The Machine Question: A Mind Doesn’t Care What It’s Made Of
Putnam built the argument for machine minds in 1967. Then he spent decades trying to take it back. Interpretability researchers didn’t notice. Now they’re checking his receipts.
The Machine Question · Classic philosophical claims about the mind, cross-examined by what today's machines actually do.
In March 2020, a small team at OpenAI published an essay with an odd title: “Zoom In: An Introduction to Circuits.” Its authors, led by a researcher named Chris Olah, had spent months staring at the individual neurons of image classifiers, the way a naturalist stares at insects under a lens. They found curve detectors, small clusters of neurons that lit up for a curved edge no matter where it sat in an image or what object it belonged to. They found neurons that fired for dog snouts, for car wheels, for high-frequency textures. What caught their attention was not any single detector but a pattern across their own past work: detectors they had already catalogued in one image classifier kept reappearing, doing the same job, inside other classifiers trained separately, on separate data, by separate teams who had never coordinated with each other or with them. The same neurons, doing the same jobs, kept turning up in networks that had never seen each other’s weights. They called this observation universality, and they were careful to call it a hypothesis, not a finding.
Two years later, Olah was no longer at OpenAI. He was at Anthropic, and he was a co-author on a different paper, “In-Context Learning and Induction Heads.” It described a circuit, a small mechanical pattern inside a transformer’s attention layers, that let the model copy a sequence it had seen a few tokens earlier and predict what would come next. The paper’s authors found this circuit again and again, in separate models trained from scratch at different scales. It was as if the same gear kept showing up inside different machines that nobody had asked to share a design.
Neither paper mentions philosophy. Neither paper needed to. But the claim underneath both of them, that the same mental or computational job can be done by physically unrelated hardware, is not a new idea in artificial intelligence. It is one of the oldest arguments in the philosophy of mind, and it is fifty-three years older than the transformer. In 1967, a philosopher named Hilary Putnam gave it a name: multiple realizability. He built it to defend the very possibility that a machine could have a mind. This is the story of that argument, what it actually claims, and whether the machines it was built to defend have started supplying evidence for it, against it, or something stranger than either.
The Position
In the years before Putnam wrote, the dominant theory of mind in analytic philosophy was not functionalism. It was identity theory, and it had a simple, almost bracing claim: a mental state just is a brain state. Pain is not caused by C-fiber activation, or correlated with it, or expressed through it. Pain is C-fiber activation, the same way that water is H2O. U.T. Place had proposed the view in 1956, in a paper called “Is Consciousness a Brain Process?” J.J.C. Smart sharpened it in 1959. For a few years it looked like philosophy of mind had found its version of chemistry’s periodic table, one clean identity to close the ancient mind-body problem for good.
Putnam thought the theory was too tidy, and in 1967 he published the paper that would undo it, originally called “Psychological Predicates” and later reprinted as “The Nature of Mental States.” His argument did not require any exotic thought experiment. It only required noticing something obvious once someone said it out loud: pain is not a state exclusive to creatures with C-fibers. A mammal feels something we call pain. So, in whatever sense the word applies to non-human minds, does an octopus, whose nervous system is organized nothing like ours, and so, arguably, does a reptile. If identity theory were right, and pain simply were a particular type of physical state, then either these creatures don’t really feel pain, which seemed to Putnam like a strange thing to insist on, or pain has to be identified with some vast disjunction of physical states, one type of C-fiber activation for mammals, some other structure for mollusks, and so on for every nervous system biology has produced or might produce. Putnam thought that second option gave away the whole game. A theory that has to keep adding disjuncts every time it meets a new nervous system is not identifying anything. It’s cataloguing.
His alternative was to stop looking at the hardware and look at the job instead. A mental state, on Putnam’s view, is defined by what it does: how it’s caused, what it causes, how it relates to other mental states. This was not a metaphor borrowed from outside his expertise. Putnam trained as a mathematician before he became a philosopher, and he thought about computation the way an engineer thinks about it, not the way a humanist reaches for a convenient image. He drew the comparison to a Turing machine deliberately. A Turing machine’s internal states are defined purely by a table of rules, this state plus this input yields that output and that next state, and the same abstract machine can be built out of vacuum tubes, silicon transistors, or, if you were patient enough, tin cans and string. What matters is the pattern of transitions, not the material carrying it. Putnam called this view machine functionalism, and its consequence for mind was direct. If mental states are functional states in this sense, then a mind is not tied to neurons any more than a Turing machine’s logic is tied to vacuum tubes. Multiple realizability was not a side effect of Putnam’s functionalism. It was the entire point. He built the argument, in part, to make room for the idea that something built from silicon could have a mind in exactly the sense a human does. He was defending, fifty years in advance, the philosophical possibility of the machines this series is about.
