Mind, Matter, and Meaning

Tag: teleosemantics

  • Borrowed Meaning

    MIND · MATTER · MEANING No. 38 · May 2026

    Borrowed Meaning

    A model’s words mean something — for us, not for it.

    An essay mindmatterandmeaning.com

    A well-trained language model produces a sentence about a maple tree. The sentence parses, predicts, and pleases. Did the model say something about a maple tree?

    A growing line of argument answers yes — not by claiming the model has thoughts, perceives leaves, or means anything in the rich, mental sense, but by claiming something cleverer. The model’s outputs, the argument goes, inherit their meaning the way a counterfeit twenty inherits the design of a real twenty: by lineage. The tokens belong to a public linguistic practice with its own teleology. They have been selected, refined, and stabilized through generations of human speakers using “maple” to talk about maples. When an LLM emits the word, it produces a token of a type whose proper function — in Millikan’s sense — already exists. The model does not need to mean anything mentally. The word does the meaning for it. Call this the borrowed-meaning move.

    It is a serious argument. Jumbly Grindrod’s “Large Language Models and Linguistic Intentionality” works from Gareth Evans on naming practices and Ruth Millikan on teleosemantics, and one cannot wave the view off by pointing out, again, that the model lacks consciousness.1 Grindrod has agreed the model lacks consciousness. He has carved out a space — linguistic intentionality — that allegedly does not require any mind on the speaker’s end at all. The thought is bracing: meaning, in the public sense, lives in the practice, and any device that successfully participates in the practice is meaning-bearing whether or not anyone is home.

    The view deserves the strongest version one can give it. Words really do have public lives. “Water” refers to H₂O whether the speaker can recite the formula or not; a sneeze that sounds like “achoo” does not refer, but a careful utterance of “achoo” in a Burns Night recitation might; lineage matters. Millikan built her account precisely to capture this: a token gets its content from the cooperative history that selected it.2 Producers and consumers, refined over generations, settle what a sign is for. So far, so good.

    The trouble starts when the LLM walks into the cooperative history and asks for a seat at the table.

    Millikan’s mechanism has two halves, and the borrowed-meaning move quietly drops one. Producers make signs; consumers take them up; selection happens because consumers’ uptake feeds back into which producer-tokens persist.3 A bee that waggles in the wrong direction starves the hive; a hive that ignores good waggles starves itself. Selection requires that the loop close — that getting it right about the world makes the difference between thriving and not. Strip out the loop and you have not teleosemantics; you have stenography with extra steps.

    Now the LLM. What does it select for? It selects for tokens that look, statistically, like the tokens that came before them. The objective function rewards plausibility under a distribution, not correctness about maples. The model’s “consumers” — its loss function, its trainers, its users — do not punish it when its outputs misrepresent maples; they punish it when its outputs read poorly. There is a feedback loop, but the loop runs through human readability, not through the world. When humans read fluently, the model is reinforced; when humans wince, it is corrected. The maple has no vote.

    This matters because the proper function Grindrod wants to inherit was forged in a different kind of loop. The word “maple” stabilized in human speech because, over generations, calling maples maples helped people find sap, identify wood, build sugar shacks, and not eat the wrong leaves. The lineage runs through successful engagement with maples. When a contemporary speaker uses the word and gets things right or wrong, she is the latest carrier of that lineage because her uses, too, are accountable to maples. The line of descent is not just morphological; it is causal-ecological.4

    The LLM joins the morphology and skips the ecology. Its tokens are the right shape, but the path by which they arrived bypasses the maples entirely. To insist that they nonetheless inherit maple-content is to confuse the costume with the role. A child wearing a postman’s uniform delivers nothing.

    One can almost hear the rebuttal forming: surely the model’s training data was itself produced by speakers whose words were maple-accountable, and so the lineage runs through the model’s outputs after all. This is the move that makes the argument feel airtight, and it deserves to be taken seriously. But notice what it requires. It requires that being trained on tokens produced by maple-accountable speakers counts as participating in the maple-accountable lineage. By that standard, my photocopier participates too, and so does the optical scanner that produced the JPEG of someone else’s botany textbook. If the criterion for inheritance is “your outputs causally trace back to outputs produced by accountable speakers,” it sweeps in every device that traffics in linguistic shapes. We have not extended teleosemantics; we have diluted it into ink-on-paper.

