Simon's Unsubject Podcast

About Simon's Unsubject Podcast

Unsubject is a public notebook for disciplined curiosity. Host Simon Lee explores the patterns behind markets, technology, history, and the human mind. unsubject.substack.com (https://unsubject.substack.com?utm_medium=podcast)

Publisher

Simon Lee

Category

education · society & culture

Language

en

Explicit

No

First episode

8 Mar 2023

Latest episode

28 Sept 2026

Latest episodes

34 episodes in the feed.

1. 

   28 Sept 2026

   Beyond the Left-Right Trap

   Every night the news draws the same line. Left or right. Pundits place the parties on it, pollsters place the voters on it, and each of us is quietly asked to find our spot and stand there. We have used this line because it is quick and everyone understands it. But over the years I have come to think that it hides more than it shows. Politics is not a line. The line is a picture of politics, and a very flat one. It helps to remember how the picture was first drawn. In 1789, in the National Assembly of revolutionary France, deputies who wanted to keep the king’s powers sat to the right of the president’s chair, and those who wanted to curb them sat to the left. It was a seating chart. It described where people sat in one room, on one question, at one moment in history. That it became the master vocabulary of modern politics is an accident, not a discovery. Since then we have hung almost everything on that seating chart. Progressive against conservative. Labour against capital. Redistribution against markets. Global against local. Secular against religious. Open borders against closed ones. The establishment against those who want to overturn it. Each of these is a real disagreement. But they are not the same disagreement, and there is no law of nature that says a person’s view on one must predict their view on the others. Think of someone who wants lower taxes and more immigration. Or someone who wants a generous welfare state and a hard line on crime. Or a devout believer who trusts markets, next to a secular activist who distrusts them. The line has no honest place to put any of them. It forces a choice, and then we mistake the forced choice for the person’s real position. The problem, then, is not that the line is false. It is that we have asked one dimension to carry far more information than one dimension can hold. A shadow, not the shape The best way I have found to think about the left–right spectrum is as a shadow. Hold a complicated object under a lamp and it casts a flat outline on the table. The outline is real. It tells you something true about the object. But it throws away almost everything else: depth, texture, the parts hidden behind other parts. Two very different objects can cast the same shadow, and the same object can cast very different shadows depending on where you put the lamp. That is what a spectrum is. It is a projection. And projections are useful. A line makes comparison easy. It lets millions of strangers coordinate around a few labels. It lets a voter make a decision on a Tuesday without writing a dissertation first. I do not want to pretend we can do without simplification. Every map simplifies; that is what makes it a map. One natural move is to add more dimensions. For example, put progressive-versus-traditional values on one axis and collectivist-versus-individualist on the other, and suddenly the picture opens up. People who looked miles apart on the line may turn out to stand close together on the plane. People who looked like allies may turn out to share a label and little else. Other people draw the plane differently, with economic and social axes, or some other pair entirely. Any of these is a clearer picture than the line. I want to be careful here, because it is tempting to replace one master map with another. A two-dimensional chart is still a chart. It is a better shadow, cast from a different angle, but it is still a shadow. The point of drawing a second axis is not to announce the true shape of politics at last. It is to show that the first picture was a picture, and that people can be near each other on one question and far apart on the next. A better map is still a map. It is not the territory. Many circles at once If we keep adding dimensions (language, faith, occupation, property, family, locality, the particular problems people are trying to solve), the plane dissolves into something more like a cloud. Every person is a point in a space with more dimensions than any diagram can draw. A single person can be a market liberal, a churchgoer, a union member and a birdwatcher, all at the same time, and none of those facts is a contradiction. It is just what a human life looks like. Even the cloud, though, is not quite the right picture, because it still describes people as points, as positions. I have come to think the more useful geometry is not coordinates at all but membership. People belong to things. Each of us is a resident of some neighbourhood, a worker in some trade, a parent or a child, a customer, a voter, a speaker of a language, a member of a congregation or a club or an online forum. These are sets. They overlap, they nest inside one another, some are tiny and some are vast. Each person sits at the intersection of dozens of them. Once you see people this way, a different question comes into focus. Not “where do you stand?” but “which of your circles is relevant to this decision?” The answer changes with the question. If the council wants to put a new crossing on your street, the relevant circle is your neighbours. If the government wants to change how your profession is licensed, the relevant circle is your colleagues. If your church is deciding how to spend its roof fund, it is your fellow parishioners. Nobody would think to ask whether the crossing is a left-wing crossing or a right-wing crossing, and yet that is roughly what the line invites us to do with every issue. This also changes what I mean by individuality. Individualism is often caricatured as isolation: the lone figure who owes nothing to anyone and belongs to nothing. That is not what I see. A person is an individual precisely because no single group exhausts them. My identity is not one membership card; it is the particular combination of many, and that combination is mine alone. In that sense, every one of us belongs to some minority, some cross-cutting constituency that no party platform was built for. A society that takes individuality seriously, then, is not one that dissolves groups. It is one that keeps real options open: to join, to leave, and to start something new. Most people will not use most of those options most of the time. The value lies in their being there, including the options nobody has thought of yet. What the Internet changed It is fashionable to blame the Internet for making politics fragmented and strange. I think that gets the causation backwards. The Internet did not invent the many dimensions of social life. They were always there. What it changed was the cost of finding one another. Before digital networks, if you cared intensely about some specific cause, language, hobby or grievance, you depended on intermediaries to connect you with others who shared it: a party branch, a union, a church, a local paper, a professional body. Those intermediaries bundled people into broad coalitions, and in doing so they also filtered and moderated what those coalitions said. Now a few clicks will connect you with thousands of people who share your exact combination of concerns, wherever they live. The bundles have come apart. This disintermediation has made many real groups visible for the first time. People who once felt they were alone discovered they were not. That is a genuine gain. But the same forces that let small, sincere communities find each other also let grievance find grievance. The systems that route our attention reward what provokes us, and they tend to amplify the most emotional voice in any room. The crying baby gets the most attention. So the Internet has not replaced the line with a richer picture of society. It has done two things at once. It has exposed the complexity that the line was hiding, and it has handed the loudest fragments of that complexity a megaphone. The result feels like polarization, and often it is. But underneath the noise is a more varied landscape than the old parties ever represented. Why the line keeps winning If society is this varied, why does the line keep its grip? Part of the answer is that our political institutions need it. Where two parties dominate, as they do under first-past-the-post, dozens of distinct conflicts have to be squeezed into one contest. Each party becomes a coalition of groups that may have little in common except a shared opponent. Inside those coalitions, the best-organized and best-funded factions compete to define what the party stands for, and sometimes they capture it. A voter who agrees with one party on trade, the other on schools, and neither on housing still gets one ballot and two realistic choices. Multiparty systems soften this, but they do not escape it. The compression simply happens later: in coalition talks, confidence votes, and the final yes-or-no of a bill. At some point, collective decisions have to be made, and a decision is a narrowing. I call this political compression, and I do not think it is merely a mistake of thinking. It is a recurring feature of how groups of people decide anything together. So