You're making a conceptual mistake here, comparing a chess tool to its operator. Deterministic tools produce superior results compared to models in many areas,

You're making a conceptual mistake here, comparing a chess tool to its operator. Deterministic tools produce superior results compared to models in many areas, so we allow models to use tooling. The correct analogy here is Fable as a second tier player assisting a SuperGM in running stockfish, then assessing its output to identify promising variations. There might be a limit somewhere that prevents the bitter lesson being axiomatic - for instance where simulations for anything can be exhaustive - so that judgement isn't needed any more as an arbiter. But while there are problems sufficiently complex or large to require a breadth models don't currently have, greater scale and compute will continue to convert to better decision making, and the bitter lesson will remain true (true enough).