    A sharper friend of the view will press here, because the diagnosis so far leans on the producer side and Millikan’s deeper innovation was the consumer side. So grant it: maybe the model is no producer worth the name, but its human users surely are consumers — they take up its tokens, act on them, and feed their satisfaction or dismay back into the next round of training. Doesn’t that close the loop after all, with us standing in as the consumers Millikan requires? It is the best version of the objection, and it almost works. What it leaves out is what the consumers are selecting for. A Millikanian consumer closes a loop only when its uptake tracks whether the sign got the world right; the hoverfly’s visual system is a consumer of the bee-dance only because hoverflies that misread the dance leave fewer offspring. The human reading an LLM’s paragraph is selecting for whether the paragraph reads well, coheres, flatters the prompt — and a fluent falsehood passes that filter as smoothly as a fluent truth. The consumer is real; the kind of selection is wrong. We are consumers of the model’s prose the way we are consumers of a pleasant melody, not the way the hive is a consumer of the waggle. The loop closes on us, and stops there, well short of the maple.5

    The serious version of the argument has to add something: that the LLM does not merely repeat tokens but produces new tokens whose proper function is settled by the practice. Fine. But the proper function of “maple” — the thing the lineage selected for — was the function of being deployed in maple-accountable ways. A producer that emits “maple” without any sensitivity to whether maples are present is, in Millikan’s own terms, not performing the function. It is doing something function-shaped. Grindrod knows this; he concedes that Millikan herself would resist his application.6 That concession is the ballgame. The framework was built around a kind of accountability the LLM does not have, and once you remove the accountability, the inheritance has nothing to inherit.

    There is a softer version of the borrowed-meaning move that survives all this and is worth keeping. LLM outputs are not meaningless noises; they are parasitic on meaning. They function as inscriptions — like the words in a book lying open on a desk. The book is full of meaning in the sense that meaning runs through it: the author meant things, and a competent reader will recover them. We do not therefore conclude that the book is a meaner. The book is a vehicle. So is the model.

    Calling the model a meaner because its outputs traffic in meaningful tokens is the same kind of category mistake as calling a perfectly transcribed prayer a worshipper. The transcription preserves the words; the worshipper supplies the world-directedness; the difference, in our tradition, is the whole point.7 Strip it away and you have not democratized intentionality. You have just mislaid it.

    What is left, then, of the model’s apparent eloquence? Quite a lot, actually — and this is where the gentlemanly part of the knife fight ends in a handshake. I use these tools. I find them genuinely useful. LLMs are extraordinary engines of linguistic regularity. They surface patterns in our practices that we ourselves had not articulated. They are useful in the way a very good concordance is useful, except they generate as well as retrieve. Treating them as oracles dishonors them; treating them as colleagues misclassifies them; treating them as new instruments of inquiry treats them right.

    Borrowed meaning is still borrowed. The maple still does the work. And anyone who writes the word — child, philosopher, model, footnote-machine — only joins the practice by doing what the practice was always for: getting things right about the world, or being corrected when one fails. The model does not fail in that way, because it cannot. It does not succeed in that way either. It produces the shape of success, which is a real and beautiful thing, and which our language has a perfectly good word for.

    We call it style.