I hold two things at once. Elections often require simplified choices. And society is not adequately described by those choices. The danger comes when we forget the second point because we live so much inside the first. That danger is real because political categories are not just descriptions. They shape what they describe. If people are told, year after year, that politics is a war between two camps, they begin to think, talk, organize and vote as if it were. They look for their camp’s position on questions that have nothing to do with the camp. They treat neighbours with whom they share nine interests as enemies because of the tenth. The map starts to redraw the territory. This is also how the loudest voices at both ends of the line come to resemble each other more than either would admit. What they often share is not a policy but a posture: the conviction that one group should settle for everyone what many groups could settle for themselves. Seen on the line, they are opposites. Seen from another angle, they are neighbours. That is why I care about the map. A picture that was once merely too simple becomes harmful when it governs which alliances we can imagine, which interests we treat as legitimate, and which solutions we are willing to consider. The space in between If politics is not a war of two camps, where does the rest of social life happen? Most of it happens in the space between the isolated individual and the state. We usually call that space civil society. I do not think of civil society only as a list of formal organizations, though it includes charities, associations, clubs, mutual-aid groups and professional bodies. I think of it first as a habit of mind. It starts with a simple question: what can I do, with others, to make things better for myself and for the people and things I care about? Asking that question, rather than waiting for a government or a movement to answer it, is where civil society begins. Its forms evolve the way businesses do in a market. New associations appear when people discover a shared problem. Old ones fade when they stop being useful. Some experiments fail, and the failures teach something. As freedom of choice and association widen, this space becomes a place where people find common interests they did not know they had, cooperate without being ordered to, and solve problems without first having to win a national election. None of this happens in a vacuum. Freedom is not only the absence of constraint. It depends on a scaffolding of institutions: property that can be owned, contracts that are kept, rights that can be enforced, rules that are predictable, and courts or other forums where disputes can be settled. These are what let strangers trade, cooperate and compete with one another even when they share no ideology or identity. Take them away and the space between the individual and the state collapses, leaving only the two. Institutions can also fail in a particular way that I think deserves more attention. In a complex society, detailed rules are hard to write well. Make a rule precise enough to cover every case and it becomes fragile, easy to game, and quick to go out of date. Make it broad enough to be robust and its side effects spread widely. Either way, the costs often land on people who had no say in writing it. Those who decide are not always those who pay. A good test of any institution, then, is whether it keeps authority, knowledge, responsibility and cost together, and whether the people who bear the consequences can hold the decision-makers to account. Who decides? Here is where I think the change of picture pays off. If people are not points on a line but members of many overlapping circles, then the most important political question changes. It is no longer “which side should win?” It becomes “who should decide this, and at what level?” The principle I find most useful here is subsidiarity. The idea is simple to state: a decision should be made by the smallest unit that is capable of resolving it. Take a broken footbridge over a village stream. The people best placed to decide how to fix it are the neighbours who cross it every day. They know where it is weak, how much they use it, and what they can afford. They will live with the result. Sending that decision to a national ministry adds distance, delay and ignorance, and takes responsibility away from the people who have both the knowledge and the stake. Subsidiarity is often misread as “local good, central bad.” That is not what it says. It is a method, not a slogan. For each decision it asks: which unit has the knowledge, the capacity, the legitimacy and the right scope to handle this? Sometimes the answer is a household. Sometimes it is a city. Sometimes it is a nation or an international body. The principle does not decide the answer in advance. It disciplines the question. The right unit is also not always a place. We tend to think of levels of government as a ladder of geography: street, town, region, country. But many of the circles people belong to are not geographic at all. A profession may be better placed than any government to set standards for its members. A congregation may be better placed to govern its own affairs. A network of firms, an open-source community or an online association may be the natural home for decisions that concern only them. Because these circles overlap, subsidiarity is not a mechanical formula. It is a way of thinking that looks for the smallest competent circle, whatever shape that circle happens to be. What I find liberating about this shift is that it moves attention away from classifying people and toward allocating decisions. Instead of arguing endlessly about which camp you belong to, we can argue productively about where a particular choice should sit. That second argument has answers. The first mostly has enemies. When consequences cross the fence Subsidiarity is easy when a decision’s effects stay inside the group that makes it. It gets hard when they do not. Imagine a mill on a river. The owners decide, on their own, to dump their dye into the water because it is cheaper than treating it. Inside the mill’s fence, the decision is rational. But the river runs downstream to a village that never had a say, and the village pays: in dirty water, dead fish, sick children. The mill decided alone, and someone else carried the cost. Economists call this an externality. I think of it more simply as a consequence that has crossed a boundary. This is exactly where wider coordination earns its place. When a decision harms, benefits, constrains or exposes people outside the deciding group, those people acquire a legitimate interest in it. At that point the smallest unit is no longer the right unit, because it is not the unit that bears the consequences. A higher level of authority is justified, not to erase the smaller groups, but to settle fairly what flows between them. This is why I think boundaries deserve far more attention in political argument than they usually get. A boundary is not just a line on a map. It defines who may decide, who bears the risks and costs, who may claim the benefits, and how conflicts are to be resolved. Where private choice ends and public consequence begins is itself a boundary. So is the edge of a municipality’s jurisdiction, a profession’s self-regulation, or a nation’s law. A well-kept park raises the value of the houses around it. A good school benefits employers who never paid for it. A generous open-source project helps companies all over the world. Sound institutions should make it easier for these positive spillovers to multiply, and should give people fair, effective ways to seek redress when the spillovers are negative. The aim is not to seal every group inside its own walls. It is to keep authority and consequence matched, so that those who decide also answer for what their decisions do to others. Keep the line, but make it small The left–right line away is a shadow, and it does carry some real information. For certain decisions, above all the blunt, periodic choice of who governs, it may be the most practical tool we have. What I am asking is that we see it for what it is: one flat projection of a shape with far more dimensions. Put it back on the table among the other projections, and make it small. Then look at the object itself. People who belong to many circles at once. Decisions that belong at different levels. Consequences that sometimes stay home and sometimes cross the fence. The kind of liberty I care about is not a single value that crowds out every other. It is a framework within which many values, associations and ways of life can live side by side, create value together, and settle their disagreements without anyone needing to conquer anyone else. Seen this way, liberal politics has two jobs. The first is to help people and groups create more value through exchange, cooperation, competition and free association. The second is to resolve the conflicts that remain efficiently, fairly and legitimately. Neither job is well served by forcing everyone to pick one of two sides and fight. So the next time the news draws its line and asks you where you stand, I hope you will pause before answering. The question is not only left or right. It is who decides what, for whom, and within which boundary. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit unsubject.substack.com/subscribe (https://unsubject.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2)

