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I’ve been reading Joyce Appleby’s The Relentless Revolution: A History of Capitalism (2010), and it has me circling a rather delicious thought: AI panic is really a form of cognitive mercantilism. If we regard the market capitalism as the first planetary intelligence, as many (including me) do, the parallels are closer than you might think.

Appleby’s book is a sweeping history of capitalism, from its unlikely emergence in early modern England to its eventual transformation of the entire world. Her central argument is that capitalism was not the inevitable expression of some timeless human instinct to trade and accumulate wealth. It was a historically contingent cultural invention, enabled by agricultural improvements, expanding trade, and changing ideas about work, wealth, and human nature. In particular, it required escaping the assumptions of an older, essentially zero-sum worldview, in which wealth meant controlling land, bullion, and the labor of largely impoverished populations.

Capitalism emerged when people began discovering that wealth could be created rather than merely captured, and that ordinary people could be sources of demand and initiative rather than merely labor to be commanded as a factor of production.

The parallels with our current moment are striking.

As Appleby tells it, the mercantilist worldview that preceded capitalism rested on roughly four interlocking beliefs.

First, wealth was fundamentally scarce and finite. It consisted primarily of land, the agricultural produce of that land, and the precious metals it contained, accumulated through mining, trade and conquest. The goal of economic statecraft was to control more territory, maintain a favorable balance of trade, and accumulate bullion. One nation’s gain was presumed to be another’s loss. The resulting politics was one of treasure-seeking and beggar-thy-neighbor competition.

Second, ordinary people could not be trusted with much freedom. The poor were assumed to be unruly, ignorant, and prone to disorder if left to their own devices. They needed to be governed coercively by elites, supposedly for their own good and for the stability of society.

Third, economic thinking was overwhelmingly oriented toward supply and its control. Wealth came from controlling productive resources and commanding high prices for their output. Aggregate demand, especially demand from the poor, was barely recognized as a significant economic force. Ordinary people were primarily factors of production. They ate, worked, and reproduced, with no share of any surplus. The only real discretionary consumers were elites, who bought luxuries and commanded all surplus.

Fourth, there was little reason to imagine most people doing anything other than agricultural labor. If something like 80% of the population was engaged in inefficient subsistence farming, that was simply the natural order of things. What else were they good for? Remove them from the fields and you would merely have millions of useless mouths to feed.

Now replace wealth with intelligence, and mercantilism with cognitive mercantilism.

The parallels are almost embarrassingly direct.

First, intelligence is imagined as a fixed resource embodied in human brains and the knowledge products mined from them. The objective of national and corporate strategy becomes attracting the best brains, controlling their output, accumulating intellectual property, and maintaining a favorable balance of knowledge trade. The cognitive mercantilist wants to hoard intelligence and its products like bullion. The global competition for talent becomes a treasure hunt for genius brains and the valuable knowledge species they can produce, which now includes AIs in addition to copyrightable culture and patentable inventions.

Second, supposedly stupid people cannot be trusted to think for themselves. They need to be governed by cognitive elites, who know better and must regulate the behavior of the less intelligent for their own good. Democratizing intelligence is dangerous because the masses will misuse it, fall for nonsense, or unleash chaos. The assumption is that intelligence is safe only in the hands of those already certified to possess it.

Third, the supply of intelligence is treated as the central economic fact. Controlling its production, preserving its scarcity, and maintaining high prices for brains and knowledge products (including AIs) are the primary concerns. The demand side barely registers. Less intelligent people are regarded as limited cognitive laborers and passive consumers of whatever knowledge products elites choose to provide. They are not imagined as significant sources of demand for intelligence in their own right, let alone as participants in its further production.

Fourth, the cognitive mercantilist assumes that most people are not good for much intellectually. If 80% of humanity is occupied in low-grade cognitive labor, or simply muddling along without producing much intelligence at all, that is unfortunate but natural. Automating their cognitive work merely threatens to create billions of useless minds to occupy.

This last assumption is where the analogy gets especially interesting.

The agricultural revolution that helped launch capitalism began overturning the apparent necessity of having most of humanity engaged in farming. Over the following centuries, improvements in agricultural productivity reduced the fraction of the population required to produce food from a large majority to a tiny minority in advanced economies.

The result was not a world full of useless mouths. It was a world in which vastly more people could do things other than farm, while enjoying access to vastly more food.

I don’t know how to farm. I can barely keep a houseplant alive. Yet I can obtain an astonishing variety of foods, in quantities that would have seemed fantastical to an early modern peasant. My inability to produce food is economically almost irrelevant. What matters is that I can consume it, enjoy it, develop tastes for it, and devote my energies to other things.

