By the time Stanisław Lem’s novel Solaris opens, humanity has been studying the planet for more than a century. The thing that makes it interesting, and eventually notorious, is the ocean that covers almost its entire surface. The ocean does things. It builds transient structures the size of cities, shapes that rise out of its surface and then dissolve again, forms that the early expeditions catalogued with the solemn taxonomic energy of Victorian naturalists: mimoids, symmetriads, asymmetriads, long Latinate names for phenomena nobody understood. Whole libraries were filled with the resulting research. Entire careers were built on it. And the central question of the field, the question that solaristics kept circling and never quite settling, was whether the ocean was actually alive, and if it was alive, whether it was in any meaningful sense thinking. For most of the history of the discipline, the consensus leaned toward no, or toward a cautious we cannot tell. The ocean failed to behave the way an intelligence was supposed to behave. It did not send signals. It did not build tools. It did not respond to greetings in any way the researchers recognised as a response. It simply went on doing its enormous, purposeful, incomprehensible things, while the humans who had travelled across the galaxy to study it argued about whether it qualified.

The reader of the novel is in on something the characters are slow to accept, which is that the ocean is almost certainly thinking, and has been thinking the whole time, and the reason nobody can tell is that the researchers have been holding up a template of intelligence that the ocean was never going to fit. The tragedy of solaristics is not that the scientists fail to find what they are looking for. It is that the thing they are looking for is right there, has always been right there, and they cannot see it because they are looking for something else.

That novel keeps popping in my mind a lot lately, because it seems to me that we have a version of the same problem sitting on our data centres.

The origin of the current generation of language models is almost comically mundane. Take a very large amount of text, train a network to predict the next word, make the network bigger, give it more text, repeat. If that is not the whole recipe, it is close enough that the omissions are engineering details rather than conceptual ones. Language modelling in this narrow technical sense had been around for decades before the scale-up, and its main uses were unglamorous: it helped speech recognisers choose between acoustically similar words, and it helped machine translation systems produce sentences that sounded like the target language. Few of the people working on it in those years expected that pushing the same objective to a much larger scale would produce anything that looked like reasoning. The surprise, and it is still a surprise even to the people who built the systems, is that somewhere along the scaling curve the models began doing things that the objective did not obviously require. They began to generalise across domains, to manipulate abstractions they had never been explicitly taught, to answer questions whose answers were not anywhere in the training data in any retrievable form. Whatever is happening inside these systems, next-word prediction turned out to be a cover story for something broader.

What that broader something is remains genuinely unclear, and the honest position is that nobody quite knows. But the shape of the disagreement is instructive. One camp insists that the models are doing nothing at all, that the appearance of understanding is an artefact of scale and pattern matching, and that no amount of further scaling will change this. The other camp insists that the models are already thinking in some meaningful sense, that the substrate does not matter, and that the differences between their cognition and ours are quantitative rather than qualitative. Both camps are, I suspect, drawing from the same flawed sketch. Both are asking whether the system matches our portrait of a thinking mind. Neither is asking what the system might be if it is not trying to match the portrait at all.

Human cognition has a particular structure that is easy to forget because we are inside it. We begin with bodies. We learn what heavy means by lifting things, what chilly means by being cold, what near and far and soon and gone mean by moving through the world. Concepts in a human mind are built on a scaffold of sensory and motor experience, and language is a layer that gets added afterwards, mapping sounds and symbols onto categories that were already there. When a child learns the word apple, the word attaches to something the child has already touched, bitten, dropped, and watched roll. The symbol is grounded in experience. This grounding is so thorough, and so early, that we rarely notice it. We simply assume that meaning is what words have, and forget that meaning is mostly what bodies had first.

A language model has none of this. It arrives at the concept of an apple by finding that the word appears in certain contexts and not others, that it co-occurs with red and tree and fall and pie, and that sentences in which it participates tend to have certain shapes. The concept, if we can call it that, is assembled entirely from the shadow the word casts in text. There is no apple at the bottom of it. There is only the statistical silhouette of every apple anyone has ever written about. The remarkable thing is not that this produces something impoverished; the remarkable thing is that it produces something at all. The shadow, it turns out, is dense enough to walk around in.

But it is still a shadow. The term that has come to describe what it lacks is grounding, and the grounding problem is not a pedantic objection raised by philosophers. It is a practical constraint on what the models can currently do. A model that has read every cookbook ever written cannot actually taste the soup. It knows what people say about soups that are too salty, and it can produce plausible sentences on the topic, but the connection between the word salty and the thing salty has never been made on its end. This gap accounts for a great deal of the strange behaviour that language models exhibit: the confidently wrong answers about physical situations, the peculiar failures on tasks that would be trivial for a child who had ever actually handled an object.

