Tag: Putnam

  • 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.
  • Twin Earth and Semantic Externalism

    MIND · MATTER · MEANING May 2026

    Twin Earth and Semantic Externalism

    Meaning isn’t in the head. The word reaches past the skull to the world.

    An essay mindmatterandmeaning.com

    Stand in the kitchen and point at the kettle and say water. Nothing about that performance feels mysterious. You meant water; the kettle holds water; the word landed where you sent it. The whole transaction belongs to ordinary life — the kind of thing a competent five-year-old manages a hundred times a day without philosophical assistance.

    Now suppose, while you weren’t looking, the kettle had been swapped for one filled with a clear, tasteless liquid whose molecular structure happens to differ from water in every respect that matters at the bench. You can’t tell. The five-year-old can’t tell. The kettle whistles. You pour. You drink. Did you mean water when you said the word, even though no water was anywhere in the room?

    This is the question Hilary Putnam asked in 1975, and the answer he gave reshaped the philosophy of mind. The short version: no. You meant water, in the way you ordinarily do, only because real water exists out in the world and your linguistic community has been pointing at it for generations. The pointing happens partly outside your skull. The meaning, accordingly, lives partly outside it too. As Putnam put it, in the line that has been quoted ever since: “meanings just ain’t in the head” (Putnam, 1975).1

    The slogan has the feel of something half-rhetorical. It isn’t. What follows explains why it turns out to be true, what bad picture it replaces, and what difference it makes — including for one of the more aggressive claims you’ll hear at the confident end of contemporary AI discourse.

    The picture we usually carry

    The bad picture is so familiar it barely registers. When you think of meaning, you probably think of something happening inside a head. A word floats up; an image, or a definition, or a feeling of recognition attaches itself to it; out the word goes, freighted with its little inner cargo. Whatever it is that makes water mean water, the picture says, is some inner state of yours — a concept, a representation, a private mental something — that the word is hooked up to.

    The picture has a long pedigree. Descartes built half his metaphysics around it. The British empiricists stocked the mind with ideas the way one stocks a pantry, and spent a great deal of energy reassuring themselves that the pantry tracked the outside world accurately. The cognitive scientists of the 1960s gave the pantry a computational paint job and called the contents internal representations. Same picture throughout: meaning sits inside the head, the head has a private inventory, the word inherits its meaning from the inner item it labels.

    What Putnam’s 1975 paper does, with one of the cleanest thought experiments in philosophy, is show that the picture cannot be right.

    Twin Earth, and why it bites

    Imagine a planet exactly like ours, down to the molecule, except that in every place our world has water, Twin Earth has a different stuff. Putnam called it XYZ. It looks, feels, tastes, and behaves indistinguishably from H₂O at the rough scale of human life. People on Twin Earth wash with it, drink it, complain about its hardness; they call it water. The chemistry beneath the surface differs, but no Twin Earther in 1750 has any way of detecting that difference.

    Now consider Oscar on Earth and his molecule-for-molecule duplicate Twin Oscar on Twin Earth, both in 1750, before chemistry exists. Stand them side by side, look inside their skulls, take inventory of every inner item the bad picture would care about: the same brain states, the same images, the same feelings, the same dispositions, the same everything. By the bad picture’s lights, when each says water, each means the same thing.

    But each does not mean the same thing. When Oscar says water, his word reaches for H₂O and lands on it, because that is the stuff the linguistic practice he inherited has been about. When Twin Oscar says water, his word reaches for XYZ. They cannot mean the same thing, because their words have different references — different things in the world that they pick out, different conditions under which what they say is true. The meaning differs even though everything inside the head is identical (Putnam, 1975).2

    Notice what the thought experiment is not claiming. Oscar and Twin Oscar do not have different inner lives; they have qualitatively identical ones, by stipulation. The point is that those inner lives, however rich, do not by themselves fix what their words are about. Something else does — namely, the actual stuff the community’s linguistic practice has been latching onto across time.

    This is what philosophers call semantic externalism — the view that the meaning of a word, and the content of a thought, depends constitutively on factors outside the speaker or thinker. Outside the skull, outside the inner inventory, outside whatever the bad picture wanted to keep tucked away in private mental space.