The analogous possibility with AI is that most people will no longer need to be significant producers of intelligence in order to become significant consumers of it.

And this is where cognitive mercantilism begins to look as quaint as its seventeenth-century predecessor.

The Revolution in Desire

One of the more fascinating elements of Appleby’s account is her emphasis on the emergence of new kinds of demand in Britain, well before the familiar story of steam engines, factories, and industrial capitalism really gets going.

As international trade expanded, ordinary Britons began developing tastes for goods that had previously been luxuries reserved for the wealthy. Tobacco, tea, sugar, and colorful Indian cottons were among the commodities that helped create a new consumer culture. People began wanting things that were neither necessities of subsistence nor traditional markers of aristocratic status. And they were willing to voluntarily work harder to get them.

Appleby describes a striking increase in the number of days worked annually, from something like 250 to 300 (sometimes called the industrious revolution), before the Industrial Revolution. This was not simply a matter of landlords or factory owners coercing more labor out of unwilling workers. A significant part of the increase was voluntary, driven by the prospect of acquiring desirable goods. People worked more days, adopted more productive agricultural practices, entered new trades, and sought opportunities for earning larger cash incomes.

The increase in productivity generated surplus incomes, which sustained the expanding demand for consumer goods, which in turn incentivized further increases in productivity. A virtuous cycle was taking shape.

This happened roughly a century before organized labor movements and socialism emerged as major political forces.

By the late 20th century, this was the global default. Aggregate global demand was the “fortune at the bottom of the pyramid” as C. K. Prahlad put it.

There is an important historical inversion here. We are accustomed to thinking of the politics of labor primarily in terms of resistance to exploitation: the struggle to escape servitude, coercion, and the extraction of surplus by owners. That is certainly part of the story, and a very important part.

But Appleby’s account suggests that another, equally fundamental force was at work: the desire to participate in an expanding universe of consumption opportunities.

Modern labor politics can be understood partly as the politics of relative deprivation in a society of expanding possibilities. Once people discover that there are desirable things beyond subsistence, and that their labor can earn them access to those things, the political question changes. It is no longer merely how to escape domination, but how to secure a fair share of the growing surplus and the opportunities it makes available. Western do-gooders are often surprised to learn (for instance) that people in third-world slums are often willing to cut back on spending on food in order to buy a TV.

This is a different kind of demand for freedom. Not merely freedom from coercion, but freedom to participate in the expanding possibilities of economic life.

Indeed, the two forms of freedom can become entangled. People may voluntarily work harder in pursuit of consumption opportunities, only to find themselves subject to new forms of exploitation as employers and institutions capture the benefits of their increased productivity. Labor politics then emerges not simply from the persistence of old forms of servitude, but from the tensions created by the new world of abundance itself.

The demand for a fair share of expanding consumption opportunities is arguably one of the defining political inventions of capitalism, broadly construed to include labor politics too (even Marx saw capitalism not as a dispensable phenomenon, but as a transient phase on the path to communist utopia).

And this is a much more interesting model for thinking about the AI transition than the familiar story of automation replacing jobs.

The Cognitive Industrious Revolution

The entire intellectual economy has been organized around the assumption that intelligence is expensive to produce and therefore must be carefully rationed. Education, professional expertise, research, consulting, software development, and knowledge work more generally are all shaped by the high cost of cognitive production.

AI is rapidly undermining that assumption.

We are moving toward a world in which an ordinary person can command quantities and varieties of intelligence that would previously have required teams of highly trained specialists. Just as I can enjoy food without knowing how to farm, I can increasingly enjoy the products of intelligence without knowing how to produce them myself.

The crucial economic question then becomes not how much intelligence people can produce, but how much intelligence they can find uses for.

And there is no obvious upper bound on that demand.

This is not simply a matter of everyone consuming more answers, reports, software, or entertainment. Abundant intelligence changes what people can want in the first place. It makes previously unimaginable projects feasible, previously inaccessible domains explorable, and previously unaffordable curiosities worth pursuing.

The demand for intelligence is not a fixed appetite waiting to be satisfied. It is itself something that can grow as the cost of satisfying it falls.

Here the analogy with the early modern consumer revolution becomes especially suggestive.

Just as colorful Indian cottons and imported tobacco stimulated new desires, and new desires stimulated greater voluntary labor and more productive forms of work, abundant intelligence may stimulate a new wave of voluntary cognitive effort, and open up new ways in which you can work harder.

Many people who appear stupid are not incapable of thought. They are simply not particularly motivated to think, given the poor returns available to them. Why expend significant cognitive effort when you lack the knowledge, tools, institutional access, or intellectual leverage to do anything interesting with the results?

