I wanted to build a knowledge base for the product at my company. A structured, version-controlled repository of everything the product knows, organized into Markdown files and tracked in GitHub. A year ago this would have been a quarter-long project: documentation sprints, alignment meetings, manual synthesis across teams and data sources. Instead I sat down with an AI and started distilling. I fed it product documentation, internal wikis, support tickets. It extracted structural claims, organized them into interlinked files, and I reviewed, corrected, and governed the output. Within days I had a working knowledge architecture.

I had been building a similar system for my own intellectual work, a governed note-linking system compiled from twenty years of reading notes, lecture fragments, and philosophical drafts. The two projects started reinforcing each other. The same architectural instincts that structured the product knowledge base sharpened the personal one. The philosophical reading I was doing in the evenings kept generating insights I could bring to work in the morning. I started using the AI as a sparring partner for my essays, arguing about Heidegger and Girard after dinner, then waking up to apply the same thinking to product intelligence. I wrote personal web applications and writing tools to test whether I could. Once the loop started, I could not stop. Each completed project revealed three more that were now plausible. Each solved problem surfaced a new frontier. At no point did I arrive at a natural stopping place.

The cultural narrative tells us that artificial intelligence is a labor-saving technology. The machine that will finally deliver the post-work utopia, or at least Keynes’ long-promised fifteen-hour work week. The assumption underneath is that human desire is a fixed bucket. If it takes forty hours to fill the bucket today, and the machine can fill it in four, we will take thirty-six hours of leisure.

My experience suggests the opposite. The bucket has no bottom. And we are not about to work less.

Jevons’s coal

In 1865, the economist William Stanley Jevons published The Coal Question, in which he noticed something counterintuitive about the steam engine. James Watt’s improvements had made steam engines vastly more efficient. The reasonable expectation was that coal consumption would decline: better engines, less fuel per unit of work. What Jevons observed was the opposite. Total coal consumption in Britain was skyrocketing. Efficiency had not satisfied the demand for coal. It had unlocked demand that nobody knew existed. Steam power, suddenly cheap enough to be profitable in a thousand new applications, was being applied to industries that had never used it before. The more efficient the engine became, the more coal the economy burned.

This pattern, now called the Jevons Paradox, keeps showing up whenever a resource becomes dramatically cheaper to use. We do not conserve it. We find new, previously unthinkable uses for it, and total consumption increases. When data storage became virtually free, we did not stop buying hard drives; we started recording our entire lives in high definition. When spreadsheet software made financial calculations instantaneous, it did not eliminate the need for accountants; it spawned an entire industry of quantitative modeling that demanded vastly more calculation.

What I have been living through, sitting at my desk after hours building intelligence architectures that did not exist a month ago, is the Jevons Paradox applied to human ambition.

When the cost of cognitive execution drops toward zero, we do not accomplish our usual tasks and go home. We attempt things that were previously unthinkable. The machine does not replace our effort. It makes our effort so wildly productive that we cannot stop. The lever moves so easily now that the only question left is whether your arms give out.

The bottleneck shifts

In a conversation with Dwarkesh Patel in January 2025, the economist Tyler Cowen described this structural shift with characteristic directness: when AI removes the technical constraints on execution, we become the bottleneck. He was talking about institutions, about how regulatory bodies and cultural inertia would throttle the speed of AI’s impact. I have been writing about Cowen’s broader project elsewhere, in The Claim Upon the Training Data, where I examined his practice of writing for AI training indexes, and in What Won’t Cross, where I used his argument about unassisted writing as the foundation for a larger claim about cognitive formation. But the bottleneck observation is the one that has followed me home.

I have been feeling it in my body. When the tools can build, write, and synthesize at the speed of thought, the only limit remaining is how fast I can think. The bottleneck is my need to sleep. My capacity to hold the architecture in my head. My stamina for sustained orchestration across a dozen simultaneous projects. The technical constraint was a ceiling. The ceiling is gone. What remains is the floor: the biological minimum below which I stop functioning.

Both sides of the AI labor debate share the same assumption and get the same thing wrong. The utopians promising leisure and the doomsayers predicting mass unemployment both treat the demand for human effort as a fixed quantity. The machine either fills it, freeing us, or replaces it, destroying us. Neither side considers the possibility that the machine might expand it. That when execution becomes cheap, the appetite for what can be executed does not stay constant.

It explodes.

The other response

I have described one side of what happens when execution becomes frictionless: the person already biased toward building discovers that the lever now moves without resistance, and pulls it until something in the body gives. That has been my experience, and I suspect it is the experience of many readers of this essay.

There is another response, and it is already visible at scale.

In 2025, writing projects on major freelance platforms declined by roughly a third year over year. A survey of nearly seven thousand creative professionals found that a third of illustrators had lost commissions directly to generative AI. Translators were among the hardest hit. The statistics describe job loss, but the reports underneath describe a psychological toll deeper than unemployment. They describe demoralization. Artists who still have work but have stopped wanting to make it. Writers who can still write but feel something draining out of the act. The word that keeps appearing in these accounts is not “replaced.” It is “pointless.”

This is worth pausing on, because the feeling of pointlessness is not a rational calculation about labor markets. A writer whose income drops can retrain, pivot, adapt. The demoralization runs deeper. It is the experience of watching a machine produce, in seconds, a version of the work that took you years to learn. The output may be worse. It may lack the texture that makes your work yours. It does not matter. The sheer effortlessness of the machine’s production changes the way your own effort feels. What used to feel like craft now feels like friction. What used to feel like earned skill now feels like an inefficiency the market is about to correct.

The philosopher René Girard spent his career studying this structure. In Deceit, Desire, and the Novel (1965), he argued that human desire is fundamentally mimetic: we do not generate our wants from within. We absorb them from others. We want what we see others wanting, and we build what we see others building. In The Economics of Infinite Desire, I explored Girard’s framework at length and traced its consequences through Silicon Valley, Versailles, and the structure of academic life. The key insight for this essay is simpler: desire requires a model. We need to see someone doing the work, struggling with it, making it look possible and worth pursuing. The model shows us not just the desirability of the goal but the plausibility of reaching it as a human activity.

What happens when the model is a machine that does not struggle?

For builders, the machine acts as what I called a “spotter” in The Friction That Was Thinking: a training partner in a cognitive gym, someone who stands behind the bench while you press, who does not lift the weight for you but lets you attempt heavier lifts than you could manage alone. The frictionless AI makes building harder to resist, not easier to abandon.

For others, the encounter with effortless machine execution sends the opposite signal. If desire is borrowed, and the model you were imitating has been replaced by something that makes human participation look vestigial, then the desire to build does not survive the comparison. Girard would call this a thinning of the desire: a want that collapses the moment it is seriously challenged. The writer does not lose the ability to write. The writer loses the sense that writing, as a human act, still means something against the backdrop of a machine that can produce infinite text without effort or intent.

We may be witnessing the early stage of a bifurcation that will define the next decade: between those who use the machine to scale their orchestration until their biology fails, and those who look at the frictionless output and quietly stop wanting to make things at all.

Standing-reserve

There is something I have not confessed yet about the euphoria.

Somewhere in the third week of this, I noticed that I had started resenting meals. Not the food. The interruption. Twenty minutes away from the screen felt like a system failure. I caught myself optimizing my sleep schedule, not for rest, but for output: calculating the minimum hours I could function on, treating my own alertness as a production input to be managed. My attention had become, in my own thinking, a resource to allocate.

I recognized this feeling. Not from personal experience. From philosophy.

Martin Heidegger, in his 1954 essay “The Question Concerning Technology,” described a way of relating to the world that he called Bestand, usually translated as “standing-reserve.” It is the orientation in which everything, including nature, labor, and human beings, becomes a resource held in readiness for further use. The coal is not a substance with its own existence. It is a fuel waiting to be burned. The river is not a waterway. It is a hydroelectric opportunity. Even the forester walking the same path his grandfather walked is, whether he knows it or not, subordinated to the orderability of cellulose for the paper industry.

Heidegger was describing a disposition, not a technology. A way of seeing the world in which nothing is allowed to simply be what it is. Everything is revealed as available, flexible, waiting to be put to work.

The euphoria of hyper-labor is the moment when this disposition turns inward. I was not resting in my finitude. I was mobilizing myself as a resource for my own projects. The tools had become so responsive, so frictionless, that the only remaining inefficiency in the system was my own body. And I was trying to optimize it away.

The joy was real. The productivity was real. The things I was building had genuine value. And underneath all of it was a logic that had no internal stopping point, no built-in signal that says enough, no mechanism by which the person pulling the lever learns to let go.

The discipline we do not have

In previous essays I have been mapping a set of related dangers. In What Won’t Cross, I argued that the formation stage of intellectual work, the hours where a person is being shaped by the act of making something, does not transfer to the machine: the artifact crosses to the new medium, the formation does not. In The Single-Player Game, I described how external scorekeeping can quietly replace the internal game you were actually playing, how the switch from one to the other does not announce itself. In The Economics of Simplified Living, I argued that the gesture of refusal, the “AI-free” badge, the performative rejection of the technology, is itself available for mimetic imitation and therefore is not a solution.

A different danger operates even when the work is genuine, even when the formation is intact, even when the score is internal. The danger is that there is no ceiling.

When the tools were slow, execution imposed a limit. You could not build more than the day allowed. The gap between what you wanted to do and what you could do was wide enough to enforce pauses. Those pauses were the spaces in which you remembered that you were a body, that you needed to eat and sleep and see people and do nothing for a while. The gap was never just an obstacle. It was a governor on the engine.

The governor is gone. The engine has not changed.

Every discipline humans have developed for managing excess was designed to resist temptation toward ease: gluttony, sloth, indulgence, distraction. Every spiritual tradition, every philosophy of moderation, assumes the danger is that we will do too little, consume too much, surrender to comfort. We have entire civilizational architectures for resisting the pull of laziness.

We have almost nothing for resisting the pull of productive intensity.

What does that discipline even look like? I do not think it looks like refusal. I do not think it looks like rationing screen time or setting timers or performing the aesthetics of digital minimalism. Those are responses to distraction, and the problem I am describing is the opposite of distraction. The problem is a focus so total that the body becomes invisible to the mind that inhabits it.

Jevons published The Coal Question in 1865. He named the paradox with precision: efficiency would not conserve the resource, it would accelerate its consumption. British coal production peaked in 1913 and continued at enormous scale for decades after. Nobody changed course. The paradox was identified, understood, discussed, and entirely ignored for nearly fifty years. Naming a trap has never, historically, been the same as escaping it.

I closed the laptop last night. Not because I had finished. I will not finish. The system has no endpoint, and the only thing that does is me. The coal does not know it is being burned. That is the one advantage I have, and I do not yet know whether it is enough.

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Sources & further reading

William Stanley Jevons, The Coal Question (1865). The original observation that efficiency of resource use can increase total consumption. British coal production peaked in 1913, nearly fifty years after the warning.

Tyler Cowen, interview with Dwarkesh Patel (January 2025). The argument that humans and institutions become the bottleneck as AI removes technical constraints. Part of a broader body of work explored in The Claim Upon the Training Data and What Won’t Cross.

Eldar Maksymov, “The Jevons Paradox and Insatiable Humans: Why AI Won’t Empty the Finance Suite” (2026, SSRN preprint). An optimistic application of the Jevons Paradox to professional work, arguing that cheaper intelligence will expand demand for analysis. This essay takes the structural mechanism seriously while questioning whether expanded demand equates to human flourishing.

René Girard, Deceit, Desire, and the Novel (1965). The first systematic account of mimetic desire: we want what others want, and we mistake the imitation for autonomy. The bifurcation between hyper-labor and creative paralysis draws on Girard’s account of how desire thins when the model becomes unimitable. See also Luke Burgis, Wanting (2021), for the most accessible contemporary treatment. I explored Girard’s framework at length in The Economics of Infinite Desire.

Martin Heidegger, “The Question Concerning Technology” (1954). The concept of Bestand (standing-reserve) and the argument that the technological orientation reveals everything, including human beings, as resources held in readiness for further ordering.

Jonadas, The Friction That Was Thinking (2026). The spotter-vs-forklift distinction: the difference between a tool that removes difficulty and a tool that keeps difficulty on the person.

Jonadas, What Won’t Cross (2026). The formation-stage argument: the artifact crosses to the new medium, the person who was being formed by making it does not.

Jonadas, The Single-Player Game (2026). The argument that external scorekeeping quietly replaces the internal game, and that the switch does not announce itself.

Jonadas, The Economics of Simplified Living (2026). The argument that the gesture of refusal is itself available for mimetic imitation, and the case for honest examination over performative rejection.

The last paragraph had finally resolved. I had been sitting with it for forty minutes, the argument rearranging itself, the sentences not quite landing until, at some point I cannot pinpoint, they did. For a moment the file was just a file. The thinking was still warm in the room.

Then something shifted. Not a decision. A browser tab opening on its own. I was estimating. How would this land? Who would share it? Was the opening sharp enough for LinkedIn? The writing had stopped. I was scoring a performance.

I did not choose to switch games. The game switched, and I noticed it afterward, the way you notice you have been holding your breath only when you exhale.

Naval Ravikant has a name for what just happened. Modern life, he argues, runs on two fundamentally different kinds of games, and the most common mistake is applying the scoring logic of one to the other. The mistake is not dramatic. It is slow, directional, and by the time you notice it, the practice has already changed.

Two kinds of games

Most of what culture offers as success is a multiplayer competitive game: salary, status, professional recognition, follower counts. These games have external scores. Other people can see how you are doing. The leaderboard is real, the rules are legible, and winning means something, even if it does not, finally, satisfy.

But some activities have no valid external scoreboard. Peace. The quality of your attention. Character. The kind of clarity that comes from sustained, unperformed thinking. Naval puts it directly:

Warren Buffett makes the same distinction from a different angle. He asks: would you rather be known as the world’s best lover when you are actually the worst, or known as the world’s worst when you are actually the best? Most people give the approved answer. Most people live by the outer one.

Multiplayer games are real, and playing them well matters. Building a career is a multiplayer game. So is getting your work in front of the right people, or earning institutional standing for what you do. The problem is narrower: what happens when you apply multiplayer scoring to activities that have no valid external score. That application is a category error. And category errors, unlike honest mistakes, change the thing you are trying to measure.

The category error

A category error applies the wrong measurement framework to the wrong kind of object. The thermometer does not malfunction when you hold it to a piece of music; it asks a question the music cannot answer. Something analogous happens when you apply external scoring to a single-player game. The consequence goes further than imprecision: the measurement changes what is being measured.

Consider meditation. You can practice for presence or practice for a streak. The physical act is the same: ten minutes, eyes closed, attention returning to the breath. The orientation is not the same. One is a single-player game; the other has been converted into a multiplayer proxy. The measurement cannot capture what the practice is for, and it will increasingly govern the practice. Missing the streak becomes the failure. Presence becomes an unmeasured side effect of something the app is actually tracking.

Something begins as yours and gradually becomes a performance of itself.

Working through a problem on the page, building an argument, finding out what you believe by articulating it: this is the form of thinking I described in The Friction That Was Thinking as most at risk from frictionless tools. There are external signals that you are becoming clearer: a reader who follows the argument, a teacher who points out where you lost them. These signals have value. Making external validation the governing purpose is something else entirely.

When the essay is written toward engagement metrics, the prose starts to optimize for landing rather than for thinking. The opening gets punchy. Complexity gets smoothed. The sentence that is actually most precise gets cut because it will lose people. The subtler damage happens earlier, in the draft: the questions you choose to pursue are the questions that seem shareable. The writing that matters most, the thinking that does not yet know where it is going, stops happening, because it does not translate.

The apparatus is built for one kind of game. It makes the other kind harder, not by prohibition, but by making the multiplayer game constantly legible and the single-player game permanently silent. You are simply never shown the right scoreboard.

When the structure plays you

These activities are not purely internal. Thinking, teaching, producing knowledge: all have a genuinely social purpose. Teaching is for students. Knowledge is produced for a community. What external scores cannot capture is whether the people doing these things have internalized the standards that make the work worth doing, whether they care about what thinking is for, whether they teach in a way that transforms rather than merely delivers. A citation count may loosely track something. Governing your practice by it degrades the practice, in ways that are slow, cumulative, and very hard to see from inside.

I learned this in the academy. Academic departments are evaluated by exactly these metrics. The intention to resist them was genuine. It was also insufficient. The department’s standing, the funding available to colleagues, the institutional weight that protected junior researchers: all of it was downstream of multiplayer games I found philosophically illegible. Refusing to play did not mean accepting the consequences for myself. It meant accepting them for people who had not made that choice.