    Why the result generalizes

    The water case is the showpiece, but Putnam’s argument doesn’t depend on natural kinds with hidden chemistry. Tyler Burge spent a career defending a more sweeping version — the view he calls anti-individualism — arguing that the same lesson runs through perception, concept possession, and the categories that structure ordinary cognition (Burge, 2010). The reason is structural. A representation succeeds or fails at hitting its target, and what counts as the target gets settled by relations the representation bears to a world. Burge’s signature example: a patient tells his doctor he has arthritis in his thigh. He is simply wrong — arthritis by definition is a disease of the joints — and crucially he is wrong about what his own word means, not because his inner state is defective but because his community’s medical practice has settled the term against him. What a word reaches for is fixed by the practice the word participates in, not by what the speaker pictures when uttering it.3

    Alex Byrne and Michael Tye have argued that on the strongest version of representationalism, even the felt character of experience — the qualia earlier philosophy treated as the last private redoubt — depends on the world the experience represents (Byrne & Tye, 2006). If they are right, even the most intimate-seeming features of mental life have an outside leg — a claim Ned Block has pressed hard against, and one the externalist has to earn rather than assume.4

    The lesson, told plainly: minds reach into a world to do their work. A mental state has the content it has partly in virtue of what, out there, it latches onto. That latching runs through causal, historical, social, and environmental relations all at once. The inside contributes half the mechanism. The outside contributes the other half.

    What the LLM defender wants to say

    Once you see why meanings can’t be in the head, an objection arrives almost immediately, and these days it usually concerns language models. A defender of the strong-AI line will say something like this: very well, meaning is not in any individual head — but it doesn’t need to be. Modern large language models are trained on the entire textual output of a civilization. The hookings, the practices, the patterns of use, are all there, distributed across the corpus. Whatever fixes meaning for human speakers should fix it for a model that has internalized the practice at scale. The model’s words reach into the same world ours do, through the same network of usage. Why call this anything less than understanding?

    Vladimír Havlík defends a sophisticated version of this view. He argues that the meanings of linguistic expressions in LLMs are grounded — in his words — “neither in the world, nor in an internal idea of the world,” but within the linguistic corpus as a whole, and that this turns out to be sufficient for what he calls referential grounding (Havlík, 2024). The picture deserves a fair hearing. If meaning lives in patterns of public use, and a model has absorbed those patterns at civilization scale, then the model — the argument runs — has whatever it takes.5

    I think the argument fails. And where it fails reveals what Twin Earth really showed.

    What Twin Earth really showed

    Putnam’s thought experiment said something stronger than meaning lies outside the speaker. It said that meaning depends on the world the practice latches onto. Oscar and Twin Oscar both participate in fluent verbal practice; both communities use water the same way; the difference is only what their respective practices are anchored to. The anchoring fixes which stuff the word reaches for. Talk that is not anchored does not reach.

    A language model has the corpus. It does not have the anchoring. It has the residue of the anchoring, frozen in token statistics, with no living relation to the stuff the tokens came from. When the model produces water, no path runs from the word back to any water — not in training, not in deployment, not even, in any straightforward sense, in the data. The data records human anchoring in compressed form; the model inherits a derivative shadow of that anchoring; the shadow does informative work, sometimes spectacularly so — but a shadow of an anchor does not anchor anything. John Searle, the Berkeley philosopher whose Chinese Room argument we will meet again, made an adjacent point four decades ago — syntax, however elaborate, does not constitute semantics (Searle, 1980) — and the externalist diagnosis converges with his from the other side: both isolate the same missing thing, a relation between symbols and the world they purport to describe.6 The model does not lack complexity. It lacks that relation.7

    This isn’t a denial of the model’s achievement, which is real and genuinely impressive in ways I don’t want to minimize. Fluent next-token prediction over a record of meaning counts for something — but it does not count as the same accomplishment as meaning. Meaning is what the record records. The record itself, separated from the activity it records, has no pointing power of its own. Twin Earth tells us so: without the right anchoring, even an inner life qualitatively identical to ours fails to mean what we mean. A model without any anchoring at all does no better.

