Emergence—the idea that reductionism can only explain so much—breathes life into many disciplines, from condensed matter physics to biology to economics. As famously described in Anderson’s More is Different, an understanding of the atom does not imply an understanding of a fluid. An understanding of a single cell does not imply an understanding of a living tissue. In nature, new principles manifest at new scales, and different branches of science are needed to understand their associated phenomena.

However, the boundaries between scales are not always so clearly defined. How many atoms must be assembled to become a fluid? How many individuals become a population? How many neurons form a consciousness? It is hard to say, and in our indefatigable efforts to describe the world around us we therefore run the risk of using language from one regime to describe emergent phenomena at a different scale: of committing a category error in which we erroneously generalize our intuition beyond its range of applicability.

Such category errors can be insidious. In the AI-exploding world of late 2026, they are particularly prevalent. It is tempting to describe an entity that can “talk” and “think” and communicate like a human with the same words that we use for each other. But there is at least one essential difference—a difference of scale—that must call comparisons into question. The temptation to capture AI in the same language that we use for the living world then reinforces a false equivalence between modern artificial intelligence and our own minds.

It is useful to think of AI as the next step in the staircase of increasing abstraction that we humans have built, layer by layer, upon the physical world. Engines and fossil fuels have allowed us to automate manual labor. Trains and cars have automated transportation. Modern AI can, in many ways, automate thought. More practically it abstracts away vast amounts of detail into an easy-to-use interface. Just as one can drive a car without knowing how an engine works, or one can use a computer without any knowledge of a transistor, in the age of AI, one can write code without knowing how to code, or create music without knowing how to play an instrument, or prove a theorem without knowing what it means.

However, compared to the other steps along this staircase toward the ether of maximal abstraction (where the ultimate reality that the modern world seeks seems to couple infinite power with vanishing knowledge, skill and experience), modern AI is remarkable and somewhat singular in terms of the scale of knowledge on which it has been trained. This scale prompts the emergence of as yet mysterious behaviors that are not simply understandable from a mechanistic understanding of the process that generated them. This phenomenon is the crux of emergence: we have, in principle, a handle on all the training data that has been fed into modern AI, we understand the workings of the algorithms and architectures that go into training these models, and yet, no one in the world can explain how these models “think” or process information in a way that distills the enormously complicated process down into some coarse-grained but useful description.

Contrast this situation with that of computation, in the standard pre-AI sense. Computers operating on tasks like word processing or numerical simulation or web browsing, though commanding billions of carefully-controlled transistors—certainly enough to exhibit emergent behavior—are essentially glorified pocket calculators: though they scale up the capacity of a handheld calculator and save enormous amounts of labor and time they do not exhibit a qualitative shift in what they are capable of, which remains something that we (albeit a rarified few computer cognoscenti) have an understanding of. The complexity of computers (in terms of their state space) is therefore not enough to produce emergence: the key is in how the state space is allowed to self-organize and modify itself. Perhaps the internet provides a pre-AI manifestation of the way in which technology can cause emergent change—through connecting huge numbers of humans and allowing them to interact in unforeseen ways—but we will stick to the example of AI for now.

As a result of this self-modification process that learning algorithms bestow upon a humble heap of transistors, a modern AI model and a glorified pocket calculator are qualitatively different, via the sheer scale of the data that have gone into the making of the AI. What are these data? Millenia of human endeavour recorded in images and text—the essentially-entire body of recorded expression and thought and reasoning—has been sucked from the reserves of the internet and fed into proprietary architectures and training protocols. If experiences could be mined directly from our brains, those too would no doubt be fed into these rapacious models, repackaged, and sold back to us. What is to come from all of this (aside from a few slipped notches on the ratchet of societal depravity)?

The outcome is an entity that looks and talks like a conscious being—in the way that we humans have defined consciousness until now—but whose nature we fundamentally do not understand. Of course, we do not understand our own nature (which is also an emergent property of the many interactions that compose us), as the field of biology reminds us, but we at least have an experiential grasp on it. The wholly distinct nature of AI poses a particularly dangerous foil because of the striking resemblance that it bears, in some respects, to living things.

Here it becomes crucial to determine how we define similarity. There is a compelling definition which posits that things that exhibit similar phenomenology should be classified together: by this definition AI fits in neatly alongside humans, as we are both capable of apparent reasoning and thought and information processing. The AI is much faster at certain things. We are (as of October 2026) still better at some things. But through the criterion of phenomenology, the AI is conscious. It passes the “duck test” in that it walks like a duck and quacks like a duck. And so it is a (conscious) duck. However there is a glaring issue. The consciousness that we humans (and presumably other animals) experience is physically manifested. It is not that of a brain in a jar. Our interactions with the physical world—our bodies—are inseparable from our minds. The AI, due to its digitalness, is bereft of such physically-mediated sensory inputs, and this difference hints at a bigger divide that must be considered if we wish to usefully evaluate the AI’s nature. Phenomenology is not enough: one must also consider phylogeny. We should be much more comfortable calling a pig conscious than an AI conscious because the pig is made from the same stuff as we are: though I cannot converse with a pig, I know that it has a body and a brain. The pig and I have much of our biology in common, and we have evolved from a common ancestor. The AI, by contrast, can hold a conversation with me but, through the lens of phylogeny, we have nothing—including our consciousness—in common. [As a corollary, any discussion of “AI rights” should be preceded by a long-overdue overhaul of animal rights, the current state of which reflects perhaps an even greater global depravity than the one exhibited by AI companies’ wanton theft and commodification of knowledge.]

This distinction between phenomenological and phylogenetic similarity throws into relief the human-AI inequivalence. Indeed the nature of our origins betrays these differences: living things have a limited set of experiences that are accrued through their lifetimes—though millennia of prior evolution can and crucially do shape our initial conditions. AI on the other hand has been reared on the entire corpus of recorded information, and it stands to reason that the mechanism of its behavior should be totally different from that of our own. If we think of an AI generating art, we might say that human artists steal from each other all the time, and what the AI is doing in its own artistic style is no different. But this misses the distinction of scale: because of the size of its training corpus, the AI perpetrates theft on a scale that no human could ever conceptualize. What the AI is doing is not similar to what we humans do: it is categorically different in a way that we do not understand. In a related vein, describing an AI model as “understanding” a concept makes a category error: understanding as we know it is something that happens in a brain, and what the AI does needs a new word to avoid conflating concepts.

AI models are essentially different from the conscious beings that we know and love and that have defined our very notion of what consciousness is. They are different both in the scale of information that they integrate and in their phylogenetic origins. Though they exhibit phenomenological similarity, one must remain wary of an over-identification of these two separate entities through the use of language that abuses terminology from one level of description to classify behavior in a different, emergent, regime. It is apparent that we have created something totally different from ourselves.



Side note: there is a question about what the phylogeny-versus-phenomenology dichotomy would say about life found on another planet, with a separate evolutionary origin. Would we need to revamp our own understanding of consciousness or invent a new set of vocabulary to describe it? Perhaps—though it is also true that certain physical constraints on biological systems are expected to hold across the universe, and therefore might limit the rate and modality of information-integration by a life form of independent evolutionary origin, making its principles of operation more similar to our own than to those of modern AI.