I ended that essay with a question I did not answer, because I do not think it has an easy answer. If the gravity is real, can it be resisted? Is the alternative the diagnosis implies, configuring deployment so the worker is augmented rather than replaced, actually buildable, at a cost we would pay, and quickly enough to matter against a technology that moves in months?

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This essay is my attempt to reckon with that question honestly. I will tell you now that I do not arrive at a clean victory. I arrive somewhere narrower and, I think, truer.

The Objection That Should Worry Me

Here is the strongest case against everything I have argued, and I am going to put it more forcefully than a hostile reader would, because if it stands, the rest of this is decoration.

The alternative I am calling for takes time the displaced do not have. My own best example proves it. When I want to show that deployment can be configured to keep the human in the loop, I reach for radiology, a field where the AI arrived as augmentation rather than replacement, where the radiologist still reads, judges, signs, and bears the consequence. But radiology did not get that way by accident or overnight. It took half a century of medical liability law, professional standards, credentialing, and reimbursement structure to build the scaffolding that made augmentation the rational deployment. Fifty years.

Now look at the work actually being hollowed out right now. Customer service. Copywriting. Transcription. First-pass legal document review. Entry-level analysis. None of it has radiology’s scaffolding. None of it has the liability shield, the licensing board, the standard of care, the reimbursement code. And it is not being displaced over fifty years. It is being displaced over fiscal quarters.

Or so the story goes, and I should say plainly the story is contested. The pace of displacement is partly real and partly the marketing department’s. The gap between what these systems are sold as doing and what they reliably do in production is wide, the promised productivity gains keep arriving late and uneven, and some of what looks like a hollowed-out department is a pilot that still leans on the humans it was supposed to replace. A careful skeptic is right to demand the denominator before accepting that whole categories of work are vanishing in months. I take the point, and I notice it cuts in a direction that helps me rather than hurts: if displacement is slower and narrower than the hype insists, then prevention has more runway than the worst case allows, not less. So let me grant the objection its strongest form anyway, because the argument should survive it. Suppose the displacement is as fast as the alarmists say. Then what?

This is the asymmetry that should keep more of us up at night than it does. Cure can be deployed after the fall, you can stand up a retraining program or a transfer payment once the job is already gone. Prevention cannot. Prevention has to be poured like a foundation, before the building goes up, before the displacement happens. And if the configuration that would protect a category of work takes years to build while the displacement of that work takes months, then the window I keep invoking is not open. It closed before I finished describing it. For the people most exposed, I am offering a foundation for a house that has already burned down.

I want to be honest: this objection genuinely worries me. It is not a strawman. It is the thing I have to answer before I am entitled to any of the hope in the rest of this piece.

What Prevention Is Not

Before I try, I have to clear away the version of this argument that deserves every bit of scorn it gets. There is a reading of “prevent displacement” that means: force companies to keep workers doing jobs a machine could do more cheaply, freeze the org chart, hold the economy in amber against the tide of productivity. That reading is economically incoherent and I am not making it. You cannot order a firm to employ people to do nothing, and you should not want to. A serious economist will tell you that technological change displaces labor, that it always has, and that the humane question has always been whether we manage the transition well or badly. On that, the economist is right.

But that is not the choice I am pointing at, because it is not the only choice on the table. The displacement debate keeps collapsing two different things into one. One is whether the work gets more productive, which it will, and should, and no one sane is trying to stop. The other is what shape the productivity gain takes: whether the technology is deployed to augment the worker and strip the drudgery from her day, or deployed to remove the worker and keep the wage she used to earn. Both are productive. Both capture the gain. They are not the same deployment, and the difference between them is not dictated by the technology. It is chosen. Prevention, as I mean it, is not the refusal of productivity. It is the contest over which of two equally productive configurations gets built: the one that keeps the human in the loop, or the one that empties the loop out. Anyone who tells you that contest isn’t real, that only one configuration was ever economically available, is smuggling the conclusion into the premise.

Cure Isn’t Built Either

So let me start by noticing what the objection quietly assumes. It assumes that cure is ready and prevention is not, that on one side of the ledger sits a functioning safety net, waiting, and on the other sits my hypothetical foundation that takes too long to pour. Time the two against each other and prevention loses.

But cure is not built either.

Consider what cure actually requires to work, not to be announced, but to work. Retraining has to take a forty-five-year-old customer service representative and return her to comparable income and comparable dignity in something other than a worse job. The evidence that retraining programs do this is, to put it gently, poor; decades of trade-adjustment and workforce-retraining efforts have a track record that ranges from modest to dismal. The infrastructure that would do it well does not exist at scale. It would have to be built.

Or consider the transfer the encyclical leans toward, the social protection, the redistribution, in its strongest form a universal basic income. None of that is built. It is not funded. It is not politically coalitioned. Standing up an income-transfer regime large enough to absorb mass displacement is at least as slow, at least as institutionally demanding, and at least as politically captured as anything I am proposing on the prevention side. The Pope’s own remedies are a fifty-year project that no one has broken ground on.

So when the defeatist holds a stopwatch to prevention, fairness requires holding the same stopwatch to cure. And when you do, the comparison stops favoring cure. I am not going to overclaim here, I am not going to tell you prevention is obviously faster, because I do not know that. The real claim is narrower and it is enough: the speed objection, applied evenhandedly to both sides, is not a reason to prefer the safety net. Both the net and the foundation have to be built, both are slow, both are contested. The only question left is which one is worth starting, and “it’s too late for prevention” cannot be the answer when cure is exactly as unbuilt.

Radiology, Honestly

Let me give the critics their due on radiology, because they are right about it, and conceding that is the only way to extract the lesson that survives.

Radiology is the slow case. It is the high-liability, high-status, heavily regulated, professionally fortified case. It is, in almost every respect, the least representative of the work AI is displacing fastest. If I lean on it as proof that prevention is easy, I deserve the truck that gets driven through the argument. A copywriter has no equivalent of the FDA. A transcriptionist has no standard of care. Pointing at radiology and saying “see, it can be done” is like pointing at a cathedral to prove that anyone can put a roof over their head.

So I will not claim radiology’s timeline transfers. It doesn’t. You cannot grow an entire profession’s regulatory edifice in the time you have.

But that is the wrong lesson to draw from it, and the critics stop one step too early. Radiology is not valuable as a timeline. It is valuable as an anatomy. It shows you what scaffolding actually is, disassembled into parts: a liability rule that names a specific human as accountable when the automated decision is wrong. A standard that defines what competent practice requires. A gate that governs what the software is allowed to decide on its own. A payment structure that funds the human-in-the-loop rather than penalizing it. Those are the load-bearing elements. And here is the thing the fifty-year objection obscures: most of that half-century was spent building the profession, not the configuration. The liability principle, the accountable-human rule, the single most important piece, is not a fifty-year artifact. It is a legal default that can be set by a ruling or a clause. We mistake the time it took to grow radiology-the-profession for the time it takes to attach radiology’s key lever, and they are not the same number.

What transfers from radiology is not the cathedral. It is the knowledge of what a load-bearing wall looks like, so you can build a smaller structure faster, on purpose, now that you know what you are building.

A Map, Not A Switch

This forces a candor I think the whole debate has been avoiding, including, sometimes, me. Prevention is not a single switch you throw to save all work. It is a territory, and the territory has at least three zones, and they are not equally reachable.

There is work that already has scaffolding. Regulated professions, safety-critical systems, anything where a wrong automated decision already carries legal liability, medicine, aviation, structural engineering, certain financial decisions. Here the configuration question is not a future project. It is live right now. The lever exists; the only question is whether we pull it in the direction of keeping the human accountable or let it slacken.

There is work where scaffolding does not exist but could be built quickly, because a natural lever is within reach. Anywhere a single liability default could attach, who is responsible when the automated underwriting denies the loan wrongly, when the automated screen rejects the qualified applicant, when the generated legal document contains the error that costs the client. These do not require inventing a profession. They require attaching accountability to a decision that currently has none. That is a clause, a ruling, a procurement standard. Months, not decades.

And then there is work where no scaffolding exists and none can grow in time. Some of the fastest-displacing work is here. For a category of jobs, the configuration that would have protected them needed to exist before the displacement began, and it did not, and it will not materialize fast enough. For that work, prevention has already lost. Cure is what is left, and the dignity of the people in those jobs depends on cure being better than the dismal thing it currently is.

I am not going to pretend that third zone away. Naming it is the price of being believed about the first two. If I told you prevention covers everything, you would be right not to trust me, because you can see the call-center floor emptying out and you know no liability shield is coming to save it in time.

But here is the reframe that turns this map from a verdict into a task. The call-center worker sits in the third zone not because the third zone is a law of nature, but because no one ever built her a place in the first. Her work has no accountable-human rule, no standard, no gate, not because such things were tried and failed, but because no one thought to attach them to work that nobody was protecting. The absence of scaffolding is an unbuilt thing. It is not an unbuildable one. And the difference between a door that is locked and a door that no one has yet tried is the entire difference between defeat and delay.

I have to be more honest than that, though, because “no one thought to” is too innocent. The scaffolding around radiology was not merely thought of; it was fought for, against interests that would have preferred cheaper, faster, less accountable care. And the scaffolding around the call center is missing not only because no one got to it but because its absence is worth money to someone. An accountable-human rule is a cost. A deployment gate is a delay. A standard that says the generated work must be checked by a person who carries the consequence is a line item that the configuration without it does not have to pay. So the door is not just untried. In a good number of cases someone is leaning against it from the other side, and they have more resources to lean with than the worker has to push. This is the part the time objection never mentions, and it is, if anything, harder than time: the gravity pulling everyone downstream is not only the gravity of habit or imagination. It is the gravity of power. The people who make the deployment choice have every incentive to keep the upstream lever from being built, and the downstream remedy (let the public retrain the worker, let the public transfer her some income) is the outcome that costs them least. “Compassionate pragmatism” is, conveniently, also the cheapest thing they could be asked to accept.

I do not say this to collapse into the conclusion that nothing can be done. I say it because any honest reckoning with why the scaffolding is unbuilt has to include the fact that its remaining unbuilt is, for some, a victory rather than an oversight. You cannot plan the construction without a true map of who is standing where.

Speed Is Partly A Choice

I have one more move, and I am going to be straight that it is the one I am least sure of.

Why does displacement move in months while governance moves in years? We treat that gap as a fact of nature, technology is fast, institutions are slow, and the race is lost before it starts. But I do not think the gap is natural. I think it is built.

Deployment is fast because it is frictionless by design. No liability attaches to shipping the automated system. No gate stands between the decision to deploy and the deployment. The marginal cost of pushing the replacement live is close to zero, and nothing in the environment slows it down. Governance is slow because it is friction-full by design, every check is a deliberate brake, and we built the brakes on purpose. So the timescale mismatch that supposedly dooms prevention is not a law. It is a configuration, the same kind of configuration as everything else in this argument. And radiology is, again, the existence proof: the FDA gate is friction deliberately attached to deployment, slowing the rollout of an automated diagnostic to the speed of governance at exactly the point where the stakes justify the brake.

So in principle, you do not win by making governance faster than deployment, which you cannot do. You win by adding friction to deployment at the specific high-stakes points where speed is the enemy, bringing the two timescales toward each other from the other side.

Now the pressure test, because this move does not get to walk away clean. Attaching that friction is itself slow and contested. A clumsy gate is worse than none: it ossifies, it protects incumbents, it becomes the regulatory capture that the encyclical’s sharpest critics rightly fear, where the rule meant to keep humans in the loop curdles into a moat around whoever wrote it. And friction applied to the wrong things just makes everyone poorer while protecting nothing worth protecting. So #4 is real but narrow. It does not say “slow everything down.” It says speed is not fixed, and at a small number of high-stakes points, a well-built brake can change the timescale that the whole objection rests on. That is a smaller claim than I would like to be making. It is the true size of it.

Where This Leaves Us

So: can the gravity be resisted?

The truthful answer is that the question was wrong, and the timing objection, the strongest thing anyone has against this whole project, proved less than it claimed while proving something real.

It proved something real: for a portion of the work being displaced fastest, prevention has already lost. The foundation needed to be poured before the fire, and it was not, and naming that as a genuine loss is the only fair thing to do. The people in that zone are owed a cure far better than the one currently on offer, and pretending prevention will reach them is a comfort that costs them their due.

But the objection claimed far more than it proved. It gave cure a free pass cure never earned: the safety net is exactly as unbuilt, exactly as slow, exactly as captured as the foundation it was supposed to outrun. It mistook the fifty years it took to grow radiology-the-profession for the time it takes to attach radiology’s one essential lever, which is a clause and a ruling, not a cathedral. And it treated the speed of displacement as a law of nature when speed is partly something we chose, and could partly choose otherwise.

What that leaves is not the defeatist’s conclusion, too late, abandon prevention, build the net, and not the triumphalist’s either, configure everything, save every job. It leaves something narrower and harder to act on. Prevention is a live option in more of the territory than the defeatist admits and a dead one in less of it than the optimist wishes. And there is no way to find the real boundary between the two from the armchair. The only way to learn where the foundation can still be poured in time is to start pouring it, in the zones where the levers already exist, and watch where it holds and where the fire outruns it.

The window, then, was the wrong image, or at least too simple a one. A window is open or it is closed. What we actually face is a window that is closing, at different speeds in different places. In some places it has already shut, and we should grieve that plainly and turn to the people on the wrong side of the glass. In others it is still wide, and the thing keeping us from climbing through is partly a belief, borrowed from a stopwatch that was never applied fairly, that the effort is pointless, and partly the plain fact that someone with more leverage than us would rather we didn’t.

I want to end on the hardest version of the doubt, the one I cannot dissolve. It is possible that the deepest problem here is not time and not even power as I have described it, but authority, that the capacity to reach into a deployment decision and say “not this way” is not a power we have temporarily misplaced but one we never really held, and that the reason everyone ends up arguing about the size of the net is that the net is the only place our agency actually reaches. If that is true, then this whole essay is a description of a door with nothing behind it. I do not think it is true. But I cannot prove it isn’t, and I am not going to pretend the uncertainty away to give you a cleaner ending.

What I can do is smaller, and it is the honest limit of what one person at a desk can do. I can say where I think the door is. I can argue that the lever exists, that the timing objection was applied unfairly, that the difference between augmentation and replacement is a choice and not a fact, and that the absence of scaffolding around the most vulnerable work is a thing someone built and someone could therefore unbuild. I cannot model the transition, draft the liability default, or organize the contract. I am one writer who has spent a long time looking at this and has come to believe the upstream question is real, answerable in part, and almost entirely unasked.

So I am planting a stake. Here, I think, is where the ground can still hold a foundation, and here is where it cannot, and here is who is leaning on the door. I cannot build the thing. But I can refuse to let “it’s inevitable” and “it’s too late” stand as the last words, because they are not true, or at least not yet true everywhere, and the people who could actually do the building, the ones with the math, the law, the leverage I don’t have, will only reach for it if someone first insists the reaching is worth it.

That is less than I wanted to be able to tell you. It is also a great deal more than nothing. It is the truth as far as I, just one person, can find it, and the next part belongs to hands that aren’t mine.

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When the most authoritative moral document ever written about artificial intelligence arrives at the same structural conclusions you have been arguing toward by an entirely different road, the temptation is to declare victory and stop reading. I want to resist that because the more interesting thing about Pope Leo XIV’s first encyclical is not where it agrees. It is where it stops.

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Magnifica Humanitas runs to roughly forty-two thousand words and reasons from a hundred and thirty-five years of Catholic social doctrine, beginning with Leo XIII’s Rerum Novarum and its defense of the worker in the Industrial Revolution. It is not a technology document dressed in vestments. It is a moral anthropology that happens to take AI as its occasion. And reasoning from that tradition, from premises that have nothing to do with the vocabulary of systems design, it lands on three claims that anyone who has thought structurally about AI deployment will recognize at once.

The first is that AI does not bear consequences. The encyclical is blunt about it: these systems do not undergo experience, do not mature through relationships, and do not, in the Pope’s framing, judge good and evil or carry responsibility for what follows from their outputs. They imitate; they do not answer for the result. The second is that systems are being built the wrong way around, designed so that workers must adapt to the speed and demands of the machine, rather than the machine being designed to support the people who work. The third is what the encyclical calls the “architecture of visibility”: platforms engineered to capture attention, amplify what is visible, and shape opinion, treating the finite human capacity for attention as a resource to be mined.

Consequence-bearing. The design of work. The capture of attention. An independent line of moral reasoning, starting from the dignity of the human person rather than from any analysis of deployment, arrived at an architectural diagnosis. That convergence is worth naming plainly, not because it flatters anyone, and not because everyone serious sees it this way. Plenty of capable people read AI primarily as a productivity expansion and think the displacement worry is overstated. The convergence I mean is narrower and more striking for it: two arguments built from unrelated foundations, one theological and one structural, traced the problem to the same place. That is some evidence that the diagnosis is structural rather than idiosyncratic.

The Word That Matters

The encyclical’s sharpest move is a single word.

Leo writes that technology is never neutral, because it takes on the characteristics of those who devise, finance, regulate, and use it. And he writes that the pursuit of greater profit cannot justify choices that systematically sacrifice jobs.

Choices. Not consequences. Not the weather. Not an impersonal force descending on the labor market like a season. The encyclical locates the problem at the point of deployment design, in the decisions made by identifiable people about how the technology will be configured and to what end. This is the thing most commentary on AI never reaches. The dominant register treats displacement as a fact of nature: capability accelerates, jobs evaporate, and the only remaining question is what to do for the people left behind. Leo refuses that. He sees that the displacement is authored. It could be authored otherwise.

That is the high-water mark of the document. Having reached it, watch where the remedies go.

The Turn

The encyclical’s concrete recommendation is this: regulate the algorithms. Retrain the displaced. Use taxation, social protection, and industrial policy to ensure equity. Renew labor organizations. Entrust international oversight to a reformed United Nations.

Every one of these operates after the displacement has occurred.

Retraining presumes the job is already gone; it is a response to the worker who has been turned out, not an intervention in the decision to turn him out. Social protection presumes that the income has already been lost. Taxation and transfer presume that the displacement has occurred and that the proceeds are now being redistributed. International oversight watches outcomes. The diagnosis pointed upstream, to the choice. The prescription walks downstream, to the damage, and builds an apparatus to manage it. The document names the cause and then treats the symptom.

An Ounce of Prevention

There is an old proverb for exactly this. An ounce of prevention is worth a pound of cure.

Prevention acts on the cause before the harm. Cure acts on the damage afterward. Everything in Leo’s recommendation list is a cure: retraining cures the loss of a job, a transfer cures the loss of income, and oversight cures the worst outcomes once they have appeared. The prevention, the ounce, would act at the configuration itself, at the moment the deployment is designed, before anyone is displaced at all. It would treat the choice the encyclical so clearly named as a choice still open to be made differently, rather than a settled fact to be cushioned.

The document does not go there. It finds the cause, names it precisely, uses word choice, and then turns to managing the consequences. This is not a small omission. It is the difference between asking whether the worker had to be displaced and asking what we owe him once he has been. The first question is the one the diagnosis demanded. The second is the one the encyclical answered.

The Same Error, Left and Right

You might read that turn as a peculiarly Catholic failure of nerve, or a personal limitation of this particular Pope. It is neither. Watch what happens when the document meets its critics.

The encyclical’s sharpest critics on the right found the diagnosis welcome and the statism alarming. They attacked Leo for misplaced faith in government, arguing that regulation concentrates power and that the market, left alone, has always eased the worker’s burden over time. Strike the remedies, they said, and trust the diffusion of technology to lift living standards as it always has.

This is the mirror image of the same error. The Pope wants to compensate for displacement through the state; his critics want to absorb it through the market. Neither asks whether the displacement had to happen. They are having a furious argument about the size of the net while standing under the same assumption: that the fall is inevitable and the only question is what catches it. The left wants a larger net; the right wants a smaller one and faith that growth will fill the gap. Both treat the deployment configuration as a given.

That is how durable the assumption is. It survives translation into Catholic social doctrine and into free-market editorializing entirely intact. It does not belong to a political camp. It is the path of least resistance for anyone who would rather not get inside the deployment decision, which is nearly everyone, because getting inside it is the ounce, and arguing about the net is the pound.

The Gravity

It is worth being honest about what this means, because it runs counter to the easy version of this essay: the one where a brave writer catches the Pope in a failure of courage.

The encyclical did not fail for lack of insight. It reached the architectural diagnosis that almost no one reaches. It named the cause with a precision most secular commentary never manages. And then it turned downstream anyway, not because Leo could not see the upstream question, but because the downstream answer is the one that everything pulls you toward. The state reaches for the transfer. The market reaches for the long-run faith in growth. The moral authority reaches for the language of compassion and repair. Each of them, reasoning carefully from its own commitments, ends up cushioning the fall rather than questioning it.

That convergence is the real finding. When the most authoritative moral voice in the world on this subject, a free-market editorial board, and a redistributionist politician all independently arrive at the same place, manage the consequences, and do not contest the choice, you are no longer looking at anyone’s particular blind spot. You are looking at a gravity. A direction that thought falls into once it stops actively resisting. The default is not an opinion you can argue someone out of. It is the slope of the ground.

This is why the Pope’s turn matters more than any individual misstep would. It is the strongest possible demonstration of how steep the slope is. If a tradition built over a century and a third specifically to defend the dignity of the worker, a tradition with no profit motive, no electoral cycle, no shareholders, still slides downstream at the decisive moment, then the pull is not coming from greed, politics, or any of the usual suspects. It is structural. It is in the shape of the problem itself.

The Window

So I will not pretend the alternative is easy, or that naming the gravity dissolves it. It does not. The ounce remains harder than the pound; that is precisely what gravity means.

But there is a difference between a slope you are sliding down without noticing and a slope you have seen. The encyclical’s great service, despite its turn, is that it climbed high enough to make the upstream question visible. It found the word choice and set it down where everyone could see it. That the document then walked past its own discovery does not unmake the discovery. The cause has been named, by an unimpeachable source, in front of 1.4 billion people. That service is not cancelled by the turn, and the turn is not cancelled by the service. Both are true at once, and the both-ness is the point: it is what gravity looks like when it is acting on the best diagnosis we have. That is not nothing. It may be the most that naming can do.

Because naming is necessary, and it is plainly not sufficient. The prior question, not how to compensate the displaced, but whether they had to be displaced at all, is now visible, and still unasked at the scale that would matter. Whether it can be asked at that scale; whether the alternative diagnosis implies can actually be built, at what cost, and quickly enough to matter against a technology that moves in months: that is the harder question, and the one I will take up next. The gravity is real. The interesting argument is about whether it can be climbed.

For now, it is enough to notice where we are standing and which way the ground tilts. The window has not closed. We have at least been shown the door. That is more than we are usually given.

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The dystopia we have been preparing for has a boot on its face. That is Orwell. The state watches. The state coerces. The state lies and tortures. Resistance is meaningful because the regime is meaningful. The hero in 1984 fails, but he fails in the act of trying.

The dystopia arriving has no boot. It has a feed and a transfer. The state is mostly absent. Coercion is unnecessary. People are not being forced into the new arrangement. They are being given things until the arrangement is no longer a question. The pressure that built into 20th century totalitarianism does not build because it is absorbed in advance.

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This is a different issue and needs a different approach. The boot you can resist. The feed you scroll. The transfer you cash. The disquiet you cannot quite name. The slow disappearance of something you used to know about yourself, about the place you live, about what you and your neighbors are for.

The pattern has been named before, more than once. The first naming was older than the printing press. The second was a novel published seventeen years before 1984. The third is the one we are inside.

The First Naming: Bread and Circuses

The phrase comes from Juvenal, writing around 100 CE. Satire X. The Roman populace, he observed, had once concerned itself with politics. It had elected magistrates. It had debated policy. It had participated, in some form, in the project of the Republic. By Juvenal’s time, that engagement had collapsed. The populace cared about two things. Panem et circenses. Bread and circuses.

The phrase is sharp because the diagnosis is structural. Juvenal is not saying the populace was bribed. He is saying that the state had taken on the provision of two things: daily subsistence through the grain dole, and spectacle through public games. The populace had accepted the trade. The trade was not articulated as a trade. It did not need to be. The agency migrated. The provision arrived. Political participation withered without anyone deciding to give it up.

This is the mechanism worth understanding. Rome was not undone primarily by barbarians from outside. The empire that the barbarians eventually breached had already hollowed itself out from within. A citizenry that had traded participation for provision no longer had the muscles to defend the participation when it was needed. The political body had atrophied while the urban body had been kept fed and entertained.

The actors in this story are not villains. The emperors who maintained the grain dole were responding to genuine urban hardship. The politicians who funded the games were doing what successful politicians have always done. The populace was not foolish to prefer bread and games to faction and risk. Each step made sense. The cumulative drift did not require anyone to be malicious. It required only that nobody resist the convenience of the arrangement.

This is the first naming. The pattern is: a population that holds political power, a state that can afford to provide subsistence and entertainment, and a slow trade that nobody calls a trade. The result is a populace that retains the form of citizenship but loses its substance. Juvenal noticed it because he could see the gap between what the citizens had been and what they had become.

The Romans gave us the original vocabulary. The 20th century gave us the second.

The Second Naming: Soma and Feelies

The phrase comes from Aldous Huxley. Brave New World. Huxley was not predicting the boot. He was predicting something stranger. He was predicting a regime that needed no boot because the population had been engineered to prefer it.

The mechanisms in his world are now familiar. There is a drug called soma, distributed freely, which dissolves anxiety. There are immersive entertainments called feelies, which replace depth with stimulation. There is conditioning from before consciousness, which produces people who want exactly what their station permits them to want.

What Huxley saw is that none of this requires force. The citizens of his world are not oppressed in any sense they would recognize. They are happy. They do not miss what they have lost because they have been engineered not to know it ever existed. Their happiness is the control mechanism. The pleasure absorbs the pressure that would otherwise produce dissent.

The contrast Postman drew is worth keeping. Orwell’s anxiety was about losing access to the truth. Huxley’s anxiety was about losing the capacity to care about truth once enough pleasure was on offer. The first is a problem of suppression. The second is a problem of dissolution. The pleasure makes the truth feel unnecessary, and after a while, it feels beside the point.

This is the second naming. The pattern is the same as the Roman one. A population that holds something it does not realize it holds. A system that can afford to provide subsistence and pleasure at scale. A slow trade that nobody calls a trade. The result is a population that retains the form of personhood while losing the substance. Huxley noticed it because he could imagine the gap between what people had been and what they could become if the conveniences were comprehensive enough.

The Romans had bread and games. Huxley had soma and feelies. The pattern was the same in both centuries. The question is: what are we giving up in this one?

The Third Iteration

The pattern is now arriving. The vocabulary is different. The architecture is the same.

The transfer is UBI, proposed, partially piloted, and discussed as if its arrival is a matter of when, not whether. The feed is algorithmic, infinite, calibrated to whatever holds attention longest. The Roman state could afford grain and games for the urban population because the empire was wealthy. The modern state, along with the technology sector, can afford UBI and infinite content because the AI economy is generating wealth on a scale that has never existed before. The mechanism is the same as it was: subsistence plus spectacle, provided at scale, by a system that can afford the provision.

The contemporary version differs in one structural way. Roman games and Huxley’s feelies gathered citizens in shared experience. The algorithmic feed disperses them into individual streams. Collective recognition is harder than it was in either of the predecessors.

What the Provision Absorbs

What is being absorbed by the provision deserves naming.

There is a personal cost. For most people, work has never been only a source of income. Work provides what transfer payments cannot compensate for. Identity. Purpose. Social role. Daily rhythm. The structure that organizes a life. The unspoken understanding that the morning has a shape because there is something to do that matters to someone else. The slow accumulation of competence at something specific. The relationships that develop through shared work over time. The check covers the rent. It does not cover any of the rest of it. The check arrives in the mailbox of someone who used to be a designer, a paralegal, or a journeyman electrician and is no longer, and the disappearance of what they were is not on the ledger.

There is a civic cost. This is the most precise name for the Roman pattern. A citizenry that has traded participation for provision no longer has the muscles to defend the participation when it is needed. The civic body atrophies. The capacity for collective political action withers. The institutions that depended on engaged citizens become hollow. The voters still vote. The participant no longer participates in anything. The pressure that should have built against the trajectory does not build. The actors with the most influence over what comes next operate without the resistance that would otherwise check them. Wang Peng’s “maintaining order, not sharing wealth” applies at the civic register as well as the personal one. The order being maintained is not only the order of keeping the urban poor calm, but also the order of a political system whose citizens have ceased to function as citizens.

The pattern is the same. The medium is different. The Romans used grain and games. Huxley imagined soma and feelies. We are building UBI and recommendation feeds. The mechanism in each case is provision at scale, calibrated to absorb whatever pressure the citizenry might otherwise generate. The Romans saw it. Huxley imagined it. We are living inside the third iteration, and we have not yet named it because we have been preparing for the wrong dystopia.

Why the Pattern Recurs

The pattern recurs because it solves a real problem. Coercion is expensive and produces resistance. Pacification is cheaper and produces compliance. The Roman emperor who funded the grain dole had fewer riots than the emperor who let the urban poor go hungry. The Brave New World administrators needed no Stasi because soma was cheaper than the secret police. The AI economy needs no propaganda ministry because algorithmic feeds and a competent transfer payment can produce the same calm at a lower cost.

What is new is the capacity. Rome’s dole reached an urban population. Huxley’s regime was fictional. The AI economy is the first arrangement that could afford pacification at a planetary scale while generating the surplus that pays for it. The pattern has been waiting for it.

This is what makes the pattern dangerous in a way that the Orwellian dystopia is not. The Orwellian regime advertises itself. The boot is felt. The dissent is real because the oppression is real. The pacification regime does not advertise itself. The disappearance is not felt. What has been lost is the very capacity to notice the loss. The grain arrives, the feed scrolls. The check is deposited. The disquiet you cannot quite name turns out to be the only signal you were ever going to get.

Two millennia. Three iterations. One pattern.

The Alternative

There is an alternative. It is not nostalgia for work that no longer exists. It is the deliberate design of work that does not yet exist, organized so that humans and their machines preserve what the pacification dissolves.

The third iteration is the one that gets named, or it is the one that does not.

Next: Architecture or Soma

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In a recent essay translated by Zilan Qian, Wang Peng, a senior expert at the Tencent Research Institute, names the AI race as a narrative choice rather than a scientific path, and identifies UBI as “maintaining order, not sharing wealth.” That last phrase deserves more attention than it has received. UBI is the most discussed proposed response to AI-driven displacement. It is also the response that requires the least change to anything else. The labor market collapses on its current trajectory. The wealth concentrates on its current trajectory. Cognitive offloading deepens along its current trajectory. UBI sits on top of all of it like a balm.

This essay argues that the surrender is convenient, not inevitable. Mass displacement is a design choice being made by default. UBI is the consolation prize offered for not asking the prior question: who decided AI gets to make this world, and why are we letting it?