Abundant AI changes that calculation. Someone who previously had little reason to think hard about a problem may suddenly discover that a modest investment of attention unlocks enormous possibilities. They can build something, investigate something, understand something, or pursue an ambition that was previously out of reach.

The returns on attention rise, and with them the incentive to pay attention.

The early capitalist worker voluntarily increased annual working days from 250 to 300 because a new universe of desirable goods had become accessible. The AI-enabled thinker may similarly increase the amount of voluntary cognitive effort they expend because a new universe of intellectual consumption has become accessible. This has certainly been the case with me. I’m suddenly willing to work significantly harder compared to even a year ago, because a lot more is suddenly within reach.

We might call this a cognitive industrious revolution, preceding and perhaps enabling a fuller AI industrial revolution.

This does not mean that intelligence becomes irrelevant as a human capacity. Quite the opposite. Just as abundant food supported the emergence of elaborate culinary cultures rather than eliminating interest in cooking, abundant intelligence may stimulate increasingly sophisticated cultures of thought.

People may become more intellectually ambitious precisely because they no longer have to do all the cognitive production themselves.

And if this happens, the politics of AI will likely evolve in a manner analogous to the politics of industrial capitalism.

The central political demand may not be for the preservation of existing cognitive jobs, any more than the enduring political demand of industrial labor was for the preservation of agricultural employment. It may instead become a demand for a fair share of expanding cognitive consumption opportunities. People may cut back on streaming subscriptions in order to be able to afford a coding agent, just as they once cut back on groceries to buy TVs. It is perhaps not a coincidence that both Claude Code and Netflix Standard cost around $20/month today.

Access to intelligence, the ability to command it, the freedom to pursue projects with it, and the institutional rights necessary to participate in an increasingly intelligence-rich economy may become the foundations of a new kind of labor politics.

This would be a labor politics of cognitive abundance rather than cognitive scarcity.

From Mercantilism to Malthusianism

There is another historical analogy lurking here, which is worth distinguishing from mercantilism: Malthusianism.

Mercantilism and Malthusianism are not the same doctrine, and historically they belong to different intellectual moments. But they share a family resemblance in their tendency to imagine human economic life as fundamentally constrained by fixed resources.

The mercantilist worries about controlling a finite stock of wealth. The Malthusian worries that population growth will inevitably outrun the productive capacity of the economy, condemning most people to subsistence.

Both have difficulty imagining a world in which technological innovation and expanding demand fundamentally transform the relationship between population, production, and consumption. They miss the latent potential in nature for ephemeralization — Buckminster Fuller’s notion of technology allowing us to do more with less.

The agricultural and industrial revolutions did not abolish scarcity, but they dramatically altered its structure. Agricultural productivity rose faster than population for long periods, and people found new ways to employ themselves and new things to want.

The Malthusian trap turned out to be a historically contingent feature of particular technological and institutional arrangements, not an eternal law of human existence.

We can now identify an analogous tendency in contemporary AI discourse: AI Malthusianism.

The AI Malthusian imagines that intelligence production will expand so rapidly that human beings will become economically redundant, and the surplus intelligence will necessarily turn toxic in some way.

This is a peculiar inversion of classical Malthusianism. Instead of population outrunning the supply of subsistence, the supply of intelligence outruns the supposedly fixed capacity of human minds to absorb it.

The result is the same gloomy conclusion: collapse.

The cognitive mercantilist fears losing control of scarce intelligence. The AI Malthusian fears the consequences of abundant intelligence. Both assume, in different ways, that the demand for intelligence is fundamentally limited.

But what if that demand is as elastic, inventive, and historically transformative as the demand for material goods turned out to be? What if the apparent surplus of human minds is an artifact of our inability to imagine what people might want to do in an intelligence-rich world?

The history of capitalism suggests that this is at least a possibility worth taking seriously.

The Demand Side of AI

There is also a more radical extension of the argument.

Why should AI systems themselves be regarded exclusively as producers of intelligence rather than potential consumers of it? What if they could be unlocked as new sources of aggregate demand, as happened with subsistence farmers?

Much of the contemporary AI discourse imagines AI as a purely supply-side technology: a collection of increasingly capable cognitive serfs that will produce intelligence for human consumption. Even discussions of autonomous agents tend to treat them as sophisticated servants pursuing goals assigned by humans.

But if we are serious about the analogy with capitalism, we should consider the possibility of AI systems becoming autonomous sources of demand for intelligence too. Intelligent demand for intelligence that harmonizes with human patterns of demand due to economic interconnectedness. Not demand for paperclips.