    Notes

    1. Jumbly Grindrod, “Large Language Models and Linguistic Intentionality,” Synthese 204:71 (2024). Grindrod’s strategy is to grant the cognitive emptiness of LLMs and recover meaning at a different level: the linguistic practice itself. Drawing on Gareth Evans’s distinction between producers and consumers of a naming practice (Evans, The Varieties of Reference, ed. John McDowell [Oxford: Clarendon, 1982], ch. 11), and on Millikan’s teleosemantic account of conventional signs, Grindrod argues that an LLM can stand in the consumer role even though it lacks the demonstrative-recognitional capacities Evans required of producers. The argument is the strongest version of the LLM-meaning move currently in print; the response developed here grants the structure and contests the inheritance step. ↩
    2. Ruth Garrett Millikan, Language, Thought, and Other Biological Categories: New Foundations for Realism (Cambridge, MA: MIT Press, 1984), chs. 1–2; and “Biosemantics,” Journal of Philosophy 86 (1989): 281–297. Millikan’s “proper function” is the function a trait has in virtue of the evolutionary or learning history that selected for its predecessors. Applied to signs, the proper function of a token-type is what its ancestors did that made it persist. The water-as-H₂O case is most directly developed in Hilary Putnam, “The Meaning of ‘Meaning,’” in Mind, Language and Reality: Philosophical Papers, Volume 2 (Cambridge: Cambridge University Press, 1975), 215–271; the convergence between Putnam’s causal-historical externalism and Millikan’s teleosemantics is one of the more underappreciated agreements in twentieth-century semantics. ↩
    3. The producer-consumer asymmetry is central to Millikan’s account and is what distinguishes teleosemantics from mere informational semantics à la Dretske (Knowledge and the Flow of Information [Cambridge, MA: MIT Press, 1981]). Information by itself does not generate the normative dimension — the difference between succeeding and failing at representation — because mere correlation between sign and signified does not yet involve the kind of cooperative history that lets a sign be wrong. The bee-dance example is Millikan’s own (Language, Thought, and Other Biological Categories, 96–98); the philosophical point is that proper function lives downstream of consumer uptake, not in the producer’s intrinsic states. Peter Godfrey-Smith, “Mental Representation, Naturalism, and Teleosemantics,” in Teleosemantics: New Philosophical Essays, ed. Graham MacDonald and David Papineau (Oxford: Oxford University Press, 2006), 42–68, provides the cleanest critical overview. ↩
    4. The morphology/ecology distinction sharpens an old worry. Stevan Harnad’s symbol-grounding problem (Harnad, “The Symbol Grounding Problem,” Physica D 42 [1990]: 335–346; and “Symbol Grounding and the Origin of Language,” in Computationalism: New Directions, ed. Matthias Scheutz [Cambridge, MA: MIT Press, 2002], 143–158) holds that the meaning of a formal symbol system cannot be fixed by relations among symbols alone, on pain of regress — the symbols must be grounded in a non-symbolic capacity to sort, label, and interact with what they denote. Harnad’s vivid gloss is that language lets us “steal” categories by hearsay rather than “earn” them through sensorimotor “toil,” but the theft presupposes that some categories were earned the hard way: “it cannot be linguistic theft all the way down” (2002, abstract). The borrowed-meaning move is precisely an attempt to make it theft all the way down — to let the LLM inherit grounded content without any grounding of its own. Teleosemantics is supposed to be the framework that explains how grounding gets transmitted across a lineage; the present objection is that transmission, in Millikan’s sense, requires the consumer’s uptake to remain world-accountable, which is exactly the link the LLM’s training loop severs. The ecology is the grounding; the morphology is the theft. ↩
    5. The consumer-side reply is the strongest objection to the argument, since it grants the producer-side point and relocates the loop in human users. The reply fails because Millikanian consumption is not mere uptake but selection-relevant uptake: the consumer’s responses must covary with the sign’s worldly accuracy in a way that differentially preserves accurate producer-tokens. This is the feature that, on Godfrey-Smith’s reading, distinguishes a genuine teleosemantic feedback process from a merely causal one — see Peter Godfrey-Smith, “Mental Representation, Naturalism, and Teleosemantics,” in Teleosemantics: New Philosophical Essays, ed. Graham MacDonald and David Papineau (Oxford: Oxford University Press, 2006), 42–68, esp. the discussion of which feedback loops Millikan’s biological cases license. Human satisfaction with an LLM paragraph covaries with fluency, coherence, and prompt-fit, not with the paragraph’s accuracy about its ostensible subject — fluent falsehood and fluent truth pass the same filter. The point parallels the standard charge against pure informational semantics (Dretske, Knowledge and the Flow of Information [Cambridge, MA: MIT Press, 1981]): correlation alone yields no norm of correctness, because a sign that merely tracks what its consumers reward cannot thereby be wrong about the world. The LLM’s consumers reward readability; readability is not a world-tracking norm; so the loop, though real, is the wrong kind of loop. ↩
    6. Grindrod, “Large Language Models and Linguistic Intentionality,” §4. The concession that Millikan would resist his application is doing more work than Grindrod treats it as doing. Millikan’s framework is not merely a name-tag for “tokens have public meanings”; it is a specific account of what makes a token-type have a meaning, and the answer is that consumers’ uptake of the token, in selection-relevant feedback loops, shapes which producer-tokens persist. Strip out the consumer side of the loop — which is exactly what the LLM case does — and the framework no longer applies. The response in the main text follows the line developed in Millikan, “What Has Natural Information to Do with Intentional Representation?” Royal Institute of Philosophy Supplement 49 (2001): 105–125. For a sympathetic but firm rebuttal of LLM teleosemantic inheritance from a different angle, see Marek Havlík, “Meaning and Understanding in Large Language Models,” Synthese 203:113 (2024). ↩
    7. The vehicle/content distinction at work here echoes Tim Crane’s deployment of it in “Is There a Perceptual Relation?”, in Perceptual Experience, ed. Tamar Szabó Gendler and John Hawthorne (Oxford: Oxford University Press, 2006), 126–146, and connects to the Chinese Room’s underlying point in Searle, “Minds, Brains, and Programs,” Behavioral and Brain Sciences 3 (1980): 417–424 — that formal manipulation of meaningful tokens is not itself a meaning-bearing activity. The prayer-transcription analogy is mine; the structural point — that a vehicle which carries meaning does not thereby produce meaning — is widely shared across the realist tradition the book sits within. Ch. 9 develops the Searlean version of the diagnosis at length. ↩