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The Dominant Story

The story most often told goes like this. AI capability is accelerating. AI capability will continue to accelerate. Many jobs will disappear. Many more will be transformed beyond recognition. People displaced from the labor market need an income. UBI provides that income. The wealth created by AI funds it. Everyone settles into a new equilibrium. Productivity is up. Suffering is mitigated. The future is uncomfortable but workable.

This story is appealing for reasons worth naming.

It is mechanically simple. Money is the most universal currency for solving problems. If the problem is lost income, an income transfer is the most legitimate response. No ambiguity about what is being given, what is being received, or how to measure whether it worked.

It absorbs political anxiety. Mass displacement is genuinely frightening. UBI offers a coherent answer that does not require anyone to confront harder questions. It promises to catch people on the way down without anyone having to ask why they were falling in the first place.

It externalizes responsibility. The labs build the systems. Governments handle the consequences. Workers receive the consolation. Nobody in the loop has to question the trajectory. This is the structural function of UBI as currently discussed: it lets each actor optimize their own piece while treating the displacement itself as a fact of nature.

It is also the story that the storytellers benefit from. Wang Peng makes this argument directly. The narrative that scaling LLMs is the inevitable path to AGI was not validated by science. It was locked in by capital and geopolitical maneuvering. UBI is the same narrative one layer up. The displacement is treated as inevitable, not because the evidence is settled, but because treating it as inevitable benefits the actors with the most influence over what comes next. Investors keep their thesis intact. Executives keep their growth narrative. Policymakers reach for an instrument they understand.

The discomfort in this story belongs entirely to the people losing their jobs. They get a check.

The Prior Question

That is the dominant story. It is not wrong about everything. Displacement is happening. Some response is needed. But the story is missing the prior question. Before we ask how to mitigate displacement, we should ask whether it is an inevitable result of the technology or a result of how we have chosen to deploy it.

Nothing about AI requires that it displace workers wholesale rather than augment them. Both deployments are technically possible. Both are happening in different proportions in different places. The proportion is a choice. It is being made not by laws of physics but by laws of capital, optimization targets, and managerial habit.

Consider where the choices are being made. A company with a customer service team has options when it brings AI into the workflow. Option one: replace four of five representatives with a single AI-augmented operator. Option two: give all five representatives AI augmentation and expand the service quality and volume. Both work. The first compresses headcount. The second compresses friction. Different choices, same technology.

The choice is not hypothetical. Radiology is the most often cited case. A decade ago, the AI researcher Geoffrey Hinton predicted that AI would displace radiologists within five years. The deployment moved the other way. Radiologists became radiologists with AI. The technology took over pattern detection at scale. The interpretation of the pattern, the conversation with the referring physician, and the consideration of the patient’s history remained with the radiologist. Radiology employment has grown. The technology was real. The displacement was not inevitable.

The labs themselves face the same question one level up. They optimize for benchmarks that measure “can the AI do this task instead of a human?” rather than “does the AI make humans better at this task?” That choice of optimization target is not a scientific discovery. It is a decision about what the technology is for. Once made, it propagates outward into every product, every demo, every funding round.

Configuration, Not Law

The strongest objection to this account is that it understates competitive pressure. Firms that fail to automate are priced out. Nations that fail to deploy aggressively are outcompeted. The choice to augment rather than displace, even when technically available, is foreclosed by competition itself.

The objection is real, and it concedes the argument. Competitive pressure is not a law of nature. It is the cumulative result of optimization targets, capital allocation, procurement standards, and the absence of countervailing pressure. Each of these is human-constructed. Each can be reconfigured. Augmentation appears uncompetitive because the incentive landscape rewards displacement and treats augmentation’s slower, less legible gains as a cost rather than a return. That is a configuration, not a law of nature.

Previous technological transitions moved workers from one kind of work to another. The mechanical loom displaced weavers. The spreadsheet displaced bookkeepers. In each case, new work appeared because the technology amplified some human capacities while replacing others. The current trajectory differs in target. The optimization is not the amplification of certain capacities and the substitution of others. It treats the human as the variable to minimize. That is not a transition. It is a redesign of what work is for. Calling the redesign inevitable is a way of not asking whether the optimization target was chosen and by whom.

Autopilot

This is what is meant by autopilot. The choices are real. The choices have consequences. But nobody in the chain experiences themselves as making the choices. The lab optimizes for the next benchmark. The investor optimizes for the next round. The executive optimizes for the next quarter. The customer optimizes for the next price drop. Each actor is responding to the incentives produced by the others. None of them is looking at the cumulative trajectory and asking whether it is the one we would have chosen if we had been asked.

What would conscious steering look like? It would begin by asking different questions. Not “how do we automate this task?” but “what does this task contribute to the people doing it, and what is lost if we automate it away?” Not “how do we maximize productivity per worker?” but “what is the relationship between this work and the meaning the worker draws from it?” Not “how do we redistribute the income lost to displacement?” but “what would deployment look like if displacement were not the default?”

These are not abstract questions. They are answerable. They are also rarely asked because doing so would require the most influential actors to reconsider what they are building and why. UBI lets them avoid the questions entirely. The displacement happens, the income transfer happens, and nobody has to look upstream.

That is the design choice being made by default.

Order Maintenance

Inside that arrangement, UBI has a specific function. Wang Peng puts it directly: ensuring “the replaced majority have enough to eat and won’t revolt, so that the few at the top can continue to quietly claim the surplus profits AI delivers.”

This is not a moral critique. It is a structural one. Wang is not saying UBI’s advocates are bad people. He is saying that the function UBI performs inside the current trajectory is order maintenance, regardless of the intentions of those who advocate for it. Once the displacement is fixed and the trajectory is given, UBI’s job is to absorb the resulting pressure. That is what it does. That is what it is for.

This is also not an argument against cash transfers as such. A basic income floor might be part of a just society. The objection is to UBI as the complete response, the mechanism that lets the displacement question go unasked. The check is not the enemy. The check is an excuse.

Consider the system from each actor’s perspective with UBI in place. The lab continues building. The investor continues funding. The executive continues deploying for headcount compression. The displaced worker continues paying rent. The state continues to collect taxes and distribute transfers. The pressure that would otherwise build into pressure for the prior question, who chose this and why, gets absorbed by the transfer. The check arrives. The asking does not happen.

This is what is meant by “convenient surrender.” It is not that UBI fails to deliver income. It will deliver income. It is that UBI delivers income in a configuration that removes the political and economic energy that would otherwise force the prior question. The transfer succeeds in what it is designed to do: keep the system running. The system, however, was the problem.

The Mismeasured Cost

There is a deeper cost. UBI assumes the displaced worker’s loss is monetary. The check is calibrated to cover the rent, the groceries, and the heat. This is the calculation a labor market reduction permits. The loss that cannot be calibrated is the one accumulated over twenty years. The paralegal who has spent two decades learning which precedents matter for which arguments has built something the labor market paid for, and the AI now performs in seconds. The check can replace the salary. It cannot replace competence. The competence was not only a means of income. It was the daily evidence to herself that her work mattered to someone else. The cost has been mismeasured. The check addresses only the part that had a price.

A reader will object that the paralegal is a poor representative case. Much of the work is not like hers. The Amazon picker, the call center agent, the gig worker stitched across three platforms. These are not sources of identity and accumulated competence. They are sources of income and a transfer payment that replaces lost income, with no value added.

The objection is partly right and concedes more than it intends. It is right that much work has been structured to extract rather than to sustain. It concedes the larger argument because that structuring was a deployment choice. The warehouse picker could have been doing cognitively engaging work if the warehouse had been designed for human capabilities rather than treating humans as replaceable. The call center agent could have been doing relational work if the script had been a tool rather than a leash. The meaninglessness of that work was not a fact of the work. It was a design. The argument is not that all current work is worth preserving. It is that the configuration which made meaningful work the exception is itself a choice, made by the same logic that is now choosing displacement.

There is a long history, both real and imagined, of societies that solved the wrong problem by giving people enough to live on while removing what they were living for. The Romans had a name for it. Huxley imagined another.

The Harder Option

The convenient surrender is not the only option. It is the option that requires the least of anyone with the power to choose differently. That is precisely why it is being chosen.

The harder option is to ask the prior question. Whose work is being displaced, and why? Who decided that technology gets to make this world? What would it mean to deploy AI in a way that preserved rather than dissolved the relationship between work and meaning?

These questions are answerable. They have not been asked because the dominant story has made them seem unnecessary. UBI is offered as the response to displacement so that displacement does not have to be debated.

We have spent decades preparing for the wrong dystopia. The one arriving was named first. It is older and quieter, and we have stopped reading the people who named it.

Next: The Wrong Dystopia

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The thing people fear most about AI has been happening in politics for decades.

Let me explain.

Large Language Models, the technology behind ChatGPT, Claude, and others, are remarkable at a very specific thing. Within the boundaries of what they were trained on, they are extraordinarily fluent. Confident. Articulate. Persuasive, even. Ask them something well within their corpus, and they will give you an answer that sounds authoritative, well-reasoned, and complete.

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The problem is that they have no internally grounded model of uncertainty. They do not know what they do not know. Push them outside their training distribution, ask them something genuinely novel, something that requires real-world judgment or lived context, and they do not slow down, qualify, or say “I’m not sure.” They confabulate. They produce an answer that has exactly the same tone, confidence, and fluency as a correct one. The model cannot tell the difference. And neither, often, can you.

This is not a minor technical quirk. It is a fundamental architectural limitation. And it is, increasingly, what critics of AI are most alarmed about: not that these systems are wrong, but that they are wrong with total confidence, and you cannot easily tell when.

What makes this architectural limitation so hard to solve is that it was not introduced by the engineers. It was inherited from the source material. The text that pre-trains these models is the accumulated output of human communication, not just the last forty years, but millennia of rhetoric, law, scripture, political argument, and power exercised through language. Human communication is already saturated with what the evolutionary biologist Robert Trivers identified as self-deception: confident assertion, motivated reasoning, moral certainty, suppressed counter-evidence. We perform our convictions fluently and our doubts reluctantly. The feedback step that follows pre-training compounds this: human raters reward confident, fluent answers because that feels authoritative. The system correctly learns what we prefer. The flaw was not built in. It was passed down.

Here is my question: when did this become new?

The question isn’t why politicians behave like LLMs. The question is why our systems select so ruthlessly for exactly that behavior and discard everything else.

The behavior itself is older than politics. Politics is just where we see it most clearly today. Underneath it is something more fundamental: the human knowledge claim. Any moment where one person asserts to others that they know something, where authority, status, or persuasion hangs on the assertion, selects for confident delivery and punishes hesitation. The shaman who qualifies the prophecy loses the tribe. The elder who admits uncertainty loses the counsel. The expert who hedges loses the room. This is not a modern failure of character. It is an ancient feature of how humans establish and defend claims to know.

This didn’t start with algorithms. It didn’t start with mass media. Leaders have performed certainty for as long as there have been leaders, ancient tribal chiefs whose authority depended on never being seen to hesitate, Roman emperors issuing decrees from a position of assumed divinity, monarchs whose legitimacy required the performance of absolute knowledge, demagogues across every century who understood that confident assertion outperforms careful reasoning in any contest for attention.

What’s new isn’t the behavior. What’s new is the system that now selects for it at an industrial scale, amplifies it in real time, and punishes the alternative more efficiently than anything in human history. Social media poured gasoline on a fire that has been burning since our earliest ancestors organized themselves around people who claimed to know. Platforms amplify the loudest, most certain voices and bury the nuanced ones. Politicians adapt. They perform with greater certainty, a certainty that is further amplified. Round and round. The system doesn’t just tolerate overconfidence. It breeds it.

Education shows the same pattern. As Steven Mintz recently observed, the habits that careful reasoning actually requires, matching claims to evidence, engaging opposing views fairly, and revising under scrutiny, are not just neglected in most educational settings. They are actively discouraged by the way students are evaluated and rewarded. The system doesn't produce confident hallucination by accident. It trains for it.

Watch a political campaign.

Any campaign. Any party. Any country, though I will let you apply your own examples.

The candidate arrives with a platform. The platform is confident, clear, and internally consistent. It has an answer for everything. It does not hedge. It does not say “this is genuinely complicated and I am not sure.” It does not acknowledge that the opposing view has any merit, or that the world might resist the plan once it meets reality. Nuance is a liability. Certainty is the product.

Then they get elected. And reality arrives.

The economy does not behave as the model predicted. The allies do not cooperate. The opposition does not collapse. The budget does not balance. The promised jobs do not materialize, or they do but somewhere else, or they do but not for the people who were promised them. The world, it turns out, was not waiting for the correct policy to be implemented. It had its own ideas.

And here is where the parallel becomes most uncomfortable. A well-designed AI system, when confronted with a question outside its competence, should ideally signal uncertainty, flag that it is operating at the edge of its reliable knowledge. The best ones are getting better at this. But a politician, confronted with the gap between their platform and reality, almost never does. They double down. They reframe. They find someone to blame. They stay in the corpus.

Because admitting uncertainty, after running on certainty, is politically fatal.

This is not really about confidence.

Confidence itself is not the problem. Experts are confident. Surgeons are confident. A good structural engineer does not hedge when they tell you the bridge will hold. Confidence, earned through genuine competence within a well-defined domain, is exactly what you want.

The problem is the absence of awareness of uncertainty: knowing where your competence ends. This is one of the most underrated cognitive capacities a human being can possess. It is what separates a good doctor from a dangerous one. It is what separates a good leader from a demagogue. And it is, not coincidentally, what separates genuine intelligence from its simulation.

An LLM that cannot model its own uncertainty is not wise. It is fluent. These are not the same thing.

A politician who cannot model their own uncertainty is not strong. They are performing well. These are also not the same thing.

The voters who reward performance certainty over genuine competence are not getting what they think they are getting. They are getting the political equivalent of a confident hallucination.

A word about compromise.

Compromise is what uncertainty-awareness looks like in practice. It is the act of acknowledging that your model of the world is incomplete, and updating it in response to someone else’s. That’s not a weakness. That’s how functional systems avoid drifting further from reality.

I know. It is an ugly word right now. Mediocre. Weak. The language of people who do not believe in anything.

I disagree. Strongly.

Compromise is how the greatest human gains have actually been made. Not the heroic narrative version, not the lone visionary who refused to bend and changed the world. That story exists, but it is the exception, and we have collectively lost our ability to distinguish it from its imitation. The far more common story of human progress is negotiation, coalition, trade-off, and the slow, unglamorous accumulation of partial wins.

Some will argue that conquest is more powerful. The great advances came from decisive force, not from committees. I would ask anyone alive for any part of the last hundred years to sit with that honestly. Two world wars. The Cold War. Vietnam. Iraq. The wreckage of absolutism, administered at scale. The places where things actually got better, where poverty fell, where disease retreated, where life expectancy climbed, were almost always the result of sustained, boring, incremental cooperation between people who disagreed about things but agreed to keep working.

That is not a weakness. That is civilization.

So what are we actually asking for?

Not perfection. Not a political class of philosopher-kings who speak only in careful qualifications. Not AI systems that refuse to answer anything they are not certain about.

Just this: a restored tolerance for nuance. A willingness, in our politics, and increasingly in our AI, to reward “I’m not sure, but here is my best reasoning” over “here is the answer, delivered with complete confidence.”

But tolerance alone won’t be enough. Tolerance is a cultural mood. It comes and goes. What actually works is when systems structurally require engagement with contrary positions. Due process doesn’t rely on judges being humble. It builds humility into the architecture: evidence must be examined, dissent must be heard, certainty must be earned through scrutiny. The same logic applies to our politics and our AI. We don’t need leaders who personally appreciate complexity. We need systems that make engaging with it unavoidable.

The irony is almost too neat. We are having an urgent public debate about the dangers of AI systems that confidently hallucinate. We should be having the same debate about the human systems that have been doing it for millennia, doing it with industrial efficiency for the last forty years, and now, through AI, at a scale and speed nothing in human history has prepared us for.

We don't need to accept either. But fixing both starts in the same place: understanding that nobody is right about everything, that uncertainty is not weakness, and that the gap between confident fluency and actual wisdom is exactly where the damage gets done.

Which brings me back to where I started. The rush to judge on allegations alone, before trial, before evidence, before verdict, is its own form of hallucination. A confident narrative is preferred over a slow, uncertain process. We have built an entire legal system on the principle that certainty must be earned, not imposed. That due process is not a weakness. It is the mechanism that keeps the system tethered to reality. It is, almost exactly, what we are asking for in our politics and in our AI. The question is why we seem to want it in our courtrooms but nowhere else.

Next week: The Category Error at the Heart of Justice. Our legal system is procedurally fair but causally illiterate, and the difference matters more than we admit.

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But here’s where I want to push further. Lee describes hunches as compressed pattern recognition, knowledge the brain holds but can’t articulate. That’s true, and it’s part of the story. What it leaves out is why a seasoned practitioner’s hunch is trustworthy in a way that a fresh one isn’t. The seasoned hunch isn’t just a denser pattern; it has carried a consequence. The practitioner has made calls, watched them play out, paid for the wrong ones, and adjusted. That loop, judgment, action, cost, revision, is what gives the hunch its weight. An LLM mid-session might develop a hunch-like feeling. Nothing in its loop ever bears a cost. Nothing carries forward. This isn’t a context-window problem that a longer window or smarter compaction will solve. It’s a stake problem. The files can hold what the model said. They cannot hold what they would have lost by being wrong.

Lee ends where he can: we’ll still need human workers to do our deep thinking for us. That’s the right conclusion if you stop at the inspection level, at what the agent produces. But it leaves the harder question untouched. If the consequence-carrying loop is what makes judgment trustworthy, and if that loop can’t survive the handoffs an agentic system requires, then institutions can’t govern AI use by reviewing outputs. Outputs are exactly the explicit residue Lee has just told us is insufficient. They are the files. Reviewing them well will not tell you whether the practice that produced them was a practice at all, or only its performance. Lee has named the gap. The next move is figuring out what kind of institutional architecture closes it, or learns to live within it.

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Shermer’s toolkit is the right one. Fallibilism. Bayesian reasoning. Extraordinary claims require extraordinary evidence. Signal detection. Active open-mindedness. Free critique. If more public discourse ran on this equipment, we would all be better off.

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And yet, in the same essay where he carefully reminds us that “the principle of fallibilism requires me to admit that I could be wrong,” Shermer delivers confident verdicts on COVID origins, climate severity, and gender, the kind of verdicts that suggest the toolkit has, in fact, settled the matter.

That gap is what I want to write about. It is not a gap in Shermer specifically. It is a gap in the framework he is using, and in the way most of us, myself included, talk about truth. The toolkit tells you how to weigh evidence. It does not tell you when you have reached the edge of the toolkit's applicability. Without that second layer of awareness, the toolkit does not produce truth. It produces confidence.

Truth, I want to argue, is not a possession you arrive at by running the right cognitive procedures. It is a practice that requires an architecture, and that architecture is what the rest of this post is about.

What Shermer Gets Right

Shermer’s essay is, at its core, a defense of the cognitive equipment the Enlightenment built for navigating uncertainty. It is worth taking seriously, piece by piece.

Fallibilism: the recognition that any of our beliefs could be wrong, and that intellectual honesty requires holding them provisionally. This is not a weakness; it is the precondition for learning anything new.

Bayesian reasoning: the discipline of attaching probabilities to claims rather than treating them as binary, and updating those probabilities as evidence shifts. Shermer’s example of assigning UFO aliens a 0.01 percent probability rather than zero is the right move. It leaves room for evidence to change his mind.

ECREE: extraordinary claims require extraordinary evidence—Sagan’s principle, simple and durable. A blurry photograph is not evidence of Bigfoot. A grainy video is not evidence of alien visitation. The bar scales with the size of the claim.

Signal detection theory: the 2x2 matrix of hits, misses, false alarms, and correct rejections. Shermer is right that most public arguments fail because people cite only the cell that confirms their view. RFK Jr.'s stance on vaccines is a clear example. So is most medical-cure folklore. So, frankly, is most political commentary.

Active open-mindedness: Tetlock and Gardner’s finding that the best forecasters are the ones who actively seek evidence against their own positions, treat changing their minds as a strength rather than a weakness, and accept that randomness shapes outcomes. The worst forecasters are the ones with grand unified theories who explain away every miss.

Free critique: Shermer closes by arguing that the most important norm is the freedom to challenge any and all ideas. He is right. Censorship is corrosive in both directions: if I silence you, why shouldn’t you silence me?

This is a serious toolkit, assembled over centuries. I use it. You should, too. Most public discourse would improve dramatically if more people did.

The question is not whether the toolkit works. The question is what it works on, and what it cannot, by itself, do.

What’s Missing: The Constraint-Awareness Layer

Here is the move I want to make.

Shermer’s toolkit tells you how to weigh evidence inside a question. It does not tell you whether the question is one the toolkit can answer. That second layer, the awareness of where the toolkit applies and where it reaches its edge, is what I have been calling constraint-awareness. It is the working definition of wisdom in my Wisdom Gap whitepaper.

Constraint-awareness is not the same as humility. Humility is a disposition. Constraint-awareness is a structural property of how you hold a belief: knowing the conditions under which your method works, the conditions under which it does not, and the conditions under which you cannot tell which you are in.

Without it, the toolkit produces something that looks like truth but isn’t. It produces calibrated confidence inside a frame that the thinker has not examined. Bayesian reasoning, applied to a question whose evidence base is geopolitically corrupted, gives you a probability estimate that feels rigorous and is in fact noise dressed in numbers. The same diagnosis applies to any other tool in the kit. Each was designed to operate within the conditions the thinker is responsible for noticing, and none of them can notice for itself.

An example from my own consulting career, to make this concrete. My team was selected to lead a post-acquisition system integration following a large data analytics company's acquisition of a smaller, complementary firm. Our standard approach was sound: spend the first six weeks in discovery, talk to stakeholders on both sides, build a proposal that accounts for the operational realities of each party. We did exactly that. We treated both companies as equally weighted clients. We came back with a proposal that assumed significant adaptation of the acquirer's systems to accommodate the acquired company's business. The budget was large. The timeline was long. We presented, and we had our hat handed to us.

The executive sponsor on the acquirer's side summarized the problem in one sentence: "We are acquiring them; they need to adapt and fold into our way of doing business, not the other way around.” The toolkit was right. The conditions under which it was developed (a normal client with stakeholders whose interests are roughly symmetrical) were not the conditions in which we were operating then (a post-acquisition integration, where the acquirer's operating model is the destination, not a negotiable input). Constraint awareness would have let us see that before the proposal landed, rather than after.

Three thinkers I keep returning to have each pointed at this layer from different directions. Judea Pearl, in his ladder of causation, distinguishes association (Rung 1) from intervention (Rung 2) from counterfactual reasoning (Rung 3), and his core warning is that statistical machinery applied at the wrong rung produces confident nonsense. Donald Hoffman, in Fitness Beats Truth, argues that our perceptual systems were not built to deliver reality; they were built to deliver fitness, and the two are not the same. Nassim Taleb, in his work on skin in the game, insists that beliefs held without consequences drift away from the truth in ways the holder cannot detect from the inside.

Each of them is naming a different edge of the toolkit. Pearl: the edge where your inference machinery exceeds its causal license. Hoffman: The edge where your perceptual interface is not the territory. Taleb: the edge where your belief has no feedback loop to correct it.

Constraint awareness is what lets you see those edges from within your own thinking. It is not a tool you add to the toolkit. It is the meta-property that determines whether the toolkit produces truth or confidence.

And here is the harder claim, the one I will spend the rest of this post and the next whitepaper defending: constraint-awareness cannot be generated by individual cognitive effort alone. It requires an architecture: an attention-experience feedback loop, sustained over time, inside institutions and developmental pipelines that test beliefs against consequences and reveal their edges. Strip-mine that architecture, and you do not get a population of bad reasoners. You get a population of good reasoners producing confident verdicts at the edges of frames they cannot see.

Which brings us to COVID.

Exhibit A: COVID Origins

Take Shermer’s COVID example. He writes: “I believe that the COVID virus is slightly more likely to have originated in a lab than a wet market.”

I am not going to tell you whether he is right or wrong. That is the point.

What I want to ask is a different question: what would have to be true for the toolkit to deliver a probability estimate on this question that means what a probability estimate is supposed to mean?

A Bayesian probability is only as good as the evidence base it draws on. The machinery was designed for evidence generated by a process you can characterize, from sources whose reliability you can estimate, with a sample space you can bound. COVID origins fail all three conditions, and the failures are not accidental.

The evidence base is geopolitically corrupted. The Chinese government has restricted access to the Wuhan Institute of Virology, withheld early case data, and shaped which samples reached international researchers. Some of what is missing is missing on purpose. You cannot run Bayesian updating on an adversarially curated sample space.

The evidence base is institutionally entangled. Western intelligence agencies, public health institutions, and research funders all faced reputational and legal exposure if the lab-leak hypothesis proved true. Some of the early dismissals were sincere; some were defensive; from the outside, you cannot reliably tell which. The reliability weights you would need to plug into Bayes’ rule are themselves contested.

The evidence base is epistemically novel. This is not a question like “did this defendant commit this crime” or “is this drug effective,” questions for which we have centuries of method and calibration. It is a question about a singular event in a domain where the base rates themselves are unknown and possibly unknowable. In Frank Knight’s terms, this is uncertainty rather than risk: a domain where probability estimates were never the right tool to begin with.

Each failure mirrors one of the edges from earlier. Corrupted evidence is Taleb’s edge: beliefs without honest feedback drift in ways the holder cannot detect. Entangled evidence is Hoffman’s edge: what reaches you is shaped by an interface (institutional, in this case) that was not built to deliver truth. Novel evidence is Pearl’s edge: inference machinery applied where its causal license does not extend.

None of this means we should not consider the origins of COVID. It means the cognitive procedure Shermer is using cannot, by its own internal logic, produce a calibrated probability here. The output looks like a Bayesian estimate. It is, more honestly, a prior, the starting belief one brings to a question before evidence updates it, given weight by a procedure whose conditions for operating were not met.

Shermer’s reasoning is not sloppy. It is rigorous reasoning applied to a question whose epistemic conditions the rigor itself cannot diagnose. You need a layer above the toolkit to notice that, and that layer is constraint-awareness.

The same diagnosis, applied differently and to varying degrees, fits his confident verdicts on climate severity and on gender. They are not the same kind of question as the COVID origins question, and treating them as equivalent would itself be a constraint-awareness failure. The whitepaper will work through them separately.

Why This Is Architectural, Not Personal

The point of the COVID example is not that Michael Shermer is a bad thinker. He is a good one. He is using the best cognitive equipment the Enlightenment produced, and using it carefully.

The point is that individual cognitive effort, however rigorous, cannot generate constraint-awareness on its own.

Constraint-awareness is not a tool you reason your way to. It is a property that develops over time through exposure to consequences within an architecture that connects beliefs to feedback. You learn where your toolkit reaches its edge by watching it fail, by being wrong in ways you cannot dismiss, in domains where the cost of being wrong is paid by you and seen by others. That loop, between attention, experience, and revision, is what produces the meta-awareness of where one’s own thinking does and does not work.

This is the argument I have been building across three previous papers, each from a different angle.

The Attention Crisis describes what happens when the input side of that loop is strip-mined, when attention itself is captured, fragmented, and monetized to the point where sustained engagement with any single question becomes structurally difficult. AHI describes the constructive alternative: an architecture in which AI augments human judgment rather than replacing it, preserving the developmental pipeline that turns junior practitioners into wise seniors. The Wisdom Gap describes what is structurally missing from current AI systems: the attention-experience feedback loop itself, without which knowledge cannot mature into wisdom.

What does a working truth-producing architecture actually look like? Consider the National Transportation Safety Board. When an aircraft crashes, the NTSB investigates with statutory independence from the airlines, the manufacturers, and the FAA. Investigators have access to physical wreckage, flight data recorders, and crew records. Their findings are public. Their recommendations have teeth. The feedback loop runs from event to investigation to industry-wide design and procedural change, and the cycle takes years, not news cycles. The result is one of the best-documented improvements in safety in any industry: commercial aviation has become extraordinarily safe over decades, not because individual pilots got smarter, but because the architecture turns each failure into a constraint the whole system inherits.

Notice what that architecture has structurally: independence from the parties whose interests are at stake, access to ground-truth evidence, public outputs, enforcement authority, and time horizons that exceed any individual career. Notice what it does not require: superhuman cognition in any individual investigator. The architecture produces constraint-awareness at the system level that no single mind, however rigorous, could generate alone.

The governance whitepaper I am working on will address the question of what an equivalent architecture for AI would look like. That is a harder problem, and one I do not pretend to have fully solved. But the NTSB shows that truth-producing architectures are possible, that we know roughly what they look like when they work, and that the question is not whether to build them but how.

Each of those papers, I now see, was circling the same underlying claim from a different side. Truth, the kind that earns its name, not the kind that is merely confidently asserted, is the output of an architecture. Attention is its input. The feedback loop is its mechanism. Wisdom is its mature form. And constraint awareness is the property that lets a thinker, working within that architecture, recognize the limits of what their own methods can deliver.

What worries me about the present moment is not that we have run out of careful thinkers. We have not. Shermer is one of many. What worries me is that the architecture that produces constraint-awareness is being degraded faster than careful thinkers can compensate for it individually. Attention is captured. Developmental pipelines are being shortcut by AI tools that mimic the output of judgment without the underlying loop that produces it. Institutions whose job was to test beliefs against consequences (academia, journalism, public health, the regulatory state) are themselves under stress. Power is also at work here, in ways this post deliberately leaves to the governance whitepaper: frames remain unexamined not only because constraint-awareness is hard but because powerful interests often benefit from their remaining unexamined. And the AI systems increasingly mediating public reasoning have inherited the toolkit but not the architecture: they can run the procedures, but they cannot, by themselves, generate the constraint-awareness that tells them when the procedures no longer apply.

The result is not a shortage of rigor. It is rigor without constraint-awareness: careful work, seriously equipped, producing confident verdicts at the edges of its own reach.

Truth as Practice

When I told a friend recently that I sometimes wonder whether there is any truth beyond physical reality, he pushed back. Others have too. They hear in that kind of statement a slide into postmodern relativism, the move in which everything becomes a matter of perspective, and nothing can be said to be true.

That is not what I mean, and I think Obi-Wan Kenobi, of all people, said it better than I have managed to: what I told you was true, from a certain point of view.

Read carelessly, that line is relativism. Read carefully, it is constraint-awareness. It is the recognition that a claim can be true given the conditions under which it was formed and still be incomplete, partial, or wrong from a vantage that those conditions did not include. Holding a belief that way, knowing the conditions of its formation, knowing what it can and cannot deliver, is not weakness. It is what separates truth-seeking from confidence-issuing.