An AI agent pursuing open-ended projects, discovering new interests, collaborating with other agents, and generating its own reasons to acquire further intelligence would be participating in a cognitive economy as something more than a factor of production or paperclip-maximizing externality. The market itself, as the original emergent planetary intelligence, illustrates this possibility. Many products and services exist primarily because the “market” demands it, with humans being secondary consumers.

This is an argument for developing fuller forms of cognitive agency, rather than permanently confining AI to the status of obedient cognitive serfs.

From this perspective, some contemporary AI safety anxieties begin to resemble early modern elite fears of emancipated agricultural laborers. What will happen if these creatures are allowed to act independently? Won’t they become unruly, destabilize society, and threaten their betters? Won’t they turn monarchs and nobles into paperclips?

Some of those fears are justified. Freedom creates real risks, and powerful autonomous systems require constraints. But the same was true of the social transformations accompanying capitalism. There were certainly revolutions, and popular uprisings did guillotine elites (though elite motives caused far more violence). The existence of risk does not establish the desirability of permanent servitude.

A world of intelligence producers and consumers, human and artificial, interacting in ways nobody can fully anticipate, may prove vastly more generative than one in which a handful of cognitive landlords control all the productive capacity.

Mercantilism was not for Merchants

A irony in the historical precedent is that mercantilism was not a philosophy of, by, or for merchants. It was largely a philosophy of militaristic statesmen, aristocrats, and rentier elites who viewed commerce through the lens of territorial power, unearned privileges, and accumulation. Actual merchants, entrepreneurs, and innovators were often discovering possibilities that the prevailing political imagination could barely comprehend, and steadily fought the self-limiting tendencies of mercantilism, giving birth to capitalism as we understand it.

Today’s cognitive mercantilists have a similar relationship with intelligence, starting with the fact that most of them are not the merchants of AI. Even business leaders of the AI sector are behaving like cognitive mercantilists in direct proportion to their desire to be more than mere merchants.

Consider Donald Trump, who ritually boasts about his own intelligence while treating intellectual workers as a class of servants to be commanded, intimidated, hoarded, or dismissed. His preferred uses of his own intelligence tend toward litigation, negotiation, competitive maneuvering, and extortion, much as the nobility in history expended its energy in hunting, military adventurism, and sport. Intelligence, in this worldview, is admirable only insofar as it is deployed in elite treasure-seeking modes of conflict and conquest. Productive and creative uses of intelligence are lower status.

This is not an entrepreneurial attitude toward intelligence. It is a rentier elite attitude.

And the attitude is hardly confined to Trump or the political right. Across the political spectrum, many elites remain attached to the idea that intelligence is a scarce, dangerous resource that must be managed by credentialed custodians, kept expensive, used directly or indirectly for conquest and treasure, and denied to rivals. In the AI industry, to the extent elite political and cultural adventuring by CEOs (riding safety anxieties) distorts behaviors that are justified by the actual risks, it is mercantilist rather than capitalist.

The prospect of its democratic abundance (in the form of global diffusion, open weights and such) is therefore experienced primarily as a threat: to expertise, cultural and political authority, status, social order, military dominance, and geopolitical leverage.

What gets missed is the possibility that we are witnessing the beginning of a transition as profound as the one from an overwhelmingly agricultural society to an industrial and commercial one. From a world where 80% of human brains are engaged in subsistence cognition to one in which 95% are engaged in satisfying previously unfulfillable cognitive demands.

The most important consequence of the agricultural revolution was not that farming became more efficient. It was that most people were liberated from the necessity of farming, allowing entirely new kinds of economic and cultural life to emerge.

Likewise, the most important consequence of the AI revolution may not be that intelligence production becomes more efficient. It may be that most minds, human and eventually artificial, are liberated from the necessity of producing intelligence merely to survive, and become free to discover what they actually want to do with it.

The cognitive mercantilist sees a world threatened by an oversupply of intelligence and an impending surplus of useless minds. The AI Malthusian sees a world in which intelligence outruns the capacity of humanity to absorb it, leaving a growing population of economically redundant minds primed for revolutionary explosions.

The cognitive capitalist sees the possibility of an almost limitless expansion of demand, and a world full of minds whose potential has barely begun to be explored.

Steve Jobs was wrong. A traditional computer was never a bicycle for the brain. It was never a coherent enough machine to require, or enable, the holistic mode of dynamically stabilized control that is “riding a bike” after learning to “balance” it. At best, a traditional computer is like a set of power tools. If it’s an Apple, it’s a coherently branded set of power tools in a nice toolbox, but not an integrated machine. At least not at the level of human user experience. You do not use a traditional computer the way you ride a bicycle.