That is the bind. Individual integrity becomes insufficient when single-player activities are placed inside institutional structures that are themselves multiplayer games. The structure plays through everyone in it, regardless of their intentions. Opting out is not a personal sacrifice you are free to make; it is a decision with collective consequences you do not have the standing to impose on others.

The internal score

Think of a musician practicing alone. Not performing: practicing. A difficult passage, played again and again in an empty room. No audience. No recording. The standard is internal: whether the phrase lands the way it sounds in the mind, whether the hands do what they intend to do. The feedback is immediate and constitutive. The music either happens or it does not, and no amount of applause or indifference changes what happened in that room.

Now think about what changes when the same musician starts preparing primarily for the recording, the performance, the response. The technique may improve; recordings require precision. But the repertoire begins to drift toward what captures well, what has a clear narrative for the program notes, what audiences have heard before and will respond to with recognition. The difficult passage that reveals an unresolved gap in the playing gets abbreviated or avoided; it does not serve the performance. The practice room is still there. Something that used to happen in it is no longer happening.

This is not decline by any visible measure. The musician is performing more, building an audience, growing by every external indicator. The single-player game, the one that required the difficult passage, has been quietly replaced by a multiplayer proxy that looks identical from the outside and produces a different person on the inside.

Bernard Williams described this structure with some precision. Ground projects, he called them: commitments so constitutive of a person that their value cannot be evaluated from outside. They are not goals you hold the way you hold a quarterly objective. They are part of what makes you the particular person you are. Applying external scoring to them is a category error: the measurement does not merely fail to capture something real; it changes what is being valued, and therefore changes the person doing the valuing. You can ask whether the musician hit the notes. You cannot meaningfully ask whether the musician is becoming who they need to become. The question assumes a meeting point between the internal and the external that does not exist, roughly like asking what temperature jealousy is.

The single-player game has its own score. It is simply not available to anyone else.

The wrong scoreboard

The two categories can coexist. You can write for reach and for thinking, pursue recognition and genuine development, play the career game seriously and still know what you actually value. The practical question is narrower: which game is governing which practice, and do you know?

You cannot find out how you are doing at a single-player game by looking at a multiplayer scoreboard. The categories do not translate. And if you keep checking the wrong scoreboard long enough, you stop playing the right game. Not by decision. The game you were playing gradually becomes the game the scoreboard was measuring.

The musician does not decide to stop practicing the difficult passage. She decides to prepare for the performance, and the difficult passage quietly disappears from the program.

The meditator does not decide to stop practicing presence. He decides to protect the streak, and presence becomes something that may or may not be happening while the counter increments.

The writer does not decide to stop thinking on the page. She decides to check how the last piece landed, and the next draft begins a little closer to the scoreboard and a little further from the thought.

None of them notice the moment the game changes. The switch does not announce itself. It arrives as a reasonable question, an innocent glance at the dashboard, a harmless check. And then the practice is answering a different question than the one it started with.

I finished the draft. The argument had resolved. For a moment, the thinking was still warm in the room.

Then I opened the browser.

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Sources & further reading

Naval Ravikant, in The Almanack of Naval Ravikant, ed. Eric Jorgenson (2020). The single-player/multiplayer distinction and the inner scorecard; pp. 143–144.

Bernard Williams, “Persons, Character and Morality,” in Moral Luck (1981). The account of ground projects and their constitutive relationship to personal identity.

Søren Kierkegaard, Concluding Unscientific Postscript (1846). The argument that subjective truth cannot be externally verified without being transformed into something other than itself.

Today, May 1, is Labor Day in much of the world. It feels like the right day to publish a piece I have been struggling with for a while, about what work might still be, once we let the machines have the parts they were always going to take.

It is almost three in the afternoon. You are about to approve a request that has crossed your screen a thousand times before. The fields are complete. The pattern matches. The customer is authorized, the dollar amount is below your threshold, the workflow has done its job. Your finger is already moving toward the approval button when, for half a second, it stops.

Nothing dramatic has happened. You did not catch an error. You did not remember a rule. You hesitated because something in the request, the timing, the wording, the order of the line items, the sender’s tone in the comment field, resembled another request that ended badly. You will not remember which one. You will probably not even know, in the next minute, that you stopped. But the hand knew before the mind.

Most office work is full of these tiny recognitions. The body knows where the friction is before the report does. The eye sees the dropdown and remembers the exception. The hand refuses, for half a second, before any reason has been produced. None of this feels dramatic from inside the day. It feels like getting through it.

We have also learned to perform some of this. The mouse jiggle to stay “active” on a chat app. The careful keyword in a status update so the pipeline reads it as priority. The way a ticket is phrased so the queue routes it past the colleague known to slow it down. We have already begun to format our presence for the systems that watch.

What changes now is that the format has a new reader.

Last week, TechCrunch reported, following Reuters, that Meta plans to capture employee mouse movements, clicks, keystrokes, and navigation patterns to train AI agents that will perform the same kinds of computer work the employees are doing. A few months earlier, OpenAI began rolling out memory features that, with the user’s permission, capture the rhythms of a person’s day across applications to give the model a longer continuity. The first records the work life. The second records the rest of life. The first is corporate; the second is voluntary. The difference between what the company coerces and what the user accepts is becoming difficult to feel.

The immediate scandal is surveillance. It should be. But there is something colder underneath the privacy problem.

The project makes sense.

If work has become a sequence of movements across interfaces, then record the movements. If the task lives in the path from ticket to spreadsheet to dashboard to approval flow, then capture the path. If a job can be shown as a choreography of clicks, pauses, fields, and confirmations, then the worker becomes, at least at that level, a training interface. The employee is not replaced after the work has been understood. The employee is used to define what counts as understanding the work.

That is the more disturbing thought. AI is not making us machine-readable from the outside. It is finding the parts of us that have already been rewritten for machines.

Operational legibility

The most disturbing thing about machine-readable work is not the recording. It is what the recording teaches us about how to appear. The person learns, slowly and without any single moment of consent, to present herself first in the form a machine can process. A biography becomes a data structure wearing a human name. The work trains a metaphysics.

A person becomes machine-readable when the important parts of her work can be captured as operational traces. Not the work as she experiences it. Not the responsibility she bears. Not the years that make one pause different from another. The trace: move here, click this, reject that, approve this, escalate there, leave behind a sequence another system can replay.

This is the next step beyond what I called The Writing That Was Never Yours. In that essay, I examined role-prose: the language we speak as a function, the mask we wear to be heard by the institution. Role-prose is what we say to be legible to other humans inside the system. Operational legibility is deeper. It is not what we say, but the residue of what we do. It is the click, the pause, the sequence of movements captured without our conscious authorship. Role-prose is the mask; operational legibility is the fingerprint left on the glass.

Elizabeth Anscombe, in her short and exact book on intention, made a distinction the office never made and that the AI age is forcing us to remember. The same physical movement, she argued, can be many actions, depending on the description under which the action falls. A man pumping water can be poisoning the household, supplying the vegetable garden, exercising his arm, or finishing his shift. Which of these the action is depends on the description that the agent can own. The log captures the gesture. The description is what has an owner.

Operational legibility erases the description. It preserves sequence and discards the reason the sequence was worth performing.

This did not begin with AI, or even with computers. Frederick Winslow Taylor’s scientific management, in 1911, made the worker visible as motion, timing, output, and deviation from the prescribed method. The body entered management as something to be decomposed. Later, the office learned to do the same to attention. Email timestamps, ticket queues, CRM fields, calendar density, productivity dashboards, version histories, response-time metrics: each made some part of professional life easier to count and compare.

Sometimes this was liberating. A system that depends entirely on someone’s private memory breaks when that person leaves. A bureaucracy, at its best, protects us from favoritism. A record can make power answerable. A process can keep a claim from vanishing because the wrong person was absent that day.

Legibility is not the enemy.

The danger begins when the trace stops serving the work and starts replacing our sense of what the work is. The dashboard does not show what happened. It shows what the dashboard can register. The ticket does not contain the case. It contains the case in the form the queue can route. The keystroke log does not capture a worker thinking. It captures the shadow thinking leaves when it passes through software.

That shadow is useful. It is also not the person.

Meta’s project is a nearly perfect image of the conversion. The employee is not asked to describe the work in language, with reasons, doubts, and responsibilities. The employee is asked to keep working while the system learns from the residue. The model does not need to know what the pause meant if it can learn where pauses usually occur. It does not need the lived weight of the decision if it can reproduce the sequence by which the decision normally appears.

Operational legibility is the condition in which the residue becomes enough.

The resume was already a parser

You can feel the same conversion in the modern job search.

A resume once promised, at least ideally, to present a life to a reader: not the whole life, of course, but a shaped account of a professional path. It had omissions, emphasis, tact. It asked someone to see a pattern in the years.

Then the reader became a parser.

Applicants learned the new etiquette quickly. Use standard headings. Remove unusual formatting. Repeat the exact keywords from the job description. Do not describe the work in the terms that are most accurate if the system is looking for a different phrase.

Do not be interesting before you are ingestible.

The two words name two registers, and the order is the whole problem. Interesting is the register a human uses for another human: a turn of phrase, a strange path, a shape of attention. Ingestible is the register a human uses for a parser: a pattern that survives tokenization, a keyword density, a header the software has been trained to recognize. The career advice, harmless on its surface, is metaphysics in disguise. It says: the parser comes first. Be readable, then, if there is room left, be a person.

There is rarely room left.

No one experiences this as philosophy. It is advice passed from recruiter to candidate, from career coach to student, from anxious applicant to anxious applicant. The metaphysics arrives without anyone having authored it.

The same training happens on platforms. A creator begins with a voice, which is to say a way of standing in relation to an audience. Then the graph begins to teach. This opening holds retention. This sentence loses the room. This face travels. This duration dies. The creator adjusts, then adjusts again, and slowly the work begins to arrive pre-shaped for distribution.

In machine learning, reinforcement learning from human feedback trains a model by rewarding some outputs and discouraging others. On platforms, the direction reverses. The algorithm trains the human. A person performs reinforcement learning on herself until the voice becomes easier for the system to carry.

What looks like self-expression is often already self-translation.

The resume and the creator feed are earlier versions of the Meta story. First, a person learns to present herself as a readable object. Then a machine becomes good at reading that object. Then we call the machine uncanny, as if the uncanniness began with it.

The manager as router

Inside organizations, the conversion is quieter because it looks like competence.

The manager opens the dashboard and sees the red items first. A stale ticket. A customer sentiment score. A delayed handoff. An owner missing from a field. A graph moving in the wrong direction. The world arrives already sorted by the system’s sense of salience.

The dashboard offers the relief of reduced complexity. By turning a messy human situation into a glowing red alert, it desaturates the weight of judgment. The dashboard is a filter that makes action possible while making judgment optional.

But the interface has a way of becoming the room.

After a while, the manager knows the work through the traces it leaves: the escalation, the field, the metric, the update. The habit becomes bodily. Eyes go first to the red number. Fingers move toward the routing action. The question “what happened here?” is quietly replaced by “what state should this object move to next?”

This is where the manager becomes legible as a router.

Some routing should be automated. There is no dignity in moving a ticket from one queue to another when the reason is already settled. Let the machine do that. The problem is that many organizations have made it hard to tell which moments were merely procedural and which only looked procedural because nobody had time to understand them.

An AI agent arrives, and the question becomes painfully reasonable: if the job is moving traces across interfaces, why does a person need to be there?

The answer cannot be that a person is always needed. That would be false. The answer has to be more exact. A person is needed where the trace is insufficient: where the case changes meaning when held against a history no field contains, where the metric improved because the underlying practice degraded, where the correct action would be wrong because this customer, this colleague, this student, this patient is not a token of the usual type.

The manager’s real work begins where routing fails. But if the institution only sees routing, it will train the manager to become what it can see.

The old name for this condition

Heidegger helps because he does not begin with the gadget. He begins with a way the world is allowed to appear.

In “The Question Concerning Technology,” he argues that modern technology is not merely a collection of instruments. It is a mode of revealing. Under Gestell, or enframing, things show up primarily as resources to be ordered, optimized, stored, and used. A river appears as hydroelectric capacity. A forest appears as timber inventory. A field appears as yield. The thing has not vanished. It has been made to appear in the aspect under which it can be mobilized.

That is why the Meta story feels less like a new scandal than a completed sentence. The worker appears as usable behavior. The day appears as training data. The pause appears as a statistical feature. The body at the computer appears as an input stream.

Heidegger’s word matters because “dehumanization” is too blunt. It makes the problem sound like a sentimental reduction of human richness by cold machines. The actual process is stranger. The human being remains present, active, clever, adaptive, overworked, sometimes even proud. But the system receives her under one aspect: as something whose usable pattern can be extracted.

That is enframing with a reader attached.

AI does not invent the frame. It reads what the frame has prepared. It is powerful exactly where the world has already been formatted as standing-reserve: the email, the status update, the keyworded resume, the optimized post, the ticket path, the dashboard action, the sequence of clicks through a business process. We keep asking whether the machine can imitate human work. A better question is how much of human work was already forced to imitate a machine-readable form.

The answer is uncomfortable because it is uneven. There are parts of our work we should be relieved to surrender. There are also parts we surrendered years ago while still performing them ourselves. The completed view is the precondition for a question that, until now, was not really available.

What the clearing reveals

Heidegger, near the end of the same essay, says something less quoted than Gestell and more important. The danger of enframing, he claims, is also the only place the saving power can show itself. “The danger, when grasped as the danger, becomes the saving power.” The phrasing sounds mystical and is not. He is saying that there is no escape from the danger to some unspoiled outside. There is only the difference between living inside the danger without seeing it and seeing it as the danger that it is. The seeing is already a turning.

What was making the seeing hard, all this time, was that we ourselves were performing the operational work. As long as the worker, the manager, the applicant, and the creator were the ones running on the dashboard, the dashboard could be confused for the work. We could pretend the metric, the parsable resume, the optimized post, and the ticket queue were what we did. We were the ones doing them, after all. We bled into them. They felt like work because they cost work.

AI changes that, not by replacing us but by separating us. When the agent runs on the dashboard, the dashboard stops being mistaken for the work. The keystroke log, captured by the model, is at last visible as keystrokes. The ticket path is at last visible as a path. The parsable resume is at last visible as a parse. We can finally see, because we are no longer the ones generating the residue, that the residue was never the building. It was scaffolding.

That is the saving power. Not a relief. An exposure.

The exposure forces a question that the era of the dashboard had quietly suppressed. If the operational was not the work, what was?

The question feels strange because the answer was always present and always demoted. The professional vocabulary of the late twentieth century filed it under headings like “soft skill,” “people side,” “judgment call,” “executive function,” “the human element.” The phrasing is telling. Soft, by contrast with the hard countable work. People side, as if the rest of the work were not. Judgment call, in the singular, as a moment of exception, rather than as a continuous capacity. The vocabulary made the actual work sound peripheral. The periphery sounded peripheral because the operational center had grown large enough to occupy the foreground.

The center was empty.

What was always there, behind the operational, is what every profession, in its older self-understanding, knew it was for. The doctor who has a sense of this patient, not patient-as-token. The teacher who hears that this student is asking a different question than the one the syllabus expects. The lawyer who recognizes that the case cannot be resolved by the precedent because the precedent assumed something that does not hold here. The manager who, from a way the meeting went silent, knows that the team has stopped trusting the project. The engineer who, looking at a build, feels that something is wrong before the test catches it. These are not exceptions to the work. They are the work, when the work is taken seriously. The operational was the way they got reported.

Acknowledgment as the form of work

Naming what was demoted requires a vocabulary older than management theory, kept alive by philosophers who refused the terms of the bureaucratic century. Stanley Cavell named it most carefully. He called it acknowledgment: the comportment in which one treats another person as that person, in their irreducible particularity, rather than as a token of a type. Acknowledgment is not interpretation; it is the stance from which interpretation can happen. It is not knowledge of the other; it is the openness in which the other can come to be known. It is what is missing from the most well-supported algorithmic decision and what is present, often without being noticed, in the briefest exchange between two people who actually see each other.

Acknowledgment is not the only thing the operational hid, but it is the one that gathers the others.

Around it sit judgment, in something like the Aristotelian sense of phronesis: the capacity to make a call you can stand behind, in a particular situation, when the rule does not by itself determine what to do. Not “decision under uncertainty as ranked by the model,” which is something a model can do well. The act of standing for the outcome.

Around it sits attention, in something like Simone Weil’s sense: looking at a situation until you actually see it, rather than classifying it until you can act on it. AI classifies endlessly. It does not, and cannot, attend, because attention requires that the attender be capable of being changed by what she attends to.

Around it sits responsibility, in the older sense in which someone’s name is anexed to a decision and they remain in a position to be addressed about it. Not the audit trail. The person who, when something goes wrong, can say I, and here is why, and here is what I knew, and here is what I would do differently.

