DeepBlue beat Kasparov with essentially raw compute thrown at alpha-beta search. That does support the premise of the bitter lesson in general. But that does not mean the bitter lesson is correctly being applied here. The point is that even if throwing raw compute at a task is better than careful human-crafted algorithms, it's still task-dependent. The current trend with the people blowing hundreds of billions of dollars is developing an all-purpose model that is everything to everyone, but you don't need hundreds of billions of dollars to create a task-specific model that outperforms their model at a given task.

> I would also be VERY slow to claim that general-purpose models will never be competitive at chess.

This is not the claim. The claim is that for the same amount of compute, a general-purpose language model will never beat a Chess model. I'm dubious, but allow for the possibility that a language model could eventually compete at a top level against humans with enough compute. However, it will never compete with a dedicated Chess model with similar resources. Training a model for a specific task with the same amount of compute will outperform training a general-purpose model with the same amount of compute. This should be common sense, right? The bitter lesson was only about compute over human algorithms, not at throwing compute at a generalised domain over throwing compute at a specific domain.

You made arguments against two claims that I did not make (that I was trying to refute the bitter lesson or that I claimed that LLMs could never be competitive against humans at Chess), so I'd like to ask you read my statements a little more carefully this time.