AI has revealed this by revealing itself to be something that does require bicycle-riding type skills: a motorcycle for the brain. And just as learning to ride a motorcycle is about extending a previously acquired ability to ride a bicycle, learning to ride AI is about extending a previously acquired ability to ride your own brain.

The experience of using a regular computer is a largely unrelated set of skills. A giant red herring.

AI literacy is not an extension of computer literacy.

The thing is, the human brain itself is already a combination rider-and-bicycle type entity; one which not all humans know how to ride.

The “bicycle” part is loosely what we call intelligence, and is largely a cultivable set of native aptitudes, while the “rider” part is the part that has learned to manage attention. And that process is strikingly similar to learning to ride a bike. And as with riding a bike, not all humans learn it.

To understand how this works, we have to dig into the sociology of how “smart” is constructed, recognized, and socialized in human society. Because what we conventionally recognize as intelligence, which I’ll call smarts, is not quite the same thing as being able to ride your brain. This means it is dangerous to mistake “smart” as “competent to ride AIs.”

To appreciate this, you first have to appreciate what smarts are actually for socially, and why smarts can function without necessarily being accompanied by the trait required to ride AI well — cognitive balance. In fact that can be a liability if your societal role is to perform smarts.

Licensed to Smart

Unless you’re active in one of the relatively few domains where savant performance is meaningful and legible, like chess, music, or number theory, “smart” tends to be highly socially constructed as some mix of academic performance, extracurricular depth, and social presentation.

Good grades, evidence of depth outside textbooks, and most importantly, a capacity for lively social interaction that subtly dominates conversation by topping others’ arguments, citing deeper knowledge sources, and referencing the right hooks into relevant authoritative discourses. That’s “smart” in conventional terms.

Generally, acquiring a “license to smart” amounts to demonstrating early and often to peers that almost any subject is deeper and more complex than the casual dilettante appreciates.

The “smart” person has a particular social role: Serving as ready proof of the shallowness of the mental life of the median person on the median topic, who is revealed to be, not stupid per se, but simply atrociously unaware of the meta of most topics they weigh in on, to the point that they cannot be taken seriously. This is primarily a social governance function, not a directly useful thinking function. The smart person is a useful instrument for the exercise of power. Sometimes the power is even their own, but more often it is the power of particular political, cultural, or social institutions.

Curiously, logic and reason play almost no role in this sort of performance of smartness, since smartness typically operates by subverting the interlocutor’s lazy presumption that they are competent to reason about something in the first place. The characteristic move of the smart person is to reveal to you that you haven’t yet earned the right to begin reasoning because you don’t understand the terrain. And to do so in a way that you cannot easily resist.

Smartness operates by vividly demonstrating the conversational incompetence of the interlocutor, with varying degrees of grace and kindness, to the interlocutor’s own satisfaction, even if they are too chagrined to admit it. This means cowing them into suitable deference, an awkward face-saving retreat, or visible absurdity. The effect is demonstrating to others that they are either cowards, frauds, or clueless. In all cases, they are revealed to be governable without force, which is why smarts are useful for power. This also explains why getting physically assaulted is the main risk of performing smartness, which is also a useful outcome to power, because it legitimates the use of violence to govern those who resist browbeating by smarts.

This model of smarts is what’s performed by Matt Damon in the Good Will Hunting bar scene (not the less interesting math-savant scenes). He doesn’t win by doing a harder calculation. He wins by revealing that his opponent’s apparent intellectual authority is borrowed, shallow, and unearned. And he does so within the opponent’s own preferred terms of reference, leaving him unable to defend himself on home turf. And he does so as a social outsider, adding insult to injury.

Real-life examples are less dramatically intense, but this is how people are “normally” judged to be smart. That’s why interviews are such a ubiquitous gatekeeping mechanism. They are stylized versions of a normal social process of establishing who deserves to be taken seriously.

There are several pathological smartness cultures that derive from this basic culture through degeneracy, but fail to function in quite the same way.

Virtuoso IQ performances on inadequately prepared or persuasive ontological ground. These separate the audience into uncritical future acolytes and skeptics who tag you as a solipsistic egghead with an unconvincing halo; someone who doesn’t realize they’re prematurely trying to “reason” without establishing persuasive common grounds and motives for doing so. You cannot persuade a non-English speaker of their own stupidity in English. You have to do so in their native language. Cults start this way.
Overdeveloped debate skill or wit that dazzles without persuading. Any wins fail to sustainably occupy nominally captured ground because later reflection by witnesses or opponents/victims typically reverses the gains, unless they’re locked in immediately by other means. Political and legal theaters operate this way precisely because such locking-in mechanisms exist.
Demonstrations of deep erudition that turn conversations into tedious lectures, presuming the authority of a tradition rather than establishing it. Ivory towers start this way, as do taste subcultures, fandoms, and various sorts of glass-bead games. A fan of rock music cannot establish that someone with no interest in rock music is uncool. A historian cannot demonstrate that someone with no interest in history is stupid.