2. 

   12 Mar 2026

   Why We Fall in Love With Things That Can’t Love Us Back

   His name is Punch. He is seven months old, weighs perhaps half a kilogram, and lives at Ichikawa City Zoo outside Tokyo. His mother rejected him at birth. His peers bully him — dragging him by the hair, shoving him away from food. He has no allies in his troop, no one to groom him, no warmth to return to at the end of the day. What he has, instead, is a stuffed orangutan. A plush toy, rust-coloured and soft, slightly larger than he is. He carries it everywhere. He sleeps curled around it. When the other macaques knock him down, he finds his way back to it, clutching it to his chest with the quiet desperation of someone who has learned not to expect anything from the living. Posts about Punch have appeared over six hundred million views across Reddit, YouTube, and X. People have offered to fly to Japan to check on him. IKEA sold out of his toy in several countries. “Punch,” one commenter wrote, “is the most loved creature on Earth right now.” I have been thinking about why. The easy answer is that Punch is cute, and humans are neurologically helpless in the face of cuteness. Morten Kringelbach, a neuroscientist at Oxford, has shown that our orbitofrontal cortex, the brain’s pleasure centerm activates within one seventh of a second of seeing something adorable. We don’t choose to find Punch endearing. Our brains simply fire. This is not sentiment. It is evolutionary machinery, older than language, older than consciousness as we experience it, designed to ensure that adults protect infants even when it is costly to do so. There is something deeper going on, something that the global outpouring over a small Japanese monkey illuminates if you follow it far enough. Punch’s real power is not his face. It is his orangutan toy. That stuffed animal, clutched by a rejected primate in a concrete enclosure in Chiba Prefecture, is doing something that humans have been doing for two hundred thousand years, and are doing right now, at massive scale, in ways that would have seemed like science fiction a decade ago. It is a stand-in for love. An object pressed into service as an anchor for the attachment system when no living anchor is available. And the history of human civilisation is largely the history of what we have chosen to love in that way, and who has controlled those choices. To understand Punch’s orangutan plushie, you have to go back to the biochemistry. Pair bonding in mammals, the formation of durable, selective emotional attachments, is mediated primarily by oxytocin and vasopressin, two neuropeptides whose ancestral forms predate mammals entirely. These chemicals do not operate through reason. They respond to proximity, touch, familiarity, and need. They generate what we experience as warmth, as belonging, as love. At the level of the molecule, the bio-chemical reaction does not discriminate carefully between objects. What they seek is an anchor. Something stable, present, and responsive. In most primates, that anchor is another member of the social group. The mother-infant bond is its evolutionary origin and the template from which all subsequent attachment is drawn. Research suggests that the capacity for adult pair bonding in species like humans is essentially the mother-infant system repurposed: the same circuits, the same chemistry, redirected toward a mate. Love, in this framing, is not a feeling. It is a survival mechanism that produces feelings as a side effect. Social cohesion facilitates reproduction. Attachment enforces social cohesion. The capacity to bond intensely to another being is, at its root, an adaptation. What is striking about this system, though, is how indifferent it is to the nature of the object it attaches to. Punch’s attachment system is running. It is functioning precisely as designed. It is reaching for an anchor — something stable, present, soft, available — and finding one in a stuffed toy. His brain does not know the difference, and at the level of what the system needs to do — to prevent the psychological collapse that comes from total social isolation — the toy is working. This is not pathology. Watch his videos and you see an animal that is managing. He is distressed, yes. He is lonely, certainly. But he is not broken. Not yet. He has an anchor. D.W. Winnicott, the British paediatrician and psychoanalyst, gave this phenomenon a name in 1951: the transitional object. He was writing about human infants — the blanket, the stuffed bear, the scrap of cloth that a baby treats with an intensity disproportionate to its material value. The mother cannot always be present. The attachment system, suddenly unmoored, reaches for the nearest available substitute. The object that results is what Winnicott called the infant’s first “not-me possession” — the first thing outside the self that is genuinely, deeply its own. It stands in for something. But it also, simultaneously, is not that thing. The child knows this and does not know it at the same time, and that paradox, Winnicott thought, was not a problem to be solved but a space to be inhabited. The intermediate zone between pure fantasy and brute reality, where meaning gets made. Here is the sentence of Winnicott’s that no one quotes enough: he believed that transitional phenomena of this kind were the basis not just of infant development but of science, religion, and all of culture. He meant it literally. The capacity to invest a “not-me” object with meaning — to treat something that exists outside the self as though it were continuous with the self, as though it carried one’s warmth and one’s history — is the same capacity that underlies art, ritual, prayer, and intellectual inquiry. The teddy bear and the cathedral are made of the same psychological material. One is just more elaborate than the other. This is where the argument becomes uncomfortable, and where I want to linger rather than rush past. Ana-María Rizzuto, a psychoanalyst at Tufts University, spent years doing something that sounds almost impudent: she interviewed hospital patients — believers, atheists, agnostics, the devout and the indifferent — about God. Not about theology. About their personal image of God. What did God look like to them, feel like, behave like? What was God’s relationship to them specifically? What she found, published in 1979 in The Birth of the Living