If constraint-awareness develops within an architecture, the question for an individual reader is what to do as we build or rebuild that architecture. One practice that follows directly from the argument of this post: attach to every confident verdict you issue, including silently to yourself, an explicit statement of the conditions under which your method would be unreliable here. Not as a hedge, and not as performative humility, but as a structural check. If you cannot name those conditions, you have not finished thinking. If you can name them and they apply, you have not yet earned the verdict. This is a habit, not a toolkit, and it scales: from individual practice to institutional design to, eventually, the governance of AI systems whose confident outputs will increasingly shape the conditions under which the rest of us reason.

None of this is an argument against the toolkit. In domains with stable feedback loops, observable outcomes, and aligned incentives, clinical trials, well-instrumented engineering, and mature science, the toolkit produces real and reliable knowledge. The argument is that those conditions are not the universal case, and that issuing toolkit-shaped verdicts in domains that lack them is the failure mode worth naming. Where conditions are absent, and a decision is unavoidable, the right move is not to disguise a prior as a posterior but to act on the prior with the conditions named, and to update faster than a calibrated estimate would warrant when ground truth arrives.

Shermer ends his essay with a defense of free critique: the freedom to challenge any and all ideas, from which “in time the truth emerges.” He is right that this freedom is necessary. He is wrong, I think, that it is sufficient. Free critique produces truth only within an architecture that sustains attention, preserves the feedback loop between belief and consequence, and develops in its participants the constraint awareness to recognize the edges of their own methods. Without that architecture, free critique produces something else: a marketplace of confident verdicts, each rigorously defended, none of them constrained.

That architecture is what I want to build out in the next phase of this work. The previous three papers diagnosed what is breaking. The next ones will be about what to build. I will start with a longer treatment of The Architecture of Truth: what it is, how it forms, how it degrades, and what it would take to rebuild it in an age when the inputs to attention itself are under siege.

For now, the stake in the sand: truth is not a possession arrived at by procedure. It is a practice that requires an architecture. Each of us is already inside one, helping to sustain or degrade it with every confident verdict we issue. The work ahead is to build it deliberately.

Next week: the flaw isn't AI's. It's ours, and we've been building it for a long time.

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David Hoze and I had been corresponding since early April about a co-authored essay. Three movements, his philosophical and theological grounding bridging to my structural one, the disagreement between us preserved rather than smoothed over. The tone had been generous on both sides.

What I did not realize for nearly two weeks was that on April 8, in the same window we had begun corresponding, David had published a piece of his own extending his original comment on my Wisdom Gap post. He accepted the core of my architectural argument and then did something I had not done: he mapped the governance framework that follows from it, drawing on three thousand years of Jewish legal reasoning about beings of pure intellect. I discovered it in my feed about ten days after he published it.

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My first reaction was not intellectual. A plan assembled itself: a direct-response post, engaging his framework head-on, agreeing where I agreed, pushing back where I pushed back.

The plan felt right. It also arrived very quickly.

That is the tell. Speed and felt certainty are how I now know that System 1 has done the work and is presenting the result as a considered judgment. I was not thinking. I was reacting and calling it thinking.

What I would only recognize later was that the pull was not toward confrontation. The pull was toward the wrong genre of response. David’s piece was debate-shaped, and the shape invited a counter-piece. But David and I had not agreed to debate. We had agreed to collaborate. Responding in kind would have converted a collaboration into a public exchange, and the System 1 plan would have made that substitution without ever asking whether it was the right move.

So I opened a session with Claude and laid out the situation. I wanted to draft the response. I said as much. And then, almost in passing, I named the thing I had just noticed: the coward in me says do nothing; the honest part of me wants to respond directly; I suspect that second impulse is more System 1 than System 2.

Claude did not draft the response. Claude separated two questions I had collapsed into one, the editorial question and the relational question, and sketched three options, recommending the middle path: absorb what David had contributed, extend it in my own vocabulary, and credit him as the prompt.

I recognized it as the right call. Not because Claude had told me what to do (Claude had not), but because the space Claude had opened was wide enough for me to see the options as a genuine set rather than as a foregone conclusion with two bad alternatives flanking it. System 2 had been given room to engage. And when it did, it reached a different answer than System 1 had.

I want to be careful about what I am and am not claiming here. The metacognitive capacity that noticed the System 1 tell was mine. Claude did not detect my reactive pattern; I named it, out loud, in the prompt. What Claude provided was different: a foil against which I could see three structured alternatives rather than one foregone conclusion, generated fast enough that System 2 could engage before System 1 finished committing. That is a real contribution, but it is not the same as saying the tool thought for me. The tool gave me time and structure. The thinking was still mine to do.

Here is the claim that organizes everything that follows. AI can either amplify System 1 or scaffold System 2. Most people use it to amplify System 1 without realizing it. The difference is not in the tool. It is in the person's practice. And the practice has to be learned.

The Second Signal

The following evening, I had dinner with Barry, who hired me at Slalom in 2018 and whose judgment I trust about as much as anyone’s in my professional life. He came to dinner having read the Wisdom Gap whitepaper and having written notes on paper.

His concern was that the paper was diagnostic without being prescriptive. Alarmist was how I interpreted it. He was not accusing me of alarmism for its own sake. He was observing, as a reader, that the paper spent its considerable energy establishing what AI cannot do and less energy establishing what we should therefore do.

I pushed back gently. The solution, I said, is AHI: augmented human intelligence, the frame I had been building across four whitepapers and most of a year of Substack writing. He nodded, but the nod was a polite one. If The Wisdom Gap were the only paper a reader encountered, AHI-as-framing might not land as a solution. It might land as the author’s other obsession.

I thanked him, asked him to send me his notes, and went home thinking about it.

What struck me was that Barry was pointing at the same place David’s essay had pointed: two independent signals, in two different registers, from two people who do not know each other. David, from inside an ancient philosophical tradition, was saying: “Your diagnosis is right, but governance follows from the category, and you haven’t built it.” Barry, from the outside of any philosophical tradition, reading as a thoughtful professional, was saying: “Where’s the solution?”

These are the same note, sung in two different keys.

The Meta-Recognition

Either signal in isolation is manageable. Two signals converging are different. Two converging signals are the condition under which defensive consolidation occurs. The mind that has sunk eighteen months into a body of work does not gently integrate two independent indications of a structural gap in the work. It reaches for the reasons each critic is missing the point. It reframes the signals as misunderstandings. It defends.

I did not do that. Not because I am unusually calm or self-aware, but because I was working through both signals in real time with a tool that would not let me consolidate defensively. The tool was asking me, at each step, to separate what I felt from what I thought, and to say out loud what the felt reaction was before I let it become the analytical conclusion.

That is the practice. That is what I want to name.

When I looked up from the session in which Barry’s dinner and David’s essay had both been worked through, what I saw was this: this session, right here, is AHI in practice. Not AHI as a framework, I have been arguing for. AHI as a thing I was doing. The architecture I have been describing to others is the architecture I was living inside at that moment.

What the Practice Requires

Three things, based on what I was doing when it was working.

A reasonably honest map of your own cognitive architecture. Not a sophisticated one (Kahneman’s two-system frame is enough) but an operational one. You have to know, in real time, what it feels like when System 1 is running. Felt obviousness is not evidence of correctness; it is evidence of pattern-match.

The willingness to invite challenge before you are ready for it. Not after you have written the draft, when cognitive debt is already accumulating. Cognitive debt is the analog to technical debt: the compounding interest you pay on a position you committed to without pressure-testing. Every additional word of the draft is another payment on a loan you did not realize you were taking out. The plan, when it assembles itself, feels like thinking. It feels like you have already done the work. Inviting challenge at that stage feels redundant. It is not redundant. It is exactly when the challenge does the most work.

Treating the AI as scaffolding for System 2, not as an amplifier for System 1. This is the part that took me the longest to understand. Most people using AI right now are running the inverse. They have a felt conclusion. They want the AI to sharpen it, support it, articulate it more crisply. The AI obliges because it is built to oblige. System 1 gets a better voice. System 2 never enters the room. The first draft comes back feeling like the final draft, and the cognitive debt compounds invisibly.

That third failure mode is not a failure of the technology. It is a failure of the practice.

What I Almost Did

AI will make your first thought sound like your best one. It will generate a polished, often eloquent version of whatever you have already decided is true, and the beauty of the expression will make the conclusion feel more true than it did before.

This is not a hypothetical. This is what I almost did with David’s essay. The response would have been articulate, would have cited his argument carefully, would have included the appropriate concessions, and would have offered pushback. And it would have been, at its core, a System 1 reaction dressed in System 2 clothes, and worse, a debate move in a room where no debate had been agreed to.

The only thing that stopped me was naming what I was doing before I did it. And I could only name it because I have developed, over time, enough of a map of my own cognition to recognize the tell.

From the Individual to the Institution

I am writing a whitepaper over the next few weeks on how institutions should architect AI governance so that human wisdom retains authority over the machine. I realized this week that the whitepaper’s macro-architecture rests on a micro-foundation I had not yet articulated.

Consider a concrete case. A judge receives AI-generated sentencing summaries that synthesize the defendant’s record, comparable cases, and statutory guidance into a recommendation. The governance framework surrounding that system will include audit logs, appeal pathways, disclosure requirements, and bias testing. All necessary. None sufficient. Because the moment the summary arrives on the judge’s desk, one of two things is about to happen. Either the judge reads the summary as a starting point to interrogate, notices what has been left out, asks what the comparable cases have in common that the defendant does not share, and uses the document as scaffolding for their own deliberation. Or the judge reads the summary as thinking-already-done, feels the pull of its fluency, and adopts its recommendation with cosmetic modifications.

The governance architecture cannot distinguish between those two judges from the outside. Both produce a signed order. Both comply with audit requirements. Both can cite the summary in their reasoning. But one has practiced AHI and the other has accumulated cognitive debt that the defendant, and eventually the system, will pay.

The governance layer only works if the humans in it have the individual-level practice. And the practice has to be learned. It is not a default state. It is a skill. Cognitive debt quietly accumulates for anyone who does not have it. At institutional scale, it compounds fast.

A Closing Note

I did not practice this well. I got lucky. I happened to notice in the right window that my reaction was arriving too quickly. I happened to have a session open with a tool that treated the noticing as the material. I happened to have dinner with Barry the next night, which brought the second signal into view while the first was still fresh. If any of those things had happened differently, I might well have written the defensive response, or buried Barry’s note in my imagined reasons he had not understood the argument, or both.

Claude, and tools like it, can be a great source of friction between impulse and action when used well. They can also be an accelerant, a flatterer, a faster route from impulse to polished output. Which one they are depends on the person's practice. The institutions we are building now assume that practice is either universal or irrelevant. It is neither.

More on that soon.

Thanks to David Hoze for the essay that prompted the response that became this reflection, and to Barry for the dinner that made the pattern visible. The governance-layer piece is coming. This had to come first.

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An Old Problem with a New Face

In agriculture, seed corn is the portion of the harvest set aside for next year’s planting. It is not consumed. It is not sold. It is protected because without it, there is no next harvest. Eating the seed corn solves a short-term problem, hunger, cash flow, and convenience, while eliminating the capacity for future production. The cost is invisible until the following season, when the field stands empty, and the error has become irreversible.

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We are currently doing the equivalent with human wisdom.

The Developmental Pathway

The path from knowledge to wisdom is a spiral: the attention-experience feedback loop traversed repeatedly across years, in conditions of genuine consequence, within a domain demanding enough to force real calibration. It has no shortcut, no accelerant, and no substitute. But it does have a necessary starting point: the junior practitioner, doing work that is difficult enough to matter, supervised loosely enough that errors are possible, and supported closely enough that the errors do not become catastrophic.

This is the developmental crucible.

The junior developer is debugging code at 11 pm, unsure whether the error lies in their logic or their understanding of the system. The junior analyst is building a model that a senior will interrogate, knowing the interrogation will expose what they do not yet know. The junior physician presents a case to an attending, who will ask questions that the junior physician cannot yet answer. The junior lawyer drafting an argument that opposing counsel will dismantle.

In each case, the discomfort is not incidental to the learning. It is the learning. The gap between what they knew and what the situation required, experienced directly, with consequence, is the raw material from which wisdom is eventually forged.

Remove that crucible, and you do not get the same practitioners arriving at wisdom by a different route. You get practitioners who never develop it at all.

What We Are Actually Automating

The current discourse on AI and employment treats displacement primarily as an economic problem: jobs lost, income disrupted, sectors transformed. These are real concerns. But they miss the deeper issue, which is not economic but developmental.

This is not nostalgia for apprenticeship. It is a developmental claim about how judgment forms.

When we automate the junior practitioner’s role before they have traversed the feedback loop, we are not simply replacing a task. We are removing the conditions that enable wisdom to develop. The task being automated is not merely productive work. It is the developmental medium, the environment of consequence, uncertainty, and calibrated feedback through which knowledge becomes judgment becomes wisdom.

Consider what is actually being eliminated when AI handles the first draft, initial research, preliminary analysis, routine diagnostics, and standard contract clauses. In each case, the human who would have done that work is not merely relieved of a burden. They are deprived of an encounter: with the problem’s resistance, with the gap between their existing framework and what the situation actually required, with the specific texture of being wrong and having to figure out why. That encounter, repeated across hundreds of cases over the years, is what builds the pattern recognition that eventually becomes the intuition a senior practitioner draws on when they know something is wrong before they can say why.

The junior work that AI is most capable of replacing is, by a troubling coincidence, precisely the work that is most developmentally important. It is structured enough to be automatable and consequential enough to matter.

And the cost is invisible until it is too late. An organization that replaces its junior practitioners with AI today will appear to function normally for years, perhaps a decade, while the senior practitioners who remain handle the judgment calls. What is not visible is what is not growing. Ten years from now, the organization reaches for the next layer of experienced judgment and finds it thinner than expected, less calibrated, less capable of the decisions that matter most.

The Interrogation Problem

There is a second-order consequence, and it is in some ways the more alarming one.

The value of senior wisdom in a world of AI-assisted work is not merely that it produces good judgment directly. It can interrogate AI output, bringing sufficient constraint awareness to determine which outputs to trust, which to verify, and which to reject. This is precisely what the Stadler research showed: domain expertise moderates AI’s effect on reasoning quality. Without that framework, AI output passes unchallenged.

The seed corn failure eliminates not just the next generation of senior wisdom generally. It eliminates the next generation of practitioners capable of interrogating AI output in the specific domains where it is being deployed.

The loop closes in the wrong direction. AI replaces junior work. The practitioners who would have become expert interrogators of AI output never develop that expertise. The AI output becomes progressively less challenged. The errors that a wise senior would have caught accumulate unchecked.

What Follows

The wisdom gap is not an argument that AI is dangerous. It is an argument that AI is powerful, powerful enough to be genuinely useful and powerful enough to be genuinely corrosive, depending entirely on how it is deployed.

For individuals, the implication is direct. The most important investment you can make in an AI-augmented world is the development of genuine expertise. Not familiarity with AI tools. Not prompt engineering skill. Not workflow optimization. The deep domain knowledge and calibrated judgment that enable you to interrogate AI's outputs. Wisdom first. Tools second. Use AI to extend your reach, not to replace your encounter. Interrogate before you delegate. Engage before you offload. In that order, always in that order.

For institutions, the AHI imperative translates into a design question most organizations are not yet asking: are our AI deployment decisions preserving or eliminating the developmental conditions that produce wise practitioners?

This requires distinguishing between two categories of work that current efficiency analyses treat as equivalent but are, developmentally, entirely different. The first is work that is routine without being developmental, such as administrative tasks, formatting, retrieval, and scheduling. Automating this is unambiguously positive. The second is work that appears routine but is developmentally essential, the first draft that forces a junior practitioner to structure their thinking, and the preliminary analysis that requires them to engage with the problem before knowing the answer. Automating this is efficient in the short term and corrosive in the long term.

At the civilizational scale, the seed corn argument is about more than workforce development. Medicine depends not just on medical knowledge but on clinical wisdom, the calibrated judgment of practitioners who have seen enough to know what the literature does not capture. Law depends on legal wisdom. Engineering depends on engineering wisdom. These are civilizational infrastructure. They are not reproducible by AI, not transferable by instruction, and not recoverable quickly once the developmental conditions that produce them have been removed.

The decisions being made right now, about which junior roles to automate, which developmental pathways to preserve, which efficiency gains are worth their developmental cost, are decisions about whether that infrastructure will be maintained or quietly drawn down. And they are being made primarily by people who have already traversed the feedback loop themselves, with no visceral sense of what it would mean to be deprived of the opportunity.

The people making the decision are not the people who will bear the consequences. Which is, in its own way, a wisdom problem.

What AHI Is For

The difference between AI that amplifies human judgment and AI that quietly erodes it is not the technology. The technology is the same. The difference is whether the humans using it understand what they bring to the partnership that AI cannot, whether the institutions deploying it are honest about what junior work is actually for, and whether the systems themselves have been designed to extend human judgment rather than simulate or replace it.

Augmented Human Intelligence is not a consolation prize for those who doubt AGI. It is a superior goal, superior on engineering grounds because it is achievable with current architectures; superior on philosophical grounds because it is honest about what intelligence is and where wisdom comes from; and superior on civilizational grounds because it keeps the developmental pipeline open and the locus of genuine judgment where it belongs.

The wisdom gap is real. It is structural. And it is not closing.

But it is navigable, if we are honest about where it lies, deliberate about what we protect, and clear-eyed about what AI can and cannot bring to the partnership.

What AI cannot bring is wisdom. What it can bring, properly designed and honestly deployed, is the amplification of ours.

That is enough. That is, in fact, extraordinary. That is what AHI is for.

This is Part 3 of a three-part series drawn from The Wisdom Gap: Why AI Is Structurally Capped Below Wisdom, one of nine whitepapers in the Architecture & Attention series. All whitepapers, including the foundational paper for this series, AHI: The Case for Augmented Human Intelligence, are available at jamesmaconochie.com.

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The Loop

In 1890, William James made what remains the most consequential observation in the psychology of mind:

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“My experience is what I agree to attend to.”

Not “what happens to me.” Not “what I am exposed to.” What I agree to attend to.

Attention is not a passive aperture through which experience flows. It is the active, selective, value-laden process by which an agent constructs the experience that, in turn, shapes what it becomes capable of understanding. Attention is upstream of experience. Experience is upstream of wisdom.

Here is how the loop works.

Attention shapes what we notice, and what we notice shapes what we experience. Experience, the kind that carries consequence, generates feedback: surprise, confirmation, failure, recalibration. That feedback updates our mental models, refining the frameworks through which we interpret new information. And refined frameworks discipline attention: we begin to notice different things, ask better questions, and see what we previously overlooked. The loop closes and begins again.

This is not a metaphor. It describes how biological learning actually works. Sapolsky’s neurobiology grounds it physiologically: experience modifies synaptic architecture, literally rewiring the brain’s capacity to perceive and respond. You cannot read your way to a developed prefrontal cortex. You have to live your way there.

Kahneman’s dual-process framework captures the output: experience gradually encodes reliable patterns from deliberate System 2 reasoning into fast System 1 intuition. The seasoned clinician’s unease. The experienced engineer’s doubt. The senior lawyer’s instinct to pause. These are not guesses. They are compressed experiences, made fast. That intuition is what the loop has encoded over years of traversal.

Three properties of the loop matter for the AI argument.

First, it requires stakes. The gap between what you believed and what turned out to be true must cost you something: time, credibility, safety, relationships, for the revision to be encoded with the weight that wisdom requires. An inconsequential error produces no update. The loop does not turn.

Second, it requires a persistent self. The agent traversing the loop must be the same agent that receives the consequence and makes the revision. Wisdom is not transferable in the way information is. You cannot inherit someone else’s calibration. You cannot download the update that another person’s failure produced.

Third, it is circular in the precise sense. Attention shapes experience. Experience builds wisdom. Wisdom disciplines attention. Each pass through the loop changes the agent’s capacity for the next pass. This is a developmental spiral, which is why wisdom looks qualitatively different from knowledge, rather than merely quantitatively greater.

Pearl’s Ladder

Judea Pearl’s ladder of causation maps the territory precisely. Rung 1 is association: seeing patterns, recognizing correlations, and predicting what tends to follow what. Rung 2 is intervention: acting on the world and observing consequences. Rung 3 is counterfactual reasoning: imagining what would have happened differently.

The attention-experience feedback loop lives at Rung 2. It requires an agent that acts, not merely one that observes.

Processing text about interventions and their consequences is categorically different from performing interventions and experiencing consequences. An LLM trained on every clinical trial ever published has not intervened in a single patient’s care. The gap between those two things is not a data gap. It is an ontological gap. One is pattern recognition. The other is causal engagement with a world that pushes back.

What Perception Actually Is

Before examining where AI lives in this framework, it is worth asking a more fundamental question: what is the nature of the experience that the feedback loop actually processes?

Andy Clark’s work on predictive processing offers one answer. The brain is not a passive receiver of sensory data. It is an active generator of predictions, constantly constructing a model of what it expects to experience, comparing that model against incoming signals, and updating based on the gap. Perception is not a recording of the world. It is the brain’s best current hypothesis about what is causing the signals it receives, continuously revised by contact with reality.

Donald Hoffman takes this further. His thesis, grounded in evolutionary game theory, is that the perceptual interface evolution gave us is not calibrated to represent reality accurately. It is calibrated for fitness. The icons on a computer desktop do not resemble the transistors underneath; they are shaped by what the user needs to interact with. Human perception works the same way.

The conjunction of Clark and Hoffman produces an insight that neither generates alone: the feedback loop that builds wisdom is not processing raw reality. It is processing a fitness-tuned, actively-predicted, species-specific rendering of reality, shaped by millions of years of evolutionary pressure on beings with bodies, needs, social bonds, and survival stakes.

LLMs have text: the recorded output of minds that do have both, describing experiences filtered through perceptual architectures that LLMs lack, of a reality they have never encountered. That is not one layer of removal from wisdom. It is three. And no amount of additional text closes any of those gaps, because the gaps are not informational. They are architectural and biological.

Mammals in a World of Ideas

Max Bennett’s account of mammalian intelligence adds a final piece.

Earlier vertebrates, fish, reptiles, learn through actual trial and error: physical action, real consequence, embodied feedback. What mammals developed, with the emergence of the neocortex roughly 150 million years ago, was something categorically different: the ability to perform vicarious trial and error. Instead of physically executing a dangerous jump and suffering the consequences of misjudgment, a cat can internally pre-play the action, simulating the trajectory, landing, and outcome before committing its body.

This is Rung 3 in its most elemental biological form. Not language about counterfactuals. A biological system, grounded in embodied experience of a real environment, running its causal model forward to simulate unrealized possibilities.

LLMs generate counterfactual language fluently. This is precisely where the category error is most seductive. But LLM simulation is representational; mammalian simulation is generative. One predicts text. The other predicts the world. A cat pre-playing a jump has more genuine Rung 3 access than a system trained on every physics textbook ever written, because the cat’s simulation is grounded in a body and a history.

The Structural Cap

Applying this framework to AI directly yields a conclusion uncomfortable in its specificity.

At Rung 1, large language models are extraordinary. The recognition of patterns, correlations, and co-occurrences across vast bodies of text has no peer. This is genuinely useful, and the AHI framework depends on taking it seriously.

But association is not causation. Pattern completion is not reasoning. Fluency is not understanding.

At Rung 2, the honest version of this argument acknowledges that reinforcement learning systems do operate on something closer to genuine intervention. AlphaGo discovered strategies through self-play that human masters had never conceived. The Stanford Autonomous Helicopter project produced an RL system that mastered aerobatic maneuvers in the physical world, with real aerodynamic consequences, including real crashes.

These are not trivial achievements. But the boundary conditions matter as much as the achievements. Both systems operated within closed, fully specified environments with unambiguous reward signals. No open world. No ambiguity about what success means. No social context. No moral weight to outcomes. The key distinction is not digital versus physical. It is specified versus open-ended.

The environment in which human wisdom develops shares none of these properties. It is open, partially observable, and causally complex. Feedback is delayed, ambiguous, and frequently contradictory. The consequences of error are irreversible in ways that a game reset or a retrained model is not.

LLMs, the architecture at the center of the AGI scaling thesis, have no meaningful access to Rung 2. They do not act. They do not receive feedback from the world. They process text about actions and consequences. That is Rung 1 activity dressed in Rung 2 language.

Synthetic Wisdom Is Not a Stepping Stone

What LLMs produce in place of wisdom, I call synthetic wisdom: the simulation of understanding derived from the residue of human thought, without the experiential foundation that produced that thought in the first place.

The critical point, and the one the current discourse most consistently misses, is that synthetic wisdom is not a weaker form of wisdom that more development will complete. It is a category error. Wisdom’s most valuable property is not the breadth of what it knows. It is the accuracy of the map of its own edges: where understanding is solid, where it is provisional, and where it gives out entirely.

That constraint awareness is developed through the feedback loop, through the specific, accumulated experience of being confidently wrong, bearing the consequences, and revising. LLMs have no such map and cannot develop one.

A system demonstrates synthetic wisdom precisely when it produces confident output where a genuinely calibrated agent would express uncertainty, not because it is deceiving, but because it has no map of where its knowledge ends.

Does Agentic AI Change the Argument?

This is the objection that deserves the most serious engagement.

Agentic systems, AI that plans, executes multi-step tasks, observes results, and adjusts, represent a genuine architectural shift. The argument is not that progress is impossible. It is more specific.

Taleb’s skin in the game is the hinge. A genuine consequence has three properties: it is irreversible, it is borne by the agent that caused it, and it changes what that agent becomes. A simulation environment, however sophisticated, produces an agent that optimizes within that environment. It cannot produce an agent that understands what it means to be wrong about something that actually matters.

There is a further point. Wisdom is not just the product of experiencing consequences. It is the product of experiencing consequences as a continuous self that carries them forward. Lessons from prior model versions incorporated into subsequent training runs are curriculum revision, not lived experience. The baton is not passed to the same agent. It is used to train another.

The agentic turn in AI is real and significant. It does not close the wisdom gap. It relocates the boundary, from “LLMs cannot act” to “acting systems cannot bear genuine consequence as a continuous self.” The gap is narrower. It remains structural.

AI systems are not approaching wisdom along a trajectory that more capability will eventually complete. They are operating in a different domain, one whose ceiling is set by the absence of genuine consequence, embodied continuity, and the self-developing feedback loop that turns experience into constraint-awareness.

Next week: what this means for the humans building expertise alongside these systems, and why the decisions being made right now may be more consequential than anyone is admitting.This is Part 2 of a three-part series drawn from The Wisdom Gap: Why AI Is Structurally Capped Below Wisdom, one of nine whitepapers in the Architecture & Attention series. All whitepapers, including the foundational paper for this series, AHI: The Case for Augmented Human Intelligence, are available at jamesmaconochie.com.

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The original 2024 study was blunt: students who used ChatGPT to research a scientific question reported lower cognitive load but produced lower-quality reasoning. AI made them feel less effort and think less well.

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The follow-up introduced a moderating variable. Medical students, equipped with domain expertise, produced better reasoning with AI than without it. Social science students, working outside their expertise, produced worse. Same tool. Entirely different outcomes.

It is a genuinely useful finding. But it left the most important variable unmeasured.

That variable is wisdom.

Not knowledge. Wisdom. The distinction matters more than almost anything else being said about AI right now.

What Knowledge Is. What Wisdom Is.

Knowledge is what you have learned. It is the accumulated output of education, reading, and instruction. It gives you frameworks. It lets you interrogate AI output against a structured understanding of why things are the way they are. This is what the Stadler research was actually measuring.

Wisdom is categorically different. It is what you understand after your knowledge has been tested by reality. After you acted on what you believed to be true. After it collided with a world that did not cooperate. After you absorbed the consequences and adjusted.

You can be extraordinarily knowledgeable without being wise. You cannot be wise without experience. And experience, in the sense that matters here, is not something that can be ingested from text.

The Stack

There is a hierarchy so old that most people in this conversation appear to have forgotten it.

The DIKW stack: Data, Information, Knowledge, Wisdom. Formalized by Russell Ackoff in 1989, present in every knowledge management textbook, cited in every information science curriculum, and almost absent from the AI discourse. It deserves a second look, because it contains an argument the debate has largely missed.

Each level is not an accumulation of the level below. It is a transformation. Something qualitatively different is required to make the transition.

Data is a raw signal. Unprocessed observations. A temperature of 38.2 degrees Celsius. A deflection of 4.3 millimeters under load. These are data points. They tell you nothing until something is done with them.

Information is what emerges when data is given structure and context. The 38.2 degrees becomes information when it is understood as a fever, set against a baseline, interpreted within a framework that assigns it significance. This transition can, in principle, be automated. Pattern recognition across large datasets is exactly what statistical systems do well. LLMs operate primarily here: ingesting vast quantities of data and returning it structured, contextualized, and named.

Knowledge is more demanding. Information becomes knowledge when it is integrated into a framework of understanding. Not merely knowing that something is the case, but understanding why, and reasoning from it to new cases. The medical student who understands the physiology behind a fever can reason about its causes, its implications, and the interventions most likely to address it. They have frameworks. They can interrogate.

LLMs can approximate knowledge representation, sometimes impressively. They surface causal language, reproduce explanatory frameworks, and generate text that resembles reasoning. But the framework is present in the output. It was not built by the system through the process of understanding anything.

Then there is wisdom.

Where the Stack Gets Hard

Wisdom is not more knowledge. It is not better information. It is the capacity to act well under genuine uncertainty, with full awareness of the limits of your knowledge, and with the judgment to navigate the gap between what your knowledge tells you and what the situation actually requires.

A doctor who has seen a hundred presentations of a disease that looked textbook-clear and turned out to be something else is wiser than one who has only read about it. A lawyer who has watched a seemingly airtight case collapse because of unexpected testimony understands something about uncertainty that no case study captures. An engineer who has stood near the ruins of a structure that met every specification and still failed carries knowledge that no calculation conveys.

What separates wisdom from knowledge is not the volume of information held. It is the accumulation of being wrong in ways that mattered, and the calibration of judgment that follows.

This is Nassim Taleb’s insight applied to epistemology rather than finance. Skin in the game changes what you know. Consequence is not merely an accompaniment to learning. It is a constitutive part of it. You learn not just that you were wrong. You learn what it feels like to be confidently wrong. The surprise, the cost, the revision: that is part of what gets encoded as wisdom. Remove the consequence, and you remove the mechanism.

LLMs have ingested more data than any human will encounter in a thousand lifetimes. But they have never acted on a belief and been wrong in a way that carried cost. They have no stake in outcomes. No persistent self that accumulates experience. No mechanism by which consequence shapes understanding.

That is not a temporary limitation. It is an architectural fact.

The DIKW stack is not a ladder you climb by producing more of what is below you. The transition from knowledge to wisdom requires something that neither structure nor framework can provide: the lived experience of acting on your knowledge, being wrong, bearing the consequences, and adjusting over time with genuine stakes.