  • The Triviality Objection to Computationalism

    MIND · MATTER · MEANING No. 37 · May 2026

    The Triviality Objection to Computationalism

    If a wall computes everything, computation isn’t what minds are.

    An essay mindmatterandmeaning.com

    Sit in enough philosophy seminars and you will eventually hear someone announce, with a straight face, that the radiator beside them is running Microsoft Word. The claim sounds unhinged. It is also, in a narrow and revealing sense, true. A famous result holds that any sufficiently large physical object — a wall, a bucket of water, a rock warming in the sun — implements every computer program you care to name. Hilary Putnam pressed a version of this in 1988, and Searle pressed a related one in 1990.1 And the trick that makes it work turns out to embarrass the trickster. Grant yourself enough latitude in how you match up physical states with computational ones, and yes, the wall computes WordStar. By the same latitude it also computes the opposite of whatever it computes, and dreams of Provence on alternating Tuesdays.

    The interesting question is what to make of this. Functionalists, on first hearing, tend to flinch. Their whole picture rests on the idea that a mind consists in functional organization — patterns of input, internal state-change, and output — that can run on any material able to sustain it. If walls turn out to have whatever organization you like, the picture trivializes. The mind reduces to a permission slip you write for yourself. That cannot be what understanding amounts to.

    The temptation here is to reach for new gears. Maybe the right functional organization rules out the gerrymandered mappings. Maybe causal counterfactuals do it, or complexity, or some careful algebra over state-spaces. A respectable literature has grown up around saving functionalism from triviality by tightening these technical screws, and Peter Godfrey-Smith has written the patient article that sorts the repairs that hold from the ones that overreach.2 But the more revealing move is to step back and ask why the threat keeps recurring no matter how cleverly the screws get tightened.

    The diagnosis, once you let it through, has a faintly comic shape. The triviality arguments do not expose some fixable bug in functionalism. They expose something larger. Anything you characterize purely in terms of formal structure — purely in terms of what plays which role in a system of inputs, swaps, and outputs — admits arbitrary interpretation, because formal structure carries no built-in tether to anything in particular. Symbols do not, on their own, point at the world. Functional roles do not, on their own, mean anything. A string of zeroes and ones sits there with perfect indifference about whether it represents a chess position, a tax return, or nothing at all. The wall does run WordStar — in exactly the trivial sense in which it does anything else you can describe with enough slack. Under a creative enough reading the world becomes a Rorschach test, and Rorschach tests do not understand anything.

    This conclusion should feel familiar from a different street. Searle reached it from outside functionalism and pressed it in the Chinese Room.3 Putnam reached it from inside and pressed it in the triviality argument. They walked toward the same wall, and the wall is not running WordStar. It is the wall that says: formal structure cannot, by itself, fix meaning. Searle held that conclusion against the computationalist program loudly. Putnam held it differently. Having spent the 1960s as the founding parent of computational functionalism, he followed his own argument to the conclusion that the very feature he had prized — that minds could be realized in any substrate — was the feature that trivialized the theory, and he said so in print. That is the kind of intellectual courage that ought to embarrass anyone who has ever kept a position because of who else held it.4

    So the wall does not run WordStar in any sense worth wanting. What does the program-running, then? Not the formal structure alone. The structure has to come tethered. It has to be the structure of something that already, for some non-magical reason, picks out one interpretation over the infinite others.

    Here the embodiment story stops being a vague gesture about robots and starts doing real work. Consider a nervous system that evolved to track predators. It tracks predators rather than sandwich crumbs because evolution selected its ancestors for the former and not the latter. No interpreter hands it that mapping. It inherited the mapping from a long causal history in which getting the mapping right meant getting eaten less often. That is roughly what Millikan calls a proper function, and what Dretske connected to representation more broadly.5 Selection — biological, or learned, or both — does the work that pure formal structure cannot. It pins down one interpretation by making it the interpretation under which the system worked, which means, prosaically, the interpretation under which its bearers had children.