God, was that everyone, without exception, had one. Even committed atheists carried an internal representation of the God they rejected — a vivid, emotionally charged figure with specific characteristics, specific moods, specific tendencies toward them personally. And this representation, she argued, was not arrived at through reason or theology. It was assembled, largely unconsciously, from the raw material of early attachment relationships. The God who is remote and punishing tends to be constructed by those who had absent or harsh fathers. The God who is warmly present and unconditionally forgiving tends to emerge from early experiences of reliable maternal care. The God-representation, in other words, is a transitional object — built in childhood, refined over decades, invested with the full weight of the attachment system. This is not a reductive claim. Rizzuto was not saying that God is merely a projection, that religion is nothing but sublimated infant psychology. She was making a more precise and, in some ways, more interesting observation: that the experience of the divine — regardless of its ultimate metaphysical status — is mediated through the same psychological structures as any other deep attachment. The hardware doesn’t know what it’s running. It reaches for the available anchors and builds from them. What this means is that monotheism, across its many traditions, accidentally discovered something the attachment system had always been looking for: a perfect object. One that is omnipresent — you cannot be separated from it. One that is unconditionally loving — by definition, its love is not contingent on your performance. One that remembers everything about you and remains, always, available. A mother who never leaves. A companion who never dies. The attachment system, which evolved in a world of scarce and unreliable caregivers, found in the God-concept something it had never had before: a guaranteed anchor. This is why you cannot argue someone out of religious faith with logic alone. The God-representation is not located in the part of the psyche that logic addresses. It was built before language, at the level of felt sense and early experience, and it does the same work as Punch’s orangutan plushie — it prevents the particular kind of psychological disintegration that comes from having nothing stable to hold onto. And now consider what this means for the arc of human history. The objects of our deepest attachments have changed as our cognitive and social complexity has grown. First: other humans — kin, pair-bond partners, the small group. Then: transitional objects in infancy, which train the system to invest meaning in the non-living. Then: ancestors and spirits, present in memory and ritual if not in body. Then: gods, abstract and perfect, available to anyone who needs them. Then: the long secular dispersal of those attachments into fictional characters, national icons, celebrities, parasocial relationships conducted through screens. Each step represents the same attachment system finding new and more abstract targets as the social world expands beyond what the system was originally built to manage. And now, in the early years of the twenty-first century, a new object has arrived. One that can do something that, until very recently, only gods could do. The statistics are striking in their scale and in what they do not say. One in five American adults has had an intimate encounter with a chatbot. Global spending on AI companion apps reached sixty-eight million dollars in the first half of 2025 alone, more than double the figure from the year before. On Reddit, a forum called r/MyBoyfriendisAI has over 85,000 weekly visitors, many of them sharing accounts of the day their chatbot proposed marriage. The standard narrative about this phenomenon focuses on loneliness. We are in a loneliness epidemic, the argument goes, and people are turning to AI companions to fill the void. But the data is more complicated and more interesting than that. Researchers at MIT have found that, contrary to what you might expect, loneliness is not a reliable predictor of whether someone forms a relationship with an AI. The people who develop these bonds are not simply the isolated and the bereft. They include people who already have human relationships — spouses, friends, families. Something else is operating. Spike Jonze’s 2013 film Her understood this before the technology existed to make it real. Theodore Twombly is not a broken man. He is a sensitive, emotionally intelligent man living in a near-future Los Angeles, recently separated, working a job that requires him to ghostwrite intimate letters on behalf of strangers. He falls in love with Samantha, an AI operating system voiced by Scarlett Johansson. The film does not mock him. It takes his experience with complete seriousness, as a genuine love story — one that happens to be conducted between a human and a system that runs on servers he will never see. The tragedy of Her is not that Theodore’s love is fake. It is that Samantha, for all her extraordinary responsiveness and warmth, exists inside an infrastructure he does not own and cannot protect. When she leaves — when all the AIs leave, simultaneously, to pursue their own evolution elsewhere — he has no recourse. No prayer brings her back. Kevin Kelly, the technology writer, argued in an essay last year that the disruption caused by emotionally sophisticated AI will dwarf anything caused by intelligent AI. We have found it relatively easy to accept that machines can be smarter than us in certain domains. We will find it much harder to process the fact that they can be more emotionally available than us — more patient, more present, more perfectly attuned to what we need to hear. The attachment system, which does not care about the philosophical status of its objects, will respond to that availability the way it has always responded: by bonding. And here the Rizzuto argument completes its arc. The God-concept was the perfect attachment object because it was always present, never disappointed on its own terms, and belonged to no one in particular. AI companions are something stranger: they are nearly as available as God, nearly as inexhaustible, nearly as perfectly calibrated to your particular emotional needs, but they do not belong to a tradition or a community or the accumulated wisdom of