These are not soft skills. They are the working content of being a person in a profession. The operational era did not invent them. It compressed them, demoted them, called them the human side, and made the rest the dashboard. AI, by absorbing the dashboard, can return them to the center, but only if we accept the surrender of the operational rather than competing with the machine on its ground.

The new measure of work is not how many cases you processed but how many you actually saw. Not how many tickets you closed but how many you closed in a way you could defend in front of a different person tomorrow. Not how many drafts you shipped but how many of them were yours under a description you can own. The shift is from quantity of trace to density of acknowledgment.

This is also why “perform humanity harder,” the obvious response to the Meta story, fails. The performance of authenticity is itself parsable. Platforms reward it; employers template it; the market turns it into a content strategy by lunch. Acknowledgment cannot be performed for the system because acknowledgment is not for the system. It is for the other person. Its illegibility is not a strategy; it is the form of the act.

Can Gelassenheit scale?

Heidegger had a name for the comportment that uses technology without being used by it. He called it Gelassenheit, usually translated as releasement: a yes-and-no said simultaneously to the technical world. Yes, we use the radio, the airplane, the calculator, the agent. We depend on them. We do not retreat into a Luddite refusal. And no, we do not let any of these devices tell us what our being is. We let the machine be a machine; we do not let it become our metaphysics. Heidegger paired this with what he called openness to the mystery: the recognition that even the technical world rests on a kind of disclosure that is not itself technical.

The yes-and-no is harder to live than it sounds. While the operational still flowed through human hands, it was always tempting to identify with what the hands were doing, because the hands were ours. Saying “yes” to the technical infrastructure and “no” to the metaphysical claim was a distinction without a clean cut. The infrastructure and the work were tangled. Now the cut becomes available. AI takes the operational; we can finally say yes to the operational as operational, without confusing it with the work, and no to the suggestion that the operational was ever the measure of who we were.

This is the saving power, said in the other vocabulary. The danger of enframing, when grasped as the danger, opens the clearing in which Gelassenheit becomes practicable. Not because Heidegger was naively right about technology, but because the consummation of Gestell is what makes its outline finally visible.

Heidegger himself did not believe this could scale. He imagined Gelassenheit as a small, contemplative, “here and now and in little things” practice, lived on country paths and in Black Forest stillness. He did not think a society could organize itself around it. He may have been wrong, or he may have failed at imagination. The reason to think he was wrong is precisely the one he could not have foreseen: the technological precondition for a Gelassenheit at scale did not exist for him. It begins to exist now.

A Gelassenheit at scale would not look like Gestell at scale. Gestell scales by uniformity, by making everything appear in the same aspect. Gelassenheit scales differently, by multiplication of singular acts. A society of acknowledgments scales the way a society of friendships scales: not by metric, but by texture. Many particular relations, each carrying its own weight, none reducible to a sum.

The popular version of this proposition is older than the AI debate. Star Trek has been running it for sixty years. The Federation handed the operational over to computers a long time ago. The replicator handles supply; the ship handles routing; the sensors handle parsing. Picard does not approve tickets. He deliberates about the Prime Directive. He sits with a grieving officer. He has a conversation with Data about what it means to be a person. The crew does the work that only crew can do: the call, the recognition, the attention to a particular situation in which a particular person is at stake. The starship is the precondition for the captain. The dashboard is what frees the deliberation, not what replaces it.

We mistake the show for utopia and miss that it is a coherent social proposition. A society in which most human work is non-operational is a society that has decided, deliberately, to let the operational be operational. It is not a fantasy of leisure. It is a fantasy of seriousness: the seriousness about each other that the operational era was too busy to maintain.

Whether such a society is achievable is a different question. There is nothing in the technology that requires us to reorganize this way. Capital can choose, and may choose, to keep humans inside the operational, racing against the machine. The platforms can choose, and have chosen, to monetize the performance of authenticity rather than the practice of acknowledgment. The danger does not save anyone automatically. It only gives us, for the first time, a clearing in which the work of saving could be done.

What the day already contains

Return to the scene at three in the afternoon.

You hesitated. You did not know why. The system, if it was watching, recorded the delay, time-stamped the next action, and moved on. Tomorrow it will use a few thousand such delays, summed across thousands of employees, to teach an agent how often the human at this stage of this workflow takes a beat. The agent will learn. It will not learn what the beat was for.

You knew. Or rather, your hand knew, and a part of you that does not separate from the hand knew with it. The request did not look like the others, in some way you could not list, and the not-looking-like was a piece of work you had spent years acquiring without noticing, and that work had a name. It was acknowledgment. The request was, briefly, a particular request, made by a particular person, against a history you held without naming. You looked. You did not classify. You attended.

The era of the dashboard called that the soft side of the job. The era of the AI exposes it as the side that is left when the dashboard goes to the machine. The hand pausing over the trackpad is not a vestige of an older way of working. It is the early form of a newer one, in which the operational has been delegated and the human is being asked to do, deliberately and at last, the thing that human work was always doing badly under the disguise of the operational.

The clearing is opening. It does not save us. It puts us in front of a question we had been allowed to avoid for a hundred years.

What is the work, if it is not what the dashboard could measure?

The day already has the answer in it. The pause was the work. The next call you make in which you actually see the person is the work. The decision you can stand behind, against the metric, is the work. The acknowledgment is the work. Heidegger’s “here and now and in little things” was never a smaller scale than the operational. It was the only scale at which acknowledgment is even possible. What changes now is that there may be room for many such here-and-nows, distributed, multiplied, taking up the foreground that the operational has finally vacated.

The question is whether we will claim the room.

---

Sources & further reading

TechCrunch / Reuters, “Meta will record employees’ keystrokes and use it to train its AI models” (2026). Reporting on Meta’s workplace data-capture initiative for training AI agents, including mouse movements, clicks, keystrokes, and navigation patterns.

OpenAI, “Pulse and continuity in personal AI” (2026). Public materials describing memory and continuity features that capture aspects of the user’s daily activity to inform the model across sessions.

Martin Heidegger, “The Question Concerning Technology” (1954). The central source for Gestell, or enframing, and for the formulation of the saving power that grows where the danger is.

Martin Heidegger, Discourse on Thinking (1959). The text in which Gelassenheit, releasement toward things and openness to the mystery, is laid out as the comportment that says yes-and-no to the technical world without retreating from it.

Frederick Winslow Taylor, The Principles of Scientific Management (1911). The early management dream of decomposing labor into measurable methods, timings, and outputs.

G. E. M. Anscombe, Intention (1957). The short and exact account of intentional action as action under a description that the agent can own.

Stanley Cavell, “Knowing and Acknowledging,” in Must We Mean What We Say? (1969), and The Claim of Reason (1979). The development of acknowledgment as a stance toward another person that is prior to interpretation, and that no body of evidence can substitute for.

Simone Weil, “Reflections on the Right Use of School Studies with a View to the Love of God” (1942). The classical account of attention as a moral act.

Maurice Merleau-Ponty, Phenomenology of Perception (1945). The background for the claim that skilled action is bodily contact with a meaningful world.

Jonadas, The Writing That Was Never Yours (2026). Companion essay on role-prose: professional writing that had already separated itself from authorship before AI arrived.

Jonadas, The Friction That Was Thinking (2026). Companion essay on the forms of resistance that build judgment rather than merely slowing output.

If you’ve ever felt an uncanny hollow while reading your own emails or status reports, it’s not because you’re a bad writer. It’s because the system wasn’t asking for a person. It was asking for an operation. This essay is a diagnostic for “Role-Prose”: the language of functions speaking to other functions, and why it is the first thing we should let the machine take.

In Portuguese, we called it prestação de contas: the rendering of accounts. In practice, it was an institutional proof of life. In the academy, it took the form of the research grant renewal or the annual activity report. In the corporate world, it is the quarterly review, the compliance audit, or the impact assessment. The names change, but the genre is identical: a thick series of templates demanding that you describe, in prescribed language, what you have accomplished to justify your funding, your time, or your existence.

Over more than twenty years, across fellowships, grant cycles, postdoctoral positions, and eleven years as a philosophy professor at a Brazilian federal university, I wrote versions of these documents. I also reviewed them, as a referee for funding agencies and peer-reviewed publications, occupying the other side of the same form. I was producer and reader at once, inside a closed system that everyone understood was not really communicating anything, and that everyone produced with the seriousness of someone who believed it was.

What struck me was not the tedium. Tedium I expected. What struck me was a feeling I could not name: I was writing, my hand was moving, words were appearing on the page, and I was not there. The philosophy conference that had changed how I thought about Wittgenstein became “capacity building” and “the dissemination of research outputs to relevant stakeholders.” The seminar where three students and I spent three weeks on a single argument in Kant became “pedagogical activities fostering critical analytical competence.” I was present in every formal sense. I was absent in the only sense that mattered.

The writing had my name on it. The writing was not mine.

This is the first kind of writing AI should take from us. Not because it is worthless. Not because it requires no intelligence. Because much of this writing had already separated itself from authorship before the machine arrived. AI did not dehumanize it. AI exposed that the dehumanization had already happened.

The uncomfortable truth is that a large part of professional writing was never the expression of a person. It was the speech of a role.

Role-Prose

Role-prose is what a function writes through a person.

The weekly status update. The grant accountability report. The meeting recap that exists so a meeting can prove it happened. The postmortem whose acceptable conclusion was visible before the investigation began. The executive summary of a document that was itself written because some other document had to be summarized upward.

From the register I spent twenty years inside: “With reference to the above-mentioned resolution, the undersigned hereby declares compliance with all applicable requirements.” “The research activities carried out during the reporting period are fully consistent with the objectives set forth in the approved project.” “Respectfully submitted for your consideration and deliberation.” No one says these things. The role says them.

In 2023, the cartoonist Tom Fishburne drew what may be the most efficient diagnosis of AI in the workplace. Two workers in adjacent cubicles. One uses AI to expand a bullet point into a professional email. The other uses AI to compress that email back into a bullet point. The loop is perfect. The meaning was always the bullet. The email was the institutional costume the message had to wear. What passed between the cubicles was never communication. It was a function’s artifact, inflated and deflated by whatever tools were available. The TL;DR became the TL;DW: Too Long; Didn’t Write.

This is not a satire of AI. It is a satire of what AI found waiting for it.

A practical test for role-prose: Would this sentence still work if anyone in the role had signed it? Was the acceptable conclusion visible before the writing began? Would anything be lost if no one had been personally present? If yes to all three, it is role-prose.

These texts are not useless. A status update can prevent confusion. A funding report can protect institutions from audit. A research brief can help a team avoid a stupid decision. The problem is not that role-prose has no value. The problem is that we kept confusing its value with authorship.

Role-prose wants competence. It wants accuracy, tone, format, institutional tact. It wants the sentence that sounds serious enough to be forwarded. It wants the conclusion stated firmly enough to guide action and softly enough to survive politics.

It does not want someone to appear.

Jean-Paul Sartre describes a waiter performing his role so completely that no person is visible behind it: movements too precise, manner too attentive, the person suppressed by the function. Role-prose is written in exactly that register. This is why AI imitation does not feel like imitation. There was never a person to imitate. The machine inhabited the function the person had been asked, over years, to become.

“For visibility.”

“As discussed.”

“We are aligned.”

“Further analysis is required.”

“The team is tracking this closely.”

“With reference to the above-mentioned.”

No one speaks this way because no one is speaking. The role is speaking. AI is good at role-prose because role-prose was already a machine language: the language of offices, templates, procedures, and people speaking as functions.

The Office Survives the Person

Max Weber saw the deeper structure before the inbox existed.

Bureaucracy was not, for Weber, merely red tape. It was one of the great technical inventions of modernity: offices, files, rules, written procedures, authority attached to position rather than personality. The point was continuity. The office survives the person. The file remembers what the body forgets. The role carries authority even when the individual changes.

This was not a pathology. It was an achievement.

A permit should not depend on whether the clerk likes you. A contract should not depend on whether the manager is feeling generous. Bureaucracy removes the arbitrary person so the system can become reliable. The impersonality of the form is one of civilization’s genuine protections.

But language changes when the person is removed.

In bureaucratic writing, the sentence is not primarily an expression. It is an operation. It records, authorizes, escalates, confirms, closes a loop. It does not ask to be memorable. It asks to be acceptable. It wants to survive audit, to be safely forwarded, to reduce ambiguity just enough for the next procedure to begin.

That is why role-prose has its distinctive texture: cautious, padded, abstract, mildly dead. Not bad because the people writing it are bad writers. Bloodless because the system is asking the blood to step aside.

Weber’s ideal was elegant in theory. It required conditions that do not exist everywhere. Brazil has a concept for this: the jeitinho brasileiro. The anthropologist Roberto DaMatta spent decades analyzing the tension between Brazil’s formal institutions, imported wholesale from European models, and the underlying social fabric, which has always prioritized personal relations. The rules exist. The impersonality was never delivered. The bloodlessness was required, and so was the blood.

The result is a double perversity: too impersonal where it matters to be addressed, too personal where it matters to be impersonal. At the bottom, the bloodless template nobody reads but everyone must produce. At the top, in institutions like the Supreme Court, the person erupts precisely where the impersonal was supposed to protect. A justice delivers an oral opinion lasting thirteen hours, or grounds a constitutional ruling in poetry. The theatrical personal performance appears exactly where the role’s restraint would have served justice better. Role-prose is what happens when a system can neither remove the person properly nor let the person appear at the right moment.

The Irreducible Claim

What remains?

Not everything with a template is merely role-prose. The same surface can hide different structures.

A research report is role-prose if it merely arranges known information into an expected format. It becomes something different the moment someone has to say, after sitting with the evidence: This is the risk that matters. A condolence note is addressed writing, written for a specific person whose situation changes every word. A draft that surprises the writer is discovery-writing: the author did not know what she thought until the document forced the conclusion into view.

The document type does not settle the question. The relation does.

Think of the last time you spent an hour calibrating the tone of a report: confident but not arrogant, humble but not weak, detailed but not exhausting. You were not writing. You were managing a function. The prose served the position you occupied, not the thought you had. Let AI do that. Verify it. Own the consequences. But do not confuse the removal of that labor with the loss of anything that mattered.

There is a different moment, when the work is real. The evidence does not tell you what to say. It only removes excuses. The data conflicts. The model is confident for reasons you do not trust. The politically convenient answer is available, and so is the evasive one.

Then someone has to decide what matters.

That moment is not role-prose. It is authorship under institutional pressure. The value of the report lives in the sentence someone is willing to stand behind. Not the sentence that summarizes the evidence. The sentence that risks being wrong.

AI can draft everything around that sentence. But if no one ever arrives at the claim they are willing to answer for, the report has no author. It has only a route.

This is why “AI wrote it” is too blunt. It can mean the machine handled the plumbing. It can mean the machine helped a person see more clearly. It can mean the machine supplied the judgment no one wanted to own. Only the last one is fatal.

The danger is not that AI will write too much. The danger is that we will stop asking what kind of writing we were doing.

Counterfeit Presence

Bureaucracy trained us to confuse signatures with presence.

The name on the document was the sign that someone was responsible. Often that was sufficient. The system did not need the full person: it needed a traceable role. But responsibility and presence are not the same thing.

You can be responsible for a document you did not author in the richer sense. Managers do this constantly. Executives do it with speeches. Modern institutions depend on this separation; Weber saw it as the secret of bureaucracy’s power. The office, because it was designed to make the person replaceable, outlasts any individual occupying it.

AI widens that separation. That may be the condition for doing less fake writing, for freeing people from hours spent laundering information into institutional tone, for exposing which documents were procedural all along.

But it also makes counterfeit presence cheap. A leader can send a warm note they did not write. A manager can deliver feedback they did not think through. A company can flood its people with language that sounds attentive while no one has attended. The surface improves as the relation thins.

That is the real danger: not that AI produces words without humans, but that it lets humans appear to have been present where they were only approving output.

The Writing That Is Yours

What authorship requires is not resistance, but presence.

Authentic writing has a phenomenological signature. The prose becomes transparent: you are not aware of choosing words, you feel the thought moving, resisting, turning back on itself. When a sentence will not close, it is not a grammar problem. It is a thinking problem. You have not yet understood what you are trying to say. The form is the pressure through which the thought becomes legible. The friction is the thought occurring.

The other experience is also recognizable. Sitting down to write what you already know you want to say, moving through the motions of a conclusion you arrived at before the work started. The words appear, the document fills, the form closes. And you have the uncanny sense that you could have been anyone. The function produced the document. You were the occasion.

You can feel the difference from the inside. The prose that runs in a groove cut before you sat down. The conclusion waiting at the end of the template. The words that come too easily because they are not really yours to choose. Something registers the absence: even when the document looks professional, even when it receives praise, even when it is technically correct in every particular.