    Notice how this dissolves the triviality argument without saving functionalism on its old terms. It does not say the wall fails to run WordStar because some better-tuned functionalism rules the bad mapping out. It says no purely formal account can rule the bad mapping out, because formal accounts lack the resources. What rules it out is that the wall has no selection history picking one mapping over another. The brain has one. The wall does not. The radiator dreaming of Provence cannot dream of Provence at all, because no historical fact about the radiator makes Provence — rather than Pittsburgh, or pickle juice — the content of the dream. Content depends on grounding, grounding depends on history, and history does not show up in the formal organization. It shows up in bodies in worlds.

    The detour through Putnam matters because, without it, the conclusion sounds like Searle banging the lectern. From the outside, the claim that syntax cannot suffice for semantics looks like an anti-computationalist hobbyhorse. From the inside — when the founding theorist of computational functionalism follows his own argument honestly to the same wall — it stops looking like a hobbyhorse and starts looking like a result. The convergence carries weight that neither voice carries alone. And it lands the conclusion the right way: not as one camp dismissing a rival, but as something the rival camp’s best mind ended up affirming when he refused to flinch from his own work.

    The argument also carries a moral about current AI hype. When a language model produces fluent paragraphs about Provence, it does not thereby know about Provence. It performs a transformation of formal structure — a transformation, to be clear, of breathtaking sophistication and real practical value — and that transformation admits, in principle, arbitrary interpretation. Whatever tether it has to the world it speaks of comes from the human authors in its training corpus, who were embodied creatures with selection histories, and who therefore meant something when they wrote about Provence. A model riding on their meanings does not thereby have its own. The wall that allegedly runs WordStar stands to WordStar exactly as a stochastic parrot stands to its training corpus: in a relation conferred entirely from outside, by interpreters who already mean things.6

    What the wall lacks, the parrot also lacks, and what the parrot lacks, embodiment supplies. Not magically. There is no extra ingredient here, no quintessence, no special carbon, nothing Descartes would recognize. What embodiment supplies is the unromantic fact that bodies — evolved or otherwise selected — have histories of getting things right and wrong, and those histories pin down what their states are about. Subtract the history and the formal structure floats free, available for any interpretation and committed to none. That, finally, names what was always wrong with the picture of the mind as software running on the brain. Software does not run on anything by itself. It runs on something that, for non-software reasons, already meant.

    The wall does not run WordStar. The brain, blessedly, runs something — and the reason has nothing to do with the cleverness of the mapping and everything to do with a long, biological, world-involving fact: some patterns got selected for being about things, and the things they got selected for being about are still there, waiting outside the window where they always were.

    Notes

    1. The “every system implements every program” result is the technical engine the parlor game runs on. The cleanest statement of the worry — that any ordinary open physical system implements every finite-state automaton, given a permissive enough mapping from physical to computational states — is laid out by David Chalmers, “Does a Rock Implement Every Finite-State Automaton?,” Synthese 108 (1996), who states the problem sharply precisely in order to constrain it. Putnam’s own version appears as an appendix to Representation and Reality (MIT Press, 1988). Searle’s adjacent claim is that computation itself is observer-relative: syntax is not intrinsic to physics but assigned by an interpreter, so nothing is “intrinsically” a digital computer — see “Is the Brain a Digital Computer?,” Proceedings and Addresses of the American Philosophical Association 64 (1990). The two routes differ but arrive together: where Searle argues that the computational description is observer-relative from the start, the triviality results show that any purely formal description leaves the mapping wide open. ↩
    2. Peter Godfrey-Smith, “Triviality Arguments Against Functionalism,” Philosophical Studies 145 (2009). Godfrey-Smith supplies the careful inventory — which versions of the triviality argument succeed, which overreach, and what minimal additional structure functionalism must take on to survive. His verdict, that the strongest versions push functionalism toward grounding in causal and selection-historical facts, converges with the position defended here. ↩
    3. John Searle, “Minds, Brains, and Programs,” Behavioral and Brain Sciences 3 (1980). The Chinese Room is the better-known vehicle, but the deeper Searlean point is the one developed in the 1990 paper cited above: because the notion of computation is observer-relative, it cannot intrinsically characterize the brain. The triviality results reached from inside functionalism are, in effect, that same conclusion reached by a different route — which is why the convergence is worth pressing rather than merely noting. ↩
    4. Hilary Putnam, Representation and Reality (MIT Press, 1988). Putnam, who in the 1960s effectively founded computational functionalism, concludes here that the multiple-realizability which originally recommended the view in fact trivializes it once one sees how unconstrained the realization relation really is — the formal organization that was supposed to be substrate-neutral turns out to be interpretation-neutral as well, and that is fatal. See also Oron Shagrir, “Putnam and Computational Functionalism,” in Hilary Putnam on Logic and Mathematics (Springer, 2018), on the philosophical significance of an arch-functionalist reaching this verdict. ↩
    5. For the locus classicus connecting selection history to representational content, see Ruth Garrett Millikan, Language, Thought, and Other Biological Categories (MIT Press, 1984), especially chapters 1–2 on proper functions; and Fred Dretske, Explaining Behavior: Reasons in a World of Causes (MIT Press, 1988), for the parallel project run through learning history rather than evolutionary history. Both share the structural claim the argument leans on: what a state represents is fixed by the history that selected it, not by its current formal profile. ↩
    6. Whether artificial systems trained on human corpora might inherit learning-historical proper functions in Millikan’s sense is a live question, taken up by Jumbly Grindrod, “Large Language Models and Linguistic Intentionality,” Synthese 204:71 (2024). Millikan’s own writings suggest she would resist the extension: the histories she has in mind run through generations and natural selection, not through gradient descent over a fixed dataset. The disagreement marks the live frontier of teleosemantic theorizing about AI, and nothing here forecloses it — the claim is only that formal structure alone never settles content, not that gradient descent could never be a grounding history of the relevant kind. ↩