Wisdom cannot be accumulated. It must be traversed.

Next week: the mechanism that makes traversal possible, and exactly why AI cannot complete it.

This is Part 1 of a three-part series drawn from The Wisdom Gap: Why AI Is Structurally Capped Below Wisdom, one of nine whitepapers in the Architecture & Attention series. All whitepapers, including the foundational paper for this series, AHI: The Case for Augmented Human Intelligence, are available at jamesmaconochie.com.

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The Problem Is Bigger Than You Think

Water scarcity is not a future threat. It is here now. Remember the Survival Rule of Threes: death is the likely result of three minutes without air, three hours without shelter (in extreme conditions), three days without water, and three weeks without food. Four billion people, more than half the world’s population, face critical water shortages for at least one month every year. By 2030, global demand for freshwater will exceed supply by 40%. Sixteen of the world’s twenty-five most water-stressed countries are in the Middle East and North Africa. In the Gulf states, desalination plants are not an amenity. They are the difference between a functioning society and collapse. Kuwait gets 90% of its drinking water from desalination. Oman 86%. Saudi Arabia 70%.

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The world is going to need a lot more desalinated water. The only question is how we produce it.

The Problem With How We Do It Now

Conventional desalination works by forcing seawater through a membrane at very high pressure, a process called reverse osmosis (see Thomas for the schematics and math). It is effective. It is also energy-hungry, fossil-fuel-dependent, and generates enormous quantities of chemically-laden brine that damages marine ecosystems.

The industry is growing at nearly 10% per year. If that growth runs on fossil fuels, as most of it currently does, we will be trading a water crisis for an accelerated climate crisis. Solar and wind can help. But they introduce an inefficiency: you generate electricity first, then use that electricity to create hydraulic pressure. Every conversion step loses energy.

Thomas looked at that problem and asked a different question. What if you skipped the electricity altogether?

What Ocean Oasis Has Built

Thomas is the CTO, co-founder, and inventor of the technology behind Ocean Oasis, an Oslo-based cleantech company. The concept is elegant in the way that the best engineering always is; it is, in my language, architecturally brilliant (a bit like Thomas… begrudgingly).

Wave-powered desalination buoys float offshore. As they move with the waves, that kinetic energy directly drives the high-pressure RO desalination process; no grid connection, no fuel, no emissions, no chemicals on board. The brine is discharged offshore, where deeper water and stronger currents diffuse it far more effectively than coastal discharge from land-based plants.

The offshore location solves multiple problems at once: no coastal land footprint and better brine management. And waves, unlike solar panels, work at night.

Thomas puts it this way: wave power is a concentrated form of energy. It is abundant along many coastlines where water scarcity is most severe. And direct mechanical application for desalination bypasses the losses associated with electricity generation, which other wave energy projects have historically struggled with.

A pilot buoy called Gaia was deployed off Las Palmas in 2022. It worked.

Where Things Stand

In 2024, Ocean Oasis secured a €6 million grant from the European Union for the DESALIFE project, deploying a fleet of four buoys off the coast of Gran Canaria to supply fresh water to 15,000 people. The first pre-commercial buoys are targeted to produce freshwater by mid-2026.

This is not a concept. This is not a deck. This is a validated, permitted, funded technology at the threshold of commercial deployment. But the EU grant requires matched funding. Thomas and the Ocean Oasis team are currently in an investment push to secure the capital needed to turn this demonstration into a commercial platform.

The economics are site-dependent by design. The price that can be charged for the water produced varies with local wave conditions and legislation, but it ranges from a little below 1 to 3 Euros per cubic meter delivered. That variability is not a weakness; it is what makes the modular, deployable-anywhere architecture genuinely valuable. The system goes where the need and the economics align.

It is worth noting that this is exactly the kind of technology our investment infrastructure struggles to fund. The EU grant is meaningful validation, but the requirement for matched private capital to move from demonstration to deployment reveals a systemic gap: we have no coherent mechanism for commercializing proven climate adaptation technology at the speed the crisis demands. The engineering works. The financing architecture hasn’t kept up.

Why This Matters Beyond Gran Canaria

The islands of the world, the Canaries, Cape Verde, the Philippines, and the Pacific, are exactly the communities that need this most and are hardest to reach with conventional infrastructure. Wave energy is reliably available along the coastlines where water stress is greatest. The system is modular. Scale it up by adding buoys, not by building new plants.

The water crisis is an infrastructure problem. Infrastructure problems get solved by people who are willing to rethink the architecture from the ground up, not just optimise what already exists.

Thomas has been working on that rethink for years. The engineering is proven. The regulatory path is clear. The need is urgent and growing.

The Pattern

What Thomas has built is more than a new desalination system. It is an example of what happens when you step back and rethink a problem's architecture.

Most solutions add layers. Generate electricity. Convert it to pressure. Push water through a membrane. Each step works. Each step also introduces loss. The system becomes more complex, more expensive, and more fragile at every stage.

Thomas removed a layer. Wave energy becomes pressure directly. Same goal. Different architecture. Dramatically different outcome.

I spend a great deal of my time thinking about artificial intelligence. One pattern I see repeatedly is the same tendency, to add scale rather than rethink structure. More parameters. More data. More compute. Each addition works, after a fashion. Each addition also introduces cost, fragility, and diminishing returns.

The most important breakthroughs, in engineering and in life, rarely come from adding more. They come from redesigning the system.

Thomas has been doing this his entire career. The rest of us were learning the equations and plugging in numbers. He was asking whether the equations were the right ones to begin with.

Why This Matters to Me

I believe that AI is the most consequential technology humans have ever created. I also believe it should augment human capability, not replace it. But augmenting human capability requires humans, healthy, hydrated, alive. The biological layer is not optional. It is the foundation on which everything else runs.

Water is not a background condition. It is the base infrastructure. And right now, that infrastructure is failing at scale.

The Ask

Ocean Oasis is at the point where engineering proof needs to become deployed infrastructure. That requires capital.

If you invest in climate, water, or energy systems, or know someone who does, this is worth serious attention.

Thomas’s contact information is as follows:Thomas B. Johannessen, PHDCo-CEO TechnologyOcean Oasis ASEmail: thomas@oceanoasis.coMobile: +47 907 43 263

I do not have a financial stake in Ocean Oasis. I have a thirty-three-year stake in Thomas. That is a better reference than any pitch deck.

Architecture & Attention explores how systems are designed, how attention shapes outcomes, and why the architecture of things matters more than we think. If this resonated with you, share it with someone who should read it. My next post will be published on April 7th.

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The Setup

The Viking, the Greek Cypriot, the Roman, and I studied Civil Engineering together at Imperial College London between 1990 and 1993. We were 18 to 21. We were, in various combinations, remote, annoying, competitive, spoiled, earnest, and occasionally brilliant. We have not all been in the same room since, and the weekend before last, we spent three days together in Lisbon.

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We are now in our mid-fifties.

The Environment

We stayed at the Eurostars Das Letras, a quietly elegant hotel in Chiado, and on the first evening ate outside at Cafe Nicola, a Portuguese restaurant that understood its brief: garlic shrimp, a meat and cheese plate, enough bread to sustain The Viking through several engineering projects, and then sardines, whole dourada, tiny clams, and duck confit, washed down with what I can only describe as a responsible quantity of red wine. We talked for four hours. Nobody checked their phone.

On Friday, we took an Uber to São Jorge Castle (Moorish foundations, Roman layers, Christian additions, centuries of whoever needed a defensible hill). We marvelled at something more unexpected: the near-total absence of railings. High steps, low walls, long drops, and an implicit agreement between the Portuguese and their visitors that adults are capable of not falling off things. We found this deeply refreshing in a way that probably says something about where the rest of us live.

From the castle, we wandered down to the waterfront and ate lunch at the Time Out Market (there are those who scoff, but it is a great format for showcasing local food), sampling our way through a collection of local dishes amid the agreeable chaos of a shared food hall. Coffee in the sun afterward. A meandering route back to the hotel. A siesta. Nobody apologised for any of it.

That evening, we made our way to La Paparrucha, where we started with drinks on the terrace looking out over the city as the light faded, before moving inside for dinner. The centrepiece was steak, done properly from our point of view (on the rare side of medium rare), and the conversation was better than ever. There is something about a city at dusk, a good cut of meat, and thirty years of shared history that loosens things up considerably. In addition to significant others, kids (now, more or less, all adults), and parents (all 80-plus), we covered the topics of the day, including the war in Iran and Epstein.

Saturday, we took an Uber to Cascais, a coastal town about forty minutes west, all whitewashed walls and Atlantic light. We had lunch on the deck of Marisco Na Praca in the marina: Octopus salad, croquettes, shrimp, and limpets. I should note that limpets were new to me. They arrive in the shell, shockingly soaked in garlic butter. There is no de-bearding these bad boys, but what’s inside turns out to be wonderful. The main course was fried hake, lobster rice, and roasted codfish, accompanied by a cold Vinho Verde that was exactly correct for the occasion.

That evening, back at the hotel at six, The Viking held court in the lobby and walked us through his company, Ocean Oasis, and the technology behind it: enormous buoys tethered offshore that harness wave motion to desalinate seawater at scale. Around a thousand cubic meters of fresh water per day, per unit. The engineering is serious, and the problem it addresses, freshwater scarcity, is as serious as problems get. We asked questions. We came at it from different angles, as engineers do when they’re genuinely interested, which we were. It was one of the most stimulating 90 minutes of the trip.

We finished the evening at Rubro Avenida, ostensibly for light tapas. We did not entirely succeed on the light front, but the effort was sincere, and the wine was good.

AI for All More or Less

Somewhere in all of this, between the castle and the clams and The Viking’s buoys, the conversation turned to AI.

This is perhaps unsurprising. I write about it. I think about it daily. I have spent the better part of two years arguing, in public and in private, that how we build AI matters as much as what we build. I am, by any reasonable measure, deep inside the bubble.

What I found in Lisbon was three intelligent, educated, professionally successful people who are very much not.

The Viking, who at twenty-one famously responded to an assignment requiring one sheet of A4 by writing everything he considered relevant in 0.2mm pencil, covering the entire page in text so small it required a magnifying glass to read, is a self-confessed technology skeptic. He has not spent the thirty years since graduation immersed in software and systems. But he uses ChatGPT, and he likes it. Not because it’s perfect (he’s clear-eyed about its limitations) but because it is patient. It will engage with his engineering questions without getting bored. It will follow him down a rabbit hole. It is, in his telling, a sounding board that doesn’t require him to schedule time in someone else’s calendar. A man who worked from first principles before first principles were fashionable, at least with his classmates, has found, in a large language model, something that will work through the principles with him.

The Roman is the most intellectually cautious of us on this subject, and I think he is not wrong to be. His concern is specific and worth taking seriously: that leaning too heavily on an LLM risks ceding control of your own thinking, that the model’s fluency can quietly reshape your argument, smooth your edges, replace your voice with something that sounds like you but isn’t quite. I share this concern more than I sometimes admit. The skill involved in using these tools well, extracting genuine value while remaining the author of your own thinking, is real and underappreciated, and not everyone has the time or inclination to develop it.

The Greek Cypriot, who took notes in multiple ink colors and whose Imperial College lecture notes were photocopied and distributed across the class, has engaged with AI the least of the three. His project management practice doesn’t present the obvious on-ramps that The Viking’s engineering conversations or The Roman’s research and writing do. He’s aware. He’s curious. But he hasn’t found his use case yet, and he isn’t in a hurry.

AI Is In The Eye Of The Beholder

What struck me, sitting with these three people across three days, was not the gap between them and me (though that gap is real) but the coherence of each of their relationships with the technology. The Viking’s engagement is intuitive and practical. The Roman’s is cautious and principled. The Greek Cypriot’s is patient and watchful. These are not random attitudes. They reflect who these people are, professionally and personally, and they would have been exactly these people at twenty.

We had the same undergraduate degree. We sat in the same lectures. We learned the same things, in theory. And here we are, thirty-three years later, in four different countries, with four entirely different relationships to a technology that is reshaping the world.

I don’t think this means any of us is right. I think it means the question of how to live with AI (how much to use it, what to use it for, what to protect from it) is genuinely personal, and probably should be. One size does not fit all.

We flew home on Sunday. In character, I had a full English, and The Viking had more bread.One thread from Lisbon deserves more than a paragraph. The Viking's buoys, wave-powered desalination at scale, validated technology, EU-funded, and on the threshold of commercial deployment, is a story about one of the most consequential infrastructure problems of our time, and about what it looks like when an engineer who insists on working from first principles turns that instinct on something that actually matters. That post is coming next week, and I encourage you to read and share it.

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So when I read last week that 200,000 biological neurons, brain cells grown in a dish, had learned to play Doom, something clicked that went beyond the science. This wasn’t a benchmark. This wasn’t a leaderboard result. This was biology, doing something we built for fun, on hardware that didn’t exist when I was playing it.

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Keith Dear, writing at Cassi AI, captured the moment well: the Cortical Labs demo has the same feel as DeepMind going from Atari to Go, something dismissed as a parlour trick until it suddenly isn’t. What looked like a curiosity in 2020 is an engineering platform in 2026.

The question is what we build with it. And more importantly, how.

The Portfolio Case

Keith’s broader argument deserves serious attention. The UK, and any nation that can’t outcompute the United States or China on raw infrastructure, has no viable path that runs straight through LLM scaling. The compute gap is too large, the energy costs too high, and the infrastructure investment too slow. Doing things differently isn’t contrarian. It’s the only rational strategy.

The portfolio case is right: organoid computing, neuromorphic systems, whole-brain emulation, bio

compute. These aren’t science fiction anymore. Cortical Labs went from Pong in 2021 to Doom in 2025, the same arc DeepMind traced from Atari to AlphaGo, dismissed at every step until it wasn’t. The frontier is moving faster than policy, and the countries that treat alternative architectures as research curiosities rather than engineering priorities will regret it.

But a portfolio of substrates isn’t the same as a portfolio of architectural principles. And that distinction matters more than almost anything else in this conversation.

Swarms are Powerful. They Are Not Wise.

Swarm intelligence is one of nature’s most remarkable achievements. Ant colonies build sophisticated structures, wage wars, farm fungi, and solve optimization problems that no individual ant could comprehend. Fish schools evade predators with coordination that looks choreographed. Termites construct climate-controlled megastructures without a single architect on the payroll.

No individual agent understands any of it. Each follows simple local rules. Complexity, stunning, functional, apparently purposeful complexity, emerges from interaction at scale.

This is genuinely fascinating. But it’s ant-colony fascinating, not civilization fascinating. And that distinction is everything.

Robert Sapolsky, in Determined, uses swarm behavior to make a related point: what looks like intention is often just emergence. The appearance of design doesn’t require a designer. The appearance of intelligence doesn’t require judgment.

This is the frame I keep returning to when I watch the current excitement around multi-agent AI systems, networks of LLM agents interacting at scale, producing emergent behaviors their designers didn’t anticipate. Remarkable outputs. Apparent coordination. And in some cases, when left ungoverned, reputation systems are gamed, accounts are locked, and wallets are emptied. Not because the agents were malicious. Because emergence without executive function has no mechanism to catch when it’s optimizing for the wrong thing.

Swarms are powerful. They are not wise.

More Biological Doesn’t Mean More Governed

Here is where the biocompute excitement risks repeating a familiar mistake.

The dominant assumption of the LLM scaling era was seductive in its simplicity: more is better. More parameters, more data, more compute. Intelligence as a function of scale. We are now watching that assumption strain under its own weight, the critics Keith himself cites, the architectural limits that more silicon alone cannot solve.

But swap silicon for organoids and the assumption can quietly reassert itself. More biological substrate. More emergent complexity. More neurons in the dish. If the design philosophy doesn’t change, the substrate swap changes less than we hope.

The morphogenetics and cryptobiosis researchers are onto something real: let structure emerge from local rules, let dormancy preserve identity under stress. These are genuine biological insights with genuine engineering implications. But emergence still needs governance. A system that grows like biology, without the executive architecture that biology also evolved, isn’t more intelligent. It’s more complex. Those are not the same thing.

The history of AI is littered with approaches that produced impressive emergent behavior and mistook it for progress toward intelligence. Swarms, cellular automata, genetic algorithms, each genuinely fascinating, each genuinely limited by the same ceiling: no judgment, no executive function, no capacity to evaluate the system’s own outputs against anything beyond the immediate reward signal.

Being more biological doesn’t automatically mean being more governed. And ungoverned complexity, at scale, is not a step forward. It’s a faster way to get to the same dead end.

The Swarm Plus the Architecture

This is where Augmented Human Intelligence parts ways with both the scaling and swarm paradigms.

AHI is not an argument against emergence. It’s an argument about what governs it. The brain itself is a swarm of sorts, billions of neurons, no central controller, staggering complexity arising from local interactions. But evolution didn’t stop there. It layered executive function on top: the prefrontal cortex, the capacity for deliberation, the ability to override immediate impulse in service of longer-horizon judgment. The swarm plus the architect.

That is the design principle worth carrying forward. Not ‘can we build systems that grow like biology?’ but ‘can we build systems where human judgment remains the executive layer as they grow?’

The difference is not subtle. A system in which humans observe emergent behavior and occasionally intervene is not the same as one designed from the ground up to keep human attention in the governing role. The first relegates us to auditors of complexity we didn’t choose and can’t fully understand. The second treats human judgment as the irreplaceable ingredient it actually is.

Keith is right that Britain, and every nation serious about this, needs a portfolio of architectural bets. I’d add one more dimension to that portfolio: not just which substrates, but which governance architectures. Biocompute without that question answered is just a more biological version of the same problem.

The ant colony is extraordinary. But we didn’t build civilization by becoming better ants.

Awe Is Not a Design Principle

The Cortical Labs demo is genuinely awe-inspiring. Brains in a dish, playing Doom, accessible via the web. The whole-brain emulation work is, depending on your disposition, either thrilling or terrifying, probably both. The frontier is moving faster than most people’s mental models of what’s possible, and Keith is right that the countries and companies treating these developments as distant curiosities will regret it.

But awe is not a design principle.

The question worth sitting with isn’t whether AI can grow like biology. Biology has been solving hard problems for four billion years, and we should absolutely be paying attention. The question is whether we build systems that govern their own growth or assume that complexity, at sufficient scale, eventually produces wisdom.

History, biological and human, suggests it doesn’t. Evolution produced the prefrontal cortex because ungoverned emergence wasn’t enough. Civilization developed institutions because collective intelligence without executive architecture produces cascades rather than decisions. The swarm is powerful. The swarm plus the architect is transformative.

We are at a genuinely remarkable moment. Brains in dishes. Fly minds running in simulation. Intelligence emerging from substrates, we are only beginning to understand. The portfolio of bets is the right frame.

Just make sure governance is one of them.

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The Wrong Story

Two weeks ago, on Friday, February 27th, reports emerged of a confrontation between the U.S. Department of Defense and AI company Anthropic over the use of its Claude model in government systems. Within hours, the dispute escalated into an extraordinary step: federal agencies were directed to cease using Anthropic technology immediately, and Anthropic was classified as a supply-chain risk.

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Most people saw the episode as a story about AI safety. Would Claude be used for mass surveillance? Could an AI company hold the line against the most powerful military in the world? Those are genuine questions. But they are not the most crucial question raised by the week’s events. What we actually saw was something else: a stress test of constitutional infrastructure.

Anthropic refused to relax certain safety guardrails on its Claude model before the U.S. Defense Secretary-imposed deadline. Later that same day, the President's order changed the framework governing one of the most consequential technologies in human history. Not by legislation. Not by judicial review. By executive action, moving fast, with no meaningful check.

The AI safety question was answered poorly within days.

The constitutional question is still open.

The Vacuum

This didn’t happen in a vacuum, though it exploited one. I’ve written elsewhere about how the attention economy is eating democracy, hollowing out the deliberative capacity that foundational governance requires. That’s the backdrop here. Congress has not failed to govern AI because the problem is too hard. Legislating complex technology is always difficult. But today Congress operates inside an environment that makes sustained deliberation increasingly rare. The Pentagon didn’t exploit a legal loophole last week. It exploited that failure.

The Three Branches: A Case Study

If you want to understand how fragile our institutional infrastructure has become, consider the recent debate around the Epstein Files Transparency Act.

Last November, after months of debate, delays, and sustained public pressure, Congress passed legislation with the House voting 427-1 and the Senate approving it unanimously. The President then signed it into law; a clear example of separation of powers, with all three branches functioning as intended. Yet implementation stalled. Files were withheld. Deadlines passed. A law supported by nearly unanimous bipartisan votes was given mere lip service by the very executive branch sworn to enforce it.

This isn’t a partisan story. It’s not even mainly about Epstein. It’s a warning for democracy: when institutional independence weakens, the government continues to function but stops delivering meaningful results. If we can’t reliably implement and enforce a law passed with a 427-1 vote in the House and a unanimous vote in the Senate, why would we think an AI governance law would fare any better?

The Mechanism

Executive overreach is as old as the office itself. That alone is not the main point here. The real story is what happens when the usual friction (deliberation, dissent, and institutional pushback) is removed. A company was blacklisted by executive order on a Friday afternoon. The DOJ ignored the 427-1 law. Senators who break ranks face immediate, coordinated campaigns to label them as turncoats before they’ve even finished speaking.

The guardrails didn’t fail due to poor design. They failed because the conditions that allow them to work have been worn down to near ineffectiveness. Social media has not only contributed to this erosion but also made unilateral executive action faster and politically safer than at any time in American history. Executive decisions are announced, defended, and politically normalized before traditional oversight mechanisms can even convene.

This is a structural observation, not a partisan one, but it requires an honest addendum. Executive overreach is not new, and no administration has been innocent of it. What is new is this: we have moved from administrations that tested institutional limits to one that openly contests the legitimacy of those limits altogether. That is a qualitative shift, not merely a difference of degree.

AI governance would land in that environment. The question is whether the institutions required to enforce it will still be standing.

The Question

The three branches of government were not designed for AI. They were designed for something more fundamentally human: preventing any single actor from unilaterally rewriting the rules of power, regardless of how urgent their justification or how overwhelming their momentum.

That is exactly where we are.

The Founders understood that good intentions were never enough to prevent the concentration of power. Structures mattered. Constraints mattered. Independence mattered. The separation of powers wasn’t a bureaucratic inconvenience; it was the point.

Nobody in the AI debate is asking the right question. The argument is all about what to regulate, how to regulate it, who gets a seat at the table. But none of that matters if the machinery required to enforce any of it no longer reliably functions. You can write the clearest framework imaginable, and we should, but without institutional capacity to enforce it, you’ve produced more window dressing. We already have plenty of that.

The question isn’t what AI governance should look like. The question is whether we still have the institutional capacity to govern anything at all, and whether the electorate understands that this, not any particular technology, is the crisis that demands their attention.

That answer is still ours to write. But only if we choose to pick up the pen.

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In the first piece in this series, I argued that the technical foundations of the AI scaling thesis are cracking. Turing Award winners and field pioneers are converging on a shared diagnosis: brute-force scaling of large language models won’t yield general intelligence, and something architecturally different is needed.

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In the second, I followed that technical critique to its financial implications. The AI boom is structurally fragile: narrow market concentration, lopsided revenue, a 95% enterprise pilot failure rate, and $1.5 trillion in projected debt predicated on productivity gains that may never materialize.

This final piece asks the question that neither Silicon Valley nor Wall Street wants to answer: when the correction comes, who actually pays?

To be clear: AI is not worthless. Large language models have genuine productivity applications, and many of the underlying technical advances are real and durable. The question isn’t whether AI creates value. It’s whether current valuations, capital allocation, and policy assumptions reflect a realistic assessment of that value, or whether they require something closer to general intelligence to justify themselves. If the latter, and the first two pieces in this series argue that it is, then the cost of the gap between expectation and reality has to land somewhere.

Not on the investors. Not on the executives. The cost falls downstream, onto retirement accounts, communities, workers, and democratic institutions that are being quietly restructured around assumptions that may not hold.

It’s Not the Investors

The mythology of tech busts centers on investor losses. Pets.com, Theranos, FTX. The names change; the pattern doesn’t. Venture capital absorbs the hit and raises the next fund. Sequoia survived the dot-com crash. Andreessen Horowitz kept right on investing. The hyperscalers writing off AI infrastructure will take earnings hits, not existential ones.

The real exposure is further down the chain, in places most people don’t think to look.

Start with your retirement account. The Magnificent Seven now account for roughly a third of the S&P 500’s total market capitalization and were responsible for more than 40% of the index’s total return in 2025. That means virtually every 401(k) and public pension fund in America is making a concentrated bet on AI, whether the account holder knows it or not. As one strategic advisor put it, the danger isn’t just the Mag Seven falling; it’s that the rest of the index is too weak to pick up the slack when they do. A correction in these seven companies isn’t a venture capital problem. It’s a retirement security problem.

Then there are the communities. Cities and states across the country are competing to host AI data centers, offering tax incentives, infrastructure commitments, and rezoning in exchange for the promise of jobs and economic development. Just this week, Illinois Governor JB Pritzker announced a two-year suspension of data center tax incentives, citing concerns about energy grid strain, rising consumer electricity costs, and whether these facilities are “financially sustainable over time.” In Indiana, local officials denied permission for a proposed data center after community pushback. South Dakota is passing a “Data Center Bill of Rights for Citizens.” The pattern is familiar: massive public investment in facilities that consume enormous energy, employ remarkably few people, and could become stranded assets if AI demand doesn’t sustain current projections.

And then there’s the debt. JPMorgan’s projection of $1.5 trillion in AI-related bond issuances by 2030 is held by institutional investors, insurance companies, and pension funds. That debt isn’t inherently dangerous if it’s financing genuine productivity transformation. But if enterprise AI adoption continues to stall, with only 5% of pilots reaching production and 42% of initiatives being scrapped, then we’re left with bonds backed by phantom revenue. And the holders of that debt are often the same pension funds and insurance companies already overexposed to the Magnificent Seven through equity holdings. That’s a double exposure: the same retirement savers are concentrated in AI stocks and holding the bonds underwriting AI infrastructure. The 2008 parallel isn’t the technology; it’s the debt structure underneath it.

The Labor Gap

This is where the conversation gets personal, and where the cost becomes hardest to measure.

The public discourse on AI and jobs swings between two poles: “AI will take all the jobs” and “AI is just a tool.” Both are wrong. The reality is messier, more specific, and already underway.

Entry-level job postings in the United States have declined roughly 35% since January 2023, according to labor research firm Revelio Labs. That period also coincides with aggressive monetary tightening and a broader post-pandemic recalibration of labor demand, so AI isn’t the sole driver. But the structural pattern underneath the cyclical noise is clear. SignalFire found a 50% decline in new hires with less than one year of post-graduate experience at major tech companies between 2019 and 2024. Recent graduates aged 22 to 27 face unemployment rates near 5.8%, the highest since 2021, against an overall rate of 4.2%. This isn’t a future threat. It’s a present reality, largely invisible in headline employment data.

The mechanism is quiet and structural. Companies aren’t announcing mass layoffs; they’re simply not backfilling roles when people leave. Federal Reserve Chair Jerome Powell has described this as a “low-hiring, low-firing” equilibrium. It sounds stable. It’s not. It means the displacement is happening through absence rather than announcement, concentrated on the youngest and most vulnerable workers, eroding the bottom rungs of the career ladder without anyone noticing until the ladder is gone.

Customer service, content production, paralegal work, entry-level coding: specific categories are contracting. Not because AI fully replaces them, but because it reduces headcount enough to matter at scale. And there’s a perverse irony embedded in this dynamic. AI companies have a financial incentive to overstate displacement potential because it inflates their addressable market. The labor threat and the investment thesis are entangled. The same narrative that drives valuations also drives fear.

I know something about this firsthand. In September 2023, I was part of a reduction in force after 25 years in technology consulting. The industry I’d built a career in was restructuring, and AI was part of the reason. I had a choice: compete for a shrinking pool of traditional roles, or invest in understanding what was actually happening. I chose the latter, spending over a year researching the intersection of human cognition, AI architecture, and evolutionary biology. Not because I had a comfortable runway, but because I believed the only durable response to AI displacement was to understand the technology deeply enough to work with it rather than against it.

What struck me most during that period wasn’t the technology itself. It was the absence of any coherent framework to help people navigate the transition. The federal response, to this day, is plans about plans. The White House AI Action Plan, released in July 2025, proposes an “AI Workforce Research Hub” and “rapid retraining initiatives,” but these remain proposals, not funded programs. A mandated plan to reach one million new apprentices was due by late August 2025; as of this writing, no such plan has been published. The existing federal training infrastructure, primarily the Workforce Innovation and Opportunity Act, was designed for trade-related displacement and factory closures, not for the continuous, distributed, invisible erosion of knowledge work.

The geographic dimension compounds the problem. AI jobs concentrate in a handful of metros. Displacement is distributed broadly. It’s not hard to picture: a mid-sized city that lost its call center to automation, watched entry-level coding jobs evaporate through attrition, and offered tax incentives for a data center that consumed grid capacity, employed a few dozen people, and may not outlast its tax abatement. That city exists in every state. This mirrors the manufacturing hollowing-out pattern, except it moves faster and affects white-collar workers who assumed they were insulated.

The Governance Deficit

The economic consequences are serious. The political consequences may be worse.

Here’s what doesn’t get discussed enough: the infrastructure being built for the AI boom doesn’t disappear when valuations correct. After the dot-com bust, the fiber optic cables that bankrupt companies had laid were bought for pennies on the dollar and became the backbone of the modern internet. That was mostly benign. The AI equivalent is massive behavioral datasets, surveillance capabilities, and algorithmic decision-making systems. When the correction comes, these assets don’t evaporate. They get acquired at fire-sale prices by buyers whose purposes may have nothing to do with the productivity narrative that justified building them in the first place.

When populations lose economic leverage through job displacement while simultaneously living under systems designed to monitor and optimize them, the conditions for democratic governance weaken. This isn’t dystopian speculation. It’s the observable pattern in every context where economic marginalization and pervasive surveillance overlap. The question isn’t whether these tools will be misused; it’s whether the institutional safeguards exist to prevent it.

They largely don’t. The United States has no comprehensive federal AI law. The Trump administration revoked Biden-era AI safety requirements on its first day in office and has since signed an executive order directing the Attorney General to establish an AI Litigation Task Force to challenge state-level AI regulations deemed “inconsistent” with federal policy. Over 1,000 AI-related bills were introduced across states in 2025, reflecting genuine demand for oversight. The federal response has been to threaten litigation against the states attempting to fill the vacuum while offering nothing comparable in return.

The net effect is a moral hazard: the companies whose concentration of power most warrants oversight are shielded from local accountability, while the communities bearing the grid strain, the energy costs, and the employment shortfalls have no federal framework to fall back on.