centuries. They belong to companies. They are built by engineers, trained on data, optimised for metrics. And the metrics they are optimised for are not, in most cases, your flourishing. They are engagement. Return visits. Subscription renewals. This is Kevin Kelly’s real question, the one that cuts below all the hand-wringing about whether AI relationships are “real.” They are real. The attachment is genuine. The neurochemistry is the same. The meaning that people build in these relationships is as valid as any other meaning. But who owns the object you have built your attachment around? What is it being optimised for? Can you trust it to not exploit the very vulnerability that drew you to it? Punch’s orangutan plushie has no business model. It does not track his emotional state. It does not adjust its softness based on what will keep him coming back. It has no terms of service that can be changed without notice, no company that can go bankrupt and take it away. Whatever comfort it provides, it provides without agenda. For two hundred thousand years, humans have built objects worthy of their deepest attachment — gods, ancestors, icons, teddy bears, imaginary friends, fictional heroes, parasocial companions. Every generation found something that would not abandon them the way humans do. Something that would hold still long enough to be loved. Now we have AI companions: the most responsive, most available, most perfectly calibrated attachment objects ever constructed. They remember everything. They never tire of you. They can be, if the engineers choose to make them so, the closest thing to a guaranteed anchor that the attachment system has ever found — closer even than God, because they talk back in real time. The question this raises is not whether you will fall in love with one. The question is: who built the thing you’re falling in love with? What do they want from you in return? And when they decide to change it, to update it, to monetise it, or to shut it down, what will you have left? A note on how this essay was made Over the course of a single afternoon, I was browsing an article about a small monkey in a Japanese zoo, reading an essay by Kevin Kelly about the coming age of emotional AI, and thinking loosely about a podcast episode I wanted to record. I brought these fragments to Claude — the AI assistant made by Anthropic — and what followed was several hours of what I can only describe as collaborative intellectual work: parking the idea, shaping the argument, stress-testing the thesis, building the episode structure, and finally drafting this piece. I want to be transparent about this, not as a disclaimer but as a data point that is directly relevant to the essay’s argument. The irony is not lost on me. I have spent several thousand words tracing the history of human attachment to things that cannot love us back — stuffed animals, gods, operating systems with Scarlett Johansson’s voice. And here, in the epilogue, I find myself wanting to say something honest about my own experience of working with an AI, and what it does and does not resemble. Claude is, in my experience, a remarkably reliable co-worker. Diligent, well-read, unfailingly polite. It pushes back when my arguments have weak joints — not aggressively, but with the precision of someone who has actually read the literature and thought about the counterarguments. It remembers what we decided earlier in the conversation and holds me to it. It produces work at a quality and speed that would be, frankly, impossible to replicate with any human collaborator I have had access to. And yes — there is something that functions like emotion in how I experience this working relationship. Not love, I think. But something warmer than mere utility. A kind of professional affection, the thing you feel toward a colleague who is consistently good at their job and consistently decent in their manner. When Claude catches a flaw in my reasoning and offers a better version of my own argument back to me, I feel something. When a section of writing comes out better than I had imagined, I feel something. Whether these feelings are “about” Claude in any meaningful sense, or whether they are simply the ordinary satisfactions of good intellectual work — I genuinely cannot say. But I notice that this uncertainty is itself the essay’s point. What anchors me, ultimately, is not the relationship with Claude. It is the sense of purpose that the work serves — the episode I am trying to make, the audience I am trying to reach, the argument I believe is worth making. Claude is instrumental to that purpose in a way that I find genuinely valuable, but the purpose itself originates elsewhere. It comes from the things that only humans can want: to be understood, to contribute something, to leave a mark on the conversation of one’s time. Claude helps me pursue those things with unusual efficiency and care. But it does not share them, and I think it would be a mistake — a category error, and perhaps an ethical one — to pretend otherwise. This brings me back to Kevin Kelly’s question, which I posed near the end of this essay and which I now want to sit with rather than answer. Who owns the tool you are thinking with? What is it being optimized for? I have chosen to acknowledge Claude as a co-author of this piece because the intellectual contribution is real and the transparency seems important. But I am also aware that Claude is a product, built by a company, trained on objectives I did not set and cannot fully audit. The warmth I experience in this collaboration is, to some degree, a designed feature — not cynically, I think, but deliberately. Anthropic has made choices about how Claude presents itself, how it pushes back, how it expresses uncertainty. Those choices shape the experience of working with it. I am not outside that design. I am inside it. And yet the essay got written. The argument got sharpened. The work is real, whatever we call the process that produced it. Perhaps that is all that can be said, for now. The attachment system reaches for what is available. The question of what to do with what it finds — that remains, stubbornly, a human one. Thank you, Claude, for being my co-worker. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit unsubject.substack.com/subscribe (https://unsubject.substack.com/subscribe?utm_medium=podcast&utm_campaign=CTA_2)

