Right, but this is somewhat different, in that we apply a simple learning method to a big dataset, and the resulting big matrix of numbers suddenly can answer question and write anything - prose, poetry, code - better than most humans - and we don't know how it does it. What we do know[0] is, there's a structure there - structure reflecting a kind of understanding of languages and the world. I don't think we've ever created anything this complex before, completely on our own.

Of course, learning method being conceptually simple, all that structure must come from the data. Which is also profound, because that structure is a first fully general world/conceptual model that we can actually inspect and study up close - the other one being animal and human brains, which are much harder to figure out.

> Basically, in mathematics, you cannot grasp all the details of a vast space just from knowing the axioms which generate it and a few things which follow from them. Elementary school children know what is a prime number, yet those things occupy mathematicians who find new surprises in that space.

Prime numbers and fractals and other mathematical objects have plenty of fascinating mysteries and complex structures forming though them, but so far none of those can casually pass Turing test and do half of my job for me, and millions other people.

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[0] - Even as many people still deny this, and talk about LLMs as mere "stochastic parrots" and "next token predictors" that couldn't possibly learn anything at all.