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It's a step in a different direction, not backwards. First people programmed computers with "play chess, like this", and because they could do it faster they eventually got better than humans at chess. Then people programmed computers with "learn to play simulatable games well", and they soon got better than humans because chess is a simulatable game, and although they also got better than the first computers it wouldn't have been a failure otherwise because the point of the exercise was the generality and the learning. Now people have programmed computers with "learn to write anything that humans might write", and yes they're still kinda crummy at most of it, but everyone's dumbfounded anyway, not because this is the way to optimize a chess engine, but because it's astounding to even find a crummy chess engine emerge via the proposition "chess play is a subset of 'anything'".
Is this a dead end? The real world isn't as amenable to simulation as chess or go, after all, and LLMs are running low on unused training data. But with "computers can learn to do some things better than humans" and "computers can learn to do practically anything" demonstrated, "computers can learn to do practically anything better than humans" should at least be imaginable at this point. Chess isn't a primary goal here, it's a benchmark. If they actually tried to make an LLM good at chess they'd be able to easily but that would just be Goodharting themselves out of data. It will be much more interesting when the advances in GPT-5 or 6 or whenever make it a better chess player than humans incidentally.
It's the claim that "computers can learn to do practically anything" has already been demonstrated that I am calling into question.
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