    Where this leaves the reader

    The point isn’t to settle the AI question in one essay — that takes a longer argument — but to remove a misleading picture that gets in the way of seeing the question clearly. The picture says meaning is a stuff inside heads, and minds reach into the world by carrying that stuff outward. The picture is wrong. Meaning is what minds do when they reach — a relation, not an inner cargo. The kettle whistles, the word lands, the kettle holds water, and the linguistic community has been latching onto water for a long time. That whole arrangement is what makes your word work. None of it lives between your ears alone.

    The mind doesn’t make meaning by storing it. It makes meaning by reaching — and a reach with nothing at the far end is not a reach. It’s a gesture.


    Notes

    1. Putnam’s argument runs through “The Meaning of ‘Meaning’” (in K. Gunderson, ed., Minnesota Studies in the Philosophy of Science, vol. 7 [Minneapolis: University of Minnesota Press, 1975], 131–193), with the slogan at p. 144. The slogan is often quoted as if it were the conclusion; in Putnam’s text it sits midway through the development, after the Twin Earth case has done its work and before the apparatus of stereotype, normal form description, and the division of linguistic labor is introduced. The full position is more structural than the slogan suggests: meaning, on Putnam’s account, supervenes on a four-element vector (syntactic markers, semantic markers, stereotype, extension), and only the first three live “in the head.” The extension — the actual stuff the word picks out — lies outside, and the extension is constitutive of meaning. The division of linguistic labor does additional load-bearing work the slogan hides: a lay speaker can mean gold without being able to tell gold from pyrite, because the community houses experts whose discriminations the lay speaker defers to. Reference is thus a collective achievement distributed across a community and its history, not a private hookup renewed in each head — a point that matters directly when the question turns to a system that has the corpus but stands in no deferential relation to any expert in it.
    2. The Twin Earth argument depends on two further commitments Putnam developed in parallel with Saul Kripke’s work in Naming and Necessity (Cambridge, MA: Harvard University Press, 1980; the lectures were delivered in 1970). Natural-kind terms like water are rigid designators: they pick out the same kind in every possible world in which that kind exists. And the identity water = H₂O is a necessary truth discovered a posteriori: not derivable from the concept of water alone, but, once established, holding of metaphysical necessity. These two commitments together explain why Oscar’s water and Twin Oscar’s water cannot have the same reference even when their inner states are identical. It bears emphasis against a standard misreading: the indexicality of water (Putnam’s “this liquid, the same liquid as that“) does not relocate the difference back inside the head as a difference in narrow content. The demonstrative reaches its referent only through a causal-historical relation to a sample, and it is that relation — not any inner accompaniment of the demonstrating — that differs between the twins.
    3. Burge’s case is developed first in “Individualism and the Mental,” Midwest Studies in Philosophy 4 (1979): 73–121, and expanded across decades into the systematic anti-individualism of Origins of Objectivity (Oxford: Oxford University Press, 2010), esp. chaps. 2–3. Burge’s claim is stronger than Putnam’s along two axes. First, it needs no hidden microstructure: the arthritis case turns on a purely social fact — that “arthritis” is, in the speaker’s community, a disease of the joints — so the externalist conclusion extends to artifact and institutional terms (sofa, contract) that have no chemical essence at all. Second, and more carefully than the main text’s compression allows: Burge’s point is not that the patient is ignorant of a dictionary entry, but that the content of his belief is fixed by his community’s practice despite his incomplete grasp of the term. The patient genuinely believes that his arthritis has spread to his thigh — a false belief about arthritis, deferring to a practice that determines what “arthritis” picks out — rather than a true belief about some idiosyncratic private concept. Strip away the community and the very identity of the concept he is deploying goes indeterminate. Content, that is, is constitutively dependent on relations the thinker bears to a wider linguistic and physical environment, not merely causally downstream of them.