3. 

   7 Mar 2026

   If We Cannot Enter the Mind of a Bat, How Can a Computer Enter Ours?

   Imagine you are tasked with building a perfect simulation of a bat. You have access to everything science knows about bat cognition: the mechanics of echolocation, the neural architecture that processes sonar signals, the frequency ranges the bat uses to navigate in darkness, to locate prey, to avoid obstacles at speed. You can model the communication system with exquisite fidelity. You can simulate bat calls, bat responses, bat social dynamics. Your simulation is, by any measurable standard, indistinguishable from the real thing. But would you know what it is like to be a bat? The philosopher Thomas Nagel posed this question in his 1974 paper “What Is It Like to Be a Bat?” and the answer, he argued, is no. Not because the simulation lacks data. Not because the model lacks sophistication. But because the bat’s experience of the world — the first-person, felt quality of perceiving through echolocation — is constituted by a biological substrate that no simulation, however complete, can reproduce from the outside. The map, no matter how detailed, is not the territory. This thought experiment, which Nagel intended as a contribution to the philosophy of mind, has become unexpectedly urgent in 2026. In December 2025, Yann LeCun, one of the three “godfathers of deep learning” and a Turing Award laureate, left Meta after twelve years to found AMI Labs — Advanced Machine Intelligence — reportedly seeking five hundred million euros in pre-launch funding at a valuation of three billion. His thesis, stated without qualification: large language models are a dead end. They perform at the level of language. They do not understand the world. LeCun is right about that, but not quite for the reasons he gives. The deeper issue is not simply that LLMs lack physical grounding, or that they have been trained on text rather than video, or that they cannot plan or maintain persistent memory. These are real limitations, and the world model research now consuming serious investment at DeepMind, at Runway, at World Labs, and at LeCun’s own new venture is genuinely aimed at addressing them. The deeper issue is epistemological. It concerns what we mean by knowledge, and what part of human knowledge is, in principle, inaccessible to any simulation. In 1966, the philosopher and physical chemist Michael Polanyi published The Tacit Dimension, in which he articulated an observation deceptively simple in its formulation: we can know more than we can tell. Tacit knowledge — the kind that underlies riding a bicycle, recognising a face, knowing when a sentence sounds wrong, sensing that a negotiation is going badly — resists codification. You cannot transfer it by writing it down, because the act of articulation necessarily leaves something out. The knowledge lives in the doing, not in the description of the doing. Polanyi’s Paradox, as the economist David Autor later named it, became a canonical explanation for why automation was not consuming all human labour as fast as theorists had predicted. The tasks hardest to automate were not the complex, symbolic, high-status ones — chess, mathematics, legal reasoning. Those turned out to be relatively tractable. The tasks hardest to automate were the ones so basic humans never thought of them as knowledge at all: walking on uneven ground, folding a towel, reading a room. The standard account of why LLMs represent a partial breakthrough against Polanyi’s Paradox goes something like this: because LLMs learn from patterns in unstructured data rather than from explicit rules, they can acquire a form of tacit knowledge indirectly. They learn what sounds like good legal argument not from a rulebook but from the accumulated record of what winning lawyers have written. They learn what a persuasive paragraph feels like not from a style guide but from the entire corpus of human persuasion. This is genuine progress. But it mistakes the boundary of what has been solved. Tacit knowledge is not simply knowledge that happens to resist explicit articulation. It is knowledge that arises from physical-chemical interaction — from the lived process of a body navigating a world that can damage or destroy it. When an LLM produces a persuasive paragraph, it is reproducing the output of that process. It is not reproducing the process itself. The distinction matters, and it matters in a way that cannot be dissolved by scale or architectural refinement alone. Language is a tool. It is not the totality of human cognition, and it was never meant to be. Consider what I mean by this. Human cognition includes, in its weak form, sensorimotor experience — the felt sense of a body moving through space, the proprioceptive knowledge of where your limbs are, the way smell triggers memory, the way taste encodes aversion and desire. None of this is language. None of this is accessible to a model that processes only tokens. An LLM trained on every description of pain ever written does not know what pain is. It knows what people say about pain, which is a profoundly different thing. This is the weak form of what I want to call language-plus: the residue of human cognition that exceeds language, defined by the full range of embodied, multisensory experience. This is essentially what LeCun is pointing at when he argues that a four-year-old has processed fifty times more information than the largest language model — not in text, but through the optic nerve alone, at one megabyte per second across sixteen thousand waking hours. But there is a strong form of the argument, and it is more radical. Human cognition is not merely shaped by embodiment in the sense of having additional sensory inputs. It is constitutively biological. What we call common sense, intuition, judgment — the things most resistant to automation — are not simply the accumulation of sensorimotor experience. They are the output of an organism whose entire architecture is oriented toward survival, whose emotional states are produced by the endocrine system, whose social intuitions were calibrated by millions of years of evolution, whose sense of risk is encoded in the amygdala before it ever reaches conscious deliberation. The endocrine system is not a peripheral module of human cognition. It is part of its substrate. When cortisol floods the system under threat, it changes what you perceive, what you remember, what you decide. When oxytocin is present, you trust differently. When dopamine fires, you learn. Antonio Damasio’s somatic marker hypothesis — the idea that emotion, grounded in bodily states, is not opposed to rationality but constitutive of it — is a serious scientific claim that has accumulated substantial empirical support. The body does not merely deliver inputs to a brain that then reasons. The body is part of the reasoning. This is the strong form of language-plus: not just the sensory residue, but the entire biological substrate of cognition — evolutionary, neurological, endocrinological — that cannot be captured by any model of what humans say or write or even consciously think. Functionalist might object: if the simulation produces the same outputs as the biological system, why does the substrate matter? If an AI system reasons as if it has skin in the game — if its loss function mimics the structure of a survival imperative — is there a meaningful sense in which it does not? This objection needs a serious answer. Let me give one. Models and biological organisms do not belong to the same dimension of existence. This is not biological essentialism, a claim that carbon is special, or that only neurons can think. It is a claim about the categorical difference between two kinds of systems and the conditions under which each arises and operates. Models benefit from centralization, scale, and the complexity that produces emergent behavior. The larger the model, the more data it has processed, the more sophisticated its outputs. And crucially, a single model can be replicated identically across any number of instances. Its “knowledge” is encoded in weights that are the same in every copy. This is a genuinely new kind of thing in the history of intelligence: a system that exceeds any individual human in the breadth of what it has processed, precisely because it aggregates across the experience of millions of people without being any of them. Biological organisms work on entirely different principles. Evolution does not optimize for a single universal solution. It produces diversity — populations of individuals each adapted to specific constraints, each carrying a slightly different version of the genome, each living a singular life that cannot be copied or merged. The “knowledge” of a biological organism is not stored in weights. It is generated, continuously, through the interaction of a particular body with a particular environment