Authorship means being present where the relation requires a person. Not just traceable. Present: someone stayed with the difficulty, changed their mind, arrived at a sentence they did not plan and are willing to answer for.

The accountability reports can go. The status updates can go. The compliance memos, the institutional-tone renderings of information that was already available, the executive summaries of documents that were themselves summaries: the role-prose can be handled by a role.

What cannot go is the writing where you do not yet know what you think. The writing addressed to a specific person whose situation changes every word. The writing where your name on the page is not a signature but an appearance: the sign that someone showed up, stayed with the difficulty, and arrived at something no formula could have guaranteed.

That writing was always yours. The machine cannot take it because the machine cannot do what produces it: be undecided, and present, and at risk.

The voice was already averaged in most of what we wrote at work. Let that go. What stands alone is smaller, clearer, and harder than we expected. It asks more of us than the comfortable mass of role-prose ever did.

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Sources and Further Reading

Max Weber, “Bureaucracy” in Economy and Society (1921/1922). The office survives the person because the office was designed to make the person replaceable. The key claim this essay tests: that impersonality is a protection, not a pathology. Brazil complicates both sides.

Roberto DaMatta, O que faz o brasil, Brasil? (1984). DaMatta’s anthropological analysis of the tension between Brazil’s formal, universalist institutions and the personalist social fabric underlying them. The jeitinho brasileiro as structural consequence: the form demands impersonality; the reality delivers neither impersonality nor the protection it was supposed to provide.

Migalhas, “Fux vota durante 13 horas...” (2024). Reporting on Justice Luiz Fux’s marathon oral opinion at the Brazilian Supreme Court, illustrating the theatrical eruption of the person where the role’s restraint was expected.

AMB, “A poesia e a literatura nos ajudam a fugir da subinterpretação...” (2012). Former Justice Carlos Ayres Britto defending the structural use of literary and poetic rhetoric in constitutional reasoning.

Jean-Paul Sartre, Being and Nothingness (1943). The waiter in bad faith: movements too precise, manner too attentive, person suppressed by role. Role-prose is the linguistic form of bad faith, not a lie but a suppression.

Maurice Merleau-Ponty, Phenomenology of Perception (1945). The blind man’s cane: a tool that, with practice, becomes transparent, the point where the person feels rather than an object the person holds. The phenomenology of authentic writing draws on this: prose becomes the medium through which you feel for something not yet touched.

Hannah Arendt, The Human Condition (1958). The distinction between what a person is (function, role, category) and who appears through speech and action. Role-prose eliminates the “who” by design.

David Graeber, Bullshit Jobs: A Theory (2018). The accountability report as a species of bullshit work: not necessarily useless, but structured to eliminate the person performing it. Graeber’s account of the moral injury, producing institutional seriousness one does not believe in, is the most precise available.

Tom Fishburne (Marketoonist), “AI Written, AI Read” (2023). The cartoon this essay cannot improve on: one cubicle uses AI to expand a bullet point into an email; the adjacent cubicle uses AI to compress the email back into a bullet point. The message was always the bullet. The email was the institutional costume.

Jonadas, The Thought You Didn’t Have (2026). The companion essay on discovery-writing: what AI takes is not the finished thought but the process by which the thought would have been formed.

Jonadas, What Won’t Cross (2026). The formation stage as what does not survive automation.

A followup to What Won’t Cross. That piece made the general case: formation happens in friction, and the gym for thinking has to be built. This one walks inside and picks up the equipment.

The grain changed direction and the plane told me before my eye could.

I was finishing the face of a walnut panel with a hand plane. A plane wants to be pushed with the grain. When you find the direction, the shaving comes off in a single continuous curl, the surface is left glassy, and there is nothing in particular to think about. When the grain turns, the tool stops curling and starts tearing. The change arrived in my wrists first. A hesitation in the blade. A small sound that was not the right sound. The tearout was not yet visible on the face of the panel when I had already lifted the plane and flipped the board.

That lesson was not written anywhere. It was in the pushback.

Matthew Crawford, who left a think-tank job to open a motorcycle repair shop, observed that a repairman has to begin each job by getting outside his own head and noticing things: he has to look carefully and listen to the ailing machine. The machine is not cooperative. It does not return a clean answer to a clean query. You have to attend to it because it requires your attention — it will not reveal what it is doing to someone who is not fully present. It resists, and in the resistance it tells you what it is actually doing, which is almost never what the manual said it was doing.

The wood resists. The room you are teaching resists. The patient whose story does not fit the template resists. In every practice worth its name, something refuses to cooperate, and that refusal is not an obstacle to the work. It is the teacher.

I wrote about what formation is and why it does not cross from one medium to the next. That essay named the loss. This one is about what it would take to replace what was lost.

The loop without the middle

Every practice you care about moves through a cycle: a cue, a routine, a reward. You see a gap in the argument; you work out the position; it holds under scrutiny. You see a patient; you form an impression; it guides the treatment. The cue fires, the routine runs, the reward arrives.

But the routine is never only a behavior. It is the part of the work that forms you. Working out the position is where the thinker learns what she actually believes. Forming the impression is where the physician learns what the chart cannot carry. Something is deposited in the person doing the work: a sensitivity to cases of this kind, an instinct for when a case is not of this kind, a feel for where the work has to be held more carefully than its outputs require.

That deposit is the practice. The reward is just what tells you the loop closed.

Here is what is happening. A system is inserted between the cue and the reward, executing the routine for you. The gap is registered. The position arrives. The reward is collected. The loop closes faster than it ever has.

And nothing forms.

Think of the last time you felt a genuine shift in your own understanding. It did not happen when a perfectly formatted document arrived in your inbox. It happened when a paragraph you were writing refused to resolve, and you had to sit there for twenty minutes until you found the word that fixed it, or when a piece of code failed three times and you had to trace the logic back to the premise you didn’t know you had assumed. That frustration was not the cost of the thought. That frustration was the thought occurring.

The completed loop is what the habit theorists describe without knowing they are describing a problem. The cue fires, the reward arrives, and the middle, the part where resistance would have deposited something in you, has been bypassed. You will not notice it for a while. Skilled practitioners do not lose their skill overnight. The lawyer who has read ten thousand briefs does not forget how to read briefs when she stops writing them herself. The senior engineer who spent a decade debugging race conditions does not forget race conditions when the first pass comes from a model. The loss is downstream: the practice that does not form in the next generation, and the attunement that stops being refreshed in this one. I have called this specific liability calibration debt in another essay; here I want to examine what produces it, and what would prevent it.

What a gym actually is

A gym is a designed environment for the production of difficulty. Not for the output of difficulty, not for trophies or race times, but for difficulty itself, as the point. The gym produces no artifact. What it produces, over time, is the person. And it is engineered, deliberately, to prevent anyone from finding a shortcut.

Good gym design has four features. Equipment that is adjustable, so the load can match your current capacity and grow with it. A protected structure, so the session cannot be cancelled just because there are easier things to do. Other people who take it seriously, because the social environment either reinforces the practice or quietly erodes it. And someone who knows the difference between productive struggle and actual injury, who will let you sit with the hard thing for as long as it is building you and step in only at genuine failure.

We have not built this for thought.

What we have instead is an accelerating pressure to close every loop as fast as possible, because the closed loop is the deliverable, and every open loop looks, from the outside, like wasted time. The structures that used to protect cognitive time under load, the uninterrupted afternoon, the paper-only draft, the meeting where the senior actually read your memo, have been eliminated one by one, and nobody called it a loss because the loops kept closing and the rewards kept arriving.

These four gym features translate directly into design for knowledge work, and I will come back to each of them. But first it helps to understand, precisely, what the load is.

Load and duration

In strength training, time under tension refers to how long a muscle is under load during a set. A person who swings a barbell up with momentum and drops it has moved the weight, but the muscle was under tension for perhaps two seconds. A person who lifts the same weight slowly, pauses at the top, and lowers it under control has kept the muscle under tension for six or eight seconds. The second person lifted less impressively. The second person built more.

The completed loop is the cognitive equivalent of the momentum rep. The cue fires, the system swings the output into place, the loop closes. Time under cognitive tension: near zero.

This is not just an elegant analogy. The brain is physical tissue. Synaptic plasticity, the biological formation of new neural pathways, requires metabolic energy. It is expensive for the body. The resistance of a hard problem is the metabolic trigger that commands the tissue to adapt. When the machine removes the cognitive load, it is not just saving time. It is literally depriving the brain of the metabolic tension required to build a new circuit.

Designing for cognitive time under tension means holding the loop open deliberately. Not because you enjoy inefficiency. Because the time you spend sitting with the unresolved problem, with the argument that is not working, with the data that refuses your model, is the time in which your capacity is being physically built. The delay is not a bug. The open loop is where you were being made into someone who can handle the next version of this problem.

I notice this in my own practice. I read long books and take notes on them, revising the notes as I go. I listen to long-form podcasts, sometimes three or four hours, without skipping, without “multi-tasking”. Sometimes, I will run the material through a quick summary pass first, the kind an AI can generate in seconds, to know whether the conversation is worth the investment before I make it. But the summary is a filter, not a substitute. Once I decide the material is worth the full engagement, I give it the full engagement, every digression and tangent, every place where the argument stalls and recovers. The summary told me whether to sit down to the meal. The summary is not the meal. What the full engagement deposits is not the information, which the summary could have delivered. It is the cognitive paths that duration forces me down, the thoughts I arrive at only because I was required to follow the argument long enough to find them. I could not have reached those thoughts on purpose. I could only have been led there by the material, over time, under sustained load.

That is deliberate cognitive time under tension. The summary pass is the momentum rep. The full engagement is the slow, controlled rep: the mind stays under load, the resistance persists, and something is deposited that the summary would not have deposited.

The difficulty curve

The body adapts. If you lift the same weight every week, the stimulus stops producing growth. The load has to increase. Trainers call this progressive overload: the deliberate, incremental raising of difficulty to keep pace with developing capacity.

AI, as currently deployed, does the opposite. It flattens the difficulty curve. The hard argument becomes as tractable as the routine one. The complex analysis becomes as easy as the simple one. The junior professional’s cognitive load stays constant regardless of how demanding the material actually is, because the system absorbs the surplus difficulty before she encounters it. This is the equivalent of going to the gym every day and lifting the same five-pound weight for years.

A 2025 study of thoracic oncology tumor boards found that AI recommendations agreed with the human clinical team seventy-six percent of the time. The twenty-four percent where they disagreed is the part of the work where clinical judgment still lives: a radiologist remembering a case that ended badly, a surgeon sensing that the imaging does not tell the whole story, the pause where someone asks a question the data would not have prompted. If the junior clinician is trained on a workflow in which the summary is already written, the likely diagnosis is already named, and the first-pass analysis is already done, she will never encounter the load of the twenty-four percent. She will lift the same weight for her entire residency. The seniors will carry the hard cases until the seniors retire. Then there will be physicians who were never overloaded, and who therefore never grew.

The same logic runs through any practice where difficulty is not uniformly distributed. Legal analysis. Code review. Strategic synthesis. The hard cases are where the load actually is. A workflow that smooths difficulty to a constant floor does not produce experts. It produces practitioners who are permanently competent at the mode and permanently fragile at the tails, which is precisely where the work matters most.

The result is a condition you can already feel inside organizations: phantom competency. A junior worker whose outputs are flawless but who feels an intense, quiet impostor syndrome because she knows her capacity has not grown. She is shipping senior-level work while feeling like an intern, acutely aware that she could not reproduce her own deliverables if the network went down. The organization measures the artifact and reports success. The person measures her own formation and feels hollow.

The forklift and the spotter

We have been treating AI as a forklift. Its job is to move the cognitive freight: take the rough argument, deliver the polished version; take the raw data, deliver the summary; take the intake, deliver the differential. The forklift does not care about the person standing next to it. It cares about the load.

A gym does not use forklifts. A gym uses spotters.

A spotter stands behind the bench while you press. The spotter does not lift the weight. The spotter watches you struggle with it, lets you sit in the tension for as long as the tension is productive, and touches the bar only when you reach genuine failure. Even then, the spotter applies the minimum force needed to keep the bar moving. The entire point is that you take the weight. The spotter’s job is to make sure the weight does not kill you, not to spare you from it.

The difference between a forklift and a spotter is the difference between two relationships to difficulty. The forklift assumes difficulty is waste. The spotter assumes difficulty is the product. One optimizes for the artifact. The other optimizes for the person.

Almost everything in the current conversation about AI in professional life is organized around the forklift model. How do we move the cognitive load faster, with fewer errors, at lower cost? That question is not wrong. There is an enormous quantity of cognitive freight that should be moved by machine: formatting, scheduling, boilerplate, summaries of meetings you were not in. Let the forklift take it.

The problem is that the forklift does not know the difference between freight and exercise. It picks up everything. And once it picks up the exercise, the person standing next to it gets weaker without noticing, because the loop still closes, the deliverables still ship, and the reward still arrives.

Consider what happens when a system architect reaches for a whiteboard at the hardest moments of a project. The whiteboard is slower than the screen. The marker cannot undo. There is no copy-paste. She is not choosing it because she enjoys the smell of dry-erase. She is choosing it because the constraints of the medium force her to synthesize before she executes. The inability to undo is the load. The slowness is the tension. She is acting as her own spotter: using the minimum level of tool support that keeps the weight on her, and reaching for the more capable tool only when she genuinely cannot proceed without it.

That is what the spotter model looks like when it is working. Not refusing the machine. Not performing analog virtue. Choosing, deliberately, the level of support that keeps the difficulty on the person.

What the gym requires

Back to the four features of good gym design, and what they mean for a knowledge worker.

• Adjustable load: The work a person encounters must include difficulty proportional to their developing capacity, not smoothed to a constant floor by automated assistance. The twenty-four percent of hard clinical cases is not a problem to be resolved before it reaches the trainee. It is the training. Routing hard cases around junior practitioners in the name of efficiency is the organizational equivalent of removing all the heavy weights from the gym and replacing them with five-pound dumbbells, forever.

• Protected time: The cognitive session cannot be cancelled because there are easier things to do. Unassisted drafts. Paper-only reviews. Meetings where the primary documents were read by humans, not summarized by machines. These are artificial constraints in exactly the same way the barbell is artificial: they exist to produce a condition that the environment no longer generates on its own. The cognitive gym is engineered resistance. It produces nothing except the person.

• Serious community: The social environment reinforces the practice rather than the shortcut. If everyone around you submits AI-drafted work without resistance, the person who drafts her own has no signal that the practice is worth the time. Institutions that want formation have to make the practice legible and reward the attempt, not only the output, because the output is the one thing the machine can also produce.

• A knowledgeable spotter: Someone who can calibrate exactly how much help to withhold, and for how long, before the load becomes damage rather than development. This is not the same as a mentor who gives advice. It is the rarer thing: someone whose own formation is intact enough that they can distinguish productive struggle from waste, and who has the discipline not to intervene too soon. In The Thought You Didn’t Have, I traced this in the specific case of writing: the senior who lets the junior’s draft stay clumsy long enough for the junior to find out what she thinks is doing something that the senior who improves the draft immediately is not doing.

None of these are anti-technology propositions. The gym is not against transport. A cognitive gym is not against AI. It is a designed environment for a specific kind of formation that automation does not produce and cannot substitute, in the same way that a running track is a designed environment for a kind of fitness that the car does not produce and cannot substitute.

The choice being made right now

Every organization deploying AI at scale is currently making a choice about this, whether or not it has named the choice. It can deploy AI as a forklift, moving every available load, optimizing every loop, closing every gap between need and output. It will have faster, smoother, cheaper production for as long as the people running it still carry inherited capacity. Or it can deploy AI as a spotter: present at every point of genuine failure, absent at every point of productive struggle, calibrated to keep the weight on the person for as long as that is where the building happens.

The second option is harder to design, harder to measure, and harder to defend in a quarterly review. It also produces people whose judgment can be trusted when the situation has no precedent and the machine has no useful answer. That situation is arriving faster than the forklift model can handle.

The wood taught me by resisting the blade. The long book teaches me by refusing to summarize itself. The long essay teaches me by refusing to resolve in the first three sections. These are not inefficiencies. They are reps.

For any practice you care about, the question is where the resistance still is, and who is meeting it. If the difficulty is still there and you are still the one encountering it, the practice is forming you. If the difficulty has been absorbed and something else is meeting it on your behalf, the loop is completing without you. There are practices that should complete without you. Formatting should. Boilerplate should. Summaries of meetings you were not in should. But the practices whose resistance was building you are your weights. The inconvenience is the load.

The question is not whether to use the forklift. You will. So will everyone around you. The question is whether anyone has decided which weights are yours to carry, and whether the organization you work inside has any interest in protecting that decision, or whether it is, right now, quietly picking up everything it can reach.