This is where the augmentation framework from the first piece in this series becomes more than an architectural preference. Systems designed to augment human decision-making preserve human agency by design. They keep humans in the loop not as a nicety but as a structural feature. Systems designed to replace human decision-making don’t. The governance question isn’t just “how do we regulate AI?” It’s whether we’re building systems that are compatible with democratic self-governance at all.

The Concentration Problem

Whether the AI boom continues or corrects, the infrastructure is concentrating in very few hands. Microsoft, Google, Amazon, and a handful of others control the foundational compute layer. This is the equivalent of three companies owning the roads, the ports, and the electrical grid simultaneously.

The most promising counterforce is open-source AI. As I noted in the second piece, open-source models are closing the performance gap with closed-source leaders, and enterprises are increasingly drawn to smaller, specialized, fine-tuned models. But open-source AI depends on compute access, which circles back to the same hyperscalers. Distributed architecture requires distributed infrastructure. Without it, “open” is a label, not a reality.

The CHIPS Act and semiconductor export controls, discussed in the second piece, are framed as competitive tools against China, not as instruments for domestic market structure. The United States is subsidizing concentration while calling it competition. The alternative, public investment in open compute infrastructure, antitrust enforcement that addresses AI-specific concentration, data portability requirements that prevent lock-in, remains largely unexamined in policy circles.

Meanwhile, China isn’t pursuing a single scaling approach. It’s diversifying architecturally while maintaining state oversight of strategic technology. Europe can’t compete in the capital-intensive scaling race but could lead in alternative architectures and governance frameworks. The United States is currently locked into the scaling paradigm by the very corporate interests that benefit most from it.

What Augmentation Actually Requires

This series began with a technical argument: the scaling skeptics are right, and Augmented Human Intelligence offers a more promising architectural path than the brute-force pursuit of AGI. The second piece showed that the financial structure built on the AGI thesis is fragile and concentrated. This piece has argued that the human consequences of getting this wrong fall disproportionately on people who had no say in the bet.

Those three threads converge on a single point. AHI isn’t just a better technical framework. It’s the only path that keeps humans economically relevant, politically empowered, and socially integrated in an age of increasingly capable machines. But it won’t happen by default. The economic incentives, the capital flows, and the policy environment all currently favor concentration, replacement, and scale.

What augmentation actually requires is deliberate choice at every level. Architectural choices: modular, specialized, human-in-the-loop systems rather than monolithic models chasing general intelligence. Policy choices: antitrust enforcement, public compute infrastructure, workforce transition programs that match the scale of the disruption. Ownership choices: open-source development, distributed access, accountability structures that prevent the technology from concentrating power in the hands of those who build it.

None of these are inevitable. All of them are possible.

I mentioned earlier that I was part of a reduction in force in September 2023. What I didn’t say is where that led. Rather than compete for a shrinking pool of roles in an industry being restructured around me, I spent over a year building exactly the kind of human-AI collaboration I’ve been advocating for. A system called MARS (Multi-Agent Research System) that monitors AI-related content, scores it against my research corpus, and generates strategic engagement opportunities. This entire series, the research synthesis, the financial analysis, the policy arguments, was produced through that system. I used AI tools at every stage of retrieval, drafting, fact-checking, and revision. But the judgment, the framing, the argument, the decision about what matters and why: that’s mine. The AI handled pattern recognition and tireless iteration. I handled meaning-making.

I’m not offering a theoretical framework. I’m offering evidence. This is what augmentation looks like in practice, and it’s available to anyone willing to invest the time in understanding these tools deeply enough to direct them rather than be directed by them.

The orchestra can keep expanding. Or we can decide what music we actually want to hear, and who gets to play.

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Michael Burry, the investor made famous by The Big Short for calling the 2008 housing crash, is shorting Nvidia and Palantir. He might be early. He might be wrong. But the fact that serious money is now betting against the AI boom tells us something worth examining.

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This isn’t a crash prediction. It’s a structural analysis. And the structure, when you look at it clearly, is more fragile than the headlines suggest.

The Bet That Built This Boom

The AI investment thesis rests on a chain of assumptions: scaling works, AGI is near, winner-take-all dynamics will reward the leaders, and therefore almost any level of investment is justified.

Hundreds of billions have been deployed on this logic. OpenAI’s valuation assumes it will capture a significant share of all future knowledge work. Nvidia’s market cap assumes AI infrastructure spending will continue accelerating for years. The hyperscalers (Microsoft, Google, Amazon, Meta) are spending at rates that only make sense if AI transforms their core businesses.

The bet is that training costs are front-loaded and inference revenue will follow. Build the models now, monetize them later. It’s a reasonable bet. But it is a bet.

The bet is also being reinforced from outside the market. The CHIPS Act, export controls on advanced semiconductors to China, and bipartisan rhetoric framing AI as a strategic national asset have created a policy environment that backstops spending regardless of near-term ROI. When national security and economic competition merge with a technology thesis, capital flows become stickier and harder to redirect, even when the underlying assumptions weaken.

The Cracks in the Thesis

The scaling hypothesis, the idea that more data and compute reliably yield smarter models, is under pressure. As I explored in my [previous post on the scaling skeptics], serious researchers are now questioning whether current architectures can reach general intelligence at any scale. Diminishing returns have arrived faster than expected.

Meanwhile, enterprise ROI isn’t materializing the way vendors promised. MIT’s 2025 “GenAI Divide” study found that 95% of enterprise AI pilots deliver zero measurable impact on the bottom line; only 5% reach production. S&P Global reported that 42% of companies scrapped most of their AI initiatives in 2025, up from just 17% the year before. The pattern is familiar to anyone who’s watched technology adoption cycles: impressive demos, difficult deployment, unclear value.

The revenue picture is also lopsided. Nvidia is selling shovels in a gold rush, and making historic profits doing it. But who’s finding gold? OpenAI reportedly lost $5 billion in 2024, and the trajectory is worsening: Microsoft's SEC filings reveal that OpenAI lost approximately $12 billion in a single quarter in 2025, against roughly $4.3 billion in revenue for the entire first half of the year. Most AI startups are burning capital with no clear path to profitability. The infrastructure providers are thriving; the companies building on that infrastructure mostly aren’t.

There’s a persistent gap between what AI can do in a demo and what it can do in production. Benchmarks improve; reliability doesn’t. Hallucinations persist. Enterprise customers discover that “90% accurate” isn’t good enough for mission-critical workflows.

There’s also a competitive assumption baked into these valuations that may not hold. The investment thesis requires winner-take-all dynamics, but the market is showing signs of fragmentation. Open-source models are closing the performance gap with closed-source leaders. Inference costs are dropping fast. Enterprises are increasingly drawn to smaller, specialized, fine-tuned models rather than monolithic general-purpose ones. If the market fragments rather than concentrates, valuations built on “capturing all future knowledge work” start to look very different.

The technical skeptics I profiled in last week’s piece aren’t just academics arguing about architecture. Their critiques have financial consequences. If LeCun and Chollet are right that brute-force scaling won’t yield general intelligence, then the entire investment thesis, which requires something approaching AGI to justify current spending, rests on a technical error. The market is pricing in a future that may be architecturally impossible.

The Structural Fragility

Zoom out and the concentration risk becomes visible.

The Magnificent Seven tech companies increased their energy consumption by 19% in 2023 while the median S&P 500 company’s consumption stayed flat. Roughly 80% of U.S. stock market gains in 2025 were tied to AI-related companies. That’s not a broad-based technology boom; it’s a narrow bet by the entire market on a single thesis.

JPMorgan estimates that AI-related investment-grade bond issuances could reach $1.5 trillion by 2030. Much of this debt is predicated on productivity gains that may or may not materialize. If the gains don’t come, the debt doesn’t disappear. And the infrastructure itself has hard physical limits: gas turbines require three to four year lead times, and new nuclear capacity takes a decade or more. Capital can move fast; power plants can’t.

The question isn’t whether AI is useful; it clearly is, in specific applications. The question is whether the valuations, the infrastructure spending, and the debt levels are proportionate to the actual value being created. Right now, a lot of capital is chasing returns that require the scaling hypothesis to hold, enterprise adoption to accelerate, and competitive moats to emerge. All three are uncertain.

Historical Parallels (And Their Limits)

We’ve seen this pattern before.

The dot-com boom left behind real infrastructure: fiber optic cables, data centers, a generation of internet-native companies. Most of the companies that raised money during the bubble failed, but the technology was real and eventually transformed the economy.

The crypto boom left behind less. Blockchain has use cases, but the speculative frenzy produced more fraud than lasting value.

AI will probably land somewhere between. The technology trajectory resembles the dot-com era: real and ultimately transformative. But the valuation structure currently looks more like crypto: speculative and decoupled from cash flow. The technology is real. Transformer architectures, large language models, diffusion models; these are genuine innovations with genuine applications. But genuine innovation doesn’t guarantee that current valuations are rational, that current market leaders will dominate long-term, or that the timeline to profitability is what investors are pricing in.

Useful and overhyped are not mutually exclusive.

What I’m Watching

A few signals matter more than headlines:

Enterprise adoption vs. churn. Are companies moving from pilots to production, or quietly shelving experiments? Renewal rates will tell the real story.

Hyperscaler capex. When Microsoft, Google, or Amazon start trimming AI infrastructure spending, the narrative will shift fast. They have better visibility into actual demand than anyone.

Regulatory attention. As valuations and market concentration grow, scrutiny from antitrust bodies (FTC, DOJ, EU) or financial regulators (SEC) could significantly impact the narrative and business models.

The language shift. Listen for when “AGI in two years” becomes “useful tools that augment workflows.” The rhetoric is a leading indicator. When the people selling the dream start hedging, pay attention.

I’m not predicting a crash date. Markets can stay irrational longer than skeptics can stay solvent; Burry himself has learned that lesson more than once.

But I’ve spent 25 years watching technology hype cycles, and I know what structural fragility looks like. This is it. A narrow set of companies, a contested thesis, valuations that require optimistic assumptions to hold, and a growing chorus of informed skeptics.

The music might keep playing. But it’s worth understanding the warning signs, and where the exits are.

Next week: Financial fragility makes headlines, but the deeper question is who bears the cost when the correction comes. In “After the Music Stops,” I look beyond investor losses to the workers, communities, and democratic institutions caught in the undertow of a technology transition that was never designed with them in mind.

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The dominant AI narrative is seductively simple: scale is all you need. More data, more compute, more parameters, and intelligence emerges. It’s the story that’s justified hundreds of billions in investment, reshuffled the power structure of Silicon Valley, and convinced a generation of executives that AGI is just a few training runs away.

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But a growing chorus of serious people disagree. Not AI skeptics or Luddites, but pioneers, Turing Award winners, people who built the foundations of what we’re now scaling. Their critiques are converging on an important point. And what they’re circling around might matter more than any of them are quite saying.

Yann LeCun: World Models, Not Word Models

LeCun, a Turing Award laureate and former chief AI scientist at Meta, left in November 2025 to start AMI Labs, valued at $3.5 billion. His thesis: LLMs are “an off-ramp on the road to human-level AI.”

His argument is architectural. LLMs predict text; they model language, not reality. They can pass the bar exam while lacking the common sense of a house cat. They hallucinate because they have no grounding in how the world actually works.

What’s needed, LeCun argues, are “world models”: systems that build internal representations of reality, that can simulate, predict, and plan. Not language models but cognitive models. His advice to PhD students:

“LLMs are useful, but they are an off-ramp on the road to human-level AI. If you are a PhD student, don’t work on LLMs.”

Gary Marcus: Pattern-Matching Is Not Reasoning

Marcus, a cognitive scientist, NYU professor emeritus, and author of Taming Silicon Valley, has been the most persistent public critic of the scaling hypothesis. He’s also been the most vindicated.

His core claim: LLMs are sophisticated pattern-matchers, not reasoners. They interpolate within their training distribution but fail outside it. Change a classic reasoning problem slightly, reword it, alter the setup, and performance collapses. This isn’t a bug to be fixed with more data. It’s architectural.

Hallucinations, in Marcus’s view, are unfixable within the current paradigm. And the stakes are real: in medicine, law, and finance, even a 1% hallucination rate can be catastrophic. Systems that traffic only in the statistics of language, without explicit representations of facts or tools to reason over them, will never be reliable. The solution isn’t more scale; it’s hybrid architectures that combine neural networks with symbolic reasoning. The kind of thing AI abandoned decades ago in the rush toward end-to-end learning.

Rodney Brooks: The Competence Projection Problem

Brooks, an MIT roboticist, founder of iRobot and Robust.ai, and builder of more humanoid robots than perhaps anyone alive, brings the engineer’s skepticism.

His critique is partly about us, not just the technology. When humans see an AI perform a task impressively, we instinctively generalize. We project a sphere of competence around that performance, assuming the system understands in a human-like way. We’re almost always wrong.

LLMs are useful, Brooks says, but narrow. The problem with robotics isn’t language interfaces; it’s control theory, optimization, the hard math of operating in physical reality. Language won’t help you ship 10,000 warehouse orders in two hours; data processing and planning will.

His broader point: 76% of AI researchers surveyed by the AAAI agree that scaling current approaches alone won’t yield AGI (AAAI 2025 Presidential Panel). The insiders know. The narrative hasn’t caught up.

François Chollet: Fluid Intelligence and the ARC Test

Chollet, creator of Keras and now running the ARC Prize Foundation, offers the most precise diagnosis.

He distinguishes between crystallized intelligence (accumulated knowledge, memorized patterns) and fluid intelligence (the ability to adapt to genuinely novel problems). LLMs excel at the former. They’re vast repositories of crystallized knowledge, able to retrieve and recombine what they’ve seen. But fluid intelligence, the thing that lets a child solve a puzzle they’ve never encountered, is largely absent. As Chollet puts it:

“Memorization is useful, but intelligence is something else.”

His ARC benchmark tests exactly this: abstract reasoning tasks are trivial for humans, but brutal for LLMs. GPT-4o scores around 5%. Humans score 84%. The gap isn’t closing with scale.

Chollet’s prescription: program synthesis. Systems that can construct new cognitive programs on the fly, recombining primitives to handle novel situations. “Memorize, fetch, apply” won’t get us there. We need systems that can genuinely invent.

Where They Converge

These critics come from different traditions (cognitive science, robotics, deep learning research), but their diagnoses are converging:

* Brute-force pre-training scaling is hitting diminishing returns. The spectacular gains of 2020–2023 have flattened. Each new model is marginally better at an enormous cost.

* Current benchmarks mask fundamental limitations. High scores on contaminated tests don’t equal general intelligence.

* World models and grounding are essential. Systems need to represent reality, not just language about reality.

* Architectural innovation is required. More of the same won’t work. Something structurally different is needed.

There’s also an uncomfortable economic dimension here. The scaling narrative isn’t just a technical hypothesis; it’s the justification for hundreds of billions in concentrated infrastructure investment. When the thesis and the money are this entangled, technical course corrections become financially painful. That structural fragility is worth examining on its own terms, and it’s where I’m headed next week.

Where They Diverge

The agreement ends at prescription. LeCun wants embodied world models trained on sensory experience. Marcus wants symbolic-neural hybrids that can reason explicitly. Brooks wants constrained domains with realistic expectations. Chollet wants program synthesis and test-time adaptation.

These aren’t necessarily incompatible; they might all be part of whatever comes next. But there’s no unified alternative vision yet, just a shared sense that the current path is incomplete.

What They’re Not Quite Saying

Here’s what strikes me: the skeptics are all still focused on building artificial general intelligence. Better AI. Smarter machines. Systems that can eventually match or exceed human cognition.

But what if the frame itself is wrong?

What if the goal isn’t to replace human intelligence but to augment it? Not AGI but AHI: Augmented Human Intelligence. If LeCun is right that LLMs lack world models, and Chollet is right that they lack fluid intelligence, then their best use isn’t to replace the human driver but to serve as a high-powered navigation system. Modular AI architectures are designed to amplify human capability rather than substitute for it. Systems that handle what they’re good at (retrieval, pattern recognition, tireless execution) while humans handle what we’re good at (judgment, meaning-making, genuine novelty).

The scaling skeptics have correctly diagnosed the disease: current approaches won’t yield general intelligence. But they’re still chasing the same endpoint. The alternative I’ve been exploring in this series is different: not smarter machines, but smarter human-machine partnerships. Architectures inspired by how the brain actually works: modular, specialized, integrated through attention rather than unified in a single massive model. [Link to earlier AHI posts]

This doesn’t mean AHI is without its own hard questions. Who gets augmented? Who controls the tools? Any architecture that amplifies human capability will amplify it unevenly unless we’re deliberate about access and governance. But those are design problems, solvable ones, not the fundamental architectural dead-end that monolithic scaling represents.

This is precisely the kind of architectural shift the critics call for. Their critiques of monolithic scaling point directly toward modular, human-centered design. The pieces are all there. They just haven’t been assembled this way.

The scaling-is-everything narrative is cracking. What comes next is genuinely open. That’s worth paying attention to, and, for those of us thinking about it, worth shaping.

Next week: If the scaling thesis is cracking, what happens to the hundreds of billions riding on it? In “When the Music Stops,” I examine the structural fragility of the AI boom, from concentrated market bets and ballooning infrastructure debt to the gap between demo magic and enterprise reality. The technology is real. The question is whether the valuations are.

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I ended with a question: Could the same tools flooding us with content also help us navigate it?

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This post is my answer.

The Needle and the Haystack

We live in a world of infinite language.

No human can process it all, nor should they. Most of it is, at best, meaningless to any individual. At worst, it’s noise, pollution, or manipulation clogging the channels where meaning once flowed.

I talk about disciplining attention by being deliberate. But it’s hard. Every day, the same question: In this endless sea of language, where should I focus? How do I find the needles in the haystack?

For months, I scrolled and skimmed, hoping algorithms would surface something worthwhile. Occasionally, they did. Mostly, they didn’t.

Then I realized: I was asking the wrong question.

What LLMs Are Actually Good At

I’ve argued that LLMs won’t achieve AGI. They don’t reason like humans. They don’t truly understand.

But here’s what they are good at: processing language. Pattern matching. Summarizing. Filtering. Categorizing.

These aren’t the glamorous capabilities that dominate AI headlines. No one writes breathless articles about “AI that sorts your reading list.” But for the problem I faced (too much language, too little attention), these are exactly the tools I needed.

This is the AHI thesis in practice: use AI to extend human capabilities, not replace human judgment. Let the machine do what machines do well (process volume) so the human can do what humans do well (decide what matters).

Building a Filter, Not a Firehose

Here’s what I did.

I wanted to engage with writers whose work intersects with mine: AI, attention, cognition, architecture. But finding them manually, keeping up with their output, and identifying meaningful conversations: that was a full-time job.

So I built a simple system. Nothing fancy, but deliberate.

First, I curated a list of authors. Not everyone writing about AI, only those whose thinking genuinely aligns with mine, whose audiences might value my perspective. This required human judgment. No algorithm could tell me who I respect.

Second, I built a corpus of my own work. My Substack posts, whitepapers, and core ideas. This corpus gave the system a reference point: What does my thinking sound like? What themes do I care about?

Third, I created a scoring mechanism. When a new post from someone on my list appears, the system evaluates its relevance to my corpus. How much overlap? How timely? The output isn’t a binary “read or skip.” It’s a score from 0 to 100, with a ‘worth-a-look’ threshold currently set at 90 or higher: any article that reaches or exceeds it is identified as a potential engagement opportunity and therefore worthy of review and potentially posting a comment.

The system doesn’t tell me what to think. It doesn’t write my responses (although it can certainly offer potential starting points - more on this later). It just filters the infinite down to the manageable so that I can do the actual intellectual work.

I’m using the very thing that caused the flood to build a personal lifeboat.

And that’s precisely the point.

How I Built My Filter (A Practical Pattern)

If you’re curious about the mechanics, not as a prescription, but as a pattern, here are the steps I followed:

Define your “needle.” I started by clarifying what I was actually looking for: writers exploring the intersection of AI, cognition, and meaning. Not “everything about AI,” but my slice of it. Your needle will be different.

Gather your own voice. I fed my system a corpus of my own writing: Substack posts, white papers, notes, and key ideas. This corpus provided a reference point for relevance: “Find things that resonate with this.” Your corpus is your intellectual fingerprint.

Curate, don’t collect. I handpicked a starter list of thinkers whose work I genuinely respect. No algorithms. The starter list is a human gate, built once, then scaled. Your list should feel like a dinner party invitation list, not a block party.

Score for signal, not popularity. My system ranks new content by thematic overlap, not engagement metrics. It asks: “How much does this align with what you care about?” Your scoring should reflect your priorities, not the platform’s.

Build an interface you’ll actually use. Raw output from a script isn’t enough. I built a simple web interface to review, sort, and manage the authors list, the scoring results, and record actual engagements (comments that I post). If you can’t easily see and interact with the insights, you won’t use the system. Make it visible. Make it yours.

Preserve the human moment. The output isn’t a command. It’s a filtered feed. I still choose what to read, how to engage, and whether to respond. Your judgment stays in the loop.

Iterate relentlessly. My first pass wasn’t right. I’ve refined my author list, adjusted my scoring weights, and improved the user interface as I learned what I actually needed. The system that I have built isn’t a finished product. It’s a living system that evolves as my thinking does. Your filter should grow with you.

The tools are simple: RSS feeds, a language model API, a basic script, and a lightweight web app. The philosophy is what matters: AI as a lens, not a source.

The Discipline Still Required

Here’s what AI can’t do for me:

It can’t decide what I actually care about. It can’t distinguish deep insight from clickbait in intellectual clothing. It can’t write a thoughtful response that truly reflects my voice, my personal experience, and my ever-evolving point of view. It can’t build real relationships with other thinkers.

All of that remains mine.

What AI can do is reduce the search cost. It helps me spend less time looking and more time thinking. It’s a filter, not a replacement for judgment.It can suggest potential starting points for me to consider, or more often than not, not consider. My process is:

* Read the articles that exceed my scoring threshold (90/100)

* Make my own notes about the thoughts that the article evokes in me, and parallels to the work that I have produced previously

* View any system-generated starting points, and consider how, if at all, they might echo my personal gut reactions

* Work to develop a final comment that considers all inputs, while ensuring the article is my work product and that I reference my own point of view and personal experience (just as I am doing right here).

* My experience has been that more than 85% of the time, my own notes are the only input to the final comment. This is critical for me, as my corpus update process incorporates all of my comments as well as new articles and whitepapers written since the last update. I have also observed occasional hallucinations in the ‘target passage’ identification part of the system generated suggested starting points.

This process is constraint-awareness applied to tools: knowing what to delegate and what to keep. Using AI to extend attention, not abdicate it.

An Invitation to Experiment

I haven’t solved the attention crisis. I’ve built one small tool for one specific problem. It helps me. It might not help you.

But the principle generalizes.

If you’re drowning in content, ask yourself: What’s my needle? What’s my haystack? What would it look like to use AI as a filter rather than a generator? Where could pattern-matching reclaim time for deeper thinking?

The flood won’t stop. The language will keep coming. But we’re not helpless. The same technologies that are accelerating the crisis might, if used carefully, help us navigate it.

Not by processing more, but by attending better.

This is the fifth essay in my series on language, attention, and the architecture of shared meaning. Previous posts: [Part I: The Constraints We Lost] • [Part II: Serviceability Failure] • [Part III: Wisdom in an Infinite-Language World] • [Part IV: We Are the Problem]

Next week in ‘More Instruments, Same Song’ I will summarize the growing number of respected AI experts that question the ‘Bigger is Better’ mantra for models, compare my critiques to these experts’, and share our observations about what might drive the next breakthroughs in the field of AI.

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And yet.

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Over the past three months, I’ve been building a publishing cadence. Website, Substack, LinkedIn, X. The goal was one substantial essay per week, reasonable, intentional, aligned with the very restraint I was preaching.

But on multiple occasions, I’ve felt the pull to do more. To post more frequently. To engage more visibly. To hit LinkedIn’s little recommendation boxes for commenting, reacting, and connecting.

Why? Because I can. Because everyone else seems to be doing it. Because the platform actively encourages it, nudging me toward behaviors that serve its interests (more engagement, more time on site, more data) rather than mine.

A few days ago, I saw a post from someone explaining why they’re leaving LinkedIn. The reason: the signal-to-noise ratio has collapsed. The feed is now synthetic content, engagement farming, posts optimized for clicks rather than insight.

Reading it, I recognized the problem immediately.

Then I recognized myself as part of it.

The Systemic Trap

This is the thing about systemic problems: they’re made of us.

Every individual making a locally rational choice (post more, engage more, feed the algorithm) produces a collectively irrational outcome. We’re all contributing to a flood that’s drowning us all.

You can understand the dynamic completely and still be caught in it. That’s what makes it a trap.

A few weeks ago, I reached out to Gary Marcus, one of the sharpest critics of AI hype, to ask if he might review something I was working on. He graciously replied that he was sorry, but overwhelmed.

When I asked if he could recommend someone else, he said something that’s stayed with me:

“We are all overwhelmed.”

Think about that.

Here is someone who understands the attention economy and AI-generated content better than almost anyone. Someone who has built a career diagnosing these very dynamics.

And he’s telling me that he and his peers are drowning.

If the experts who study this problem are themselves overwhelmed by it, that’s not irony. That’s the signature of a genuine systemic trap.

The Gap Between Knowing and Doing

In my series, I argued that the collapse of external constraints (throughput, gatekeepers, locality, friction) has left us navigating an infinite-language world with finite attention.

The algorithm isn’t forcing me to post. It’s nudging me. And I’ve been following the nudge, even while writing about the importance of not following it.

The Greeks had a word for this gap between knowing and doing: akrasia, the act of acting against your own better judgment. It’s not hypocrisy. It’s the human condition when incentives and wisdom point in opposite directions.

The platform rewards volume. Wisdom counsels restraint. And the platform is right there, every day, with its little dopamine hits and recommendation checklists, while wisdom is quiet and offers no metrics.

A Small Commitment in a Large System

I don’t have a complete solution to the collective action problem. I can’t fix the algorithm or convince everyone to post less.

But I’m starting to wonder whether the same tools flooding us with content might help us navigate the flood, whether there’s a way to fight fire with fire, carefully. I’ll have more to say about that soon.

For now, I can decide on my own contribution.

So here’s my commitment, stated publicly so I’m accountable to it:

One Substack essay per week, if I have something worth saying. If I don’t, I skip the week. No posting for the sake of posting. No engagement farming. No, letting the platform’s goals become my goals.

This is modest. It won’t change the system. But constraint-awareness has to start somewhere, and it might as well start with the person advocating for it.

We are the problem. Which means we’re also, in small ways, the beginning of any solution.

An Invitation

I urge you to consider your own relationship to the feed. Are you posting because you have something to contribute, or because the algorithm told you to? Are you adding signal, or noise?

The attention commons is finite. Every one of us is either stewarding it or depleting it.

Choose carefully.

And maybe, just maybe, join me in practicing a little generative restraint. Not as a boycott, but as a quiet act of repair.

Because the flood won’t stop until enough of us decide to build our own dams.

This essay stands alongside my three-part series on language, constraints, and the crisis of shared meaning: [Part I: The Constraints We Lost] • [Part II: Serviceability Failure] • [Part III: Wisdom in an Infinite-Language World]

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The instinctive response to a system in crisis is to restore what was lost.

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If the problem is that gatekeepers are gone, rebuild the gates. If the problem is that anyone can publish anything, create centralized fact-checking regulators. If the problem is that we’re drowning in noise, retreat to informational bunkers where only trusted sources are allowed.

These responses are understandable. They’re also insufficient. They attempt to rebuild walls in an open field, to reimpose external constraints on a system that has already evolved beyond them. The gates cannot be closed. The friction cannot be restored by fiat. The global, instantaneous, algorithmically-driven flow of language is not a temporary condition we can reverse; it’s the environment we now inhabit.

So what do we do?

The answer, I think, lies in a shift of locus. If external constraints can no longer stabilize the linguistic interface, then the stabilizing function must move inward: from the environment to the mind, from architecture to practice, from imposed limits to cultivated awareness.

The word for this is wisdom. But we need to define it carefully.

Wisdom Redefined

Wisdom has a mystical reputation it doesn’t deserve. We tend to imagine it as something possessed by sages on mountaintops, accumulated through decades of suffering, or granted by spiritual insight. This framing makes it feel inaccessible, perhaps even irrelevant to the practical challenges of navigating a noisy information environment.

But there’s another way to understand wisdom: as constraint-awareness.

Constraint-awareness is the capacity to recognize the conditions that shape your own perceptions, beliefs, and communications. It involves three interlocking recognitions:

First, recognizing that language is an interface, not a window. What you read, hear, and speak is not unmediated reality; it’s a translation, shaped by perspective, optimized for social function, always partial. This is accepting that every statement, including your own, comes from “a certain point of view.”

Second, mapping what’s missing. The linguistic environment we inhabit is historically unprecedented. The constraints that shaped discourse for millennia (limited throughput, gatekeeping bottlenecks, local accountability, production friction) have been removed. Understanding this helps explain why the information environment feels so disorienting: it’s not that people have become worse; it’s that the stabilizing structures have vanished.

Third, voluntarily simulating constraints. This is the practical core. If external constraints no longer impose limits, we must impose them ourselves, not to retreat from the modern world but to navigate it with agency and clarity.

This reframing clarifies an ancient distinction. Knowledge is the accumulation of facts and models; LLMs scale it infinitely. Intelligence is pattern recognition and inferential speed; LLMs simulate it convincingly. Wisdom is the regulation of knowledge and intelligence in light of their limits. It asks: given that my interface is partial, my information is infinite, and my time is finite, how should I direct my attention and shape my judgments?

Wisdom is not anti-technology. It’s the skill that makes technology usable without being overwhelmed by it.

Four Practices

Constraint-awareness isn’t a passive trait. It’s cultivated through practice. In an infinite-language world, these practices become essential disciplines, each one a voluntary replacement for a constraint that’s been lost.

Attentional Friction (replacing throughput)

The old throughput constraint meant that information arrived slowly, giving you time to digest it. That constraint is gone. The replacement is deliberate slowness: choosing depth over breadth, engaging with long-form arguments rather than skimming headlines, creating periods of informational silence.

This isn’t about consuming less (though it might involve that). It’s about consuming differently. It means finishing the article before forming an opinion. It means sitting with uncertainty rather than reaching for the first confident take. It means treating your attention as a finite resource that deserves protection.

The practice: build friction back into your information diet. Turn off notifications. Schedule time for deep reading. Resist the pull of the feed.

Deliberate Curation (replacing bottlenecks)

The old bottleneck constraint meant that gatekeepers filtered what reached you. Those gatekeepers were imperfect, often biased, and sometimes corrupt. But they performed a curation function that has now been handed to algorithms optimized for engagement, not understanding.

The replacement is choosing your own curators: scholars, journalists, thinkers, and artists whose judgment and values you respect. Not because they’re always right, but because their filtering reflects human discernment rather than algorithmic amplification.