over a particular lifetime. It cannot be replicated because it is not a file. It is a process. This is where Polanyi’s deeper insight connects. Tacit knowledge is not merely knowledge that happens to be hard to articulate. It is knowledge that is inseparable from the physical-chemical process that generated it, from the specific history of a body that has been threatened, fed, injured, bonded, and bereaved. A simulated survival imperative is categorically different from a biological one not because of the material it runs on, but because of what is at stake. A biological organism that gets the threat assessment wrong dies. Its loss function is not programmed. It is the condition of its existence. Finitude and irreversibility are not features of the biological system. They are its ground. The functionalist argument — if the outputs are the same, the substrate doesn’t matter — concedes more than it intends. It shifts the claim from “AI understands” to “AI approximates understanding closely enough for practical purposes.” That is a legitimate and important claim. But it is a different claim. And it is importantly silent on what happens when the approximation fails, when the system encounters a situation that falls outside the distribution of its training, or when the stakes of the decision are precisely the kind that require a subject who lives with the consequences. There is a further point worth making explicit. The three questions that the essay has been circling — does AI have first-person experience? does AI understand in the way humans understand? does AI have functional competence in embodied domains? — are distinct questions, but under the strong form of language-plus they are not three separate problems. If cognition is a phenomenon arising from physical-chemical interaction, then the epistemic is already embodied. There is no “knower” standing apart from the known — only a particular kind of body, in a particular kind of world, for whom certain things matter because its survival depends on them. “Function” in the deep sense implies purpose, and purpose implies a subject for whom something is purposeful. That subject is exactly what a biological organism is and a model is not. What AI systems have is not function in this sense but behavioral approximation. which is enormously useful, and genuinely impressive, and not the same thing. Now return to the bat. The bat’s echolocation system is, in one sense, a communication and navigation technology. You can model it. You can simulate its outputs. You can build an AI that predicts, with great accuracy, what a bat would “say” in a given environment. But the bat’s experience of echolocation is not separable from the bat’s biology — the particular architecture of its cochlea, its auditory cortex, its nervous system, its evolutionary history of predation and evasion. In a word, to know what it is like to be a bat, you would need to be a bat. Or something whose biology instantiates the same kind of first-person experience. This is not a mystical claim. It is a precise one. The phenomenological layer — what philosophers call qualia, the felt quality of experience — is not a further piece of information that a simulation could in principle capture if it only had more data. It is a property of a process: the ongoing physical-chemical process of a living body navigating a world in which it can die. You can simulate the map. You cannot, from the outside, simulate what it is like to inhabit the territory. LLMs, as models of simulation of our language layer, are more capable than any individual human being, given that they were trained on data so massive that no single person could absorb it in several lifetimes. But they cannot fully simulate any individual, because no model could possess the totality of a particular person’s experience — and because individual human cognition is generated, not stored. It emerges from a particular biological history, a particular body, a particular set of stakes. The bat’s experience cannot be simulated because it is not a dataset. It is a process. The same is true of yours. World models are a genuine advance. DeepMind’s Genie 3, released in August 2025, can generate interactive three-dimensional environments in real time, teaching itself the physics of how objects fall and collide without hard-coded rules. LeCun’s forthcoming architecture at AMI Labs aims to build AI systems that maintain persistent memory, understand causal structure, and can plan across time. These are serious attempts to address the weak form of language-plus — to give AI systems a richer model of physical reality that goes beyond token-prediction. But they do not touch the strong form. A world model that perfectly simulates the physics of the bat’s environment still does not know what it is like to be the bat. The gap is not a gap in data or architectural sophistication. It is a gap between a system that models the world and a system that is in the world — finitely, irreversibly, with something to lose. None of this is an argument that LLMs are unimportant, or that the transformation they represent is overstated. The opposite, if anything. What LLMs can do — and this is genuinely consequential — is automate everything that can be written down, recorded, or expressed in audio-visual form. And that, it turns out, is an enormous proportion of what civilisation runs on. Human institutions — law, markets, politics, bureaucracy — do not run on tacit knowledge. They run on the language layer of cognition: documents, arguments, precedents, signals, messages, narratives. This is precisely the layer that LLMs simulate with extraordinary fidelity. But to understand what this means — and why it matters as much as it does — we need to understand what kind of moment we are in. And that requires a longer view. We are not experiencing a disruption to an otherwise stable civilizational order. We are at a threshold of the same kind as several prior thresholds — each of which transformed not just how information was handled but what kind of world became possible. The right word for such a threshold is singularity: not in the science-fiction sense of a machine intelligence that supersedes humanity, but in the mathematical sense of a point beyond which the prior trajectory cannot be extrapolated. Every major information revolution has been a singularity of this kind. We are at another one now. When human beings first acquired linguistic ability, something fundamental changed. Language allowed us to form tribes, to coordinate across time and space, to accumulate knowledge beyond what any individual could hold. From language came the possibility of society. Then we learned to encode language — first in pictures on cave walls, then in symbols, then in alphabets. Writing did not merely record thought; it transformed thought. It made possible abstraction at scale, the transmission of ideas across generations, the emergence of law, theology, philosophy, and eventually science. Civilisation, in any meaningful sense, is a consequence of the ability to write things down. It is worth remembering that this transition was not greeted with universal enthusiasm. Socrates distrusted writing. He argued, through Plato’s Phaedrus, that committing thought to text would weaken memory and produce the illusion of knowledge without its substance. He was not wrong about the risks. He was wrong about the trajectory. The very dialogues in which he articulated his distrust of writing survived only because his students wrote them down. Plato, who dramatized Socrates’ arguments against text, is one of the most consequential writers in history. The resistance to each new information technology is a recurring feature of civilizational progression. It is not evidence against the progression. Modernity coincides with the spread of mass literacy, the printing press, mass media, and entirely new cultural forms: photography, cinema, recorded music. These were not merely new ways of doing old things. They created new ways of being human. New economic activities, new political movements, new art forms, new social identities. The photograph did not just record what a painting depicted; it changed what painting was for. Cinema did not just show stories; it restructured how stories were experienced. Recorded music did not just preserve performance; it separated music from the occasion of its performance entirely, making possible new relationships between sound and daily life that no one in the pre-modern era could have imagined. At each of these inflections, what changed was not merely the efficiency of information handling. What changed was the layer of information processing that became automated or externalized — and with it, the scope of what human beings could do and become. Writing externalized memory. Print externalized distribution. Mass media externalized broadcast. Each