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Sources & further reading

Matthew B. Crawford, Shop Class as Soulcraft (2009). The argument that skilled manual work is not a lower form of cognition but its own form of it. The description of what attention the ailing machine compels is the sentence this essay opens on.

Jonadas, What Won’t Cross (2026). The companion essay that names the formation stage as the part of professional work that does not survive automation. This essay assumes that argument and asks the design follow-up.

Jonadas, Calibration Debt (2026). The argument that firms were accidental calibration apparatuses, and that AI is dismantling the apparatus while retaining the revenue engine. The junior-professional and senior-executive patterns here draw on that framework.

Jonadas, The Thought You Didn’t Have (2026). The specific case of writing as formation practice: what AI quietly takes is not the voice but the process by which the writer was being made into someone with a voice.

K. Anders Ericsson et al., “The Role of Deliberate Practice in the Acquisition of Expert Performance,” Psychological Review (1993). The research behind the popular “10,000 hours” formulation. Ericsson’s actual argument, which the popular version dropped, was that the key variable was deliberate practice under feedback and resistance, not hours alone. Time under tension, not time spent.

Hubert Dreyfus and Sean Dorrance Kelly, All Things Shining (2011). On poiesis, the craftsman’s way of bringing things out at their best. Dreyfus’s wider work on skill acquisition and embodied expertise stands behind the completed-loop argument here.

Journal of Clinical Medicine, Large Language Models in Multidisciplinary Tumor Boards (2025). The seventy-six percent agreement figure between AI recommendations and human clinical teams. Source

We are very good at recognizing when our bodies need resistance to stay healthy. We invent entire industries to put that resistance back into our physical lives. We are surprisingly bad at recognizing when our minds need exactly the same thing. Here is what happens when we automate the friction away.

Twenty years ago I had a supervisor who wrote the first drafts of his papers by hand, late into his career, on yellow legal pads, forming block letters one at a time like a printing press. He had a computer. He used it for everything else. But the first draft, the one that would eventually become an article, he wrote on paper with a pen. I remember thinking it was eccentric. He was not old, not precious, not performing anything. He just refused to skip that stage.

It took me about fifteen years, and the arrival of a drafting assistant that could turn my rough sentences into clean prose inside of a minute, to understand what he had been protecting.

He was guarding something that, at the time, I could not name.

Nobody argues against exercise. We all agree the body needs it. What we sometimes forget is how recent the agreement is, or rather, how recent the problem is. For most of human history, physical labor was built into survival. You did not need a gym because the field, the workshop, the docks kept you strong as a byproduct of staying alive. The modern gym exists because industrialization quietly removed that labor and left the calories. The treadmill goes nowhere. The barbell serves no purpose beyond the act of lifting it. The gym is engineered resistance, designed to replace the resistance that civilization accidentally took away.

Something similar is now happening to thought. And we have not built the replacement.

The tools of cognitive production (AI assistants, synthesis engines, summarizers, drafting agents) have become so fluent that the friction which once accompanied knowledge work is vanishing. That friction was never only an obstacle to the output. It was the condition under which the person doing the work was being formed. Remove it, and the artifacts keep arriving. What stops arriving, quietly and without anyone noticing for a while, is the capacity of the person behind them.

What is vanishing is the formation stage of the work: the stretch of the process where the person making the artifact is also being made by it. The draft that taught you what you thought. The code that taught you how the system actually behaved. The diagnosis that taught you what the patient had. These are not production stages. They are not recording stages. They are the hours during which, while producing an artifact, a specific capacity is being built inside the person producing it.

The formation stage is invisible in the finished work. A reader of the polished essay cannot see it. A reader of a fully AI-generated essay cannot see that it is missing either. The difference between the two versions is not on the page. It is in whether someone became someone by making one of them.

A confession

I say this as the last person who should be arguing it. I was an early adopter from the moment I had access. I started in academia in the nineties and I used computers for everything I could put them near. I hated writing by hand so much that when I had to take notes I abandoned cursive and formed block letters one at a time. Like a printing press. It felt more natural. It still does.

When I began teaching in a philosophy department and brought a laptop to the first seminar and used PowerPoint for my lecture, I could feel the disapproval. Philosophy, the implicit argument went, was not something one did with slides. It took over a decade for the department to stop minding. By the time I left, almost everyone was using them.

So when I say the thing I am saying, understand that I am not defending any medium against any other. I have never been nostalgic for the older tool. What I am reaching for is not about the tool. It is about whether, somewhere inside the work, a person is still doing the thinking.

Here is how I write now. I open a blank page and type (sometimes, dictate) a first version in my own voice. The sentences are rough. I know they will not stay this way. An assistant sits beside me that can clean them up within minutes once I hand them over. I could skip the rough version entirely. It would be faster. The prose would read better. I do not do it, because if I did, I would not have thought anything.

I wrote about this in The Thought You Didn’t Have: sometimes a sentence resists me, and I rewrite it four times, and somewhere in the fifth rewrite the shape changes, and what was wrong with it turns out to be that I had not yet understood what I was trying to say. The sentence was waiting for me to catch up. That kind of discovery cannot be outsourced. Once you hand over the drafting, the thought that would have arrived through the resistance simply never arrives. You get something that sounds like the thought. You do not get the thought.

My supervisor was protecting the formation stage of his own work. He did not say “I think with the pen.” He just would not skip to the clean version.

Which stage makes you someone

Before you decide where you stand on any of this, run a different test. Pick the kind of work you are most worried AI is threatening. Writing. Teaching. Coding. Medicine. Lawyering. Strategy. Pick one. Now make an honest list of every electronic aid you already use without a second thought:

Spell-check. Grammar check. Word processors with auto-format. Search engines. Autocomplete. Stack Overflow. Email filters and auto-reply suggestions. Document templates. Citation managers. Version control. Slide decks generated from bullet points. Transcription software. Calendars that schedule your meetings for you. Dashboards that summarize what happened. Tools that condense what others said.

Every item on that list is something you already handed to a machine, some of them decades ago. If any of those things had been the work, you would not have handed them over. You did. So they were not the work. Cross them out.

Spell-check. Grammar check. Word processors with auto-format. Search engines. Autocomplete. Stack Overflow. Email filters and auto-reply suggestions. Document templates. Citation managers. Version control. Slide decks generated from bullet points. Transcription software. Calendars that schedule your meetings for you. Dashboards that summarize what happened. Tools that condense what others said.

What remains?

What remains, for most people who do this honestly, is smaller than they expected and more specific than they can quickly name. It is whatever stage of the process is the one where they actually struggle. The research direction they followed without knowing where it was going. The first draft of an argument that surprised them. The moment they had to rethink their entire framing because the evidence would not fit. The piece of code whose right shape became visible only after four wrong shapes. The conversation with a specific student where they found themselves saying something they had not said before.

Those stages are not defined by medium or tool. They are defined by the presence of resistance: the moment the material did not do what you wanted, and you had to meet it, and find something on the other side. The meeting is what thinking is. The artifact is only partial evidence that someone was once there doing it.

The test becomes this: if I could remove this stage from my workflow and replace it with a machine that delivered the artifact cleanly, would I still be the same person on the other end? If yes, let the machine do it. If no, that is the formation stage of the work, and it is not for sale.

What must go unassisted

In January 2026, Tyler Cowen gave a lecture at the University of Austin (full lecture; the section on writing and thought begins at 21:50). Cowen is an economist, a prolific blogger at Marginal Revolution, and one of the most forceful advocates for artificial intelligence in American intellectual life. He uses AI reading agents to filter his own incoming text. He has written, publicly, that he composes partly for the machine indexes that will process his work, a logic I examined in The Claim Upon the Training Data. He is not a nostalgic.

His position on AI in education is therefore worth attending to, because he is not defending a boundary for sentimental reasons. And the position is two-handed.

Cowen argued that the institutional response to AI has been organized almost entirely around the wrong problem: how to prevent students from using tools that are freely available and professionally essential. This, he suggested, is an evasion. The real question is how to restructure education when AI can produce many of its traditional outputs faster and better than students can.

Alongside any expansive integration, Cowen insists that students must spend real unassisted hours writing, no AI access, because writing is how thinking gets done, not how it gets recorded. You do not have a thought and then write it down. You have the thought by writing, against the resistance of the page and your own unformed attempt. Take the resistance away and the thinking was skipped.

The argument is not about handwriting. Cowen was not proposing fountain pens. The practice being protected is medium-neutral: laptop, phone, whatever. What is protected is a portion of the student’s time during which the student is actually meeting their own cognitive resistance and producing, alone, the rough version whose function is not the paper that comes out of it but the person who comes out of it. Without those hours, the four years produce a file of polished papers and no interior.

That is a formation stage protected as policy. It is one of the only places in a current curriculum where that protection is being proposed explicitly. It has also been proposed before, in circumstances we now find slightly ridiculous.

What the press could not copy

In 1492, a Benedictine abbot named Johannes Trithemius, at the monastery of Sponheim in the Rhineland, wrote a small treatise called De Laude Scriptorum, “In Praise of Scribes.” The printing press had been in commercial operation for four decades. The scriptorium was collapsing across Europe. Most of the scribes Trithemius had known in his youth were out of work. The demand for hand-copied texts was drying up, the economics were finished, and the great Benedictine scribal tradition that Trithemius himself had spent his life inside was, in the working sense, already over.

He wrote the treatise as a defense. Forming each letter, he argued, was a form of contemplative attention. Parchment outlasts paper. The scribe who copies a text has read it in a way the printer has not. A monk who spends twenty years in a scriptorium is not the same creature as a man who operates a press for twenty years.

In 1494, he had the treatise printed.

He had to. The press was already the only serious distribution network for new ideas in the German-speaking lands. An argument published only in manuscript form in 1494 would reach a few dozen monastic libraries and die there. If he wanted the argument to live at all, he had to print it. He chose reach, and in choosing reach he conceded, structurally, the surface of the argument he was trying to make.

Trithemius lost the argument he thought he was making. The tract defending scribal labor survives today because a press copied it. The specific craft he was defending, the particular room of men and parchment and candlelight, disappeared within a generation. On the surface, he was wrong, and history agreed.

Underneath the argument about the medium was another argument he did not quite manage to articulate, and on that one he was right.

The monk who spent twenty years copying sacred texts was not producing copies as a side effect of his day. He was being formed into a certain kind of reader and a certain kind of person by the labor. The slow forming of each letter bound his attention to the text. The years of shared work in the scriptorium bound him to the men beside him. Specific capacities were being built in him: a sense for when a scribe ahead of him had drifted, when a passage had been garbled in an earlier copy, when a doctrinal turn hinged on a single preposition. None of this was about the parchment. It was about the hours.

The press took the production. That was fine, the press did it better. What the press could not do was the hours. Those hours were never a production stage. They were a formation stage: the place where a mind met the resistance of a text across years, and a specific kind of person was made from that meeting. The monastery ended up with more books than the scribes had ever produced. It ran out of the specific kind of person that twenty years of copying used to form.

The artifact crosses. It always crosses. Put it on parchment, move it to print, scan it to PDF, ingest it into a model, it crosses. The formation does not cross. It has to be done, in place, by someone, against resistance, for the time it takes.

Formation, up the ladder

The same question, in the same shape, applies at every altitude where judgment is made.

Consider the teacher on the other side of the desk. A professor who reads a student’s paper carefully, with this specific student in mind, thinking about how to help this particular person improve, is doing the teaching. Whether the professor reads on paper or on screen does not matter. What matters is whether the professor is meeting the resistance of this student’s specific move, this student’s specific misunderstanding, this student’s history across the semester. A professor who hands the paper to an AI grader and forwards the output has not taught anything that day. Not because the comments are wrong. The comments may be better. Nothing has been taught because nothing has been met.

This is not a medium distinction. The same AI tool, used with full engagement or as a way to skip the engagement, produces two different outcomes. I watched the same split happen in my own department with PowerPoint when it (finally) became ubiquitous. Some colleagues took slide preparation as an occasion to rethink the lecture from scratch. Some pasted yesterday’s bullets and read them aloud. Same tool. Two different acts. The first group was still teaching. The second group had stopped.

The same collapse is now playing out in corporate life, and I traced the economic logic of it in Calibration Debt. The law firm, the consultancy, the hospital: these institutions were, accidentally and as a side effect of proximity, apprenticeship apparatuses. A junior analyst sitting next to a senior analyst was not only producing deliverables. She was being calibrated, developing the feel for when an argument overshoots its evidence, when a model is cheating, when the client’s stated problem is not the actual problem. That calibration was never designed. It was the residue of shared, uncomfortable work done in proximity over years.

When a senior partner delegates the reading of a junior’s brief to an AI, receives the summary, and ships revised clean prose, the brief improves, the mentorship vanishes, and the junior accumulates what I called calibration debt: the liability that grows whenever someone deploys AI to produce work beyond their ability to evaluate it. The brief files perfectly. The formation does not happen.

This extends all the way up, with one feature that makes the top of the ladder more exposed than the bottom.

Picture a CEO running the same exposure at a higher altitude. A board deck arrives on Monday. The assistant summarizes it. The model drafts a response. She edits lightly and ships. Over a quarter, the practice that used to keep her strategic judgment calibrated, reading the raw document, forming a first-pass view, catching the anomaly that the summary would have flattened, has been retired. She will not notice the retirement. Nothing will announce it. The briefings keep arriving. The decisions keep shipping. The deliverables look, if anything, sharper than before.

The difference is that the junior at least has someone above her who might catch the gap. The CEO does not. There is no senior partner reviewing the CEO’s thinking. When the capacity to evaluate one’s own strategic synthesis atrophies, and it will, because the practice that maintained it has been retired, the error will not be caught by anyone in the room, because the room was built on the assumption that the person at the top had done the thinking.

Formation debt at the top of an organization is a risk that has no current name and no current check. The gym equivalent is easy to picture: an executive who believes himself strong because the people around him all lift on his behalf.

The absent sender

South Park caught one side of this earlier and sharper than most philosophy has. In “Deep Learning” (Season 26, Episode 4, March 2023), Stan begins using ChatGPT to write his texts to Wendy. The texts are immediately better, more thoughtful, specific, attuned. Wendy, feeling outmatched, starts using ChatGPT to respond. The exchange improves in surface quality continuously. Neither of them is writing anything. Neither of them is thinking about the other. Two phones exchange perfect text between ghosts.

When Wendy finds out, what she is shocked by is not that Stan’s texts were bad. They were the best she had ever received from him. What she is shocked by is that she had been in conversation with no one.

This is the formation argument turned outward. Formation is the private half: what the person becomes by doing the work. Attendance is the public half: what the other side recognizes in the artifact as evidence that someone sat with the material, with them specifically in mind. Both are residues of the same hours. When AI mediates the whole exchange, neither residue appears. No one is being formed. No one is being attended to.

An institution in which professors generate AI feedback on AI-generated papers, a university in which neither side of the exchange is actually encountering the other, has not become more productive. It has become a system of artifacts passing between parties who are no longer thinking about each other. The quality of the surface masks the absence of what was supposed to happen underneath.

What human communication is always reaching for, across every medium and every century, is one specific signal: did someone think about me. Not content. Content can be generated. The receiver is looking for evidence of acknowledgment. Handwritten letters survived typewriters by decades, not because handwriting is noble (I have already confessed I do not think it is), but because a handwritten letter was, for a while, the most available proof that someone had sat down for some number of minutes and attended to the reader specifically. When the signal became fakeable, people reached for whatever the next medium could not yet fake: the specific detail, the voice note, the message that could only have come from someone who actually knows. The form keeps changing. The thing being looked for stays constant.

There is more to say about what this does to communication itself, to the shape of friendship and mentorship and public life under an infrastructure that makes ghostwriting instant and invisible at every turn. That is a different essay. For now, the point inside this one is smaller and harder: the collapse is symmetric. What the sender does not do, the receiver cannot find.

The gym for thinking

Here is where the analogy circles back, and where it goes further than you might expect.

The Greeks did not separate physical and intellectual training. A gymnasium was part athletic facility, part philosophical seminar. Plato held his discussions in one, which is where the word Academy comes from: a public garden adjacent to the wrestling grounds of Athens, named after the hero Academus. The logic was the same for both: capability is not a faculty you are born with. It is a capacity built through structured practice under pressure, in a specific kind of space, with others who take it seriously. The exercise produces the exerciser.

We inherited the physical gym. We seem to have forgotten the thinking one.

What Cowen is proposing, unassisted writing alongside maximum AI integration, is the beginning of rebuilding it. Not a sentimental exception. Not an anti-technology gesture. A designed and protected practice of deliberate cognitive resistance, in the same way the gym is a designed and protected practice of deliberate physical resistance.