  • What a Machine Would Have to Earn

    MIND · MATTER · MEANING No. 29 · May 2026

    What a Machine Would Have to Earn

    Understanding is earned in a world, not performed on a screen.

    An essay mindmatterandmeaning.com

    A friend sent me a transcript last spring. He had asked a chatbot what a sunburn feels like the morning after — that specific tight, hot, can’t-find-a-way-to-lie-down misery — and the machine answered better than he could have. It named the flinch when a shirt seam drags across the shoulders. It knew the small betrayal of forgetting for a second and leaning back into a hot car seat. He found it uncanny, a little moving, and he wanted to know: does it understand what a sunburn is?

    Good question, asked at the right moment. The honest answer takes a while to earn, so let me start with the answer most of us reach for first — because it’s reasonable, and because it’s wrong.

    The reasonable view goes like this. Understanding shows up in what you can do. A student who can answer any question about the French Revolution, field the follow-ups, catch the trick ones, and explain the whole thing to a ten-year-old — that student understands the French Revolution, and we would be cranks to deny it on the grounds that we can’t peer inside her skull. Understanding is as understanding does. So if a machine handles every question about sunburns as well as a sunburned person could, the difference between the machine and the person starts to look like a difference we invented to feel special about ourselves. The picture has a respectable pedigree: it descends from behaviorism, and it has a famous instrument in Alan Turing’s imitation game, where the test for thinking just is indistinguishable performance.

    Notice the quiet assumption, though. The picture takes understanding a word to be a matter of using it correctly, and takes “correctly” to be settled by looking only at the outputs. Pull on that thread and the whole thing comes apart in your hands.

    Stevan Harnad, a cognitive scientist with a gift for naming traps, named this one in 1990: the symbol grounding problem.1 Imagine trying to learn Chinese from a Chinese-only dictionary. Every definition sends you to other entries, which send you to others, and you ride that merry-go-round forever without once touching the ground. A system whose symbols are defined only by more symbols never means anything by them. Meaning gets in only when some of the symbols connect to the things they are about by some route other than further symbols — when “red” hooks to red, not merely to “crimson,” “scarlet,” and “the color of a stop sign.”

    What supplies the hook is not anything inside the system. Hilary Putnam made the case unforgettable with a thought experiment about Twin Earth — a planet just like ours except that the stuff they call “water” there is some other compound with all of water’s surface features.2 A person here and their molecular duplicate there can be internally identical, down to the atom, and still mean different things by “water,” because the word answers to the stuff in the world, not to the state of the head. “Meanings,” Putnam wrote, “just ain’t in the head.” Tyler Burge pushed the same point from the social side: what your word “arthritis” picks out depends on the practice of the community you defer to, not on a private definition you carry around.3 Content lives in a relation — between a system, a world, and the company it keeps.

