracking custom data points for your GTM needs

(43:19) Building custom, self reinforcing battle cards based on call learnings

(56:44) Marketing's Role in GTM Engineering

(01:03:38) Building and evaluating GTM Engineering Skills

(01:14:38) The long term value of a CRM and conclusion

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Ted Eltringham worked in growth at Rippling and Samsara before starting Architect to build an agentic AI website. He has been using AI in his go to market campaigns since the day that (OpenAI first model) launched. We talk about how to use AI judges to find the best models and eliminate hallucinations, his learnings from growth at Rippling & Samsara, and how to become a GTM engineer.

In this podcast, we discuss:

* Using AI to build automatic reply handling for cold outbound campaigns

* The concept of an AI judge to pick the best models and reduce hallucinations at scale

* Some of Ted’s most creative growth campaigns while at Rippling and Samsara

* How to start building GTM engineering skills and evaluate those skills

* One practical piece of advice every company can follow to better integrate AI in their business

Episode highlights:

* Ted built automatic reply handling for his cold outbound campaigns to reply within minutes and increase reply rates.

* Finding the best AI models for your needs by finding an initial winner and comparing all new model launches against the baseline.

* Ted uses multiple AI judges for any of his more complicated or important workflows. This reduces the chance of error, and it allows him to train an agent on what amazing looks like.

* Using traffic accident APIs to build cold outbound campaigns at Samsara and how to build customer lookalike audiences after every closed won deal.

* The concept of an AI automation engineer that can hop between different business departments to help them automate workflows and better incorporate AI.

Where to find Ted Eltringham:

* LinkedIn

Transcript details:

(00:00) Introduction to Ted and the conversation

(02:36) Ted's journey into growth and GTM engineering work

(05:04) Incorporating AI in GTM work, including reply handling and how to structure prompts

(06:43) AI in Outbound and Email Automation

(09:29) Finding the best models and using AI Judges to QA at scale

(15:27) The most important skills to maintain with the rise of AI

(17:50) What it’s like working in growth at first growth startups and some of Ted’s most creative experiments

(26:23) Building valuable GTM engineering skills

(31:53) How to build the GTM engineering muscle inside of your business and evaluate talent

(37:23) Underrated tools and the future of GTM engineering

(40:50) Architect as the first agentic website and conclusion

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai

Emre Kavaloglu started Waterfall.io to help companies more efficiently and cost effectively find, store and maintain their company and contact databases. He works with some of the fastest growing companies in tech and has a comprehensive view about building your TAM (total addressable market). We get into the nitty gritty details about TAM building - from new ways to find good-fit companies, to building a system that gets smarter over time. Emre also shares his views on common misconceptions about GTM engineering and how to become an effective GTM engineer.

In this podcast, we discuss:

* Why most companies overlook 20–40% of their TAM—and how to find it

* How to pick (or build) a data stack and why you should be thinking about opportunity cost, not price per contact

* How much effort to put into TAM building based on your company size, stage, and product market fit

* All of the places that a GTM engineer can add value along the entire funnel

* How to build GTM engineering skills from scratch, and advice for founders looking for their first GTM engineer

Episode highlights:

* Smaller business without product market fit shouldn’t be concerned about maximum TAM coverage. Instead, they can think about the need for TAM building as a function of the different active acquisition channels. For outbound or cold email, having a large database is materially more important than if you’re driving all of your revenue through partnerships.

* To find the right company list for your business, you need to look beyond the basic industry, location and employee size demographics. You can find more companies by looking for good-fit contact titles (e.g. “VP of human resources”), and by deploying manual enrcihment efforts against possible-fit lists until you identify the patterns that can scale with AI.

* Decide what you are going to use intent data for before spending time and money capturing it. Once you start tracking intent, aim to retrieve + act on it as quickly as possible after the signal actually happens.

* GTM Engineers can (and should) be thinking about how to deliver value across the entire funnel, not just cold outbound. The fastest way to do this is to go talk to internal folks interacting with customers (customer success, AEs, AMs, etc), be curious, and find ways to add value.

* If you want to build your own GTM engineering skillset - be curious, play with workflow tools like N8N, and be intentional about honing your business intuition.

Where to find Emre:

* LinkedIn

* Waterfall.io

Transcript details:

(00:00) Introduction to Emre and the conversation

(05:15) Defining what GTM engineering is NOT

(12:45) Framework for building and maintaining your TAM

(18:29) Practical ways to increase your TAM coverage

(23:55) Database hygiene best practices

(25:46) The common mistakes and best practices when tracking intent signals

(30:15) Using intent across the entire funnel of your business

(32:05) Account prioritization frameworks

(36:56) TAM building for early stage companies

(39:55) Other parts of the business a GTM engineer can add value

(47:29) Building GTM engineering skills

(52:46) Hiring and scaling GTM engineering for founders

(55:41) Underrated tools and the future of GTM engineering

(59:35) Conclusion and Final Thoughts

For inquiries about sponsoring the podcast and to recommend any guests, email noah@thegtmengineer.ai