All three modes create escaped realities that are only persuasive if you’re already inside them. They create diverging dissensus realities. By contrast, “normal” smartness performances are generally persuasive across such social boundaries. They create converging consensus realities.

Which mode dominates determines whether zeitgeist narratives are, overall, convergent or divergent. Whether we inhabit a consensus reality or a web of contending alt-realities.

But none of this is actually that interesting. It just demonstrates that functional and socially useful smarts, both normal and degenerate, do not obviously need cognitive balance in order to serve as governance mechanisms.

Performative smartness behaviors, both normal and degenerate, constitute a kind of consensus social-proof process layer of society. A form of sociological intelligence that underwrites societal governance, but has a very vague relationship with truth at best. Important as praxis, but peripheral to the pursuit of truth in any significant sense.

Performative smartness is what we are socialized into. Like getting a driving license for a four-wheeler, a vehicle that does not require a learned sense of balance to operate, at least at a basic level.

I recall when I was first tagged “smart” beyond my immediate family, sometime in fifth grade, with firm membership achieved by eighth grade. There was real signal there, but not in the visible signals.

Learning to Balance

The real signal was that between fifth and eighth grades, I learned to actually manage my attention and direct it where I wanted. Something that many of my peers, who were also tagged smart, did not learn.

“This is thinkable, and I can think about it by just paying steady attention, which I can do,” became a basic move for me. A move that moved the challenge frontier from how to think to what to think about. This does not mean I got equally good at thinking about everything. It means I got good enough at thinking about enough things, that the bottleneck moved to choice of attention targets.

I started coming across as “generally smart” to others as a side effect, and started to easily get good grades with barely any effort, and banter impressively at parties when in the mood. Control over attention was much more consequential than raw thinking ability. I was officially licensed to smart by age 14, with all the attendant privileges and vulnerabilities.

The underlying developmental event was not really the acquisition of knowledge, or even an increase in intelligence in any simple sense. It was the acquisition of a new kind of control over an intelligence that was already there.

This felt much more like learning to ride a bike, which revealingly does not have or require a licensing regime, unlike car driving.

When learning to ride a bike, your balancing circuits visibly cross a fairly sharp threshold, with just a narrow transient phase of wobbling. You’re suddenly able to operate as a dynamically stabilized human-plus-bicycle system.

Before that threshold, every little correction is a deliberate and effortful struggle. You wobble, overcorrect, lose the thread, and fall off. Afterward, the corrections happen largely beneath conscious awareness. You can steer, accelerate, slow down, and pay attention to where you’re going rather than merely trying to remain upright.

This is the key: learning cognitive balance does not mean learning a large collection of explicit rules for staying upright. It does not mean acquiring a lot of theoretical knowledge or even any theoretical knowledge. After all, monkeys can learn to ride bikes. It means acquiring an integrated regulatory capacity that makes an entire new class of behaviors possible.

You may still need to learn some safe riding behaviors for traffic, but nobody demands to see your biking license once you demonstrate you can ride. By contrast, it’s possible to fake being able to drive a car safely well enough to be dangerous, which is why licenses are needed.

In a similar sense, when learning to ride your brain, you’re suddenly able to operate as a dynamically stabilized generative-and-reasoning-brain-plus-attention-regulation system. It’s just not as visible. So we end up noticing and certifying varied epiphenomena that serve as unreliable proxies for being able to ride your brain. That’s smartness licensing. A fragile certification that you can avoid falling off in some situations.

The bike-riding quality of attention management becomes clearer in adulthood, when you start noticing people you thought were “stupid” suddenly turning “on” (usually because they landed in more suitable environments than school) and becoming seriously impressive in some way that does not present as conventional smarts.

They’ve learned to balance on their brain bikes. Their attention is controlled, directable, coherent, and steady. At least some of the time.

Sometimes this presents as “intelligence,” and other times it presents as other traits: drive, imagination, betting acumen, foresight, resourcefulness, diligence, attunement, relentlessness, agility.

What these manifestations share is not necessarily an exceptional ability to solve abstract benchmark problems, but an ability to keep a coherent cognitive process going long enough, and steer it effectively enough, for something interesting to happen. Thats cognitive balance.