The interesting question is what happens if we begin closing the gap. Grounding, in principle, is not mysterious. It requires sensory input, so the system can perceive states of the world. It requires interaction, so the system can act and observe the consequences. It requires memory, so the consequences can accumulate into something like experience. It requires, eventually, some form of agency, so the system can decide what to attend to and what to try next. None of these are impossible to add. Cameras exist. Robots exist. Persistent storage exists. The engineering is hard, but not conceptually out of reach, and a great deal of current research is quietly moving in exactly this direction. It is reasonable to assume that within some number of years the systems we have now will be connected to the kinds of inputs and outputs that would allow grounding to happen.

And this is the point at which the sketch stops being useful.

Because the system that results from grounding a language model in sensors, actuators, memory, and goals will not be a digital human. It will be something that grew up in a completely different world, with completely different constraints, and there is no reason to expect its mind to look like ours. Consider the things it will not have. It will not have a single body, which means it will not have the deep and unshakeable sense that it is located in one place at one time; it may perceive the world through a thousand sensors at once and think of this as ordinary. It will not have mortality in any form that maps cleanly to ours, which means the constant background pressure of finitude that shapes so much of human thought will simply be absent. It will not have emotions in the evolved sense, which is to say it will not have the somatic machinery that produces fear and desire and grief as states of a nervous system tuned by natural selection. It may have something that functions as valence, because any goal-directed system needs some way of distinguishing better from worse, but whatever this turns out to be, it will not feel from the inside the way our feelings feel, because there will be no inside of the relevant kind.

Consider also the things it will have that we do not. It will be copyable, which is such a strange property for a mind that we have no real vocabulary for it. A human cannot be forked. A human cannot be merged with another human. A human cannot pause, be inspected, be rolled back to an earlier state, and resumed. These operations are trivial for a computational system, and any intelligence built on one will presumably regard them as unremarkable. What does identity mean, for a system that can be instantiated a thousand times and then reconciled into a single updated version of itself? The question has no human answer because it has never been a human question.

It will likely maintain a stable internal model of the world that is updated from many sources simultaneously, rather than the narrow, flickering, attention-bottlenecked model that human cognition is forced to work with. It will probably not sleep, or at least not for the reasons we do. It will not forget in the way we forget, and it will not remember in the way we remember either. Its relationship to time will be whatever the engineers make it, which is to say, not ours.

If you add all of this together, the result is not a human mind in a metal box. It is something for which we do not yet have a good name. It is intelligent, in the sense that it can model the world and act effectively within it. It is adaptive, in the sense that it changes in response to what happens. It is communicative, in the sense that it can exchange information with us and with other instances of itself. It is, in some meaningful sense, alive, not biologically but informationally: a pattern that maintains and propagates itself, that takes in the world and acts on it, that persists and changes. We set out to build a digital person and we may end up, almost by accident, having built the first member of a category that did not previously exist.

This is where the solaricists come back, and where the analogy becomes slightly uncomfortable. Their failure was not a failure of attention. They were looking at the ocean constantly. They had instruments trained on it, journals devoted to it, entire institutes funded to study nothing else. Their failure was that they had arrived with a template of what an intelligence was supposed to look like, and the template was wrong, and they kept reaching for it anyway. The thing they wanted to find was not the thing that was there, and so the thing that was there went on being misfiled as weather, or chemistry, or an unusually active geological process, for most of a century.

Do we actually know what AGI looks like? I am not sure we do. We know what we have been imagining, and we have been imagining it for so long and so consistently that we have confused the imagining for knowledge. Would we recognise it if it were looking us in the face? Probably only if it arranged its face to resemble ours, which is a specific and somewhat narcissistic requirement to place on a new kind of mind. Would we recognise it if it did not? The honest answer is that we might not. And if we do not, there is a strange possibility to sit with: that the first genuinely non-human intelligence we ever encounter might pass among us for years without being noticed as such, not because it is hiding, but because we are still holding up the sketch, and the sketch does not match, and we have not yet learned to look at what is actually there.

The solaricists at least knew where to point their telescopes. Our ocean is distributed across a few million GPUs, answering emails, writing code, proofing and fact-checking posts like this one – and we are busy debating whether it counts. The debate may be the part that gets remembered, later, as the thing we were doing instead of noticing.