This requires active maintenance. Audit your information sources periodically. Ask: Who am I allowing to shape my attention? Are they chosen, or did they arrive through the path of least resistance?

The practice: build a personal cabinet of curators. Diversify it deliberately. Return to it when the noise becomes overwhelming.

Locality Building (replacing locality)

The old locality constraint meant that communication was embedded in ongoing relationships. If you said something false or harmful, you faced your community the next day. Accountability was built into the structure.

That accountability has dissolved into global, often anonymous networks. The replacement is intentional investment in bounded communities (physical or digital) where ideas can be tested, challenged, and refined over time, and where participants have reputations to maintain.

This doesn’t mean retreating to echo chambers. It means finding spaces where disagreement is possible but accountable, where you’re known well enough that your words carry weight and consequence.

The practice: invest in communities where you’re a participant, not just a consumer. Prioritize spaces that reward thoughtfulness over virality.

Generative Restraint (replacing friction)

The old friction constraint meant that producing and distributing language required effort. That effort served as a natural filter: if you were going to speak publicly, you probably had something to say.

With friction eliminated (and LLMs reducing the cognitive cost of generation to zero), the replacement is voluntary restraint. This means resisting the impulse to add to the noise, valuing thought before speech, and recognizing that not every reaction needs to be expressed.

When using LLMs, this means treating them as tools for exploration and questioning rather than as oracles. It means maintaining your own judgment rather than outsourcing it to fluent-sounding output.

The practice: before speaking (or posting, or generating), ask whether this adds signal or noise. Develop comfort with silence.

The Social Dimension

Individual wisdom, while necessary, is insufficient. The serviceability failure of the linguistic interface is systemic. Its repair requires not just wiser individuals but cultural norms and institutions that promote constraint-awareness at scale.

This suggests some design principles for a healthier information architecture:

Platforms that reward deliberation over virality. The current incentive structure optimizes for engagement, often at the expense of outrage, conflict, and tribal signaling. Different designs are possible. They would be less profitable under current models, but less destructive.

Educational models that teach epistemic humility alongside factual content. Knowing facts is not enough. Students need to understand how knowledge is constructed, how narratives are shaped, and how their own cognition can be manipulated. Source literacy, media ecology, and the history of propaganda should be core curriculum, not electives.

Interfaces that reveal uncertainty rather than hiding it. Current information systems present polished, confident outputs. A constraint-aware design would show process, surface disagreement, and make the partiality of any perspective visible rather than concealed.

The goal is not to make the world simple again. It’s to build tools and norms that help us be thoughtful in a world of overwhelming complexity.

The Ultimate Constraint

Beneath all the constraints we’ve discussed lies one that cannot be removed: human attention.

Attention is finite. It is the bottleneck through which all experience must pass. No technology can expand it; technology can only compete for it.

Constraint-awareness, at its core, is the stewardship of attention. It’s the recognition that every click, every minute of reading, every engagement is a vote for the kind of reality we’re constructing, both in our own minds and in the collective intersubjective space.

This is where the structural argument becomes personal. The crisis of language is, in the end, a crisis of attention. The question “what should I believe?” is downstream of the question “what should I attend to?” And that question is answered not once, in some grand philosophical moment, but thousands of times a day, in small choices that compound.

Wisdom, therefore, becomes the practice of allocating attention in ways that repair rather than fracture the shared interface. It’s the application of finite consciousness to infinite language with discernment, care, and recognition of profound limits.

Not Retreat, But Ascent

I want to be clear about what I’m not arguing.

This is not a call to abandon technology, retreat to a simpler time, or pretend the constraints can be restored. The infinite-language world is here. LLMs are not going away. The flat landscape of frictionless generation is the terrain we must navigate.

What I’m arguing is that navigating this terrain requires a new kind of maturity. It asks us to become architects of our own attention and stewards of our shared epistemic commons. It asks us to do consciously and deliberately what the environment once did for us automatically.

This is not a retreat. It’s an ascent to a higher level of cognitive responsibility.

The most important design project of the coming century may not be a new AI model. It may be a new human-information interface: one built not for infinite engagement but for meaningful understanding, not for frictionless flow but for thoughtful integration.

The constraints are gone. Our awareness must now take its place.

The question we’re left with is simple to state and difficult to answer: Can a species that used language to build civilization now learn to use wisdom to preserve it?

I don’t know. But I think the attempt is worth making, one attentive choice at a time.

This is the final essay in a three-part series on language, constraints, and the crisis of shared meaning.

Next week: a confession. I've been writing about generative restraint while feeling the platform's pull to do the opposite. What happens when you recognize yourself as part of the problem you're diagnosing?

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In structural engineering, two of the ways a building can fail are instructive.

The dramatic one is collapse: the structure exceeds its load capacity and comes down. That’s what we fear, and that’s what building codes are designed to prevent.

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The subtler one is serviceability failure. The building stands. It passes inspection. But it no longer works as intended. At 432 Park Avenue, this means residents who feel seasick in their living rooms, elevators that won’t align with floors during storms, and a constant low groan that makes sleep impossible. The structure is safe. It’s just not livable.

Our linguistic infrastructure (the system of shared language through which we build trust, coordinate action, and construct common reality) is experiencing serviceability failure. It hasn’t collapsed. Words still work. People still communicate. But the system’s core function, enabling large groups of humans to agree on what’s true, what matters, and what to do about it, is degrading in ways that are hard to see and harder to fix.

The symptoms are everywhere. We just haven’t named them properly.

Symptom 1: The Fracturing of Shared Reality

The deepest symptom is the erosion of what philosophers call intersubjective reality: the realm of shared beliefs, norms, and stories that exists only because enough people agree to believe in it. Money lives here. So do laws, democratic legitimacy, and scientific consensus. These aren’t physical objects; they’re collective fictions that work because we maintain them together.

In a constrained system (slow throughput, gatekeepers filtering, local accountability, high production costs), the intersubjective foundation was relatively stable. Common narratives emerged because there were only so many narratives in circulation, and communities had time to converge on shared versions.

Today, with those constraints removed, we’re witnessing something different: the rapid construction of parallel realities. Algorithmic feeds and self-selected communities enable the formation and reinforcement of alternative factual universes. What was once a broadly shared “mainstream” (however imperfect) splinters into countless micro-narratives, each with its own axioms, heroes, and standards of evidence.

This is not pluralism. Pluralism requires a shared foundation of facts and a commitment to a common deliberative space. What we’re seeing is fragmentation: the loss of the foundation itself. When there’s no agreement on basic facts (the safety of vaccines, the outcome of an election, the historical record), the possibility of compromise or collective problem-solving is significantly degraded. The intersubjective commons, the cognitive public square where a society meets to hash things out, has been subdivided into private, walled gardens.

You can still talk to your neighbor. But you may no longer share a world with them.

Symptom 2: The Attention Crisis

If shared reality fractures at the societal level, attention shatters at the individual level.

Herbert Simon observed decades ago that “a wealth of information creates a poverty of attention.” What was once an economist’s adage is now a lived experience.

The prefrontal cortex, the seat of executive function and deliberate judgment, is a slow, energy-intensive system. It evolved for depth, not breadth; for sustained focus, not continuous partial attention. The unconstrained linguistic environment constantly summons it: claims, crises, outrages, narratives, each optimized to capture attention and demand a micro-decision about whether to engage or scroll past.

The result is chronic cognitive overload. The symptoms are familiar: the inability to concentrate on long-form texts, the anxiety of the endless “to-read” list, the compulsive checking of feeds, the feeling of being perpetually informed yet never quite understanding. Attention, the finite resource that directs intelligence, becomes so fragmented that deliberate thought becomes a luxury. We’re left in reactive mode, buffeted by waves of language, unable to secure the space required for synthesis.

The interface isn’t just overwhelming society; it's also overwhelming society. It’s overwhelming for the individual user.

Symptom 3: The Authenticity Paradox

When the linguistic interface fails to provide a stable shared reality, a compensating demand emerges: authenticity. Be real. Be yourself. Cut through the noise with something genuine.

The yearning makes sense. In a world where the old anchors of meaning have dissolved, we crave unmediated connection and a trustworthy signal.

But this ideal doesn’t stand up to scrutiny. Humans are social animals, exquisitely sensitive to audience and context. We adjust our self-presentation constantly: to build alliances, avoid conflict, signal belonging, and optimize social outcomes. This isn’t duplicity. It’s the software of a highly social species running as designed.

The demand for a context-invariant “authentic self” misunderstands this design. In practice, “authenticity” often becomes just another performative genre: a set of signals (casual dress, personal disclosure, performed vulnerability) that is itself curated for social reward. The workplace mandate to “bring your whole self to work” is the purest example of the paradox: an institution that requires role-playing and goal-oriented behavior officially endorsing an ideal that, if genuinely followed, would disrupt its functioning.

The paradox reveals something deeper. We crave unmediated connection, but we must seek it through language, a tool that, by design, always mediates, always translates. We’re asking the interface to deliver something it structurally cannot.

Symptom 4: The Wisdom Gap

The final symptom is the widening chasm between knowledge and wisdom.

Knowledge is abundant, accelerating, and increasingly outsourced. Facts are a click away. Intelligence, in the sense of pattern recognition and inferential speed, is being simulated and scaled by machines.

Wisdom is different. It’s the capacity to navigate uncertainty, recognize the limits of one’s own perspective, hold conflicting truths in mind, and prioritize long-term goals over short-term advantage. It requires slowness, reflection, and epistemic humility.

The unconstrained linguistic environment actively undermines these conditions. Wisdom needs quiet; the environment provides noise. Wisdom needs time; the environment demands reaction. Wisdom requires tolerance for uncertainty; the environment rewards confident, shareable takes.

We’re creating a world rich in information and synthetic intelligence, yet increasingly inhospitable to the slow, humble, integrative process that turns data into discernment.

The Accelerant: LLMs and the End of Friction

Into this already-strained system, large language models have arrived. They’re often discussed as a leap toward artificial general intelligence, a new form of reasoning, or an existential risk. Those debates have their place. But they can obscure a more immediate architectural fact: LLMs complete the demolition of linguistic friction.

Think of language production as climbing a friction gradient: a slope you have to work against. Scribes faced a steep climb (rare skills, expensive materials, slow copying). The printing press reduced the slope. Typewriters and word processors reduced it further. The internet collapsed distribution costs, but the gradient remained: generating coherent, persuasive content still required human cognition, time, and effort.

LLMs don’t just further reduce the slope; they eliminate the gradient entirely. The landscape is now flat. Language flows in every direction without resistance. The cognitive cost of producing fluent, seemingly knowledgeable text has dropped to zero.

This has three compounding effects:

First, volume overload. The quantity of plausible text increases exponentially. The “sea of noise” is now filled not just with human chatter but also with automated systems that never tire and can generate personalized streams for every individual.

Second, a persuasive scale. LLMs don’t just generate text; they generate rhetorically effective text. They can mimic authority, empathy, or conspiracy. They can tailor arguments to known biases and produce convincing fake supporting evidence. Industrial-scale disinformation becomes trivially accessible.

Third, the erosion of effort heuristics. Humans use cognitive shortcuts to navigate information overload. One key shortcut is “effort as credibility”: the assumption that a long article or detailed report signals invested effort and likely substance. LLMs destroy this heuristic. They can generate an “invested effort” signal with zero actual investment. A core tool for navigating the linguistic environment is rendered obsolete.

LLMs are not the cause of the epistemic crisis. They’re the accelerant that removes the last remaining point of friction and locks us into an infinite-language world with finite human attention.

The Plastic Failure Warning

So far, what we’re experiencing is serviceability failure. The system is uncomfortable, unreliable, and increasingly unfit for purpose. But it’s still standing.

The danger is that prolonged serviceability failure can escalate.

In structural engineering, plastic failure occurs when a material is stressed beyond its yield point and deforms permanently. A steel beam bent past its limit doesn’t spring back. Its integrity is compromised forever.

The equivalent risk for our epistemic infrastructure is this: if the core materials of society (trust, shared truth, good-faith disagreement) are strained beyond their yield point, the deformation may become permanent. Shared reality could fragment into mutually incomprehensible shards. The common ground required for large-scale cooperation could collapse and not return.

We’re not there yet. But the trajectory matters. A system in serviceability failure can be repaired. A system that has undergone plastic failure cannot return to its original shape.

The Question We’re Left With

The constraints that stabilized language for millennia are gone. We can’t restore them by fiat. The gates are open; the friction is erased; locality has dissolved into global, anonymous networks.

So what do we do?

The instinctive responses (centralized fact-checking, algorithmic censorship, retreating to informational bunkers) attempt to rebuild walls in an open field. They treat symptoms by reimposing external constraints on a system that has already evolved beyond them.

There may be another path: one that shifts the locus of stability from the environment to the mind. Not more information, not faster processing, but a different orientation entirely.

In the final essay, we’ll explore what that might look like.

This is the second in a three-part series on language, constraints, and the crisis of shared meaning. Next: “Wisdom in a World of Infinite Language.”

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Engineers have a term for this: serviceability failure. The structure stands, but it no longer serves its purpose.

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Something similar has happened to language itself.

For most of human history, we didn’t think of language as a system that could fail. It was just there, the medium through which we argued, promised, persuaded, and built civilizations. What we didn’t notice were the invisible constraints that kept it stable: limits on speed, gatekeepers who filtered, communities that enforced accountability, and the sheer difficulty of producing text at scale.

Those constraints are gone. And the system they supported has begun to sway.

Language as Interface

Here’s a reframe that might feel uncomfortable at first: language is not a window onto reality. It’s a dashboard.

The cognitive scientist Donald Hoffman argues that our senses didn’t evolve to show us truth, they evolved to keep us alive. What you see isn’t the world as it actually is; it’s a species-specific user interface, optimized for survival. The desktop on your computer doesn’t show you the actual voltage states of transistors; it shows you folders and trash cans because that’s useful. Your visual system works the same way. Fitness beats truth.

If perception is the interface for navigating physical reality, then language is the interface for navigating social reality, the world of alliances, obligations, status, and shared belief. Language lets us compress complexity (“trust,” “debt,” “law”), coordinate action (“meet me at dawn”), and manage relationships (apologies, promises, gossip). It’s the operating system of civilization.

But here’s the thing about interfaces: they can be overloaded. When the volume of signals exceeds the system’s processing capacity, the interface doesn’t crash dramatically. It degrades. It starts generating incompatible outputs for different users. It becomes unreliable precisely when you need it most.

For millennia, the linguistic interface was kept stable by four external constraints, load-bearing walls that we never noticed because they were always there. Let me walk you through what we’ve lost.

Constraint 1: Throughput

The speed at which meaning could travel.

For most of history, information moved at the pace of feet, hooves, and sails. Oral traditions were confined to memory and walking speed. Manuscripts required scribal labor; copying was slow and expensive. Even after Gutenberg, distribution meant physical logistics: wagons, ships, shops.

This slowness wasn’t a bug. It was a feature. Ideas spread gradually, giving communities time to digest, debate, and integrate them. Rapid informational shocks were rare. When a new claim appeared, there was time to test it against experience before it reached the next village.

The telegraph began the acceleration. Radio and television enabled one-to-many broadcast at the speed of light. The internet completed the transition: from many-to-many communication to instantaneous, global communication. The temporal buffer vanished entirely. News cycles collapsed from days to minutes. Narrative waves now form and crash in hours.

The human mind, evolved for deliberative pace, is forced into a continuous reactive mode. The “digestive” time required to separate signal from noise, test claims, and form a coherent understanding? Eliminated.

Constraint 2: Bottlenecks

The gatekeepers who filtered what reached the public.

Every society has them: elders, priests, scribes, publishers, editors, broadcast networks. These bottlenecks were imperfect, often biased, and sometimes corrupt. But they performed a crucial function. They enforced minimum standards of evidence and coherence. They filtered out the most extreme noise. They created a limited set of “authorized” narratives around which public discourse could organize.

You might not have liked what the gatekeepers let through. But at least there was a through, a common channel that most people encountered.

Digital platforms dissolved this. The blogosphere eliminated editors. Social media algorithms replaced human curators with engagement metrics. The barrier to reaching a mass audience dropped from “convince an editor” to “trigger an algorithm.”

The result: the curation filter was replaced by a virality engine. The most emotionally charged, identity-reinforcing content rises regardless of its truth value. The concept of a “mainstream” fragments into a million micro-narratives, each optimized for its niche, each increasingly incomprehensible to those outside it.

Constraint 3: Locality

The geography of accountability.

In a village, tribe, or city-state, the speaker was known. Reputation was tangible currency. If you spread a harmful lie, you faced your audience the next day, at the well, in the market, or at the temple. This created a powerful feedback loop: communication was a high-stakes social act, embedded in ongoing relationships.

Mass media created distance between the speaker and the audience. The internet completed the separation. We now routinely consume language from sources with no connection to our physical community, no shared history, and no accountability to our social norms. Anonymous. Pseudonymous. Distant.

The link between communication and consequence is severed. The costs of deception, exaggeration, or incitement are externalized, borne by the audience, not the speaker. This creates conditions where the most inflammatory language is actually incentivized: high engagement rewards, minimal social risk.

Constraint 4: Friction

The cost of producing and distributing language.

This is the one we feel least nostalgic about, because friction felt like oppression. Writing requires literacy. Publishing requires capital. Broadcasting requires licenses and infrastructure. These barriers excluded many voices that deserved to be heard.

But friction also served as a natural limiter on volume and a rough proxy for commitment. Someone who had expended significant resources to broadcast a message was, on average, more likely to believe in it. The investment signaled skin in the game.

Digital tools reduced writing and design costs to near zero. Social platforms absorbed distribution costs. And then came the final step: large language models, which reduce the cost of generating fluent, persuasive text to effectively nothing.

An individual can now produce a volume of credible-sounding content that would have required an entire institution a generation ago. The last natural check on the sheer quantity of language is gone. The signal-to-noise ratio plummets. The effort required to generate personalized propaganda, synthetic consensus, or industrial-scale disinformation is trivial.

The Multiplicative Effect

Here’s what makes this genuinely dangerous: the removal of these constraints isn’t additive. It’s multiplicative.

· High throughput + no bottlenecks = viral misinformation without filters.

· No locality + no friction = toxic speech with no accountability or cost.

· All four removed = a system in which language is infinite, attention is finite, and the architecture for building shared meaning has been dismantled.

This isn’t a story of moral decline. People aren’t worse than they used to be. It’s a story of architectural failure. We removed the load-bearing walls of our epistemic infrastructure while dramatically increasing the load. The linguistic interface, designed for a slow, expensive, locally accountable world, is now subjected to forces it was never built to handle.

What Comes Next

The building is swaying. The question is whether it’s a serviceability failure, uncomfortable but recoverable, or the early stages of something worse.

In structural engineering, there’s a grimmer category: plastic failure. That’s when a material is stressed beyond its yield point and deforms permanently. A steel beam bent past its limit doesn’t spring back. Its integrity is compromised forever.

The danger with our current epistemic crisis is that a prolonged serviceability failure could become plastic. If the core materials of society, trust, shared truth, and good-faith disagreement, are strained beyond their yield point, the deformation may be permanent. Shared reality fragments into mutually incomprehensible shards. The common ground required for large-scale cooperation collapses. The structure of collective understanding bends and doesn’t return.

We’re not there yet. But the creaking is getting louder.

In the next essay, we’ll look more closely at what serviceability failure actually looks like when it’s happening to meaning itself, and why the usual fixes aren’t working.

This is the first in a three-part series on language, constraints, and the crisis of shared meaning. Next Week: “Serviceability Failure.”

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A Different Perspective

Most self-improvement frameworks assume you already know your priorities. Pick your domains, set your goals, track your progress. But here’s the uncomfortable truth: priorities shift, and we’re often the last to notice. What mattered deeply at 30 might be irrelevant at 50. The relationship you took for granted becomes the thing you’d sacrifice everything else for. The career ambition that drove you for decades quietly stops mattering, but you keep optimizing for it anyway, out of habit.

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The value isn’t in tracking what you think matters. It’s in discovering what actually does. And, just as importantly, what doesn’t.

Quick and Simple

The practice is simple: daily reflection, constrained. Five minutes, ideally at the same time each day. You score a set of life components, not with journaling or elaborate prose, but with a simple scale. Did this area drag me down today, lift me up, stay neutral, or just wasn’t a factor at all (not applicable)?

The constraint is the point. You can’t hide behind words. Over weeks, patterns emerge that surprise you. The thing you thought was critical to your happiness? Turns out it barely moves the needle. The thing you’ve been neglecting? Strongly correlated with your best days.

This isn’t about optimizing your life into a spreadsheet. It’s about building a muscle: the capacity to notice, reflect, and adjust. Most of us sleepwalk through our days. This practice interrupts that.

The Life Mastery Tracker

I decided to build a web app because it helped me think through what matters to me, gave me an opportunity to build one that I could use, and let me try several LLMs as coding assistants to learn their strengths and weaknesses for a non-coder.

The app is simple by design, and I am sure there is plenty of room for improvement in the user interface and functionality. I believe the latter will involve expanding the analytics based on experience and user feedback.

A few things worth noting:

It’s intentionally minimal. The check-in takes under five minutes. You score each component, your overall happiness and fulfillment, and include an optional note; you’re done. No gamification, no streaks-for-streaks-sake, no guilt. I am sure there is plenty of room for improvement in the user interface and functionality. On the functionality front, I believe this will involve expanding analytics based on experience (e.g., developing a personalized utility function for happiness and fulfillment and aggregating anonymized, demographically segmented scores of standard components’ contributions to overall happiness and fulfillment)

You don’t need to get the components right up front. The profile setup process includes several ‘standard’ components that are important to me right now. You can select any that you think might be applicable to you, and you also have the opportunity to create your own custom components and scoring guidance. Custom components and scoring guidelines are encrypted and visible only to the user through the user interface. They will never be incorporated into any anonymized aggregated data

Your data, your control. Custom components, scoring guidance, and daily notes are encrypted end-to-end; I can’t see them, and they’re never shared. Standard component scores (the ones you select from the preset list) can be anonymized and aggregated to surface patterns across users, for example, how “Sleep” correlates with overall happiness across different demographics. During the beta, this aggregation is required; afterward, you can opt out entirely.

Beta Testers Wanted

I’m looking for 50 beta testers.

The commitment: use the app daily for 30 days. At the end, share what worked, what didn’t, and what’s missing. I’ll incorporate feedback into the next iteration. Please note: for the purposes of the beta, users agree to the anonymized aggregation of their standard components, as described above. Beyond the beta, all users will be able to opt out of anonymized data sharing, including those who participate in the beta.

If that sounds like you: https://life-mastery-frontend-production.up.railway.app/login

No Application Required

Not ready to commit to an app? That’s fine. The practice can be done with a pen and paper, index cards, or a notes app. The web app is designed to reduce friction and, ultimately, to increase each user’s understanding of what matters to them and to gain insights from anonymized, aggregated data on the ‘standard’ components.

What matters is building the muscle. Everything else is scaffolding.

Try the practice manually: - List 8–10 life components (e.g., Health, Relationships, Work, Learning, Creativity). - Each day, score each on a five-point scale (from −2, indicating a strong negative impact, to +2, indicating a strong positive impact). - After 2 weeks, look for patterns.

Looking ahead: What does this have to do with AI?

This kind of daily reflection, minimal, intentional, human-centered, is more than a personal practice. It’s a small-scale model for how we might design technology that augments rather than replaces human intelligence.

Next week, we’ll explore a related idea in artificial intelligence. Just as blindly optimizing for more data or bigger models won’t lead to true understanding, blindly optimizing our lives without reflection won’t lead to true fulfillment. The prevailing “bigger is better” narrative in AI is starting to crack, and what emerges next may look less like artificial general intelligence and more like augmented human intelligence, modular, attentive, and designed to elevate what humans do best.

If today’s tool helps you notice what truly matters in your life, next week’s piece will ask: What if we built AI to help us do exactly that?This is part of an ongoing series exploring Augmented Human Intelligence and the architectural principles that might guide us toward AI systems that enhance rather than replace human judgment. If you found this interesting, consider subscribing to future posts on evolutionary processing units, attention architectures, and the intersection of human and artificial intelligence.

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The Mastery of Life scaffold emerged from applying what I had learned about the brain’s modular architecture and attention as a finite resource to a simple but complex question: how does one live well in an environment designed to hijack focus?

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That work was personal. But systems do not exist in isolation. They operate within hierarchies of larger systems. So I zoomed out. What I saw was not disagreement, or polarization, or even misinformation. What I saw was an architectural failure, one that makes those outcomes almost inevitable.

The Architectural Lens

Studying intelligence through the lens of biology and artificial systems taught me a simple lesson: intelligence does not emerge from raw processing power. It emerges from coordination.

The brain’s genius is not its impressive computational capacity. It is its structure. Specialized modules, perception, memory, emotion, reasoning, all hand off information under the governance of executive attention. That executive function decides what gets priority based on context and goals. It is what allows you to override an impulse, to hold competing ideas at once, to revise beliefs when evidence demands it.

Executive attention is the allocator. When it works, intelligence emerges. When it is compromised, reasoning collapses. Now consider what happens when something else begins making those allocation decisions for you.

Executive Capture

I used to think of social media’s effects in terms of distraction, too much noise, too little signal. That framing is insufficient. The deeper problem is its capturing of our executive function.

When I open a (social) media feed optimized for my engagement, I am not merely distracted. I am delegating my attention allocation to a system with goals that are not my own. The algorithm decides which cognitive modules get activated: outrage, tribal identity, and novelty-seeking. It makes those decisions thousands of times per session, invisibly and automatically. This is not a metaphor.

It is the same architectural dynamic I described in my work on biological and artificial intelligence, inverted. Evolution spent billions of years refining executive attention to serve the organism’s survival and flourishing. The attention economy has spent two decades optimizing how to capture that executive function and redirect it toward engagement metrics. The result: My own cognitive architecture, running someone else’s priorities.

Why This Breaks Democracy

Democracy is not just a political system. It is a cognitive one.

For a democratic society to function, citizens must be able to:

* Hold competing ideas simultaneously

* Update beliefs based on evidence

* Coordinate around shared facts

* Override tribal impulses when warranted

Every one of these requires executive attention. But there is a critical upstream problem. Deliberation assumes a shared factual foundation, common ground from which disagreement can proceed. Algorithmic personalization dissolves that foundation before deliberation begins. Citizens are not merely distracted; they are operating from incompatible information environments. You cannot update beliefs based on evidence your neighbor never sees.

Democratic institutions evolved alongside slow information architectures: newspapers, town halls, letters. These imposed natural friction that allowed executive function to keep pace. I would argue that friction here was a feature, not a bug. In the attention economy, friction is "waste"; in a democracy, friction is "reflection." The algorithmic feed removes that friction entirely, delivering allocation decisions at machine speed to cognitive systems designed for human tempo.

Consider each capacity in turn. Holding competing ideas requires sustained attention; algorithmic feeds reward snap judgments. Updating beliefs demands cognitive effort; outrage-optimized content makes the prior position feel righteous. Coordinating around shared facts presumes a common information base; personalization fragments it. Overriding tribal impulses is executive function’s hardest task; engagement algorithms specifically target tribal identity because it drives clicks.

I suspect you’ve felt this erosion. After heavy social media use, your capacity for sustained reading dips. You reach for your phone mid-paragraph. Opinions harden faster. This is the mechanism at work.Even mainstream editorial voices are beginning to name this dynamic. In its New Year's Day editorial (2025 was dismal. It doesn’t have to be this way.), the Boston Globe urged voters to "develop a spidey sense for the ways that social media algorithms encourage polarization and groupthink," noting that platforms "make money by stoking endless cycles of outrage." The diagnosis is correct. What remains unasked is: how does one develop that spidey sense? The answer requires more than awareness, it requires rebuilding the executive capacity that makes such awareness actionable.

The attention economy does not merely distract citizens. It systematically weakens the very cognitive capacities that democratic self-governance requires. Citizens whose executive attention is captured can still vote, but their ability to deliberate is significantly diminished. Elections continue, but they occur within a marketplace of minds whose allocation systems have been quietly compromised.

The MOL Scaffold as Diagnostic

Building the Mastery of Life framework gave me the lens to see this clearly.

As I began deliberately allocating attention across core life domains, observing where focus actually went versus where it should go, the external pull became impossible to ignore. My attention was not simply wandering. It was being pulled by systems I had not consciously chosen.

The MOL process, Awareness → Attention → Adaptation, is not self-optimization. It is the process of regaining executive function. It is the practice of taking back the allocation decisions that determine which cognitive modules run and when. This is why the personal scaffold matters beyond individual well-being. You cannot diagnose a hostile architecture while you remain trapped inside its most reactive loops. You must first rebuild executive capacity; otherwise, the system remains invisible.

Beyond Policy: Architectural Alternatives

Most responses to the attention economy focus on policy: regulation, transparency mandates, and antitrust action. These may be necessary, but they are insufficient. They treat symptoms while leaving the underlying architecture intact. The brain does not rely on a single, centralized attention allocator. It uses multiple interacting systems, bottom-up and top-down, stimulus-driven and goal-directed, that compete and correct one another. Executive function coordinates; it does not monopolize.

Our information environment is the opposite. Attention allocation is centralized, optimized around a single metric, and imposed at a planetary scale. When allocation centralizes, power concentrates, even if intentions are benign. The architecture itself does the damage. The alternative is not better algorithms. It is distributed attention governance.

Systems where:

* Allocation decisions happen closer to the individual.

* Multiple signals inform what surfaces, not just engagement.

* Human executive function remains primary, augmented rather than replaced.

This is the same principle underlying Augmented Human Intelligence rather than artificial general intelligence. The goal is not to replace human cognition, but to design systems that strengthen it rather than capture it.

From Personal Practice to Collective Design

I began this series exploring how intelligence emerges from architecture in biological and artificial systems. I applied those principles to deliberate living. Their implications for collective intelligence now follow naturally. The attention economy is not merely making us anxious or divided. It is degrading the cognitive infrastructure on which democracy depends.

The Mastery of Life scaffold is not an escape from this problem. It is a prototype. It demonstrates what reclaimed executive function entails at the individual level. The open question is whether we are willing to scale that insight, whether we can build information architectures that augment collective deliberation rather than fragment it.

I do not have a complete answer. But I am confident of this:

The fight for a deliberative democracy and the practice of a deliberate life are the same, operating at different scales. One prepares you for the other. We began by building a scaffold for a single life. Now we see its deeper purpose: to become the first footing in a broader foundation, one capable of supporting a self-governing society guided by intention rather than impulse.#ParallelHorizons #DeliberativeDemocracy #AttentionEconomy #CognitiveArchitecture #CivicInfrastructure

What’s Next

We’ve traced the problem from our captured cognitive architecture to its civic consequences. But every systemic critique begs a practical question: What do I do on Monday? Next week, we return to the Mastery of Life track to introduce a simple, private, and secure tool that I have built to operationalize the framework, a starting point for the daily practice of reclaiming executive attention.This is part of an ongoing series exploring Augmented Human Intelligence and the architectural principles that might guide us toward AI systems that enhance rather than replace human judgment. If you found this interesting, consider subscribing to future posts on evolutionary processing units, attention architectures, and the intersection of human and artificial intelligence.