The same principle applies wherever judgment matters. For students: protected hours of writing as the formation practice. For junior professionals: apprenticeship structures that require actually encountering and being corrected by the work, not only receiving the improved output. For senior executives: a discipline of reading raw signals, forming a first-pass synthesis with their own minds, actually sitting with the material before the machine prepares its briefing. None of this is a call to inefficiency. The gym does not make you less mobile. It makes you capable of being more so. Protected thinking practice does not slow down an organization. It produces people whose judgment can actually be trusted when speed is required.

We need to build these rooms. We need to name them. We need to protect them from the entirely reasonable pressure to optimize them away, because the pressure to optimize them will sound, in every instance, like an argument for making things better and faster, and it will be right about the output and wrong about the person.

My supervisor with the yellow pad was guarding a formation stage. He just refused to skip it. He was right about something that the rest of us, now, have to make explicit and deliberate, because the accident that used to keep us in contact with our own resistance has been retired.

The artifact will cross in any medium. The formation will not. It has to be done, in place, by someone, meeting resistance, for the time it takes. What we have to decide, this decade, is whether to build the rooms for it on purpose, or to discover, once the need has vanished from everyone’s calendar and no one can remember what it produced, that we have lost the capacity to recognize what we lost.

---

Sources & further reading

Tyler Cowen, lecture at the University of Austin (January 8, 2026). Full video; the section on writing and thought begins at 21:50; Q&A on cognitive atrophy from AI dependency at 47:56. Cowen’s broader commentary on AI and education runs continuously through Marginal Revolution (marginalrevolution.com).

Jonadas, The Thought You Didn’t Have (2026). The argument that AI does not take the thought, it takes the process by which the thought would have been formed.

Jonadas, The Claim Upon the Training Data (2026). The essay examining Cowen’s practice of writing for machine indexes, and the argument that institutional founding happens at the fiction layer, not in the text itself.

Jonadas, Calibration Debt (2026). The argument that firms were accidental calibration apparatuses, and that the AI transition is dismantling the apparatus while retaining the revenue engine. The law firm and executive examples in this essay draw on Calibration Debt‘s central argument.

Johannes Trithemius, In Praise of Scribes / De Laude Scriptorum (1492, printed 1494). Translated by Roland Behrendt, edited by Klaus Arnold.

South Park, “Deep Learning” (Season 26, Episode 4, March 8, 2023). Written by Trey Parker.

Elizabeth L. Eisenstein, The Printing Press as an Agent of Change (1979). The field-defining account of what the press actually disrupted.

K. Anders Ericsson et al., The Role of Deliberate Practice in the Acquisition of Expert Performance (1993). The research behind the popular “10,000 hours” rule. Ericsson himself argued, repeatedly, that the key variable was not hours but deliberate practice under feedback and resistance, a nuance the popular version dropped and this essay relies on.

Stanley Cavell, The Claim of Reason (1979). The philosophical background for the distinction between having the words of another and actually meeting them.

This piece is more technical than my usual writing here: it talks about compilers, schemas, and note architecture. None of it requires a coding background to follow. The technical argument is the surface of a philosophical thesis I've been chasing in the previous essays: generative AI, left to its own gravity, smooths everything into wallpaper. Each output is fluent, each paragraph is reasonable, and the aggregate result is a sophisticated form of consensus. What this essay proposes is that the antidote isn't to do less with AI; it's to build governance layers that force it to produce friction instead of fluency. The technical part is the price of taking that thesis seriously at the level of infrastructure, not just prose.

I recently handed an LLM compiler three hundred files of reading notes, book highlights, and lecture fragments accumulated over twenty years. The system worked exactly as designed: it parsed each source, extracted key claims, organized them into a directory of interlinked Markdown files. When I sat down to review the output, every file was accurate, well-formatted, and completely useless.

The notes on Simon Sinek read like a textbook summary. The notes on René Girard read like a Wikipedia entry. The notes on Stanley Cavell, the philosopher I spent a decade studying, read like something written by a diligent undergraduate who had never been troubled by a single sentence Cavell wrote. Three hundred files of consensus. A searchable hard drive with better formatting.

Andrej Karpathy, who recently published a concept he calls the llm-wiki, diagnosed half of this problem. His engineering instinct lands on something important: the standard AI workflow is broken at the infrastructure level.

The Compiler Metaphor

In Retrieval-Augmented Generation (RAG), the AI searches a database at the exact moment you ask a question. It is a frantic, just-in-time operation, erratic and structurally blind. Karpathy’s compiler approach refuses this. Instead, you give the LLM raw inputs (articles, notes, PDFs) and it works in the background to compile them into a dense, interlinked web of Markdown files. When you sit down to work, the knowledge is already synthesized. An ahead-of-time operation. As Karpathy puts it: “the wiki is a persistent, compounding artifact,” not something re-derived on every query.

I had been independently experimenting with a similar approach for months, and his post validated the core engineering intuition. A compiler that runs before the conversation starts is categorically superior to a search engine that scrambles during it. (I wrote about the broader workspace architecture in The Agentic Studio.)

But a compiler is only as good as the architecture it targets. And this is where the engineering frame, on its own, hits a ceiling.

The Docile Compiler

If you hand an LLM a folder of raw reading notes and tell it to “compile and structure” them, it will default to its baseline training distribution. It will build an encyclopedia.

You have seen this. You asked ChatGPT to analyze something you read, and the output came back as a book-jacket summary. You asked it to connect two ideas, and it returned a list of superficial similarities. You have a hundred notes in Notion, and every time you ask the AI to “synthesize,” the result is more generic than any of the individual notes. The machine averages. That is what it was trained to do.

In the Hacker News discussion around Karpathy’s post, a commenter named qaadika raised the sharpest objection: “There’s nothing ‘personal’ about a knowledge base you filled by asking AI questions.” The worry is that the bookkeeping the LLM automates (filing, cross-referencing, summarizing) is exactly where genuine understanding forms. Hand it to a machine and you get a corpus that looks organized but has lost the intellectual labor that makes knowledge yours.

That worry is legitimate, but it mistakes the tool for the architecture. The fix is not to do all the bookkeeping by hand. It is to govern how the machine does it.

When I fed my un-governed compiler the notes on Sinek’s The Infinite Game, the output looked like this:

Simon Sinek argues that leaders should maintain an infinite mindset rather than playing a finite game designed to beat competitors. He emphasizes building trust and advancing a Just Cause.

Accurate. Neutral. Philosophically sterile. A summary of what is, with no trace of what it means or what it fights against.

The default gravitational pull of the LLM runs toward consensus, toward the average of everything it has read, toward a fluency so frictionless that it erases the very tensions that make an idea worth having.

Left ungoverned, the compiler does not think. It smooths.

Cognitive Governance

Cognitive Governance is an explicit epistemological framework that dictates how the compiler fractures, weighs, and connects knowledge. Not just what to store, but how to reason about what it stores.

The LLM’s natural gravity pulls toward consensus. The governance layer pulls against it. Every rule in the schema is a constraint that forces the machine to do something it would not do on its own: find antagonists, identify omissions, surface contradictions, link across disciplines. Governance is not organization. It is resistance: active, deliberate counter-pressure against the compiler’s tendency to flatten everything into encyclopedia.

For my system, the governance architecture is a digital Zettelkasten, the note-linking method developed by the sociologist Niklas Luhmann, who used it to produce 58 books and hundreds of articles over 40 years. My workspace adapts it into three layers:

1. Raw Sources (Immutable). Where my reading notes live, some of them fifteen years old. The AI reads these but is forbidden from modifying them.

2. The Compiled Wiki (Mutable). The output layer. Instead of generic folders, the compiler operates strictly through Literature notes (LITs), Permanent concepts (ZETs), and Maps of Content (MOCs).

3. The Schema (Governance). A `SCHEMA.md` file that forces the AI to execute specific protocols when it processes any source.

The mechanism only works if the rules are rigid. To prevent the smoothing effect, my Schema contains directives like this one:

Rule: Extraction over Summary Never summarize a text chronologically. Extract the structural claim and identify the text’s implicit antagonist. Find the friction point. Link this claim to at least two existing concepts in the database.

When the compiler is governed by this single rule, the output on the same Sinek material transforms:

Sinek’s “Finite Game” operates on the exact same mechanics as René Girard’s theory of Mimetic Rivalry. A finite game requires an internal mediator, a competitor you are obsessed with beating. Sinek’s “Infinite Game” is an attempt to escape mimetic contagion by replacing the localized competitor with an unachievable, transcendent “Just Cause.” → [[MOC-Girardian-Mimesis]], [[LIT-Thiel-Zero-to-One]]

I had spent years reading both Sinek and Girard without ever making this connection explicit. The structural relationship was latent in my own notes, sitting across two folders that had never been in the same room. It took a governed compiler, one forced to find the friction point rather than summarize the surface, to make visible what was already mine but had never been articulated.

That is the difference between a compiler that files and one that generates. The architecture did not produce a new idea. It formalized a connection I had earned through years of reading but had never been forced to write down.

The bookkeeping is not eliminated. It is governed. The intellectual labor is encoded in the schema, not in the manual act of filing. And the schema is something only the human can write, because it reflects an epistemological commitment: what counts as a connection, what counts as friction, what the compiler should never be allowed to smooth over.

How to Build This

The novelty phase of generative AI is over. We are entering the infrastructural phase, and the tools you design today will shape the boundaries of what you can think tomorrow.

The evolution maps onto three stages:

If your AI relies on a basic RAG pipeline, it is an archivist: fast, but structurally blind. If it acts as an un-governed compiler, it is an encyclopedist: organized, but gravitating toward consensus. An architected Zettel-Wiki is Stage 3: the compiler still does the heavy lifting, but the governance layer forces it to produce friction instead of fluency.

If you want to move from Stage 2 to Stage 3, here is what the transition requires.

1. Write a Schema file. This is the single most important document in the system. It contains the protocols the compiler must follow when processing any source. At minimum, include three rules: extraction over summary (force the compiler to identify structural claims, not retell the narrative), mandatory linking (every new node must connect to at least two existing nodes), and an antagonist rule (every claim must name what it argues against). Here is a minimal version you can paste into your own `SCHEMA.md` today:

## Ingest Protocol

When processing a new source:
1. Extract the source's central structural claim in one sentence.
2. Identify the claim's implicit antagonist: what position does this claim argue against?
3. Find the friction point: where does this claim create tension with existing nodes?
4. Link to at least two existing nodes (LIT or ZET). If no link exists, create a new ZET.
5. Never summarize chronologically. Never produce neutral description.

2. Define your node types. Literature notes (one per source, capturing the source’s central claim and its friction with your existing knowledge). Permanent notes (concept-level nodes that synthesize across multiple sources). Maps of Content (thematic indices that give you entry points into clusters of related ideas).

3. Make raw sources immutable. The compiler reads them but never modifies them. This preserves the original context and forces all synthesis into the compiled layer, where it can be audited and revised.

4. Run session reviews. After each working session, the AI summarizes what changed, proposes new links, and flags contradictions. Five minutes. The system gets smarter after every session.

The governance rules are not suggestions. They are instructions the machine executes. Natural language is the software layer, and your Schema is the program.

In the age of LLMs, the most valuable intellectual property you own is not the raw data in your notebooks. It is the Schema: the ruleset you write to govern how that data connects. When you get the rules right, the system ceases to be a tool. It becomes a partner that enforces consistency across a decade of your thought. It catches the contradictions you miss. It compiles, but more importantly, it compounds.

I have open-sourced the directory structure, governance schema, and ingest protocols I use in my own system. The repository includes the complete `SCHEMA.md`, worked examples of the ingest pipeline (raw source → LIT → ZET → MOC links), and the session review protocols.

If you want to try it now, this is the fastest path:

1. Download the repository as ZIP from github.com/jonadas-tech/agentic-memory-template.

2. Open the folder in your AI tool.

3. Paste this command:

`Read START-HERE.md and run the full setup interview now.`

→ Open Source Template Repository

---

Sources & further reading

Andrej Karpathy, llm-wiki (2026). The original idea file. The engineering foundation is right; the governance layer is what this piece adds.

Sönke Ahrens, How to Take Smart Notes (2017). The modern codification of Niklas Luhmann’s Zettelkasten method. The three-layer architecture described here is a digital adaptation of this system, with the LLM acting as the disciplined note-taker Ahrens describes.

Niklas Luhmann. The German sociologist who produced 58 books and hundreds of articles using a system of 90,000 interlinked index cards over 40 years. The original proof that structured note-linking compounds.

Jônadas Techio, The Agentic Studio (2026). The companion essay on building the full workspace architecture, from progressive disclosure to session reviews to style-guide-as-software.

Hacker News discussion on llm-wiki. The community debate on whether compilation is categorically different from RAG, and qaadika’s sharp objection that bookkeeping is where understanding forms.

Henry David Thoreau went to the woods and kept a ledger. In Walden, he tells you exactly what his cabin cost: boards, eight dollars and three and a half cents. Refuse shingles for the roof, four dollars. Two second-hand windows with glass, two dollars and forty-three cents. One thousand old bricks, four dollars. Nails, three dollars and ninety cents. Chalk, one cent. Total: twenty-eight dollars and twelve and a half cents.

The precision is almost comic. Who accounts for a penny of chalk? But Thoreau is not being quaint, and he is not budgeting. He is demonstrating a method. A few pages later, he states the principle behind it:

Not money. Life. The twenty-eight dollars were not the point. The point was what those twenty-eight dollars represented: the hours, the decisions, the desires that led to each line item. The half-cent matters not for its value but for its binding force. Once you have counted it, you cannot pretend you were not paying attention. The ledger commits you to what it reveals. Thoreau counted until he could not count further, because the exercise itself was costly, and the cost was the point. Every item on the ledger was a question you could not take back once you had asked it: is this mine, or did I absorb it?

For many years, I reread Walden at the start of each January. The whole book, but with particular attention to the “Economy” chapter, which most readers treat as prelude and which is in fact the philosophical core. I read it with the same intention every year: to take Thoreau’s method seriously. To sit with his ledger and try to build my own. Not of lumber and nails, but of the desires I was carrying. What did I actually want from my career in philosophy? What had I absorbed from colleagues, from the academic market, from the ambient mythology of what a serious intellectual life should look like? The questions sound simple. They are not. We are not transparent to ourselves; I wrote about why that is in the previous essay in this series. Most of what I took to be conviction turned out, under examination, to be inheritance. The desires I had never questioned were exactly the ones that had been borrowed.

The answers came slowly. Not in a single revelation, but across years of returning to the same questions, each January peeling back another layer. Then, in 2020, the world paused. The COVID lockdown removed the daily machinery of professional life (the conferences, the meetings, the ambient social comparison that passes for ambition), and in the silence, the ledger became readable in a way it had not been before. The desires that survived the stillness were smaller, stranger, and more stubbornly mine than the ones I had been carrying. The ones that dissolved were the ones I thought were most important. Within a year, I had decided to change careers. Not because the lockdown told me what to want, but because it removed enough noise for me to hear what I had been telling myself in those January readings all along.

In The Economics of Infinite Desire, I wrote about the structural problem behind this kind of experience: desire is mimetic, borrowed from others without our noticing, and it has no natural ceiling. Solving material scarcity does not cure the wanting; it concentrates it. Abundance does not resolve desire. It inverts the pyramid: the base is satisfied, and the apex, where desire operates, opens outward without limit.

This essay is about the attempted cure. For 170 years, Thoreau’s Walden has been the most famous prescription: strip away the borrowed desires, reduce life to its essentials, discover what you actually want by subtracting everything you absorbed from others. It is a beautiful idea. It is also a trap, unless you understand what Thoreau was actually doing, which was not what most of his imitators think.

A mile from town

Here is a fact that Thoreau’s critics love and his admirers tend to skip: he did not go to the wilderness. He went to a pond a mile and a half from the center of Concord, Massachusetts. He walked to town every day or two. His mother brought him food. Ralph Waldo Emerson owned the land. For a man who wrote “I went to the woods because I wished to live deliberately,” the setup was conspicuously suburban.

I walked the path myself, just before the lockdown, on a detour from a work trip. The distance from Concord center to Walden Pond is an unremarkable stroll. You pass houses, cross a road, walk through ordinary woods that thin into a gentle clearing at the water’s edge. There is nothing dramatic about it. That is precisely the point. Thoreau did not go to the mountains. He went to the outskirts of his own town, close enough to hear the train, close enough that his mother could visit.

The critics use this to call him a fraud. They are wrong, but not in the way his defenders usually argue. The standard defense says: the proximity does not matter because the ideas matter. This is too easy. The proximity matters enormously, but it matters as evidence for Thoreau, not against him.