The argument did not stay confined to pain and C-fibers for long. In 1974, the philosopher Jerry Fodor generalized it into something with much larger stakes, in a paper called “Special Sciences.” If mental states can be multiply realized, Fodor argued, so can the subject matter of any higher-level science: economics, biology, psychology. A recession is not identical to any specific configuration of atoms, and a species is not identical to any specific arrangement of molecules, in the same way that pain is not identical to any specific arrangement of neurons. Each higher-level science studies patterns that can be built out of physics in more ways than physics alone would ever predict, which is why those sciences get to exist as autonomous fields rather than being swallowed into physics departments. Multiple realizability, in Fodor’s hands, stopped being a narrow fix for the mind-brain problem and became an argument for why most of the sciences we have are not, and should not be, reducible to the one underneath them. That is the scale of the claim now sitting on the load-bearing wall this piece is testing.
But “functionalism” is not one theory, and it matters which machine he was defending. Putnam’s own version, machine or computational functionalism, is a specific claim about mental states as Turing-style functional roles. It sits alongside cousins that make different commitments: the analytic or causal-role functionalism developed by Smart himself once he moved past identity theory, and later by David Armstrong and David Lewis, which analyzes our ordinary concept of pain in terms of its typical causes and effects rather than any computational architecture; and psychofunctionalism, which cares less about common-sense concepts and more about the functional organization revealed by actual cognitive science. The three views share a family resemblance and a common enemy in identity theory, but they answer different questions, and an objection to one does not automatically land on the others. This distinction will matter later in this piece. For now, the claim on trial is Putnam’s own: mental, or mind-relevant, states are individuated by functional role alone, independent of the physical medium that implements them.
The Claim
Stated in its testable form, Putnam’s thesis says this: if a state is truly defined by its functional role rather than its physical substrate, then that same functional role should be capable of arising in substrates that share no lower-level structure with each other. Applied to artificial neural networks, the thesis makes a specific and checkable prediction. Two networks that share no weights, no initialization, and in the strongest version of the claim, no architecture, should nonetheless sometimes converge on the same functional unit doing the same computational job. Not a similar job. The same one, playing the same role in the same kind of larger process.
This is a claim interpretability research is now in a position to check directly, because it can open a network and look at what is inside it in a way neuroscience has never been able to open a brain. That is the load this section will place on Putnam’s wall.
Two features of the claim matter for reading the evidence honestly. First, it is a claim about recurrence, not about similarity. Two networks solving a task in roughly similar ways is not enough. The bar Putnam’s argument sets is the same functional unit, doing the same specific job, showing up in systems that did not share the training run that produced it. Second, the claim gets stronger evidence from bigger differences between the substrates being compared. A finding that two runs of the identical model, differing only by chance at initialization, converge on a shared unit is weaker support than a finding that two models of different sizes, different architectures, or different training objectives converge on the same unit. The interpretability literature, as it stands today, has much more of the first kind of evidence than the second.
Loading the Wall
The technical case starts with the word Olah’s 2020 paper used carefully: universality. The paper distinguished a weak and a strong version of the hypothesis. Weak universality says only that there are underlying computational principles networks tend to discover, without insisting that any two networks implement them in identical form. Strong universality says more: that the same specific features and circuits will reliably appear across models trained on similar tasks, almost regardless of the particular run. It is the strong version that lines up with Putnam’s claim, because it is the version that predicts literal recurrence of the same functional unit across different hardware, not merely similar solutions to similar problems.
The induction head result is the best evidence strong universality has. An induction head is a specific attention mechanism that lets a model notice a repeated pattern, such as a name that appeared earlier in the text, and predict that whatever followed it before will follow it again. Anthropic’s 2022 paper found these heads forming, in roughly the same functional shape, across models that had nothing in common except the general recipe of transformer training. Later work found the same pattern again in models built for entirely different tasks, repurposed for problems their designers never anticipated. If you were looking for a single circuit that behaves the way Putnam’s argument needs a mental state to behave, defined by its role and indifferent to its housing, an induction head is close to the cleanest example currently on record.
But the most direct test of the hypothesis, and the one this piece leans on hardest, comes from a 2024 paper with an unglamorous title: “Universal Neurons in GPT2 Language Models,” by Wes Gurnee and seven co-authors. The method was simple and exact. Train five copies of GPT-2, identical in architecture, identical in training data, differing only in the random seed used to initialize their weights. Then compute the correlation between every neuron in one copy and every neuron in every other copy, across a hundred million tokens of text, and ask how many pairs of neurons across models consistently fire on the same inputs. The paper’s own answer, stated in its abstract without hedging: one to five percent of neurons qualify as universal by this measure. The rest are apparently free to solve the same overall problem in whatever idiosyncratic way five separate training runs happened to stumble into.