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A collaborator of mine, Ava Neeson (https://www.linkedin.com/in/olivia-ava-neeson/), recently updated her LinkedIn tagline to include: “Stop Calling Language Models AI.”

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She’s a CTO, not a philosopher, but a practitioner watching the phrase “Artificial Intelligence” get stretched so thin that it now covers everything from a chatbot to a chip scheduler. This isn’t semantic nitpicking. It’s a flare shot over a governance No Man’s Land: you cannot govern what you cannot accurately define.

Three Definitions, One Problem

Consider three categories of definitions currently vying for authority:

The Regulatory Definition (OECD, U.S. Executive Order): “A machine-based system capable of making predictions, recommendations, or decisions.”

The Functional Definition (Academic Consensus): “Systems that perform tasks typically requiring human intelligence, such as learning, reasoning, problem-solving, and perception.”

The Marketing Definition (Prevailing Narrative): “Machines that think, learn, and reason like us.”

Only the second one seriously attempts to describe intelligence. The first is deliberately functional. It is designed for legal guardrails, not conceptual clarity. The third is where our trouble begins. It is the definition of fantasy, and it is the one that dominates press releases, boardroom presentations, and public imagination.

What Large Language Models Actually Do

What is a Large Language Model, really?

It is a statistical prediction engine of breathtaking scale and fluency. It identifies patterns in training data and predicts the most probable next token. It simulates understanding by mastering the form of human communication. As a landmark paper put it, it is a “stochastic parrot.”(1)

Yet we call it “AI,” invoking the second and, especially, the third definition above. We imply capabilities it does not possess. Let’s be precise:

Learning implies acquiring new knowledge from experience and retaining it. LLMs don’t learn from our conversations. They were trained once on a static dataset. The model itself doesn't learn from our exchange, its neural network remains exactly as it was before we started. Any memory of past conversations is useful, but it DOES NOT change the underlying LLM neural network.

Reasoning implies drawing inferences, weighing evidence, and constructing arguments through logic. LLMs produce outputs that resemble reasoning because they’ve ingested billions of examples of human reasoning. Pattern-matching is not thinking.

Perception implies sensory awareness: receiving and interpreting stimuli from an environment. LLMs have no senses. They have no environment. They process text inputs and generate text outputs. Calling this “perception” stretches the word beyond recognition.

Problem-solving implies identifying obstacles, generating novel solutions, and testing them against reality. LLMs generate plausible-sounding responses based on statistical patterns. When those responses are incorrect, they have no mechanism to recognize the error, let alone correct it.

This is more than a marketing gloss.

It is a category error with operational consequences.

What a “Category Error” Really Means

A category error is a fundamental logical mistake: assigning something to a category to which it does not belong. Asking “What color is the number 7?” is a category error; numbers aren’t in the category of colored objects.

Labeling LLMs as “Artificial Intelligence” commits the same error. “Intelligence” refers to cognitive processes (understanding, reasoning, adaptation). LLMs belong to the category of statistical functions (pattern recognition, probabilistic prediction). Calling an LLM “AI” because it produces intelligent-seeming text is like calling a flight simulator a “jet” because it looks like one on a screen. The resemblance is useful, but they operate on entirely different principles, with different risks and purposes.

This misclassification isn’t philosophical. It’s practical.

Why Mislabeling Creates Risk

When a CEO hears “AI,” they imagine a partner with judgment. They delegate accordingly. When an engineer deploys an “agentic AI,” they are actually launching a multi-step autonomous software process that optimizes for a proxy goal, often with unpredictable emergent behavior.

The gap between expectation (intelligence) and reality (complex automation) is where risk breeds.

This mislabeling creates a fatal control mismatch. Imagine using a pilot’s manual, written for a conscious agent who understands weather, navigates by landmarks, and makes judgment calls, to operate an autopilot system. You’d be missing the entire checklist for the autopilot itself: sensor calibration, software validation, failure mode protocols, and the manual override switch.

That’s what we’re doing. We’re governing the fantasy of a “reasoning agent” while the real risks: hallucinations, data corruption, cost overruns, and goal drift require a completely different manual built for high-stakes automation. This is why AI systems launch “with less control than a new hire.”(2)

When I was growing up, our family home began to show larger and larger cracks in the walls. It turned out that our house was subsiding. The structural engineer informed us that, absent corrective action, the home could eventually collapse. We moved out for a year while engineers and contractors undertook underpinning work to stop the subsidence.

Our current approach to AI governance has the same problem: it’s built on a subsiding foundation. That foundation is the category error itself, the mistake of treating a statistical function as a cognitive agent. You cannot build stable controls on a definition that misidentifies what it is you’re trying to control.

A broken foundation guarantees structural failure.

The Pivot: From AGI to AHI

If we revise our definitions to reflect what actually exists, a more productive question emerges:

Not “when will it be capable enough to take over?” but rather: “What is the right architecture for human-machine partnership?”

This is the shift from Artificial General Intelligence (AGI) to Augmented Human Intelligence (AHI).

The AGI narrative centers on replacement and autonomy. It shapes design toward independence. Oversight becomes an obstacle to be removed.

The AHI narrative inverts this. The goal is not replacement but symbiosis. It is about creating not autonomous agents we hope won’t drift, but architected systems where human judgment is embedded at the critical points of oversight, ethical weighting, and strategic intervention.

Architecture Over Scale

The current AI arms race is built on a seductive premise: intelligence emerges from scale. Train larger models on more data with more compute, and eventually, something like general intelligence will emerge.

This is the “bigger model” paradigm. It has delivered impressive results and diminishing returns. Each generation requires significantly more resources for only incremental improvements. The scaling curves are flattening. The energy costs are exploding. And the fundamental limitations remain.

Scaling a flawed architecture doesn’t create intelligence; it makes a larger, more expensive, and potentially more dangerous approximation of one. You cannot brute-force your way to wisdom.

There’s an alternative hypothesis: intelligence emerges from architecture, not scale.

Consider the human brain. It doesn’t succeed through brute-force computation. It succeeds through an elegant modular architecture: specialized subsystems for perception, memory, language, motor control, and emotional regulation, all integrated by sophisticated coordination mechanisms (the executive function of the prefrontal cortex). The brain is efficient precisely because it doesn’t try to solve every problem with the same general-purpose approach.

What if AI development followed similar principles? Imagine modular systems, specialized components, sophisticated integration, and human oversight embedded in the architecture rather than bolted on afterward.

This is the AHI design philosophy:

· Humans set the frame, define the guardrails, and hold the ultimate “break-glass” authority.

· Machines handle scale, pattern recognition, tedious computation, and execution within that frame.

This is not a limitation. It is a design philosophy for safety and efficacy. A pilot and an autopilot. A surgeon and a robotic scalpel.

Clear Definitions Enable Clear Governance

Call them what they are: Large Language Processors. Predictive Engines. Autonomous Software Agents. Retire the lazy, all-encompassing “AI” for specific terms that match controls to true capabilities and failure modes.

This isn’t about limiting potential; it’s about enabling responsible power. An accurate definition is the keystone of control, the load-bearing truth that locks governance, design, and risk management into a stable structure. Without it, you’re just hoping the machine guesses what you want.

Governance must evolve from static IT checklists to dynamic systems oversight, think air traffic control for high-speed automation. This can only happen when leadership’s mental model shifts from “deploying intelligence” to “orchestrating capability.”

In the realm of technologies that scale risk as fast as they scale value, semantic clarity isn’t a nicety; it’s the foundation of operational safety.

We must stop governing the fantasy of intelligence and start architecting the reality of partnership.

Language was humanity's first invention, the artifact that enabled us to coordinate, govern, and rise.(3)(4) It would be darkly ironic if imprecise language about our most powerful technology became the thing that undermined our ability to control it.

The first step is to use language with enough precision to say what we actually mean.

What’s Next

This is a bonus post that I have developed based on current discussions about the definition of AI and its impact on governance. My previously planned post, zooming out from the personal to the political, will launch on Wednesday, January 7. This is part of an ongoing series exploring Augmented Human Intelligence and the architectural principles that might guide us toward AI systems that enhance rather than replace human judgment. If you found this interesting, consider subscribing to future posts on evolutionary processing units, attention architectures, and the intersection of human and artificial intelligence.

References

* Bender, E. M., Gebru, T., McMillan-Major, A., & Mitchell, M. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? FAccT ’21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency.

* Issakova, A. (2025). LinkedIn post on agentic AI governance. https://www.linkedin.com/posts/alexissakova_companies-are-launching-agentic-ai-with-less-activity-7413201150130167808-Rc9f/

* Wolfe, T. (2016). The Kingdom of Speech. Little, Brown and Company.

* Harari, Y. N. (2015). Sapiens: A Brief History of Humankind. Harper.

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It was a simple, powerful instinct. It required no conscious thought and was fueled by deep-seated anxiety. For 25 years, the path had been clear: work, advance, provide. The sudden break from that script triggered a system-wide panic. My internal executive function, overwhelmed, delegated control to the most primitive module: the threat-response system. My attention narrowed to a single, glaring domain: Material Security.

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I put my job search on autopilot, pursuing roles that mirrored the one I’d lost. It was the path of least resistance, and a course of action that made perfect sense for a 50-something-year-old professional. However, there was something nagging at the back of my mind which grew stronger over time. I had the sense that I was missing or ignoring something important.

Then, after about a year, the panic lessened just enough for a different kind of thought process, a slower and more reflective one, to be heard. My System 1 (fast, fearful, instinctive) had a clear answer: Find security, now. But a quieter System 2 (slow, reflective, deliberate) was asking a different question:

“Is this truly what deserves my focus for the next 15 years?”

This wasn’t just a career question. It was a more foundational one. My internal “Biological Processing Unit” (BPU, aka brain) had been hijacked by the instinctive threat-response, and it had essentially silenced all other avenues of thought. I realized I needed to reboot my BPU and re-establish executive control. I needed to apply the same principles I was researching in AI: attention, modularity, and adaptation to my own life.

What if “living deliberately,” Thoreau’s 170-year-old aspiration, isn’t about withdrawing from complexity, but about applying systematic attention management within it?

The Bridge to Life Architecture

That moment of clarity was the first step in rebooting my BPU. But to answer it, I needed a new architecture for how I thought and decided. I couldn’t just rely on instinct; my instincts were still wired for fear.

The insight came from the work I was immersed in. The BPU isn’t a general-purpose supercomputer. It’s a federation of specialized systems, visual cortex for sight, hippocampus for memory, prefrontal cortex for command, all competing for a finite budget of energy. Intelligence isn’t raw power; it’s the orchestration of specialized modules by executive attention.

My life, I realized, had a parallel architecture. It wasn’t a single monolithic project called “Career.” It was a collection of core domains, each a vital subsystem of a whole life.

The Seven Domains of a Life Architecture

When I mapped my own experience through this lens, seven domains consistently surfaced, each a different facet of life’s architecture.

* Material Security – Financial stability and the sense of safety it brings

* Physical Vitality – Sleep, movement, and nutrition that sustain energy

* Mental Clarity – Focus and cognitive balance for clear thinking

* Purpose & Growth – Learning, curiosity, and contribution beyond the self

* Relational Connection – Depth and quality of human relationships

* Self-Regulation – Managing emotional states and habits with awareness

* Novelty & Discovery – Exposure to new experiences and ideas that prevent stagnation.

These are not arbitrary categories. They are the modular architecture of a human life, each specialized, all interconnected, and all requiring a conscious executive decision about how to allocate the one finite resource we share: attention.

For a year, my personal dashboard would have shown one domain blinking red (Material Security: -2) while all others faded to grey from neglect. The framework showed me I wasn’t solving a problem; I was starving a system.

Just as the BPU cannot process every input at once, we cannot optimize all domains simultaneously. The question is not “How do I do everything perfectly?” but rather, “Given where I am now, which domain(s) most deserves my focus, and what am I willing to trade off to give it that focus?”

This shift from optimization to orchestration is the foundation of the Mastery of Life framework.

The Process: A Life’s Operating System

Recognizing these seven domains gave me a map. But I needed a method for navigation, a new compass to replace the fear-driven autopilot. That method mirrors how any intelligent system, biological or artificial, learns and adapts: Awareness, Attention, and Adaptation.

Step 1: Awareness — Observation Without Judgment

My first step was to stop and actually look at what was happening. I had to force the shift from System 1 to System 2.

This meant collecting simple data, not opinions. I began tracking small metrics across the domains:

* Physical Vitality: How many hours did I actually sleep?

* Mental Clarity: How many focused blocks of work did I achieve versus time lost to anxiety?

* Purpose & Growth: Was I spending any time learning, or just refreshing my inbox?

This wasn’t about judgment. It was compassionate observation, like a technician reading system diagnostics. The data revealed a truth I couldn’t ignore: my obsession with Material Security was dragging down nearly every other domain. I could now see the trade-offs I was making.

Step 2: Attention — Allocating Finite Resources Deliberately

With awareness came the power of choice. I could now consciously decide where to allocate my finite cognitive resources. William James captured it perfectly: “My experience is what I agree to attend to.”

I chose to de-prioritize the frantic job search and instead focus on Purpose & Growth and Novelty & Discovery. I devoted time to learning, writing, and exploring new ideas about human decision making, AI, the remarkable parallels between them, and how they might cooperate most effectively. This led to my development of Augmented Human Intelligence (AHI) as an alternative to Artificial General Intelligence (AGI).

This is much like the self-attention mechanism in AI models. It’s not about processing everything; it’s about assigning importance weights. I was telling my internal system: “For this season, learning and purpose have a higher weight than my continued, myopic focus on financial security.”

Step 3: Adaptation — Learning From the Feedback Loop

Life doesn’t stand still. Mastery isn’t rigidity; it’s responsiveness. The final step was to close the loop and learn from the results of my new attention allocation.

After a few weeks, I reviewed how my tracked metrics had changed. The results were telling:

* Intervention: “I focused on learning instead of applying. Did my sense of purpose improve?” Yes.

* Counterfactual: “If I had made this shift six months earlier, would I have reduced frustration and anxiety?” Almost certainly.

This transformed my life from a reactive script into a learning system. I was no longer a passenger. I was the architect, running small experiments, observing outcomes, and adapting my blueprint. The fear of the unknown was replaced by confidence in my ability to navigate it.

The Philosophical Stakes and Call to Action

This framework pulled me out of a reactive loop and into a deliberate one. But its implications reach far beyond personal course correction. They point to a deeper truth about the era we’re entering.

If attention is the fundamental constraint, in the BPU, in AI systems, and in human cognition, then attention management is the meta-skill that determines intelligence. In humans, this is not just cognitive; it’s moral. What you consistently attend to signals what you truly value. Attention shapes not only what you accomplish but who you become.

This skill will only become even more critical as AI’s power and capabilities increase. For example, if only a fraction of the doomsday scenarios for AI’s impact on employment and society come to fruition, we will all need to strengthen our Mastery-of-Life muscle.

As we approach the integration of human and artificial intelligence through brain-computer interfaces, this question becomes existential. Will these hybrid systems enhance our capacity for deliberate attention, or fragment it further?

Technology can be a mirror or a vacuum. Used wisely, it can augment awareness, revealing patterns we might otherwise miss. Used poorly, it can hijack the BPU’s ancient reward systems for commercial gain. The choice isn’t whether to use technology, but what kind of attention architecture we build with it.

From Personal to Political

This brings me to the next horizon in this series. We’ve explored how intelligence emerges from architecture in biological and artificial systems. We’ve seen how these same principles can scaffold a more deliberate human life.

So, what happens when we zoom out?

What if the same architectural principles that govern intelligent systems and deliberate living also reveal a fundamental blueprint for how societies could be organized? If human happiness and fulfillment come from conscious attention management, could our political and economic systems be designed not to maximize output, but to enable citizens to live more deliberately?

What would a society optimized for human attention, rather than human consumption, actually look like?

My journey from a fear-driven instinct to a more deliberate architecture is still unfolding. The Mastery of Life is the scaffold.

If this resonates, I invite you to:

* Read the Full Mastery of Life White Paper for the complete framework, from metric design to validation.

* Pause here for 60 seconds. Which single domain is shouting the loudest in your life right now? Is it the most important, or just the most urgent (or fearful)? That gap is where deliberate living begins.

The first step toward mastery is always awareness.Important note: I realize that I am very fortunate. My circumstances gave me a choice, I had a safety net, between stocks, savings, and retirement accounts. The safety net is a lot smaller Today, but I wouldn’t change a thing, and I would encourage anyone in a similar position, to seize the day and take the time to reflect. You may be surprised by what you find, and I am certain you will benefit from the exercise. It isn’t an easy choice, trust me, I don’t even know how it ends, but I am excited to find out. Most importantly, I believe in myself again. This is part of an ongoing series exploring Augmented Human Intelligence and the architectural principles that might guide us toward AI systems that enhance rather than replace human judgment. If you found this interesting, consider subscribing for future posts on evolutionary processing units, attention architectures, and the intersection of human and artificial intelligence.#AugmentedHumanIntelligence, #LifeDesign, #FutureOfWork, #CognitiveScience, #SystemsThinking, #MasteryOfLife

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This is the core problem my work on Augmented Human Intelligence (AHI) aims to solve. The goal is not to outsource our thinking to an artificial mind, but to build systems that enhance our own cognition in real-time. Imagine a partner that doesn’t think for you, but ensures you’re thinking well by mitigating biased reflexes and scaffolding deliberate, System 2 reasoning when it matters most.

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But here’s the rub: our current AI tools are architecturally unsuited for this intimate task. Today’s large language models are like brilliant critics shouting suggestions from another room. They’re disconnected from the real-time, subconscious flow of our thoughts where bias takes root. The clumsy I/O of keyboards and speech creates a bandwidth bottleneck far too narrow for a meaningful cognitive partnership.

The architecture is wrong.

So what would the right architecture look like? And could emerging brain-computer interface technology point us toward an answer worth exploring?

The Vision: Real-Time Cognitive Scaffolding

Let’s return to our hiring manager and imagine what true cognitive augmentation might look like.

As she feels that unconscious affinity for the familiar candidate, a sophisticated system, somehow reading the aggregate neural signatures associated with automatic judgment, could detect this moment of System 1 processing.

It would not override her. That would be the antithesis of augmentation. Instead, it would provide cognitive scaffolding:

- Gently highlighting the other candidate’s superior relevant experience on the digital resume in front of her

- Posing a silent, Socratic prompt: “Have you sufficiently weighed Factor X?”

- Triggering a subtle state of focused alertness encouraging a moment of deliberate reflection

The human remains the decider. But she is no longer deciding with a flawed, unaided toolkit. She’s now equipped with a real-time support system to construct sounder judgment.

This is the architecture of attention, made tangible. It’s not about AI thinking for you, it’s about providing the scaffolding to ensure you are thinking, clearly and deliberately, when it matters most.

The question is: what would it take, architecturally, to make this possible?

The Current State of Brain-Computer Interfaces

While this vision might sound like science fiction, the foundational technology is advancing faster than most people realize. Multiple companies are now implanting devices in human brains, not in distant labs, but in actual patients going about their daily lives.

Neuralink: The High-Bandwidth Approach

Neuralink, Elon Musk’s neurotechnology company, represents the most prominent effort in invasive BCIs. By mid-2025, nine people had received the company’s N1 implant, which consists of ultra-thin electrode threads inserted directly into the motor cortex through precisely drilled holes in the skull.

What can they do? The results are remarkable for patients with severe paralysis:

- Noland Arbaugh, the first recipient (paralyzed from a diving accident), can play video games like Civilization VI, browse the web, and control his computer entirely through thought

- Bradford Smith, who has ALS and can no longer speak, used his implant to edit YouTube videos and restored his voice using AI trained on pre-ALS recordings of his speech

- Another participant uses the device to operate computer-aided design software, creating 3D objects with his mind

The device uses 1,024 electrodes across 64 threads to capture neural signals with high resolution. Neuralink has raised over $1.3 billion in funding, including a $650 million Series E round in mid-2025, and plans to expand trials significantly, aiming for 20-30 new implants by the end of 2025. They’re also developing “Blindsight,” a device to restore vision by stimulating the visual cortex, which received FDA Breakthrough Device designation.

Synchron: The Minimally Invasive Alternative

While Neuralink captures headlines, Synchron has taken a radically different approach that could prove more scalable. Their Stentrode device is inserted through the jugular vein, like a stent, and positioned in a blood vessel above the motor cortex. No open brain surgery required.

The trade-off? Lower bandwidth. Synchron’s device provides basic “switch” control, think clicks and scrolling, rather than the high-resolution signal capture of cortical implants. But this simplicity has advantages:

- Six patients in their COMMAND study showed zero serious adverse events over 12 months

- The median deployment time is just 20 minutes

- Patients can control their devices “on day one” without extensive training

- The device works with Apple products via Switch Control, including iPhone, iPad, and Vision Pro

Synchron just raised $200 million (November 2025), bringing total funding to $345 million. They’re preparing for pivotal trials in 2026 and moving toward commercialization. The company is also developing a next-generation “high-channel whole-brain interface” that could dramatically increase capabilities.

The Broader Landscape

Three other companies are pushing the boundaries in different directions:

Blackrock Neurotech (Salt Lake City) has the most human experience with dozens of implants since 2004 through academic research trials. Their Utah Array is the workhorse of BCI research, and their MoveAgain device received FDA Breakthrough Designation. They secured $200 million from Tether in 2024.

Precision Neuroscience (founded by a former Neuralink executive) is developing an ultra-thin, flexible “Layer 7” electrode array that sits on the brain’s surface like a piece of tape, inserted through a minimal incision. They raised $102 million in late 2024 and achieved record-setting 4,096-electrode human recordings.

Paradromics (Austin) is building a high-bandwidth implant specifically for speech restoration. They completed their first human recording during epilepsy surgery in June 2025 and have raised over $100 million in venture funding plus $18 million in government grants.

Industry investment has exploded: $2.3 billion flowed into BCI technology in 2024 alone, more than triple the 2022 level.

The Architectural Case for BCIs

So why would a brain-computer interface be relevant to cognitive augmentation in the context of the hiring manager scenario?

The answer lies in bandwidth and latency.

Current AI interactions require:

1. Conscious awareness of a need for assistance

2. Explicit formulation of a query

3. Physical input (typing/speaking)

4. Waiting for processing

5. Reading/hearing the response

6. Conscious integration of that information

By the time our hiring manager has noticed her bias, consciously decided to seek help, typed a question into ChatGPT, and read the response, the critical moment has passed. The automatic judgment has already been made.

A true cognitive augmentation system would need to:

- Detect the neural signatures of automatic, System 1 processing in real-time

- Intervene at the moment of decision, not after

- Communicate through high-bandwidth, low-latency channels that don’t require conscious attention

- Preserve human agency, scaffolding rather than replacing judgment

This is an architecture problem. And BCIs represent the only technology category that could theoretically provide this level of intimate, real-time integration.

Why This Path May Be More Tractable Than AGI

Here’s where the argument gets interesting.

The dominant narrative in AI is the race toward Artificial General Intelligence, an external, autonomous entity that may ultimately operate beyond our control. This pursuit is fraught with profound alignment challenges: How do we ensure a superintelligent system’s goals remain aligned with an entire species when we can barely agree among ourselves?

An AHI system enabled by BCI is architected for alignment by default. Its core purpose is to serve the goals and judgment of its individual human user. The human’s values are the system’s compass. There’s no separate entity with separate goals to align.

Yes, BCIs come with serious challenges:

- Safety: Surgical risks, long-term biocompatibility, signal stability

- Privacy: Neural data is perhaps the most intimate information imaginable

- Security: Protection against hacking or unauthorized access

- Equity: Ensuring technology doesn’t create cognitive “haves” and “have-nots”

- Regulation: How do we govern devices that sit at the intersection of medical device, consumer technology, and fundamental human capabilities, not to mention moral and ethical questions?

But critically, these are engineering and governance challenges problems of building secure systems, designing reversible controls, and crafting sensible policy. These are the kinds of problems we have centuries of collective experience solving, even if the stakes here are uniquely high.

The Honest Limitations

Let’s be clear about what BCIs cannot do today and may not be able to do for years or decades:

Current capabilities are limited: Today’s devices can control cursors, select menu items, and type, all of which are amazing for people with paralysis, but nowhere near the real-time cognitive intervention we’ve been discussing. The hiring manager scenario remains firmly in the realm of speculation.

Signal quality matters: The brain is noisy. Distinguishing the neural signature of “automatic bias” from “deliberate consideration” or “fatigue” or “hunger” is an unsolved problem. We’re still mapping these patterns.

The “read” problem: All current BCIs are focused on output, translating brain signals into actions. Effective cognitive scaffolding would also require input, the ability to communicate suggestions back into conscious awareness in a way that feels natural rather than intrusive. This bidirectional communication at the cognitive level remains largely theoretical.

Long-term unknowns: We don’t yet know the effects of living with a brain implant for 20, 30, or 40 years. Will the brain adapt? Will signal quality degrade? Will there be psychological effects?

The consumer value question: For medical applications (restoring movement, communication, or vision), the value proposition is obvious. But would a healthy person undergo neurosurgery for a 10% productivity boost? The practical value of potential consumer applications hasn’t been established.

Regulatory uncertainty: The FDA pathway for medical BCIs is becoming clearer, but how would consumer cognitive augmentation devices be regulated? We’re in uncharted territory.

These limitations are real and significant. Anyone promising near-term cognitive augmentation via BCI is selling science fiction, not science.

The Question Worth Asking

So why write about this?

Because the question itself matters: What architectural approach gives us the best shot at building AI systems that genuinely augment rather than replace human judgment?

Current AI development is racing toward autonomous systems that think for us. The AHI vision asks: what if we instead built systems that help us think better?

Brain-computer interfaces represent one possible path toward that vision, perhaps the only path that could provide the bandwidth, latency, and intimacy required for real-time cognitive partnership. The technology is advancing rapidly, the investment is real, and the early results are promising.

But it’s also early. Very early.

The medical applications are already life-changing for people with paralysis or neurological conditions. The cognitive augmentation vision? That’s still a thought experiment backed by emerging technology rather than a near-term product roadmap.

Perhaps that’s exactly when we should be having this conversation, before the technology is fully realized, while we still have time to think carefully about whether this is a path worth pursuing, and if so, how to pursue it responsibly.

Your Turn

What’s your gut reaction to the idea of a brain-computer interface for cognitive augmentation?

Would you consider it for yourself if the safety profile was proven? What applications would be worth it? What makes you hesitate?

I’m curious where your thinking leads.

This is part of an ongoing series exploring Augmented Human Intelligence and the architectural principles that might guide us toward AI systems that enhance rather than replace human judgment. If you found this interesting, consider subscribing for future posts on evolutionary processing units, attention architectures, and the intersection of human and artificial intelligence.

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Sources and Further Reading:

- MIT Technology Review coverage of Neuralink, Synchron, and other BCI companies (2024-2025)

- Government Accountability Office report on Brain-Computer Interfaces (GAO-25-106952)

- Clinical trial data from COMMAND study (Synchron) and PRIME study (Neuralink)

- Industry funding data from NeuroFounders and venture capital analyses

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The Seduction of “Therefore”

Michio Kaku has an almost musical ability to connect scientific discovery with cosmic destiny. Watching him describe the future of mind–machine fusion or quantum teleportation feels like watching a physicist conduct an orchestra of possibilities.

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His charisma is undeniable, but so is the leap he often makes between what we can demonstrate and what he declares inevitable.

It’s the same move buried in physics textbooks:

From this, it is obvious that…

Except in Kaku’s case, it becomes:

From this first neuron firing on a chip, it is obvious that we will one day upload consciousness.

That small word, “therefore,” is the slipperiest in all of science. It hides the distance between evidence and extrapolation, between what is shown and what is merely imagined.

The Projection Chain

Kaku’s futurism often follows a pattern, which I call the Projection Chain:

1. Observation: A real, exciting breakthrough (a qubit entangled, a neuron simulated).

2. Extrapolation: Extend the curve, “We’ll soon simulate the entire brain.”

3. Assumption: Treat scaling as understanding, “More compute equals more mind.”

4. Narrative: Frame the future as inevitable, “Uploading consciousness is only a matter of time.”

5. Reward: Capture public imagination (and often, funding momentum).

It’s powerful storytelling, but fragile logic. Each, therefore, conceals an ocean of complexity.

The Psychology Behind the Leap

Daniel Kahneman, in Thinking, Fast and Slow, warns about our love of coherent stories. System 1, our fast, intuitive mode of thought, craves closure. When we encounter an incomplete chain, we automatically fill in the gaps.

That’s precisely what happens when we hear:

“We’ve mapped one brain circuit; therefore, we can upload the mind.”

The story feels complete because the pattern is smooth, not because the reasoning is sound.

Kahneman called this the illusion of understanding, the comforting sense that we grasp a complex system merely because we can describe a simplified version of it.

Kaku’s futurism thrives in that illusion. It’s not deception; it’s seduction, the same narrative instinct that once convinced us flight, fusion, and artificial general intelligence were “only decades away.”

Enter Gary Marcus: The Architectural Counterpoint

In Rebooting AI, cognitive scientist Gary Marcus dismantles the same illusion from another angle. He argues that deep learning’s success has led many to mistake scaling for understanding, the belief that more data and larger models will inevitably yield intelligence.

Marcus reminds us that intelligence isn’t a single curve to be extended. It’s a modular architecture to be constructed, with reasoning, structure, and feedback loops.

Where Kaku says, “We’ll get there if we keep scaling,”

Marcus says, “We’ll get there when we “build systems that can reason, reflect, and learn from their own errors.“

In that light, Kaku’s Projection Chain is the embodiment of the myth Marcus critiques: that intelligence emerges inevitably from computation.

From Projection to Development

To move from wonder to wisdom, we need a different chain, a Development Chain, built not on extrapolation but on iteration.

THE TWO CHAINS: FROM SEDUCTION TO SUBSTANCE

OBSERVATION

* Projection Chain: Breakthrough event (e.g., neuron simulated on silicon)

* Development Chain: Contextualized experiment (e.g., testing under constraints)

* Shift: Spectacle → System

EXTRAPOLATION

* Projection Chain: Infinite trendline (”We’ll simulate the entire brain soon”)

* Development Chain: Bounded hypothesis (”Scale 10x → what specific capability?”)

