Earlier quoted context omitted.
I was replying to this: The conclusion of the bitter lesson would be that a large language model trained on chess commentary as well as being trained on millions of chess games would outperform stockfish which is only trained on millions of chess games. If you can draw any lessons from chess commentary, I think it’s very reasonable to call it “hand-crafted heuristics.”
Hand-crafted even if you're feeding in the raw commentary? That seems like a weird way to consider it. Wouldn't that make LLMs in general "hand-crafted"? And raw games plus raw commentary is all the data you have. You can make more games but those can be fed to both stockfish and the LLM competitor. So it seems like a valid interpretation of the bitter lesson to me.
The key point I was trying to get at is that the human insights don't contain anything that can't be mined from vast amounts of gameplay. Every human insight can eventually be rediscovered and made rigorous by data (in chess, at least!) In the short term, those insights are useful, but in the longer term, they add nothing at all.
Note also that "raw gameplay" here can mean new games -- you can generate as much data as you need, you don't need to rely on real recorded games.