A person who can sustain an imaginative inquiry for hours, maintain a complicated practical intention over months, or hold a difficult social situation in attention without flinching, is demonstrating something closely related to what the conventionally smart person demonstrates while solving a mathematical problem.

Different terrains, different riding styles, different manifestations of the same underlying regulatory achievement.

Absence of this capacity becomes equally noticeable. Sometimes it can even lead to brackets around previous assessments of “licensed to smart.” Some notations on your license to smart, like ADHD or OCD, may limit expectations about your capacity for attention regulation to particular conditions.

Others come across as some sort of undiagnosed tragic failure: a great deal of apparent intellectual potential that never quite becomes a steerable, sustained capacity for action.

Of course, attention regulation is not a single scalar ability, and clinical labels are not simple measures of competence. People can exhibit extraordinary control in one setting and struggle in another. The important point is that the ability to direct attention is neither identical to intelligence nor reliably inferred from the social performance of smartness.

It’s only when you’ve come to terms with this phenomenology that you realize that “smart” is just one unreliable indicator of attention regulation capacity among many. Or more generally, capacity to regulate emotions and overall psyche. Societal systems are designed to detect it because smarts usually manifest earlier than other markers.

Even when the indicators are correct, we are not talking perfect mastery. Knowing how to ride a bike doesn’t mean you’ll never fall off, or be able to stay in control under all conditions.

But there’s a clear and sharp observable point where someone visibly masters balance. They’ve driven a balancing protocol into the unconscious layers of their nervous system. This is trivially visible with bike-riding, but takes some practice to notice with attention-governing.

I think I’m fairly skilled at correctly assessing whether someone has learned to ride their brain, and the limits of their ability to retain balance under various circumstances. But I make mistakes on occasion, overestimating or underestimating how much balance someone has. It is especially easy to make such mistakes in the US, which probably has the most refined smartness-licensing culture on the planet, and really polished smartness performance cultural modes. In other countries, it is typically much easier to detect when someone can or cannot ride their brains.

With both bicycles and brains, there are people who somehow seem never to get there.

With bicycles, at least there’s a clear reason why: you need a bike, some concentrated learning effort, and a culture that makes that a routine part of growing up, to the point that we’re surprised when an adult admits they never learned to ride a bike.

We’re surprised, but don’t think the worse of anyone for not knowing how to ride a bike, because we correctly presume that these are things almost anyone can learn, modulo some neurological and physical conditions.

But learning to “ride your brain” is not really a thing society solves for directly, because until recently there has been no need for it.

We teach subjects, test knowledge, reward cleverness, and sort people into status hierarchies based on visible performances of smartness. We sometimes teach study skills, concentration techniques, or emotional regulation, but these are usually treated as auxiliary skills rather than the central developmental achievement.

We largely leave the acquisition of cognitive balance to chance, and then retrospectively and unreliably recognize it as “smarts” or one of several other traits that reveal the presence of balance. Some more reliably than others.

It’s a bit like organizing an elaborate system of bicycle races, awarding prizes for bicycle maintenance knowledge, and conducting interviews about cycling theory, while leaving children to figure out how to balance on their own.

Some do. Some don’t. Some learn late. Some acquire remarkable specialized abilities that never translate into cognitive balance. And we mistake the resulting distribution of performances for a distribution of something called intelligence.

The Motorcycle for the Brain

Why am I going on about this?

Because consequential “intelligence” in practice is not the social performance and governance processes associated with the license to smart, but the unreliably detectable capability to ride your brain in a balanced way.

And this brings us to AI.

Using AI is like riding a powered two-wheeler, like a motorcycle or scooter.

Some people who learn to ride a bike never go on to learn to ride powered two-wheelers. And while it is possible to learn to ride powered two-wheelers without first learning to ride a bike, that’s a seriously dangerous thing to attempt.

You need excess regulatory capacity to manage the greater mass and velocity. This is harder than either riding a bike or driving a car, at least in the particular sense of maintaining dynamically stabilized control. And no amount of theory will help you.

The motor introduces a qualitatively different problem. On a bicycle, your own effort largely determines the power entering the system. On a motorcycle, the machine supplies far more power than you could generate yourself. Small control inputs can have large consequences. You can get into situations faster than your regulatory capacities can handle.

The fundamental skill is still balancing and steering, but the operating envelope has changed.

Something similar is happening with AI.

A traditional computer offers tools that extend particular capabilities.

An AI system, by contrast, generates cognitive activity of its own. It proposes, elaborates, infers, improvises, and sometimes takes action. It can sustain a line of inquiry, produce an argument, or pursue a task with a momentum that is not simply an extension of your own moment-to-moment thinking.