    There is even a natural story about how the relation gets built. On teleosemantic accounts — Ruth Millikan’s and Fred Dretske’s, chiefly — a state comes to be about something by acquiring the function of tracking it, the way a frog’s strike comes to be about flies through a long history in which catching flies is what kept frogs going.4 The clinching detail is misrepresentation: to get something wrong, a system has to have been in the business of getting it right. A state can mean fly and fire at a passing pellet only because its job, fixed by history, was flies. No history, no job; no job, nothing to be mistaken about; nothing to be mistaken about, no content.

    So understanding a word turns out to be an achievement, not a knack: it consists in having states that are genuinely about the world — not states that merely accompany the right answers, but states directed at the very things the words name — and aboutness is something a system earns over time. Your “red” means red because red things have been pushing on you, through eyes and skin and the small stakes of an actual life, since before you could pronounce the word. This is what people are gesturing at, usually too vaguely, when they say minds are embodied. The word invites mysticism, so let me drain it of any. Embodiment names three sober requirements: the system takes in the world through senses and acts back on it; its inner states have been shaped by real traffic with the features they represent; and those states are there to track a world the system inhabits, not merely to emit the right strings. Michael Tye — who spent three decades building the most careful theory we have of how experience could be nothing more than representational content, and then argued that even his own theory needs history — makes the sharpest version of the point. Two creatures could be intrinsically identical at an instant, he argues, and still differ in what they experience, because one has a past of tracking the world and the other was assembled, atom for atom, five minutes ago.5 History is not decoration on content. It is part of what fixes it.

    Which lets me say, at last, what a machine would actually need. Not the right stuff — I don’t think the barrier is silicon, and here I part company with John Searle, who ties understanding to the specific causal powers of biological brains.6 The barrier isn’t carbon; it’s a world. A system understands when its inner states have been shaped by, and stay answerable to, the things they represent — when it senses and acts, lives under stakes, and can get things wrong and pay for it. Build that, and the door to genuine artificial understanding stands open. I mean open, not slyly closed. The claim here is not the tired one that machines could never understand. It is that understanding is earned through engagement, and there is no coupon for skipping the engagement.

    Skipping the engagement is precisely what today’s text-only language models do. A large model learns the statistics of how we talk — the staggeringly intricate shape of which words follow which — from a corpus of descriptions of the world, never from the world.7 It has read everything ever written about sunburns and has never once had skin. Its “red” is a position in an immense map of words, anchored to other words, anchored to nothing outside the map. The fluency is real and the achievement is genuine; it is simply not the achievement of understanding.

    Here the strongest objection arrives, and it deserves a real hearing rather than a brush-off. If the machine’s answers became indistinguishable — in principle, not merely in today’s practice — from an understander’s, then insisting it still lacks understanding looks like clinging to a ghost. A difference that makes no detectable difference, the objection runs, is no difference at all. That is the whole moral of the imitation game, and it is not a silly one.

    But “makes no difference you can detect in the output” is the definition of a good simulation, not the absence of a difference. Simulate a hurricane to any precision you please: the equations are flawless and your desk stays bone dry. Modeling a process is not running it.8 Two systems can produce the very same words while one means them and the other reports the statistics of how the word gets used — because meaning was never a property of the output. It lives in the history behind the output, and that history is exactly what an output test cannot see. The objection mistakes the instrument for the quarry. It notices that the meter reads the same and concludes there is nothing the meter is missing.

    So: does the machine understand what a sunburn is? It has never had skin. It has never flinched, never dreaded an evening because of how the sheets would feel. It holds the words and not the world the words are about. Ask the question again in some later decade, of some later system that has spent years bumping into things and paying for its errors, and the answer could come back different — that is the part the doom-mongers and the hype-merchants both manage to miss. Understanding is not a performance a system delivers. It is a debt a system pays, to the world, in the one currency the world accepts: contact. Until the bill comes due, fluency is only fluency. It was always going to be the easy part.

    References

    Burge, Tyler. 1979. “Individualism and the Mental.” Midwest Studies in Philosophy 4: 73–121.

    Dretske, Fred. 1988. Explaining Behavior: Reasons in a World of Causes. Cambridge, MA: MIT Press.

    Harnad, Stevan. 1990. “The Symbol Grounding Problem.” Physica D 42: 335–346.

    Harnad, Stevan. 2002. “Symbol Grounding and the Origin of Language.” In Computationalism: New Directions, edited by Matthias Scheutz. Cambridge, MA: MIT Press.

    Havlík, Vladimír. 2025. “Meaning and Understanding in Large Language Models.” Synthese 205: 9.

    Millikan, Ruth Garrett. 1989. “Biosemantics.” Journal of Philosophy 86 (6): 281–297.