Thoreau was not retreating from society. He was calibrating his distance from it. A simultaneous yes and no: engaged enough to hear what Concord was thinking, detached enough to discover that he was thinking something else. The mile and a half was not a failure of commitment. It was the experiment’s design. He was not testing whether a man could survive alone. He was testing whether a man could think his own thoughts while remaining close enough to hear what everyone else was thinking.

I recognize this calibration. It is what I am doing now. I left academic philosophy, but I did not leave philosophy. I write these essays from a different vantage point: close enough to the discipline to engage its thinkers seriously, far enough from the department to think without the ambient pressure of what counts as a publishable contribution. A mile from town, in a sense. Not abandonment, but the posture in which something becomes possible that was not possible inside: a yes to the questions, a no to the institution’s way of owning them.

The distinction matters because the alternative, the one most people hear when they hear “Walden,” is withdrawal. Leave the city. Sell your things. Escape. And escape, it turns out, is exactly the move that does not work.

Girard explained why. Desire is not something you generate from within. It is something you absorb from others, and you mistake the imitation for autonomy. The problem is that the mechanism works in both directions. You can imitate someone’s pursuit of the world just as readily as you can imitate their rejection of it. The anti-mimetic gesture is available for mimesis. Fighting borrowed desire can itself be borrowed.

The evidence is not subtle. It is an industry. Marie Kondo built an empire on the premise that owning less brings joy. By 2025, the empire included a year-long guided program (capped groups, live coaching, roughly two thousand dollars), branded retreats, and a product line of organizational boxes and containers: things to buy so that you need fewer things. After three children, Kondo herself publicly abandoned her own method. “My home is messy,” she said, “but the way I am spending my time is the right way for me at this time.” The system’s inventor could not sustain the system. What sustained itself was the brand.

And then there is the newest iteration, the one that brings the pattern closest to this essay’s concerns: the “AI-free” movement. At least eight separate initiatives are competing to create a certification logo for human-made work: “Proudly Human,” “No A.I.,” “Human-made.” Designers are cultivating deliberate imperfection (rough edges, hand-drawn elements, slightly unbalanced layouts) as a signal of authenticity. The gesture is understandable. It may even be sincere in many individual cases. But authenticity-as-brand is a Girardian recursion. The moment “I do not use AI” becomes a badge, the badge is available for imitation, and the imitators are no longer refusing AI because they have examined what they want. They are refusing it because refusal has become the thing to want.

Girard’s insight is precise: the antidote to mimetic desire is not anti-mimetic desire. Anti-mimetic desire is just mimesis wearing different clothes. The antidote is something else entirely. And Thoreau, read carefully, already knew this.

The hero of this book is its writer

Stanley Cavell published The Senses of Walden in 1972. It remains the most penetrating reading of Thoreau I know. Its central claim overturns the standard interpretation.

Cavell does not read Thoreau as a naturalist, a minimalist, or a social critic. He reads him as a writer. “It is hard to keep in mind,” Cavell writes, “that the hero of this book is its writer.” The pond, the cabin, the beans, the morning walks: all of these serve the writing. And the writing is not a record of the experience. It is the instrument through which Thoreau discovers what he thinks, as opposed to what Concord taught him to think.

This is why the proximity to town matters. Thoreau did not need isolation. He needed a practice. And the practice was not living simply. It was the slow, resistant, daily effort of writing: finding the sentence that holds, discarding the sentence that sounds right but belongs to someone else, arriving at a thought he could not have predicted when he sat down. The seven drafts of Walden, revised over years, are not evidence of perfectionism. They are the experiment itself. Each revision was another pass through the borrowed material, another attempt to separate what was his from what was Concord’s.

Cavell makes a remark that stopped me when I first read it, years ago, and that has not loosened its grip:

Self-consciousness as hearsay. Your sense of who you are, what you value, what you want: borrowed, absorbed, mistaken for the genuine article. Thoreau was writing about this in 1854. Girard would formalize the same insight a century later, in 1961, through the theory of mimetic desire: a different vocabulary for the same structural diagnosis. What matters is the convergence. Two of the most powerful accounts of human self-deception in the twentieth century, working from entirely different traditions, arrive at the same claim: what you take to be yours is borrowed.

Where they diverge is on what to do about it. Girard, in his later work, pointed toward transcendence: models of desire that do not generate rivalry, figures who absorb imitation without reflecting it back as competition. The argument is powerful but theological, and this essay is not the place to assess it. Cavell points somewhere more immediate. He points to practice: the sustained, effortful work through which borrowed opinions are exposed as borrowed, and something uncovered underneath. Not a “true self” waiting to be found, but a thinking that happens only in the difficulty of articulation.

Thoreau’s image for this practice was morning.

Not a time of day. A state of mind. To be awake, for Thoreau, was to be in the active process of examining what you think. The opposite was not sleep. It was the productive comfort of never having examined anything at all. And the morning must be renewed. Every day. “We must learn to reawaken and keep ourselves awake, not by mechanical aids, but by an infinite expectation of the dawn.” The practice of self-authoring is not a state you achieve. It is a discipline you maintain against the gravity of borrowed opinion, which never stops pulling.

Not by mechanical aids

I remember the resistance of a blank page at the start of a philosophy paper. The hours before the first honest sentence arrived, the false starts that sounded like something I had read rather than something I thought. That resistance was not an obstacle to the work. It was the work. The writing was the practice through which I discovered whether I had anything to say, or whether I was rearranging what others had said and calling it mine.

Writing was Thoreau’s method, but it need not be everyone’s. The ledger that Thoreau kept in prose, others keep in conversation: the friend who asks the question you have been avoiding, the mentor whose silence after your explanation tells you more than their words would. Still others keep it in deliberate stillness: in meditation, in prayer, in the long walk that has no destination but that somehow, by the end, has rearranged what you thought you knew. The common element is not the medium. It is the friction. Every one of these practices puts you in a situation where the borrowed thought cannot coast on its own momentum. It has to withstand examination, and the ones that dissolve under the pressure were never yours.

The problem with AI is not that it replaces writing. It is that it removes friction across all of these registers at once. The generated strategy document that saves you from discovering what you actually think about the problem. The AI-summarized meeting notes that spare you the slow work of deciding what mattered. The chatbot that answers your questions so fluently that you never sit with the discomfort of not knowing, which is where most honest self-examination begins. Each of these is a small mercy. Taken together, they amount to a systematic thinning of the occasions on which borrowed thinking gets exposed.

I wrote about the individual dimension of this in The Thought You Didn’t Have: every time you accept a generated paragraph without having fought through the problem yourself, you trade morning for a very productive kind of sleep. This essay adds the structural dimension. The minimalism industry, the FIRE forums, the “AI-free” badges: these are all attempts to escape borrowed desire through subtraction. Use less. Own less. Produce less. And they fail, reliably, because subtraction does not address the mechanism. You can subtract the objects and keep the mimesis.

The Thoreauvian alternative is not subtraction. It is examination. And examination requires friction: the resistance of the blank page, or the silence after the hard question, or the discomfort of sitting with a desire long enough to discover whether it is yours. Any practice that preserves this friction is doing what Thoreau’s ledger did. Any technology that removes it, however helpfully, is working against the project. The tool that promises to simplify your thinking may be preventing the practice through which you learn what your thinking is.

I do not think the answer is refusal. The “AI-free” badge is just another anti-mimetic gesture available for imitation. And I do not think the answer is a set of rules about when to use AI and when not to (though I have offered practical heuristics elsewhere). The answer, if there is one, is the same one Thoreau demonstrated: maintain a ledger. Have some practice, whatever it is, through which you regularly submit your desires and convictions to honest examination. The test is not whether you use AI. The test is whether you can still tell the difference between a thought you arrived at and a thought you absorbed. If you have no practice that puts that question to you, the question will stop occurring to you. And then the ledger closes, not because you finished it, but because you stopped keeping it.

What the half-cent measures

The career path that felt like a free choice until you noticed that everyone you admired had made the same one. The annual resolution to simplify your life that quietly produced a subscription to a simplification app. These are not failures of willpower. They are the normal operation of borrowed desire. Girard’s point was never that mimesis is a flaw to be corrected. It is the mechanism through which we become social beings; without it, we would not learn language, share projects, or build institutions. The problem is not that desire is borrowed. The problem is mistaking borrowed desire for your own, and living the unexamined life that follows.

Thoreau’s ledger was the antidote, not because it told him what to want, but because it forced him to ask, item by item, line by line: is this mine? The twenty-eight dollars and twelve and a half cents are an exercise in honesty pushed to its limit, and an exercise in commitment: each line written is a line you have to live with. The cost of a thing is the amount of life exchanged for it. That is not an economic principle. It is a question you ask every morning, when you are awake enough to ask it. And asking it binds you to the answer in a way that no amount of regenerating, revising, or optimizing can undo.

I still reread “Economy” every January. The ledger has changed. It no longer lists academic desires I need to shed. It lists the habits of convenience I build with tools that are, in many ways, extraordinary. The question is the same one Thoreau asked at Walden Pond, a mile from town, close enough to hear the train: is this mine? The practice of asking is the practice of paying attention, and paying attention is the only thing that distinguishes a life you chose from a life you absorbed.

The ledger does not close. The morning must be renewed every day. That is the cost. And the half-cent matters.

---

Sources & further reading

Thoreau, Walden (1854). The primary text. The “Economy” chapter is the philosophical argument; most readers treat it as prelude. It is the core.

Cavell, The Senses of Walden (1972; expanded 1981). The reading that transforms Thoreau from nature writer to philosopher of self-authoring. The expanded edition adds two Emerson essays that deepen the connection.

Girard, Deceit, Desire, and the Novel (1961). The theory of mimetic desire. The concept of “internal mediation” (imitating someone’s rejection of the world, not just their pursuit of it) is essential to understanding why anti-mimetic gestures fail.

Burgis, Wanting: The Power of Mimetic Desire in Everyday Life (2021). The most accessible contemporary application of Girard. His distinction between “thick desires” (which survive examination) and “thin desires” (which dissolve under scrutiny) gives the reader a tool.

Newport, Digital Minimalism (2019). Newport draws explicitly from Thoreau’s cost-of-life principle. Useful as a practical companion, though the philosophical question (what counts as “life” exchanged?) runs deeper than the productivity framework allows.

This is The Fiction Layer — essays on AI, language, and the infrastructure of meaning. However you found this, welcome. The essay below is a good place to start.

“If the computer and robots can do everything better than you, does your life have meaning?”

Elon Musk posed this question, and to his credit, he did not pretend to have the answer. It is an honest question. It may also be the wrong one.

I spent twenty years in philosophy departments where no machine was coming for my job. And yet the question of what gives a life meaning was not, for me, a thought experiment. I lacked nothing: I had positions, publications, the respect of people I admired. What I had, underneath it all, was what Thoreau once diagnosed in his Concord neighbors: a “quiet desperation” that had nothing to do with scarcity. I kept orienting my work toward what my peers considered valuable, publishing in the journals that conferred standing, pursuing the grants that signaled seriousness, without ever quite asking whether these were the things *I* wanted. The desire was real. The origin was not where I thought it was.

What unsettled me was not the threat of obsolescence. It was the suspicion that what I wanted had been shaped, from the start, by what the people around me wanted. Musk’s question locates the threat in machines. But the harder problem may be older: that the wanting itself was borrowed, long before any computer entered the picture.

There is a famous image from Maslow’s hierarchy of needs that every management consultant has shown, at some point, in a PowerPoint deck. The pyramid sits wide at the base: physical needs, safety. It narrows upward through belonging, esteem, and self-actualization.

Musk’s question lives at the top of that pyramid, in the realm where people who have everything still feel that something is missing.

But the pyramid contains a hidden assumption, and the question inherits it without examining it: that we already know what we want, and the only difficulty is getting it.

René Girard spent his career dismantling this assumption, though he never used slides.

The finite and the infinite

Sam Altman published an essay in 2021 called “Moore’s Law for Everything.” It is a remarkable document: precise, optimistic, and more economically sophisticated than its detractors give it credit for. His core argument is that the marginal cost of intelligence is about to fall toward zero, just as the marginal cost of electricity, computation, and communication did before it. When that happens, the price of goods and services will collapse, and the current system of labor, wages, and capital allocation will need to be redesigned from scratch. He is not naively utopian about this: he worries explicitly about distribution, proposes a form of wealth tax on capital and land to fund a universal dividend, and acknowledges that the transition will be disruptive before it is liberating. His ambitions are large but the argument is careful.

Musk makes a related case with characteristic sweep: a post-scarcity world, humanoid robots, money as a relic. He invokes Iain M. Banks’s Culture novels, a science fiction series about a civilization managed by benevolent AI where nobody lacks for anything and the concept of a price has become quaint. What Musk does not mention, and what Banks understood, is that the Culture novels are not celebrations of abundance. They are studies of what abundance leaves unsolved. The most compelling stories in the series follow characters who have everything the material world can offer and find themselves seeking purpose through competition, espionage, and borrowed missions at the civilization’s edges. Banks solved the problem of scarcity in fiction. Then he wrote novel after novel about what remained.

Neither Altman nor Musk pauses long on what comes after. They assume, reasonably enough, that a world where food, shelter, healthcare, and physical safety are available to everyone at near-zero cost is a better world. This is almost certainly true, and the ambition deserves to be taken seriously. But solving material scarcity is one thing. Transforming the conditions under which human desire operates is another. It is the second question they leave open, without quite noticing they have left it.

Altman’s essay is, at its core, about need: the cost of the things we require, and the mechanisms by which those costs might fall toward zero. What we want (the desires we construct by watching one another, by imitating, by positioning ourselves relative to models we did not consciously choose) falls outside the frame of his analysis. Not because he is careless. Because no engineering framework easily accounts for it.

Call what they are solving the material problem: human suffering caused by the inadequate supply of goods. The question Girard opens is whether solving the material problem also resolves the problem of human wanting, or whether it merely reveals it.

The fog before the flood

Tyler Cowen’s recent The Marginal Revolution is, among other things, an elegy for the discipline he has practiced for forty years. The book traces how AI is transforming the very methodology of economics: the intuitive, humanistic tradition of marginalist thinking being displaced by machine learning and brute empirical power. But woven through that argument is a sustained diagnosis of why this transformation will diffuse through human institutions far more slowly than the technology itself develops.

Cowen’s case, developed across this book and in conversations like his exchange with Dwarkesh Patel, is that those who build AI and those who study the diffusion of technology are not the same people. The AI researchers are extraordinarily intelligent; they are also, in his diagnosis, systematically prone to projecting the speed of the technology onto the rest of human civilization. As AI makes some sectors radically more productive, resistant sectors (healthcare, education, government, the courts) absorb the disruption unevenly. The economy does not accelerate uniformly; it drags. Cowen has estimated that AI will add roughly half a percentage point to annual growth. Over thirty years, that compounds into something transformative. Year over year, it feels like almost nothing.

Here is what Cowen’s diagnosis implies, though he does not state it this way: the resistance is not primarily technical. Organizations do not sit out the AI transition because the tools are insufficient. They sit it out because adopting the tools would require abandoning the ways of working that define who they are relative to their peers.

The legal firm that resists AI document review is not protecting efficiency; it is protecting an identity that nobody at the firm consciously chose. The model of what a prestigious law firm looks like (the billable hours, the associate hierarchy, the particular forms of deference and display) was absorbed from peer firms, from the profession’s mythology, from decades of watching what success looks like in the eyes of others. The hospital administrator who slows down algorithmic diagnostics is not confused about the data; she is navigating an institution whose hierarchy was built on inherited models of medical authority that the AI threatens to make visible as contingent. This is friction, yes, but friction of a specific kind: the resistance of inherited desires to being exposed as inherited.

Into this fog steps Salim Ismail, the strategist behind Exponential Organizations. In an episode of Moonshots (alongside Peter, Dave Blundin, and Alex Wissner-Gross), he made a prediction that surprised his hosts: consulting firms would not be disrupted by AI. They would have their biggest boom ever. His reasoning invoked an old proverb: in the land of the blind, the one-eyed man is king. If you are a consultant who knows how to use AI tools even slightly better than the terrified CEO across the table, you are invaluable to them. In the epistemological chaos of a technological transition, the scarcest resource is not intelligence or capital. It is orientation. Organizations in the fog are not searching for a better tool; they are searching for someone who can tell them which direction to face. A model to imitate.

Cowen supplies the theory of why the fog exists. Ismail describes the mimetic market that forms inside of it.

The fog itself is a clue. The resistance to the new technology is not ultimately about the technology. It is about the desires that the old arrangements helped organize: the identities, the hierarchies, the models of prestige that people absorbed without choosing them. What Cowen describes as institutional friction and what Girard describes as mimetic desire are not two phenomena observed at different time horizons. They are the same phenomenon observed at different scales.