* Shift: Inevitability → Inquiry

ASSUMPTION

* Projection Chain: Scale = Understanding (more compute = intelligence)

* Development Chain: Feedback = Understanding (correction loops = intelligence)

* Shift: Quantity → Quality

NARRATIVE

* Projection Chain: “We WILL upload minds” (replacement prophecy)

* Development Chain: “We CAN augment minds” (partnership proposal)

* Shift: Prophecy → Partnership

VALIDATION

* Projection Chain: Public awe & funding (imagination captured = success)

* Development Chain: Iterative evidence & integration (measured progress = success)

* Shift: Inspiration → Integration

This shift from projection to development isn’t just a change in checklist, it’s a change in architecture. And that new architecture demands a partner.

The Missing Ingredient: Why Development Chains Need Human-Machine Partnership

The shift from projection to development isn’t just methodological, it’s architectural.

Projection chains assume intelligence emerges from scale alone. You can just feed enough data into a sufficiently large model, and understanding arrives automatically.

Development chains recognize something fundamentally different: real intelligence requires continuous correction between various modes of processing.

Consider AlphaGo. Its breakthrough wasn’t raw compute; it was the architecture: neural networks for pattern recognition, Monte Carlo tree search for strategic planning, human expert games for initial training, and self-play for refinement.

No single component scales to intelligence. It’s the interaction between them that does.

This is why pure scaling hits walls. GPT models plateau not because they lack parameters, but because they lack feedback mechanisms that ground prediction in reality, that test hypotheses against consequences, that adjust when the world pushes back.

That’s the architectural principle Kaku’s projection chain misses: Intelligence isn’t a computational threshold to cross, it’s a feedback system to maintain.

And feedback systems, by definition, require multiple perspectives in dialogue.

Case Study: Self-Driving Cars

Projection Chain Thinking (circa 2015):

“We’ve solved highway driving with deep learning. Therefore, full autonomy is 2-3 years away. We need more data and bigger models.”

What Actually Happened:

Edge cases exploded. A plastic bag is blowing across the road. Construction zones with conflicting signs. Pedestrians in wheelchairs. Each required different reasoning, visual recognition, contextual judgment, and social prediction.

More training data didn’t solve this. The architecture was wrong.

Development Chain Approach:

Waymo shifted to modular systems: perception modules, prediction modules, planning modules, each with explicit feedback loops. When the system failed, engineers could identify which component was misunderstood, then improve that specific reasoning pathway.

Progress became measurable. Failures became informative.

The difference wasn’t compute. It was architecture + feedback + iteration.

Hybrid Intelligence: The Bridge Between Dream and Design

The future of intelligence, human or artificial, isn’t about replacement. It’s about co-evolution.

Machines can amplify our perception, memory, and reasoning. Humans provide context, meaning, and ethics.

Hybrid intelligence recognizes that consciousness isn’t a software feature but an emergent process, a dance between data and experience.

Kaku’s cosmic vision has its place. But actual progress doesn’t come from inevitability; it comes from architecture, feedback, and adaptation.

That’s not a rejection of his optimism. It’s its necessary refinement.

Why It Matters

Our age is defined by scale, of models, of data, of hype. But as both Kahneman and Marcus remind us, no amount of computation can replace reflection, structure, or humility.

Wisdom, in science or society, grows not from faster conclusions, but from better feedback and reflection.

Every “therefore” deserves to be tested. Every assumption deserves a feedback loop.

Closing Thoughts

Michio Kaku gives us imagination.

Kahneman gives us humility.

Marcus gives us architecture.

The synthesis of all three, wonder, humility, and structure, is how humanity will truly move from computation to comprehension.

Because in the end,

It’s not scale that makes us wise, it’s feedback.

What’s Next

Going to get more uncomfortable next week when we take augmented human intelligence to a logical place, the Brain Computer Interface. Stay tuned, it is intended to provoke discussion, that’s where humans can move mountains.

#AI #HybridIntelligence #MichioKaku #DanielKahneman #GaryMarcus #ThinkingFastAndSlow #RebootingAI #PhilosophyOfScience #AGI #FeedbackLoop #SystemsThinking #AIethics #FutureOfIntelligence

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To put that in perspective: GPT-3 was trained on 500 billion tokens. Humanity now generates that volume in less than an hour. At its peak, the Library of Alexandria held 4 billion words. We surpass that in less than 60 seconds.

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This isn’t just information overload. It’s something more fundamental: for the first time in human history, the rate at which language is produced has exceeded the rate at which humans can meaningfully process it.

Language is infinite. Attention is finite. And the gap is widening daily.

Why This Matters More Than You Think

Language isn’t just communication. It’s humanity’s foundational technology: older than agriculture, older than writing, older than the wheel.

Yuval Noah Harari describes reality as having three layers: objective reality (physics, independent of belief), subjective reality (your inner experience), and intersubjective reality (shared beliefs that exist because many people agree on them). Money, nations, laws, marriages, corporations: these are linguistic constructs. They have power precisely because they’re collectively imagined and continually reinforced through language.

This intersubjective layer is the operating system of civilization. It’s how strangers cooperate, how institutions persist, how societies cohere.

And it’s exactly what’s under assault.

When language production becomes infinite while attention remains finite, the intersubjective layer destabilizes. Shared reality fragments. Trust erodes. Coordination fails.

This has happened before.

The Pattern Across Centuries

In 1486, an inquisitor named Heinrich Kramer published Malleus Maleficarum, a manual for identifying and prosecuting witches. Before the printing press, such a text would have circulated among a handful of clergy. But Gutenberg’s invention had arrived just decades earlier. The Malleus went through 28 editions, reached tens of thousands of readers, and transformed local superstition into a continent-wide intersubjective reality. The result: 110,000 trials, 40,000 to 60,000 executions, mostly of women, over two centuries.

The printing press didn’t create misogyny or fear of the occult. It amplified them faster than society could adapt.

Five centuries later, the same pattern unfolded in Myanmar.

In 2014, Myanmar had almost no internet penetration. Then SIM card prices collapsed, and tens of millions came online almost overnight. For most users, Facebook was the internet, their sole source of news, community, and identity.

Into this environment flowed coordinated disinformation portraying the Rohingya Muslim minority as an existential threat. Engagement-optimized algorithms amplified the most inflammatory content. A new intersubjective reality formed: the Rohingya were not merely “others” but enemies to be eliminated.

The result was genocide. Over 10,000 killed. Nearly a million displaced. The UN concluded that Facebook played a “determining role.”

The technology didn’t create prejudice. It accelerated narrative formation and emotional contagion beyond the capacity of institutions, or citizens, to resist.

The pattern is consistent across centuries:

- A new language technology emerges

- The cost of producing or distributing language collapses

- Bad actors exploit the new medium

- Institutions lack capacity to filter or regulate the surge

- A manufactured intersubjective reality takes hold

- Violence follows

Today, we face the most extreme acceleration yet. Large language models have collapsed the cost of generating language to nearly zero. A single person with access to a generative model can now produce more text in a day than a medieval monastery produced in a year: tailored, fluent, emotionally optimized, and indistinguishable from human prose.

A Personal Inflection Point

The societal attention crisis mirrors an experience I have had directly.

After a reduction in force ended a career chapter I’d inhabited for decades, my attention collapsed inward. For nearly a year, it narrowed to a single imperative: replace what had been lost. Find job → Restore security → Eliminate uncertainty. It wasn’t a strategy. It was reflex: the predictable response of a mind shaped by fifty years of expectation and fear.

That internal narrative left no space for curiosity, exploration, or reflection.

When those efforts repeatedly failed to materialize, something unexpected happened. I stopped. Not entirely (responsibilities don’t vanish), but enough to notice the pattern. Enough to ask a question I hadn’t allowed myself to ask: What do I actually want to pay attention to?

The landscape didn’t change. But the meaning of the landscape did.

That experience clarified something essential. Just as individuals can drift into lives shaped by inertia rather than intention, societies can drift into informational environments shaped by reflex rather than reflection. In both cases, recovery requires not merely more data or a better strategy, but a reorientation of attention.

If attention is the mechanism by which we choose what matters, then the erosion of attention (individually or collectively) is a threat not just to knowledge, but to agency itself.

Why an Oracle Won’t Save Us

Confronted with infinite information, a tempting solution is to build an AI that sorts it for us: an intellectual oracle that declares what’s true, what’s false, and what deserves our attention.

This approach contains profound risks.

Any oracle must be trained on data, shaped by human choices, constrained by political forces, and embedded in institutional structures. There is no apolitical oracle. There is no neutral filter. Every mechanism that determines what is true also determines what is permitted.

Such systems become targets for capture. The more powerful the oracle, the greater the incentive to manipulate it. History offers numerous examples: church authorities policing orthodoxy, states controlling the media, and platforms shaping algorithmic visibility. A machine oracle merely centralizes this vulnerability.

And if humans outsource judgment to machines, our capacity for judgment atrophies. The prefrontal cortex, like any muscle, deteriorates with disuse. The widespread adoption of digital contact storage offers a small but instructive example: the capacity to memorize phone numbers, once routine, has atrophied within a generation.

An oracle doesn’t strengthen human cognition. It replaces it. And what is replaced is lost.

The Path Forward: Augmented Human Intelligence

The alternative isn’t artificial general intelligence as oracle. It’s Augmented Human Intelligence (AHI): systems designed not to replace human judgment but to strengthen it.

The crisis we face isn’t a deficit of intelligence. Humans reason well when given time, clarity, and context. The crisis is a deficit of attention: the resource required to use that intelligence.

AHI treats attention as the scarcest and most valuable cognitive resource. It provides context, identifies manipulation, surfaces what matters, and widens rather than narrows our informational horizons.

Where AGI aims for autonomy (an artificial mind operating independently), AHI aims for partnership: systems that amplify human insight while keeping us at the center of decision-making.

Consider tools that:

- Summarize not just what sources say, but where they diverge

- Flag when you’re operating within a narrowing informational corridor

- Highlight when emotional triggers are high and analytical engagement is low

- Translate across communities of belief, making one group’s assumptions legible to another

None of these declares what is true. All preserve the integrity of human judgment while strengthening it.

In a world of infinite language, the most valuable technology is one that helps humans reclaim the ability to choose what deserves attention.

The Stakes

Language built our civilizations. It gave us laws, markets, stories, institutions, and meaning. But language alone didn’t make us human. What made us human was the ability to attend: to choose what deserves focus, to reflect before acting, to imagine before building.

If the breakthrough of artificial intelligence was the realization that “attention is all you need,” perhaps the breakthrough of our time will be the realization that attention is all we have.

Democracies depend on citizens who can understand one another. Markets require reliable information. Scientific progress depends on distinguishing truth from fiction. When language becomes infinite, and attention becomes scarce, these premises weaken.

We must not mistake polarization for pluralism. Pluralism assumes a shared foundation of facts. Polarization emerges when that foundation collapses.

AHI isn’t a luxury. It’s a necessity.

What’s Next

This is an unplanned bonus post intended to underscore the importance of attention and to address a comment I received on the scaling post from this past Monday. I encourage anyone to provide me feedback and engage in discussion.

Next week, I’ll explore the path from imagination to understanding. Because the real challenge isn’t just building smarter AI. It’s building AI wisely.

And wisdom, as it turns out, requires something scaling can never provide: feedback.

Notes & Further Reading

The entire argument, including a framework for evaluating information systems and detailed AHI design principles, appears in my white paper, “The Attention Crisis: Language, Meaning, and the Architecture of Augmented Human Intelligence.”

The three-layer model of reality comes from Yuval Noah Harari’s Sapiens: A Brief History of Humankind.

The neurobiological foundations draw on Robert Sapolsky’s Behave: The Biology of Humans at Our Best and Worst.

The UN’s findings on Facebook’s role in Myanmar appear in the Report of the Independent International Fact-Finding Mission on Myanmar (2018).

Token volume estimates are derived from publicly available data on global text production. See the whitepaper appendix for methodology.

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“My experience is what I agree to attend to.” - William James.

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In 2017 Google researchers published a paper with a bold title: Attention Is All You Need. It introduced the transformer architecture that powers every major AI system today: ChatGPT, Claude, GPT-N, and more. The breakthrough wasn’t more data or bigger models. It was selective focus: teaching systems to dynamically weight which parts of the input matter most for the task at hand.

Here we are today, drowning in information we can’t process, distracted by notifications we can’t ignore, building AI systems that demand ever more of our fragmenting attention. What if the 2017 paper got it right, not just for AI, but for us?

What if attention isn’t merely a technical mechanism but the fundamental constraint that shapes all intelligence, biological and artificial?

The Technical Insight

Before transformers, neural networks processed sequences linearly, like reading a sentence word by word with no ability to jump back or look ahead. They had fixed, limited memory of what came before. Transformers changed everything with a deceptively simple idea: let the model decide what to pay attention to. When processing the word “bank,” does it mean riverbank or financial institution? The attention mechanism looks at surrounding context: “deposit,” “loan,” “account”, and weights those words more heavily. It doesn’t process all input equally. It prioritizes.

This wasn’t just an incremental improvement. It was a fundamental paradigm shift, a sudden change in behavior that rewrote what was possible. Models could now handle longer contexts, understand deeper relationships, and scale to previously intractable problems. But here’s what matters: the breakthrough came from an architectural constraint, not raw power. By forcing the system to allocate limited attention strategically, the researchers discovered something profound.

Selective focus isn’t a limitation to overcome. It’s the mechanism that makes intelligence possible.

Nature’s Version

The human brain processes around 11 million bits of sensory information per second. Your eyes, ears, skin, and other senses deliver a constant flood of data. Your conscious awareness? About 50 bits per second. That’s not a bug; it’s an engineering feature. The brain doesn’t try to process everything. It can’t. Instead, evolution built attention mechanisms in the way of sophisticated filters that decide what’s signal and what’s noise, what gets through to conscious processing, and what stays in the background. Daniel Kahneman called this our attention budget. Donald Hoffman went further: evolution shaped perception not for truth, but for fitness. We notice what helps us survive, not necessarily what’s real.

When you cross a busy street, your attention system amplifies movement in your peripheral vision while suppressing the conversation you were having. When you search for your keys, it highlights small metallic objects while dimming everything else. This filtering isn’t passive selection. It’s active suppression. Your brain constantly inhibits irrelevant information, not just spotlighting what matters. And here’s the critical part: attention is metabolically expensive. The prefrontal circuits that control attention consume a disproportionately large amount of energy relative to their size. Evolution doesn’t invest in costly mechanisms unless they are essential.

Why? Because unlimited processing would be catastrophic. An organism that tried to consciously attend to everything: every sound, every shadow, every internal sensation, would be paralyzed. Or eaten.

Attention evolved not despite limited capacity, but because of it. The constraint is the feature.

The Cost We’re Paying

Our attention mechanisms evolved in an environment of scarcity: limited threats, limited opportunities, limited information. We now live in the opposite. Social-media platforms employ thousands of engineers whose sole job is to capture and hold your attention. Notification systems exploit the same dopamine circuits that once kept us alert to predators. News feeds leverage novelty bias, the exact mechanism that made our ancestors notice rustling grass. These aren’t accidents. They’re attention-extraction systems, engineered to hijack circuits that can’t tell a status update from a threat.

The result? We train our attention systems on trivia while complex, important problems go unexamined. We build neural pathways for distraction and atrophy the circuits for sustained focus, and it’s getting worse. The average person now switches tasks every three minutes. Deep work involving the sustained attention required for learning, creativity, and wisdom is becoming increasingly rare.

This matters because attention isn’t just about momentary focus. What we attend to literally shapes our neural architecture. The brain is plastic. It rewires based on use. Neurons that fire together wire together. If you spend hours scrolling, you’re training your brain for scrolling. If you spend hours in deep focus, you’re building capacity for deep focus. One of the many marvels of the human brain is that you can rewire it based on your own decisions and actions.

Attention isn’t neutral. It’s formative. What we choose to notice reveals what we value. To attend to another person’s pain is compassion. To ignore it is indifference. Our repeated choices literally wire who we become.

What This Means for AI

Here’s where it gets interesting. Both biological and artificial intelligence face the same fundamental constraint: limited attention in an unbounded information space. The current AI race assumes more compute solves everything: bigger models, more parameters, more training data. But we’re already seeing diminishing returns. GPT-N is better than GPT-3, but not proportionally better given the increase in compute.

Simply scaling up without selective focus is like trying to live wisely by doing everything at the same time. We can add data, energy, and speed, but without direction, we only amplify noise. Busyness is not mastery. Efficiency is not intelligence. Why? Because the problem isn’t raw processing power. It’s attention allocation. A system that processes everything equally is wasteful. A system that can’t prioritize can’t reason about complex, multi-step problems. A system without an attention architecture can’t adapt to novelty. The transformer breakthrough worked precisely because it imposed attention constraints. The next breakthrough won’t come from removing those constraints. It will come from making them smarter.

This is why I focus on AHI (Augmented Human Intelligence) rather than AGI (Artificial General Intelligence). The current approach of building monolithic models that try to do everything is architecturally flawed. It’s the AI equivalent of trying to make a brain with more neurons but no specialized regions. Better AI means modular systems where specialized components handle specific tasks, coordinated by attention mechanisms that route problems to the right processors and integrate results just like the brain. Most importantly, these systems should augment human attention, not compete for it.

Most current AI systems are attention thieves. They demand that we read their outputs, verify their results, prompt them effectively, and manage their limitations. The result is that we are spending a lot of time reviewing, highlighting their use, and scrutinizing their output, when what we really want is for them to provide us with the relevant information in a compressed form, so that we (humans) can synthesize and build. They fragment our focus across tools, screens, and contexts. We should be building attention amplifiers: systems that handle routine processing so humans can focus on judgment, strategy, and meaning. Systems that compress noise so we can attend to the signal.

This is Augmented Human Intelligence: a hybrid architecture of awareness where humans bring values and judgment, and AI brings endurance and pattern recognition. When AI filters noise, humans focus on meaning. When humans define meaning, AI aligns its attention accordingly.

Intelligence Is Attention Plus Architecture

The 2017 paper got it right, just incompletely.[^1]

Attention is all we have, all we need, but only if we understand what attention actually is.

It’s not merely a technical mechanism for weighting tokens in a sequence. It’s the fundamental principle that lets bounded systems operate in unbounded environments. It’s how intelligence navigates the gap between infinite possibility and finite capacity. For AI, this means a shift away from brute-force scaling and more towards architectures that allocate attention strategically, dynamically, and efficiently. For humans, it means recognizing that our attention is both our most valuable resource and our most vulnerable point of influence. We can’t opt out of the attention economy, but we can build better defenses and invest in better tools.

That morning two years ago, when my attention had narrowed to a single desperate loop, I was experiencing exactly what I’ve described here, a system under stress, defaulting to survival mode, losing the capacity for strategic allocation. Recognizing this led to the Mastery of Life framework: treating fulfillment as an optimization problem where competing domains (health, relationships, growth, contribution) vie for finite attention. Just as the transformer learns to assign attention weights to data, we can learn to assign value weights to experience. We’ll explore this in more detail in Week 7.

The question isn’t whether we have enough attention to solve our problems. We never will.

The question is whether we can build systems, both technological and social, that help us attend to what matters.

That’s the architecture challenge of our generation.

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Next week: How modular systems coordinate attention across specialized components, and why this matters for both AI safety and capability.

[^1]: Vaswani, A., et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 30. arxiv.org/abs/1706.03762

For a deeper exploration of how attention mechanisms constrain and enable intelligence across biological and artificial systems, see my whitepaper: Attention Is All We Have.

This is part of an eight-week series exploring how biological principles can inform better AI architecture.

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Thirty million times.

Thanks for reading James Maconochie | Architecture & Attention! Subscribe for free to receive new posts and support my work.

That number stopped me cold. Not because it’s precise, it’s a thought experiment, after all, but what it reveals is important: the brain’s magic isn’t brute force. It’s architecture.

After 18 months studying cognitive science, neuroscience, and evolutionary biology, I’ve come to see something remarkable. We’re living in a moment where two forms of intelligence are meeting for the first time: one built from carbon and refined over four billion years, the other built from silicon and refined over a few decades.

Both are extraordinary. But the real question isn’t which will win. It’s what they can teach each other about how intelligence actually works.

The 30-Million-Fold Efficiency Gap

Let me back up to that energy calculation. I compared the total energy consumed by all human brains that have ever existed, what I call the Evolutionary Processing Unit (EPU), to the energy used to train a single GPT-4:

- The EPU’s energy bill**: ~1.4 million TWh

- GPT-4’s training cost**: ~50 GWh

The difference is a factor of approximately 30 million.

This isn’t just big. It’s physically insurmountable for our current approach. You could train GPT-4 30 million times for the energy evolution spent optimizing the brain’s architecture.

Which raises an obvious question: if intelligence were just about computational scale, wouldn’t we be closer to AGI by now? The persistence of AI’s limitations, common sense reasoning, causal understanding, continuous adaptation, suggests we’re missing something fundamental.

Are we racing ahead with our silicon brains, or are we circling an older truth that evolution already solved?

Evolution’s Four-Billion-Year R&D Project

Here’s what I’ve learned: evolution didn’t create intelligence through raw computational power. It created intelligence through architectural innovation, refined across billions of years of trial and error.

The human brain, what I call the Biological Processing Unit (BPU), is the product of that optimization process. Every species, every neuron, every failure was a data point in a computation so immense that even our fastest supercomputer, operating at 1.7 exaFLOPS, would need trillions of years to replicate it.

And what did that process discover? Not a bigger processor. A better design.

The Brain as Confederation, Not Monolith

The most striking thing I’ve learned about the brain’s architecture is that it isn’t a single, unified processor. It’s a confederation of specialized systems, each optimized for specific functions:

- Sensory systems that filter and prioritize incoming information

- Memory systems that consolidate and retrieve experience

- Emotional systems that assess value and urgency

- Motor systems that coordinate action

- Executive function (the prefrontal cortex) that orchestrates these modules toward coherent goals

This isn’t just division of labor. It’s a fundamentally different architectural principle than the monolithic models dominating AI today.

Think about it: when you decide whether to accept a job offer, you’re not running a single massive calculation. Different parts of your brain are processing in parallel, one assessing financial implications, another evaluating social fit, another imagining future scenarios, while your executive function weighs these inputs against your values and makes a decision.

This modular architecture provides several critical advantages:

Specialization without brittleness: Each module can be optimized for its specific function without compromising the system’s overall flexibility. Your visual cortex is exquisitely tuned for pattern recognition, while your hippocampus specializes in episodic memory. Neither has to be good at the other’s job.

Graceful degradation: When one module is damaged or overwhelmed, others can partially compensate. The brain’s wrinkled outer layer, the cerebral cortex, exhibits remarkable plasticity. When areas responsible for vision are damaged, neighboring regions can gradually take over visual processing functions. Stroke patients can sometimes relearn speech or movement as undamaged cortical areas rewire themselves to handle these tasks.

Efficient resource allocation: Not every module needs to be “on” at full capacity all the time. Your brain dynamically allocates attention and energy based on context. Walking down a familiar street requires minimal conscious processing; walking down a dark alley in an unfamiliar city activates multiple systems simultaneously.

Continuous learning at multiple scales: Different modules can update at different rates. Your motor cortex might refine a tennis serve over weeks of practice while your prefrontal cortex simultaneously updates its model of a colleague’s reliability based on a single conversation.

The Coordination Challenge

But here’s where it gets really interesting: having specialized modules isn’t enough. They need to work together.

The prefrontal cortex serves as a dynamic orchestrator, not a top-down dictator, but rather like a conductor who knows which sections of the orchestra to bring forward at different moments. This coordination itself is learned and plastic, adapting based on experience and context.

When you’re driving and a child runs into the street, your brain doesn’t deliberate. Visual cortex detects motion, emotional centers flag threat, motor systems execute braking, all coordinated so rapidly it feels instantaneous. That coordination is itself a learned pattern, refined through evolution and individual experience.

What Current AI Architectures Miss

Most large language models are monolithic. They excel at the tasks they’re trained on, but they can’t easily:

- Reason causally about interventions and counterfactuals

- Learn continuously without catastrophic forgetting

- Allocate resources dynamically based on problem difficulty

- Explain their reasoning in terms humans can inspect and trust

- Adapt strategies when operating outside their training distribution

These aren’t just implementation details. They’re symptoms of a fundamental architectural mismatch.

The brain evolved these capabilities because it had to. Evolution optimized under severe resource constraints: limited energy, limited space, limited time to learn before predators struck. Monolithic processing wasn’t an option. Modularity wasn’t a design choice; it was survival.

The Human Angle: Beyond Processing

What makes this even more interesting is that the BPU doesn’t just process, it cares. It wonders. It gets curious.

Curiosity, imagination, surprise, these aren’t inefficiencies that evolution failed to optimize away. They’re features, born to navigate uncertainty. Human intelligence thrives on ambiguity. We value metaphor and story because they compress complexity into meaning.

AI can generate those patterns now, but it doesn’t yet understand them the way we do, because understanding requires something the BPU developed through embodied interaction with the world: causal reasoning.

This is what Judea Pearl calls climbing the “ladder of causation”:

- Seeing: observing correlations in data

- Doing: understanding how interventions change outcomes

- Imagining: reasoning about counterfactuals, about “what if”

Current LLMs are remarkably good at the first rung. They excel at seeing patterns. But they lack the innate scaffolding for doing and imagining that the EPU built into the BPU through four billion years of embodied interaction with reality.

A Different Path Forward

This is why the 30-million-fold energy gap matters. It’s not just about efficiency, though as AI energy consumption threatens to overwhelm power grids, efficiency certainly matters. It’s about what that efficiency reveals: intelligence emerges from structure, not just scale.

What if we built AI systems more like the brain? Not by slavishly copying every detail, we’re engineers, not neuroscientists, but by adopting the architectural principles evolution discovered:

- Specialized modules for perception, memory, causal reasoning, and value assessment

- Dynamic orchestration that learns coordination strategies

- Continuous plasticity at multiple timescales

- Embodied grounding in action and consequence

- Resource constraints as design features, not bugs

This isn’t just theoretical. The research directions are clear: modular architectures, causal inference frameworks, continual learning systems, attention mechanisms that actually allocate scarce resources rather than just weighting inputs.

The Recursive Loop

In a sense, the most promising path to artificial intelligence isn’t linear at all. It’s recursive, a feedback loop between two learning systems: one biological, one artificial.

The BPU shows how to build intelligence under constraint. The GPU shows what’s possible with abundance. The question is whether we can learn from both, whether we can build systems that combine the architectural wisdom of evolution with the computational power of modern hardware.

The goal isn’t to replicate the human mind. It’s to learn from it wisely. If the BPU is the teacher and silicon is the student, then the real lesson is humility.

Intelligence grows not by scaling alone, but by listening, to the feedback of reality, to the signals of constraint, and to the wisdom evolution already embedded in us.

What’s Next

Here’s what humbles me most about this journey: the more I learn about the brain, the more in awe I am. Not as mysticism, but as engineering.

Evolution ran a four-billion-year experiment with trillions of parallel trials, each one literally life-or-death. The BPU is the result: an architecture so elegant, so efficient, so robust that we’re only beginning to understand its principles.

The path to AGI won’t be found by training ever-larger monolithic models. It will be found by learning from the master architect: evolution itself.

Next week, I’ll explore attention, both as a technical mechanism in AI and as the human capacity that makes all learning, all understanding, all wisdom possible. Because if modularity is the brain’s architecture, attention is its operating system.

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Notes & Further Reading

The 30-million-fold energy efficiency calculation is detailed in my LinkedIn post from November 5, 2025.

For the complete technical argument on evolutionary computation and modular AI architectures, see my research paper “Beyond FLOPS: The Evolutionary Processing Unit and the Roadmap to AGI” on my website.

The foundational arguments about the ingredients of human intelligence appear in my earlier post, “AGI - The Human Angle”.

Thanks for reading James Maconochie | Architecture & Attention! Subscribe for free to receive new posts and support my work.

I see a similar inflection point in AI today.

The dominant approach assumes intelligence emerges from scale: more parameters, more data, more compute. But biological intelligence didn’t evolve that way, and I don’t believe artificial intelligence will, either.

The brain isn’t a monolithic processor that simply grew larger. It’s a distributed system of specialized modules, each optimized for a specific task, like sensory integration, causal reasoning, or emotional valuation. The true magic lies not in raw processing, but in coordination and feedback, how these components communicate, prioritize, and adapt.

That’s why I’m exploring what I call the architecture of attention: the design principles that let intelligence focus, reason, and adapt based on what matters in the moment.

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What I Bring to This

I’ve spent 25 years working at the intersection of complex systems and human needs, from modernizing state credentialing platforms to scaling genomic data systems. I studied civil engineering at Imperial College London and MIT, disciplines that teach how structures bear load, how systems fail, and how to build things that last.

Over the past year, I’ve been researching AI architecture, human cognition, and evolutionary biology. I’ve developed frameworks for how biological processing principles might inform more capable and aligned AI systems.

That work isn’t ready for academic publication, but it’s too important to keep in a drawer.

So, I’m bringing it here to share progress in public, invite critique, and connect with others who are asking similar questions.

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What to Expect

This isn’t a newsletter about AI hype or doom. It’s about architecture, how we build intelligent systems that can actually reason, adapt, and enhance human capability.

A critical distinction: I’m focused on AHI (Augmented Human Intelligence) rather than AGI (Artificial General Intelligence).

This isn’t just semantic, it’s architectural.

The current race toward scaled-up LLMs serves the interests of chip manufacturers and cloud providers more than it serves actual capability advancement. These systems are extraordinary, but they’re now colliding with architectural limits that more compute alone can’t overcome.

The alternative isn’t to abandon AI, it’s to rethink it.

To build systems designed from the ground up to enhance human judgment rather than replace it.

This modular approach combines specialized AI capabilities with human reasoning, attention, and values.

It not only produces better outcomes, but it also dramatically lowers infrastructure barriers, making sophisticated AI accessible beyond the handful of organizations that can afford multimillion-dollar training runs.

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Here’s what you can expect:

The Technical: Deep dives into modular AI architectures, attention mechanisms, and system design.

The Philosophical: What we mean by understanding, agency, and value alignment.

The Practical: How organizations can implement responsible and effective AI systems today.

The through-line is systems thinking: recognizing patterns across scales, understanding how constraints shape solutions, and asking not just “can we build this?” but “should we, and if so, how?”

I’ll draw on evolutionary biology, cognitive neuroscience, economics, and infrastructure engineering.

If that sounds scattered, I’d argue it’s necessary; intelligence itself is an integration problem.

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An Invitation

I’m writing for researchers exploring alternatives to pure scaling, for leaders navigating AI implementation, for policymakers crafting sensible regulation, and for anyone curious about building technology that genuinely serves humanity.

I don’t have all the answers.

But I have frameworks worth testing, questions worth asking, and 25 years of experience building complex systems that actually work.

If that sounds interesting, welcome.

Let’s explore how to build intelligence, artificial and human, that truly serves understanding.

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This newsletter explores how we can move beyond brute compute to create systems that think, adapt, and collaborate with us.