The Bitter Lesson is specifically about AI. The lesson restated is that over the long run, methods that leverage general computation (brute-force search and learning) consistently outperform systems built with extensive human-crafted knowledge. Examples: Chess, Go, speech recognition, computer vision, machine translation, and on and on.
I think it oversimplifies, though and I think it’s shortsighted to underfund the (harder) crafted systems on the basis of this observation because, when you’re limited by scaling, the other research will save you.
This is correct however I’d add that it’s not just “AI” colloquially - it’s a statement about any two optimization systems that are trying to scale.
So any system that predicts the optimization with a general solver can scale better than heuristic or constrained space solvers
Up till recently there’s been no general solvers at that scale