The induction head is not an isolated case, either. Researchers have since found specialized attention heads for related jobs, tracking whether a word is being repeated, tracking whether one number is greater than another, and found that models of noticeably different sizes tend to grow these heads at roughly the same point in training, measured in tokens processed rather than parameters held. Other work has found that circuits built to solve one task get reused, unmodified, to solve an entirely different one the model was never explicitly trained on, as if the network had built a small library of general-purpose parts rather than one bespoke solution per problem. Putnam would not have called any single one of these a proof. Together, they are exactly the kind of recurrence his argument predicts, if it is true.
One to five percent is a real number, and it deserves an honest reading in both directions. Read one way, it is evidence for Putnam. It says something in these networks is not accidental, that a small set of functional roles gets rediscovered again and again even when nothing forces it to, the way induction heads keep appearing. Read the other way, it is a limit on how far that evidence reaches. The Gurnee study did not compare different architectures, or different training objectives, or, in the spirit Putnam’s argument actually requires, physically unrelated substrates. It compared five copies of the same architecture, trained on the same data, differing only in a random seed. That is the narrowest possible test of substrate independence available to modern machine learning, a controlled experiment holding almost everything constant except the coin flips at initialization. And even under that generous condition, the great majority of neurons showed no cross-model correlation at all.
Work using sparse autoencoders complicates the picture further, in a way that cuts toward Putnam rather than away from him. Individual neurons in a transformer are usually polysemantic: one neuron often does several unrelated jobs at once, which makes a neuron-to-neuron comparison a blunt instrument. When researchers instead extract cleaner, more monosemantic features using sparse autoencoders and compare those, they find a higher degree of alignment across models of varying size and architecture than raw neuron correlation ever showed. And a more recent refinement goes further still, arguing that features and circuits do not universalize as literal, coordinate-for-coordinate matches at all, but as what one 2026 paper called rotation-equivalence classes: two networks can learn the identical feature in the mathematical sense that a rotation of one network’s internal coordinate system lines it up with the other’s, even though the raw, un-rotated numbers never match. Seen this way, the 1-5% figure may understate how much real functional convergence is happening. It may simply be measuring convergence in the wrong coordinate system.
There is a reason this question is worth more than philosophical tidiness to the people building these systems. If functional structure really does recur across independently trained networks, even in the narrow, same-architecture sense the current evidence supports, that has a price tag attached. It is the difference between treating every new model as a fresh, unrelated object that must be probed and understood from scratch, and treating model families as variations on a smaller number of underlying computational solutions that can be found once and recognized again. Interpretability teams already lean on this bet when they reuse a technique discovered on one model to go looking for the same circuit in the next one. Distillation, the practice of training a smaller model to reproduce a larger one’s behavior, is a related bet: that whatever the large model is functionally doing can survive a change of physical scale. Every one of these practices is a working assumption that Putnam’s claim, or something close to it, is at least approximately true. None of them depend on it being true in the strong, philosopher’s sense. But the weaker the underlying convergence turns out to be, the more each new model is, in a real and costly sense, its own separate problem.
None of this settles the question. It does something more useful. It tells you exactly where the disagreement now lives: not in whether independently trained networks share some functional structure, which by 2026 looks hard to deny, but in how much of that structure survives the harder test Putnam’s own argument was built for, substrates that don’t merely differ in a random seed, but differ in kind.
Where It Bends
Even inside philosophy, Putnam’s argument was never accepted without a serious internal challenge, and the sharpest one came from a former student of his, Ned Block. In a 1978 paper called “Troubles with Functionalism,” Block asked what would happen if you built a system that satisfied functionalism’s criteria to the letter using the least likely material imaginable: not silicon, not neurons, but people. Imagine, Block wrote, that the government of China agreed to simulate a single human mind for one hour. Give each of a billion citizens a two-way radio, wire them into the same pattern of causal relations a human brain’s neurons occupy, and let a satellite display stand in for the bulletin board a mind would otherwise write to itself. Block called the resulting system functionally isomorphic to a person, because it satisfies exactly the criterion functionalism cares about: the same pattern of inputs, internal transitions, and outputs. And then he asked the obvious question. Does this collection of a billion people relaying radio signals actually feel anything? Block’s own answer was no, and he took that intuition to expose what he called functionalism’s liberalism, its willingness to hand out mentality to any system with the right abstract shape, whether or not anyone would seriously believe it was conscious.