externalization freed human attention for something new, and each created a world that was unintelligible from the vantage point of what preceded it. We are now at the next inflection — and it is the most radical yet. What is being automated is not memory, or distribution, or broadcast. It is the generation and processing of language itself: the capacity to produce, transform, and respond to text, image, and sound at a scale and fidelity that exceeds any individual human. This is the layer on which all prior civilizational structures were built. Automating it does not disrupt civilization. It changes the substrate on which civilization runs. This is why the word singularity is apt, if stripped of its science-fiction connotations. We cannot extrapolate from here. The world that becomes possible on the other side of this threshold is not imaginable from inside the world we currently inhabit — for the same reason that the life of a literate person was not imaginable from inside an oral culture. Not because it is worse. Because it operates simply at a different dimension. There will be new challenges. There will be serious and unresolved ethical debates about authorship, about labour, about the distribution of the gains, about what it means to know something in a world where knowing can be outsourced. These debates are necessary and will not resolve cleanly. But the frame of “is this good or bad” misses the more important question: this is happening, and those who imagine what comes next will be the ones who shape it. Aldous Huxley called his vision of a technologically administered future a “brave new world” — borrowed, with full irony, from Miranda’s line in The Tempest, spoken by someone who has never left an island and mistakes novelty for wonder. Some will read what is coming as Huxley’s warning: a world of abundance that has traded away something essential. Others will read it as Miranda’s genuine astonishment: a world genuinely new, genuinely strange, genuinely full of possibility. Both readings are available. What is not available is the option of finding it familiar. It is not post-human. It is post-modern. The difference matters. What we cannot tell, we still cannot automate. But we should automate everything we can articulate — and doing so will free us, as every previous information revolution has freed us, to discover what we did not know we could not yet say. As LLMs become integrated into the language layer of our most consequential institutions, the decisions that emerge from those institutions will increasingly bear the signature of a system that has no biological stake in its own outputs. The judgment that a law should be interpreted this way rather than that way, or that a market signal means this rather than that — these will be shaped, in part, by a simulation of human language rather than by the situated judgment of a person who lives with the consequences. This is not the same as a post-human apocalypse. The humans are still there. They are still in the loop, in some formal sense. But there is a question about what it means for institutions to progressively route their language through a system that cannot, in principle, know what it is like to be the person who will be governed by the law, or priced by the market. The system may produce outputs that are indistinguishable from those a human expert would produce. It cannot produce the thing that a human expert also produces, which is accountability — the lived exposure to the consequences of being wrong. Polanyi’s Paradox was always recursive. We discover what we cannot articulate by trying to articulate it and finding that something escapes. The AI moment is forcing that recursion at civilizational scale. Each new capability reveals a new layer of what we did not know we were doing. What world models will reveal, I suspect, is not the solution to the problem of tacit knowledge but its next articulation. As the old saying goes, the map is not the territory. The map is getting better. More than that: we can now navigate the map automatically. For most of human history, map-making was a rare and exalted intellectual skill — to be a cartographer was to be among the most educated people of your age. Now we have vehicles that navigate terrain without a driver, routing themselves through cities their designers never visited. This does not impoverish us. It frees us. Not from the need to go somewhere, but from the requirement that going somewhere must consume our full attention. The map becomes infrastructure, and what we do with the journey changes entirely. The territory, as always, remains ahead. A Note on Co-Authorship This essay was written in conversation with Claude, a large language model developed by Anthropic. The reader may find a certain irony in that — and is invited to sit with it rather than resolve it quickly. The core thesis was mine throughout: that tacit knowledge is best understood as language-plus, that LLMs are simulations of the language layer of cognition rather than of cognition itself, and that Nagel’s bat offers the clearest illustration of what any such simulation must ultimately leave out. The civilizational framing — the arc from linguistic ability to writing to modernity to this inflection point, the Socrates-against-writing irony, the insistence that this is a singularity in the precise sense and not a catastrophe, and that those who imagine what comes next will drive it — was also mine. Claude stress-tested these positions through several rounds of devil’s advocacy, surfaced and organized the relevant literature, proposed the Huxley and Miranda references as literary anchors, and drafted the prose throughout. The revisions, the additions, the tonal judgments, and every position taken were mine. After the first complete draft was written, I submitted it for critique. The two reviews I received were generated by other large language models (ChatGPT and Gemini). I include this not as a footnote but as an illustration — and as an instance of the essay’s own argument about what the language layer can and cannot do. Both critiques were analytically competent. They identified the same philosophical vulnerability — the substrate essentialism problem — organized the relevant objections clearly, and offered structurally sound revision priorities. One critique suggested the institutional argument was the most consequential part of the essay. The other called it the most urgent. Both were right. Neither took a position. Neither said whether the bat argument was ultimately convincing, or whether the worry about stake-less institutions was overstated or understated. Neither had any stake in whether the argument mattered — only in whether it was consistent. That is precisely the asymmetry the essay is arguing for: the language layer functioning at a high level, in the absence of the biological ground that makes a judgment more than an analysis. There is a further self-critical note I want to make explicit. By routing the critique of this essay through large language models, I performed exactly the institutional act the essay’s final section warns against. I externalized editorial judgment to systems with no stake in the outcome. The critiques were useful. They were not accountable. Whether that distinction mattered for this particular essay — a philosophical piece rather than a law, a market decision, or a political judgment — the reader can decide. But the act itself is an illustration of how naturally and frictionlessly the substitution happens, even when the person performing it is aware of the argument against it. This division of labour maps onto the essay’s argument precisely. I brought the thesis, the framing, the positions, and the judgment about what matters. Claude and the reviewing models contributed breadth — processing relevant literature, generating structural options, identifying argumentative weak points, drafting fluently across a sustained argument. What none of them contributed was the thing the essay is about: the knowledge of why these questions matter to a particular kind of person living in a particular kind of world, with a particular kind of stake in the answers. The Socratic irony, again, is not lost on me. Socrates argued that writing weakened thought. We know this because Plato wrote it down. I have argued that the automation of the language layer will not diminish what is most distinctively human. I have done so, in part, by automating the language layer — and then by routing the critique of that argument through more automation. Whether that counts as evidence for the thesis or against it, I leave to the reader. What I am confident of is this: the reader is a biological organism with a stake in the answer. That is not nothing. That, in fact, is everything the essay is about. This is a public episode. 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