What makes you categorically say that "AIs can't"?

Based on my experience with present day AIs, I personally wouldn't be surprised at all that if you showed Gemini 2.5 Pro a video of an insect colony and asked it "Take a look at the way they organize and see if that gives you inspiration for an optimization algorithm", it will spit something interesting out.

It will 100% have something in its training set discussing a human doing this and will almost definitely spit out something similar.

That's a good point but all it means is that we can't test the hypothesis one way or the other due to never being entirely certain that a given task isn't anywhere in the training data. Supposing that "AIs can't" is then just as invalid as supposing that "AIs can".

> Humans can observe ants and invent any colony optimization. AIs can’t.

Surely this is exactly what current AI do? Observe stuff and apply that observation? Isn't this the exact criticism, that they aren't inventing ant colonies from first principles without ever seeing one?

> Humans can explore what they don’t know. AIs can’t.

We only learned to decode Egyptian hieroglyphs because of the Rosetta Stone. There's no translation for North Sentinelese, the Voynich manuscript, or Linear A.

We're not magic.

What makes you categorically say that "humans can"?

I couldn't do that with an ant colony. I would have to train on ant research first.

(Oh, and AIs can absolutely explore what they don't know. Watch a Claude Code instance look at a new repository. Exploration is a convergent skill in long-horizon RL.)

That's what benchmarks like ARC-AGI are designed to test. The models are getting better at it, and you aren't.

Nothing ultimately matters in this business except the first couple of time derivatives.

humans also eat