Block pushed the same intuition from another angle in the same paper, imagining a brain kept alive in a vat, its neurons intact but disconnected from any body, receiving none of the inputs and producing none of the outputs functionalism says a mind requires. He argued such a brain would go on having a rich mental life regardless, which is exactly backward from what a strict functionalist should predict, since the brain in the vat has lost its functional relations to a body while keeping every relation it has to itself. Between the China Brain, which has the right functional shape and, Block insists, no mind, and the vatted brain, which has lost its functional shape to the outside world and, Block insists, keeps its mind anyway, functionalism looked to him like it was getting the wrong answer in both directions at once.
Not every philosopher took Block’s intuition at face value, and the standard functionalist reply is worth stating before moving on, because a fair reading of this thought experiment cannot only report the objection. Daniel Dennett, in a 1988 paper called “Quining Qualia” and later in his 1991 book Consciousness Explained, argued from inside the functionalist camp that qualia, defined the way Block’s argument needs them, private, incorrigible, directly and infallibly knowable from the inside, may not pick out anything coherent at all. If Dennett is right about that, Block’s argument never gets off the ground, because it depends on readers trusting a shared intuition that the China Brain obviously lacks something real. Dennett went further than merely doubting the intuition. He was willing to say a China Brain, correctly organized, would have a mind, and that the reluctance to grant it one says more about the limits of human imagination scaled up to a billion people than about any principled line functionalism has actually crossed.
The China Brain is a genuine problem for Putnam’s argument, but its reach is narrower than it first appears, and getting that limit right matters for how much weight the thought experiment can carry here. Block’s target was what he called commonsense functionalism, the view that mental states are defined by the causal roles we intuitively, pre-theoretically attribute to them. A psychofunctionalist, who instead defines mental states by the actual functional organization revealed by cognitive science rather than folk intuition, is not obviously committed to Block’s conclusion at all, because the China Brain’s organization bears no resemblance to the specific mechanisms psychofunctionalism cares about. The thought experiment is a real strain on the common-sense version of the view Putnam held. It is not automatically a strain on every descendant of it, and treating it as a knockdown of functionalism as such overstates what Block actually showed.
The stranger complication is not Block’s. It’s Putnam’s own. In the decades after 1967, Putnam kept working, and he did not stay where he started. By 1988, in a short book called Representation and Reality, he had turned against computational functionalism in print, arguing that it could not account for the intentional, meaning-laden character of belief and thought, the way a mental state is about something in the world. Part of what drove the reversal was a technical worry about the theory’s own discriminating power: Putnam came to argue that, on a sufficiently permissive reading of what counts as implementing a computation, an ordinary physical system could be shown to implement more or less any finite automaton you liked, which threatened to make “this system computes X” too easy to satisfy to do the philosophical work functionalism needed from it. A theory invented to draw a principled line between mind and non-mind should not, on its own author’s later accounting, be so easy to satisfy that almost anything crosses it. Putnam spent the second half of his career dismantling the argument that opened this piece, and did so for reasons that echo, from the inside, the same worry about promiscuity Block raised from the outside.
Taken together, the two objections point in a related direction without landing in the same place. Block’s China Brain says functionalism is too generous about what counts as a mind. Putnam’s later self-critique says functionalism, or at least his machine-functionalist version of it, may be too generous about what counts as a computation in the first place, before mentality even enters the picture. Neither objection depends on artificial neural networks, since both predate them by decades. Block was writing about brains, radios, and billions of citizens in 1978, a decade before anyone had trained a network deep enough to have an interesting circuit inside it, and Putnam wrote his self-critique before the transformer existed at all. But both should make a reader hesitate before treating any finding of shared functional structure inside a network as automatic evidence that something Putnam-style is going on. The interpretability evidence in the previous section shows real, non-random convergence. It does not, by itself, show that the concept doing the convergence, “the same functional role,” is drawing a line anyone should trust. Putnam spent the second half of his life arguing it wasn’t. The machines he was defending in 1967 have only just started to weigh in.
The Verdict
The Verdict: Strains
Position tested: Putnam’s machine functionalism and the multiple realizability thesis it was built to support.
Technical evidence: Independently trained neural networks show real, non-random convergence on shared functional units, most cleanly in induction heads and in sparse-autoencoder-derived features. But the most direct measurement, Gurnee et al.’s 2024 study, found only 1 to 5 percent of neurons meeting even a narrow bar: five copies of the identical architecture, trained on identical data, differing only in random seed. That is a far weaker test than the cross-substrate independence Putnam’s argument actually requires, and even it clears only a small minority of the network.
What would change the verdict: If future work found reliable, high-overlap convergence between genuinely different architectures, not just different seeds of the same one, the case would move toward Holds. If the convergence already observed turned out to be an artifact of shared training data and shared optimization objectives rather than evidence of any deeper computational structure, the case would move toward Breaks. Right now, the evidence sits in between, which is a truer place for it than either clean verdict would be.
The Machine Question. Nine philosophical positions on mind, tested one at a time against what current AI systems actually do.