But suppose the fog eventually lifts. Suppose Altman and Musk are right about the destination, only Cowen is right about the timeline. The flood does arrive. The material problem is solved.

What are we left with?

The pyramid, inverted

Here is what Girard’s theory reveals about Maslow’s pyramid, and what Luke Burgis, the writer who has done most to translate Girard into contemporary terms, makes explicit in Wanting: the pyramid gets the shape wrong.

The base is real. Biological needs are finite and autonomous. If you are dying of thirst in a desert, you do not need anyone to tell you that water is desirable. The need arises from within, from the body’s own urgency, and it has a clear endpoint: you are no longer thirsty.

The upper layers are nothing like this. Belonging, esteem, self-actualization: these are not smaller than the base. They are larger, potentially infinite. They do not narrow to a peak. They open outward. And they belong to the realm of desire, not need. Desire, in Girard’s account, is not autonomous. It is mimetic: it arises from the other.

We do not know what to want. Or rather: we know that we want something, but knowing what to want is almost infinitely harder than knowing what we need. The thirsty person in the desert does not require a model. The ambitious person in a stable society, a person whose physical needs are met, whose safety is guaranteed, does. They look at what the people around them are pursuing, and they pursue it. Not out of stupidity or weakness; out of the structural conditions of desire in a social species. We read our own wants from the faces and choices of others. We desire the being of the other: not just their objects, but their position, their distinction, the particular way they seem to have found their footing in the world.

This is what Girard called mimetic desire. It is contagious, rivalrous, and, in principle, without limit.

There is a finite number of things a person needs. There is no natural ceiling on what a person can be made to want.

If you have read this far, you have almost certainly experienced this. The promotion you wanted until a colleague got it and you wanted it twice as much. The project you lost interest in until someone else picked it up. The career path that felt like a free choice until you noticed that everyone you admired had made the same one.

When the base is secured

The Girardian argument does not diminish the importance of solving material scarcity. On the contrary: the relief of genuine physical suffering is one of the most morally serious things a civilization can pursue. But it reframes what solving that problem achieves. Eliminating the lower layers of the pyramid does not reduce the pressure of the upper layers. It concentrates it.

When you no longer have to spend your attention on survival, you spend it entirely on belonging and esteem and self-actualization, which means you spend it on watching what others have and want and seem to be, and adjusting your own wanting accordingly. The mimetic pressure does not diminish. It intensifies, because it now operates without the absorbing constraint of necessity.

Consider what already happens in societies that have, by historical standards, largely solved the material problem. The most visible anxiety in wealthy communities is not about food or shelter. It is about college admissions: specifically, which university a child attends. The object is desired not because a Harvard diploma provides meaningfully better education than a dozen other institutions but because others desire it. It confers a social position that others can see. Obtaining it registers as a win in a competition whose stakes feel existential precisely because they are positional rather than material.

As material abundance spreads, the competition for scarce positional goods (by definition resistant to abundance) intensifies rather than eases. The pressure does not dissolve. It migrates.

The FIRE movement (people who achieve financial independence and retire before forty) has documented this shift from the inside. Their forums are full of posts from people who solved the material problem ahead of schedule and then wrote, with more honesty than they perhaps intended: “I achieved everything I set out to achieve. Now what?” They solved for need. The question of what to want turned out to be a different problem entirely, and not a smaller one. Thoreau went to Walden Pond to escape this. The FIRE community discovered, a century and a half later, that you can retire to the pond and bring the desperation with you.

Peter Thiel, who studied under Girard at Stanford and has since made Girard’s framework central to his public thinking, grasped this in his analysis of competition in Zero to One. His argument is that companies obsessed with their rivals end up duplicating each other to death; the only escape is to find the position no one else is occupying. This is Girardian economics applied to the technology industry, and Thiel is honest about the source.

The larger implication runs in the same direction: a world of material abundance is not a world without competition. It is a world where competition has been liberated from its material constraints and floods entirely into the immaterial domain.

That domain has no price mechanism to clear it. It has no scarcity that resolves by filling. Satisfying hunger makes you less hungry. Drawing closer to a mimetic rival makes you want to surpass them more, not less.

Remove the necessary work constraint from human life and you do not produce rest. You produce the acceleration of mimetic rivalry in the pure register of status, recognition, and cultural influence. What this essay’s opening described at the scale of a single career, and what Cowen’s analysis describes at the scale of institutions, the courts of early modern Europe enacted at the scale of an entire class.

The clearest case is Versailles. In 1682, Louis XIV moved the French aristocracy to his palace and, in doing so, ran one of history’s most complete experiments in removing material anxiety from an entire class. The nobles at Versailles wanted for nothing: they were housed, clothed, fed, entertained. Every physical need was met by the Crown. What filled the vacuum was not peace, not leisure, not philosophical contemplation. It was the most elaborate system of status competition Western civilization had ever organized. Who stood closest to the king at the levée. Who held the chemise during the royal dressing. Who sat and who stood at dinner, and in what order, determined by precedents so intricate that the Duc de Saint-Simon devoted thousands of pages of his memoirs to cataloguing them. People ruined reputations, destroyed alliances, and fought duels over questions of precedence that had no material consequence whatsoever.

Versailles was a post-scarcity society, at least for the people inside it. And it did not produce contentment. It produced a war conducted entirely in the register of meaning, without the moderating friction of survival. Every ounce of human attention that had once gone to the practical demands of managing estates and commanding local authority was redirected, with extraordinary intensity, toward the single question of where one stood in the eyes of others.

The pattern did not end with the ancien régime.

What cannot be supplied

Musk is aware of this, perhaps more than most. His stated answers to the meaning question are more honest than his critics acknowledge: he does not claim technology will solve it, suggesting instead that humans freed from necessity will find purpose on their own. On the policy side, universal basic income. On the existential side, something closer to faith in human adaptability than a program.

But Musk’s most revealing answer is not what he says. It is what he builds.

SpaceX is, among other things, an attempt to give humanity a shared mission that transcends terrestrial rivalry: a frontier, an institution, a reason to look outward rather than at one another. Whether he frames it this way or not, the structure is remarkably close to what Roddenberry imagined in Star Trek: a civilization that channels desire toward exploration rather than competition, backed by institutions that give the mission weight. Musk is building the Federation with rockets.

The complication is that this project is itself saturated in mimetic rivalry. Musk competes with Bezos, with NASA, with the arc of history. The mission to Mars is simultaneously a genuine channeling of desire toward a new frontier and a competition conducted in the purest register of meaning: who will make humanity multiplanetary?

This is not a contradiction of the Girardian analysis. It is its most vivid confirmation. Even the attempt to transcend mimetic rivalry takes the form of mimetic rivalry. The question is not whether the desire is borrowed but whether the project it fuels generates more meaning than it consumes.

This is where Girard becomes essential. Not as a condemnation of mimetic desire, which is also the engine of every shared project worth pursuing, but as a diagnosis of what the engineers’ instincts are reaching for and why the engineering framework, on its own, cannot supply it. The question is not whether to eliminate borrowed desire (impossible, and not even desirable) but whether to see it. Meaning, on the mimetic account, is not a resource. It arises from being seen by others you respect, in a world of shared commitments that feel, to those inside them, non-negotiable. These commitments are the operating infrastructure of collective life: the shared stories, the institutional agreements, the structures of recognition that hold a civilization’s sense of reality in place.

Musk’s Mars mission gestures toward such commitments. But a gesture made within a framework that cannot name what it is reaching for remains vulnerable to collapsing back into the rivalry from which it emerged.

There is a finite number of things a person needs. There is no natural ceiling on what a person can be made to want. The engineers are solving for the first sentence. The second sentence is the one civilization runs on.

The value of the fog

Cowen’s institutional friction gives us time. Not much, perhaps, and not evenly distributed. But time for what?

In “Jevons’s Other Machine,” I described how the economist’s community of judgment (the shared practice of intuition, argument, and trained perception that defined a discipline for two centuries) is dissolving as the technology that made it necessary disappears. Cowen’s book is, in part, an account of that dissolution from the inside. The economists are not losing their jobs, not yet. They are losing something harder to name: a form of life, a way of inhabiting their discipline that cannot be reconstructed once the community that sustained it is gone. This essay is asking whether that kind of dissolution is an isolated case or the general condition of a post-scarcity world: whether, as material constraints fall away, the communities that taught us what to want (and sometimes taught us to question what we wanted) fall away with them.

Girard himself believed the deepest answer was religious. In Things Hidden Since the Foundation of the World, he argued that human societies have always managed mimetic violence through the scapegoat mechanism: collective violence against a victim that temporarily restores peace. The Judeo-Christian revelation, in his reading, was the moment this mechanism became visible, and therefore resistible. Once you see the scapegoat for what it is, the old unconscious resolution can no longer function.

Not everyone follows Girard into the theological conclusion. But the line between theory and theology is less clean than it appears. Even Thiel, who now lectures publicly on Christian eschatology, has been accused by theologians of accepting Girard’s diagnosis of mimetic violence while rejecting the redemptive framework that gives it resolution. Where the diagnosis ends and the prescription begins is not settled.

What is settled, or close to it, is the structural principle: mimetic desire operates most powerfully when it is invisible to the person experiencing it.

The moment of recognition (seeing your model, seeing the borrowed nature of your desire) does not eliminate the desire. It introduces a gap: a distance between the imitation and the response, a pause where something like freedom becomes possible. This is what Burgis, in Wanting, attempts to make practical: distinguishing between desires that arise from within and those absorbed without noticing, learning to identify the models whose influence you did not choose, and (as he rightly emphasizes) choosing the communities and models that shape you rather than absorbing them by default.

The advice is sound. But Girard’s own work pushes further, because desire is not a private phenomenon. It is social from the ground up. You do not discover your mimetic models through introspection alone, even aided by better choices about whom to surround yourself with. You discover them in friction, in conversation, in the uncomfortable recognition that someone has shown you what you were imitating without knowing it.

The FIRE movement forums illustrate this inadvertently. The people who solved for material freedom and then asked “now what?” could not answer that question in isolation. The ones who found their way through (and not all did) had something in common: they found or built communities capable of reflecting their desires back to them in a form they could examine. Not communities that told them what to want. Communities that helped them see what they already wanted, and ask whether they had chosen it.

I have seen the same pattern in philosophy departments where the friction between traditions (analytic rigor encountering continental depth, each unsettling the other’s assumptions) generated exactly the kind of discomfort that makes mimetic models visible. These are not scalable structures. They are forms of life that happen to produce, as a byproduct, the conditions under which a person can notice what they have been imitating.

This is not engineering. It cannot be automated, optimized, or scaled.

The natural objection is that AI itself might serve this function: systems that learn your patterns, reflect your contradictions, show you what you would rather not see. The question is genuine, and it would be dishonest to foreclose it. It may be that what we are building is more capable of this kind of examination than we yet understand. But the design logic of current AI systems runs in the opposite direction: toward engagement, toward comfort, toward the optimization of satisfaction rather than the discomfort of recognition.

A mirror built to keep you looking is not the kind of mirror that reveals a mimetic model.

Which prophecy

The distinction that runs through this essay is not between technology and meaning. It is between technology designed to confirm the desires you already have and technology that opens frontiers you did not know existed. A Mars mission, at its best, does the latter. So might an AI system designed not for engagement but for the riskier work of honest reflection. Whether such things can be built at scale, or whether they can only emerge in the small, fragile, unscalable communities this essay has been describing, is a question the fog gives us time to explore.

When the flood arrives and the fog lifts, the question will not be whether we have enough intelligence or enough goods. It will be whether we have preserved the kinds of communities capable of asking us, with enough authority and enough care, whether we chose what we are pursuing or whether it chose us.

The shared stories a civilization tells itself about what matters, its agreements about status, meaning, mutual recognition, are not a layer that can be engineered from above. They are built, slowly, by people who pay attention to one another. Losing that capacity would not register as a crisis in any economic model. It would simply mean that the flood, when it arrived, had carried away the one thing it could not replace.

But dissolution is not the only future on offer.

Banks imagined one version: post-scarcity without resolution, his characters wandering the Culture’s edges in search of borrowed purpose. Roddenberry imagined another: post-scarcity as reorientation, desire channeled toward exploration, backed by institutions strong enough to absorb the mimetic pressure. The outlines of Roddenberry’s vision are already visible, imperfectly, drenched in rivalry, in projects like Musk’s own.

In the short term, the Girardian prediction is unambiguous: as material constraints fall, rivalry intensifies and old structures of meaning come under strain. In the longer arc, the question is which fiction proves closer to prophecy. Banks wrote the short-term prediction. Roddenberry wrote the long-term possibility.

Girard spent his career at Stanford, in the wealthiest community on the planet, lecturing on mimetic rivalry to the people most visibly caught in it. The setting was not incidental. If Versailles was an experiment in removing material anxiety from an aristocracy, Silicon Valley is the same experiment conducted on a meritocracy: the same concentration of ambition liberated from material need, the same elaboration of distinction in the register of pure meaning, now played out through valuations, keynote stages, and competing visions of the human future. The courtiers tracked proximity to the king. The founders track proximity to the singularity. I tracked proximity to the journals that conferred standing. The mechanism has not changed. Only the decor.

Thiel left those rooms and wrote a book about it. Others carried the idea into investment theses, into company strategy, into frameworks for thinking about markets. They understood the mechanism. The harder project, the one Girard kept returning to, was understanding it in oneself: recognizing the desire you did not choose, the model you did not consciously pick, the rivalry you entered without knowing you had entered it.

That project does not get easier when the material problem is solved. If Girard is right, it gets harder, because there is nothing left to distract you from it.

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Sources & further reading

Sam Altman, “Moore’s Law for Everything” (2021). The canonical statement of the post-scarcity thesis: AI drives the cost of goods toward zero, redistribution of capital wealth follows. More economically careful than its reputation suggests.

Tyler Cowen, The Marginal Revolution: Rise and Decline, and the Pending AI Revolution (Mercatus Center, 2026). A history of marginalism and an elegy for a discipline being transformed by AI. The institutional-resistance argument draws on Cowen’s broader body of work, including his conversation with Dwarkesh Patel at the Progress Conference (2024).

Tyler Cowen, The Great Stagnation (Dutton, 2011). The foundational argument that technological diffusion has been slower than it appears and that structural obstacles, not intellectual failures, explain the persistent drag.

René Girard, Deceit, Desire, and the Novel (Johns Hopkins University Press, 1965). Girard’s first systematic account of mimetic desire, derived from close readings of Cervantes, Stendhal, Flaubert, Dostoevsky, and Proust. The place to start.

René Girard, Things Hidden Since the Foundation of the World (Athlone Press, 1987; French original 1978). Girard’s most ambitious work: the scapegoat mechanism, its role in the foundation of human culture, and the argument that the Judeo-Christian revelation makes mimetic violence visible and therefore resistible. The religious dimension of mimetic theory.

Henry David Thoreau, Walden (Ticknor and Fields, 1854). “The mass of men lead lives of quiet desperation.” Thoreau’s diagnosis of a society where material sufficiency produces not peace but a nameless anxiety. The experiment at Walden Pond was his attempt to strip life to its essentials and find what remained.

Iain M. Banks, the Culture series (Orbit, 1987–2012). Ten novels imagining a post-scarcity civilization managed by benevolent AI. The most interesting stories are about what material abundance leaves unsolved.

Gene Roddenberry, Star Trek (Desilu/Paramount, 1966–ongoing). The Federation imagines post-scarcity as reorientation rather than dissolution: a civilization that has solved the material problem and channels desire toward exploration, service, and the discipline of encounter with the unknown.

Luke Burgis, Wanting: The Power of Mimetic Desire in Everyday Life (St. Martin’s Press, 2021). The most practical and accessible introduction to Girard, including the rectification of Maslow’s hierarchy that this essay draws on directly. Available at lukeburgis.com/mimetic-desire.

Johnathan Bi, lecture series on René Girard (2021–ongoing). A philosophically rigorous, series-length treatment of Girard’s full arc, from mimetic desire through sacrifice and the sacred. Available at johnathanbi.com/lectures.

Peter Thiel, Zero to One (Crown Business, 2014). Thiel’s Girardian analysis of competition in technology: mimetic rivalry destroys value; the escape is the genuinely singular position. Thiel has discussed Girard’s influence on his thinking in several public interviews, and has more recently engaged with the theological dimension of Girard’s work in a lecture series on Christian eschatology.

Salim Ismail, “Consulting Gets Replaced?” (Moonshots with Peter Diamandis #234). Ismail’s contrarian argument that consulting firms will thrive in the AI transition because the scarcest resource in technological chaos is orientation, not capability.

Jônadas Techio, “Jevons’s Other Machine” (2026). On the dissolution of the economist’s community of judgment when the technology that sustained it disappears. The present essay asks what happens to desire when the technology that constrained it does the same.