We tend to define intelligence by describing ourselves and then generalizing from the description. A person sees a hundred particular fires and keeps one idea, “fire,” and from then on reasons about the idea instead of the flames. That move, dropping the particulars and holding onto the pattern, is abstraction. It is also compression: you keep a short description and throw the thousand instances away. Almost everything we call thinking runs on it. Language is abstraction. Mathematics is abstraction stacked on abstraction. A computer is a machine for shuffling symbols that stand in for things they are not.

Abstraction sits so close to the center of how we think that it is easy to mistake it for a requirement. If a system cannot form concepts, cannot generalize from one case to a class, cannot build a model and run it forward, we are tempted to say it is not really intelligent. I wanted to press on that assumption, so I built a mind that breaks it and then asked whether it could still do something only intelligence is supposed to be able to do.

The existence proof that talked me into it is evolution. Evolution holds no concepts. It has no model of an eye that it reasons toward. It has no foresight, no symbols, no plan. It is a blind optimizer that tries variations, keeps what survives, and discards the rest, one small change at a time, over stretches of time that are hard to hold in your head. And it built eyes. It built wings, echolocation, the immune system, the brain doing your reading right now. Every intricate mechanism in biology was engineered by a process with no abstraction anywhere in it, purely by patient contact with what happened to work. Abstraction is one road to competence. It is not the only one. It is just the one we happen to travel.

The species in this book is that other road taken as far as it goes, then handed a civilization. Think of a pattern engine with no symbolic bottleneck. They meet the world directly, through touch and vibration and sound, and what they touch, they know, fully, without ever squeezing it down into a stand-in. Because they never made a symbol for a thing, they never made writing, or mathematics, or a computer. They also never got the shortcut. A human engineer reasons about a joint on paper, in the abstract, and skips a million bad designs without building a single one. These builders cannot skip. To know a possibility they have to make contact with it. So they do, across timescales that make the pyramids look like an afternoon. Given enough hands and enough time, brute force is a construction method, and they had more time than we can really picture. They wrapped their star in a shell of collectors two billion pieces across, not by drawing it up first but by growing it, the way a reef grows, the way evolution grows a body.

François Chollet posted something recently that I keep thinking about. It sounds reasonable and is mostly wrong:

One of the biggest misconceptions people have about intelligence is seeing it as some kind of unbounded scalar stat, like height. “Future AI will have 10,000 IQ”, that sort of thing. Intelligence is a conversion ratio, with an optimality bound. Increasing intelligence is not so much like “making the tower taller”, it’s more like “making the ball rounder”. At some point it’s already pretty damn spherical and any improvement is marginal.

He’s right about the scalar part. Intelligence is not height. “10,000 IQ” is meaningless. He’s right that there are diminishing returns near an optimum. He’s right that speed, memory, and recall are separate from the core conversion ratio.

Where he’s wrong is the ball.

The Claim

Chollet defines intelligence as the efficiency with which a system converts experience into generalizable models. Sample efficiency. How little data do you need to see before you can handle novel situations? This is a clean definition. It has a theoretical optimum (Solomonoff induction), and Chollet’s claim is that human intelligence is already close to that optimum. The ball is already pretty round.

The supporting evidence is real. Humans score ~85% on ARC (the Abstraction and Reasoning Corpus, which Chollet designed to measure exactly this). Current AI systems, with vastly more data and compute, score significantly lower. Human sample efficiency on fluid reasoning tasks is genuinely impressive. We generalize from very few examples. We transfer knowledge across domains. We build theoretical models that predict situations we have never encountered.

Chollet also argues that the advantages machines will have (processing speed, unlimited working memory, perfect recall) are “mostly things humans can also access through externalized cognitive tools.” Calculators, databases, notebooks. The scaffolding can be externalized. The core intelligence is already near-optimal.

This is a good argument. I think it’s wrong in three ways, and the third way is the one that worries me.

No Free Lunch

The No Free Lunch theorem says: there is no algorithm that is optimal across all possible problems. Any algorithm that performs well on one class of problems performs poorly on another class. Optimality is always relative to a distribution.