    4. Byrne and Tye, “Qualia Ain’t in the Head,” Noûs 40, no. 2 (2006): 241–255. The argument runs as an externalist extension of Tye’s strong representationalism (Ten Problems of Consciousness [Cambridge, MA: MIT Press, 1995], esp. chaps. 4–5): if phenomenal character is identical to representational content of the right kind, and if representational content is itself externally determined (per Putnam, Burge, and the wider tradition), then phenomenal character cannot be wholly internal to the perceiving system. The strongest objection is Ned Block’s “mental paint” line (Block, “Mental Paint,” in M. Hahn and B. Ramberg, eds., Reflections and Replies: Essays on the Philosophy of Tyler Burge [Cambridge, MA: MIT Press, 2003], 165–200): there are, Block argues, intrinsic phenomenal features — the “paint” on the inner canvas — that vary independently of any represented worldly property, as in cases of phenomenal inversion or in afterimages, so that two experiences could represent the same scene yet differ in felt character. Met at full strength, the objection is answered, not conceded, by holding the line on the identity claim: the cases Block adduces are redescribed as differences in what is represented (a difference in represented hue, an afterimage represented as a colored region of the visual field) rather than as residue left over once representational content is fixed. “Mental paint” names exactly the inner cargo the externalist denies; to grant it as an independent variable would be to smuggle the bad picture back in under a new label. The present essay endorses the strong representationalist line and takes the burden Block identifies — to redescribe every putative case of paint without remainder — to be one the view can carry.
    5. Havlík, “Meaning and Understanding in Large Language Models,” Synthese 204, article 71 (2024). Havlík distinguishes three candidate locations for the grounding of LLM meanings — the world, an internal world-model, and the linguistic corpus itself — and argues that the first two cannot be required of an LLM without begging questions about what counts as grounding; his positive proposal is that meaning in LLMs is grounded intra-linguistically, within the corpus, so that referential success becomes a property of distributional structure rather than of any speaker-world relation. The objection pressed in the main text grants Havlík his negative point — referential grounding is indeed not the only way to fix meaning for a symbolic system — while denying the positive one: intra-linguistic structure can do real semantic work (disambiguation, inference, paraphrase) precisely because it inherits the compressed trace of relations the original speakers bore to the world, but it cannot do the constitutive work the externalist tradition has identified, because the relation that did that work was severed at training time and the trace is not the relation. Compare the converging diagnosis of Emily M. Bender and Alexander Koller, “Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data,” Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020): 5185–5198, who argue from the side of form what externalism argues from the side of reference: a system exposed to form alone, however much of it, has no route to communicative intent or to the world that intent is about.
    6. Searle, “Minds, Brains, and Programs,” Behavioral and Brain Sciences 3, no. 3 (1980): 417–424; followed up in “Is the Brain a Digital Computer?” Proceedings and Addresses of the American Philosophical Association 64, no. 3 (1990): 21–37. For the present essay’s externalist diagnosis, Searle’s two arguments converge from different directions: the 1980 paper isolates the syntax/semantics gap from the side of what symbol manipulation alone delivers (a syntactic engine, however fast, never crosses into semantics by running faster); the 1990 paper isolates it from the side of what counts as a symbol in the first place (syntax is not intrinsic to physics — it is assigned by an interpreter, which threatens any account that hopes to read off semantics from a system’s formal structure). The externalist arrives at the same gap from a third direction: even granting determinate symbols, their reference is fixed by relations to a world, and those relations are not among the system’s formal properties. Three independent roads, one missing ingredient.
    7. The relevant technical distinction: training statistics encode the distributional facts about how speakers in a corpus deploy tokens relative to one another, but do not encode the referential facts about which extensions those deployments succeeded in picking out. The two are correlated — because the human speakers were anchored — but the correlation does not survive the move from the speakers to the trained model: the model has access to the shadow the anchoring cast on usage, not to the anchoring. This is why the natural reply — “but a model can be grounded, via multimodal training, robotic embodiment, or retrieval against a live environment” — is not a counterexample but a concession in disguise. Each such proposal works precisely by restoring some causal-historical relation between the system’s symbols and the world, which is the externalist’s point: reference is purchased by anchoring, and where genuine anchoring is added the verdict can change (cf. Michael Tye, “How Can We Tell if a Machine is Conscious?” Inquiry [advance online publication, 2024], https://doi.org/10.1080/0020174X.2024.2434856, on the embodied conditions under which machine reference could succeed). The claim of this essay is narrow and exact: a text-only model trained on a static corpus has no such relation, and so its fluency, however vast, is fluency over a record of meaning rather than an instance of it.