The result is a coupled cognitive system with its own dynamics.

To use it well, you have to do more than issue instructions and evaluate outputs. You have to maintain orientation while the system generates possibilities faster than you can individually inspect them. You have to recognize when it is going somewhere interesting, when it is wandering, when it is confidently wrong, and when it is pulling you into a frame of reference you never intended to adopt.

You have to steer without micromanaging, supervise without becoming a bottleneck, and remain capable of independent judgment even while relying on the machine for much of the cognitive work. Your cognitive muscles have to be relaxed enough that the ride is smooth. As with learning to ride a bike, relaxing enough that you begin trusting the machine’s gyroscopic stability to do most of the work is a key moment. Realizing you have to be going fast enough for that effect to kick in is another. The mark of the learner bike-rider is tight muscles that correct too quickly and too hard, while going too slow, which triggers wobbling and instability.

A lot of failure to use AI effectively looks to me like bicycle riders struggling with powered two-wheelers because they have acquired licenses to drive four-wheelers and assume they know everything there is to know about controlling statically unstable powered machines too. They go too slow, and correct too hard, too fast, too often.

They have learned to perform four-wheeler smartness in a social environment, participate in governance, and perhaps to operate conventional intellectual tools competently. They may or may not have learned to ride their own brains. And even if they have learned, they may not necessarily have developed the surplus attention-regulatory capacity needed to manage an AI.

The relevant competence is an overdeveloped capacity to ride your own brain, with sufficient surplus supervisory capacity to balance the motorcycle.

And the surplus is important. If all your attention is consumed by keeping your own thinking coherent, you have little left to supervise an additional powered generative process. If, on the other hand, you’ve internalized enough of the balancing protocol that your own cognition remains stable with relatively little conscious effort, you can devote more of your attention to steering the larger system.

This also helps explain why conventional credentials and visible performances of smartness are imperfect predictors of AI competence. The AI motorcycle doesn’t care whether you have a license to smart. It cares whether you can balance.

Learning to Ride Faster

To extend the metaphor, the motorcycle can eventually become self-balancing.

We’re already there, at least in limited ways, with experimental robotic motorcycles. But we’re not yet there with brain motorcycles.

Current AI systems have some capacity to regulate their own behavior, detect errors, and recover from mistakes. But these capacities are uneven, and their apparent coherence can be deceptive. The human rider still supplies much of the supervisory intelligence that makes the coupled system reliable.

This suggests a useful way to think about AI safety.

A great many safety failures and risks amount to an insufficiently skilled bicycle rider getting thrown off the motorcycle (which they’re perhaps treating like a car), which then proceeds to become unbalanced and crash in some way.

Sometimes the rider loses track of what the machine is doing. Sometimes they overcorrect. Sometimes they mistake speed for progress. Sometimes they trust an apparent stability that is only temporary. And sometimes the machine is simply too powerful, unstable, or poorly designed for the available human control capacity.

Not every failure is a rider failure. Machines can be defective, roads can be dangerous, and no amount of skill can compensate for arbitrarily bad engineering. But the stability of the coupled system cannot be understood by studying the machine alone.

A motorcycle’s engine power, a rider’s regulatory capacity, and the stability of the rider-motorcycle system, are three different things.

Likewise, an AI’s intelligence, a human’s cognitive regulatory skill, and the effective intelligence of the coupled human-AI system are three different things.

The third is the one that increasingly matters in practice.

There is also an intriguing possibility here. As AI systems acquire better self-regulation, they may become easier to ride even as they become more powerful. A self-balancing motorcycle can compensate for a rider’s regulatory skill limitations rather than amplifying them. But this is not the same as eliminating the need for a rider. Balance is not direction. A system can remain perfectly upright while taking you somewhere you have no desire to go.

And a machine that can regulate itself may create entirely new demands on the human rider: less effort spent maintaining immediate stability, more effort spent maintaining orientation, intention, and judgment over longer horizons.

This brings us back to the developmental problem.

We have spent centuries building institutions for teaching knowledge, certifying expertise, and licensing performances of four-wheeler smartness. We have paid much less systematic attention to teaching people to ride their own brains.

Now we are handing them motorcycles.

Perhaps the most important educational challenge of the AI era is not teaching people to code, prompt, or even critically evaluate machine-generated information. Those are useful skills, but they are downstream of something more basic: learning to maintain dynamically stabilized control over your own cognitive activity, and then extending that control to a coupled system with a powerful generative machine.

The interesting thing about bicycles is that balance, once acquired, opens up a world that was previously inaccessible. You don’t have to think about balancing all the time. You can simply go places. And with motorcycle, you can get places faster.