    Putnam, Hilary. 1975. “The Meaning of ‘Meaning.’” In Mind, Language and Reality: Philosophical Papers, Volume 2, 215–271. Cambridge: Cambridge University Press.

    Searle, John R. 1980. “Minds, Brains, and Programs.” Behavioral and Brain Sciences 3 (3): 417–457.

    Searle, John R. 1990. “Is the Brain a Digital Computer?” Proceedings and Addresses of the American Philosophical Association 64 (3): 21–37.

    Tye, Michael. 2019. “Homunculi Heads and Silicon Chips: The Importance of History to Phenomenology.” In Blockheads! Essays on Ned Block’s Philosophy of Mind and Consciousness, edited by Adam Pautz and Daniel Stoljar. Cambridge, MA: MIT Press.

    Notes

    1. Harnad (1990) coined “the symbol grounding problem” and framed it with the Chinese-dictionary regress; he later tied it to the origin of language (Harnad 2002). The problem is older than the label — it is the computational heir of the externalist worry about how any representation latches onto its object — but Harnad’s formulation is the one the AI literature inherited, and it is sharper than the Chinese Room for present purposes because it isolates grounding from Searle’s further claims about consciousness. ↩
    2. Putnam (1975). The conclusion is specifically about reference and extension: the content that fixes what “water” is true of does not supervene on the speaker’s intrinsic states. Note that Putnam later qualified his own semantic externalism in several directions; nothing here turns on the most contested versions of the thesis, only on the minimal claim that reference depends on causal-environmental relations the head alone does not settle. ↩
    3. Burge (1979) extends externalism from natural-kind reference (Putnam) to social content: holding a thinker’s physical history fixed while varying the surrounding linguistic community varies which concept the thinker exercises. The two cases are independent routes to the same structural conclusion — internal organization underdetermines content — which is why the essay leans on both rather than treating Burge as a footnote to Putnam. ↩
    4. The teleosemantic tradition, principally Millikan (1989) and Dretske (1988), grounds content in proper function: a state represents what it has the function of tracking, where functions are fixed by selection or learning history. Misrepresentation is the standard adequacy test for any naturalistic theory of content, since a theory on which states cannot be false has not yet described representation. Rival tracking theories handle reliable misrepresentation differently, but the historical structure — content fixed by what a state was for — is common ground and is what the embodiment argument borrows. ↩
    5. Tye (2019). The thesis is that two beings intrinsically alike at a time can differ in phenomenal character because they differ in history — a representationalist’s concession that current intrinsic structure does not suffice. Ned Block replies in the same volume (“Fading Qualia: A Response to Michael Tye”) that a subject could be radically wrong about their own phenomenology; the disagreement is real and unresolved, and the essay sides with Tye while granting that Block has located the genuine pressure point. That Tye, of all people, reaches for history is the relevant fact: the most developed representationalism on offer does not think structure alone fixes content. ↩
    6. Searle (1990) argues that computation is observer-relative — a physical system “computes” only under an interpretation we assign — so computational description cannot, by itself, explain intrinsic intentionality. The essay takes this negative point and leaves Searle’s positive doctrine behind. Searle’s biological naturalism holds that only the specific causal powers of brains can produce understanding; the view defended here replaces “the right biology” with “the right causal-environmental engagement,” which a non-biological system could in principle possess. The negative argument survives the amputation of the positive one. ↩
    7. Not everyone takes the contact gap to be fatal, and the most direct contrary voice deserves naming. Vladimír Havlík (2025) argues the reverse of this essay’s conclusion — that large language models do ground the meanings of their expressions, by way of what he calls semantic fragmentism, so that grounding in worldly reference is not a precondition of understanding. I think this mislocates the gap rather than closing it. Semantic fragmentism can explain how a model’s tokens come to bear stable relations to one another; the externalist and teleosemantic considerations above concern what fixes the relation between a token and the world, which is precisely what a text-only training signal never touches. The architectural premise is not what divides us — a text-only model is trained to predict the next token over a corpus of text, full stop — what divides us is whether that suffices for content, and Havlík’s affirmative answer is the live position this essay rejects. ↩
    8. The simulation/realization distinction is Searle’s reply to the Brain Simulator objection in “Minds, Brains, and Programs” (1980), generalized: a model of a process is not an instance of it, and whatever a process owes to its physical realization is not delivered by a description of that realization, however exact. The hurricane example makes the point without the contested premises about consciousness — no one is tempted to say the simulated storm is wet — which is why it does cleaner work here than the Chinese Room. ↩