I'm not so sure Stockfish is a good example. The fact it can run on an Iphone is due to Moore's law, which follows the same pattern. And Deepmind briefly taking its throne was a very good example of the Bitter Lesson.
Stockfish being so strong is not merely a result of scaling of computation with search and learning. Basic alpha-beta search doesn't really scale all that well with compute. The number of nodes visited grows exponentionally with the number of plies you look ahead. Additionally alpha-beta search is not embarassingly parallel. The reason Stockfish is so strong is that it includes pretty much every heuristic improvement to alpha-beta that's been thought of in the history of computer chess, somehow combining all of them while avoiding bugs and performance regressions. Many of these heuristics are based on chess knowledge. As well as a lot of very clever optimisation of data structures(transposition tables, bitboards) to facilitate parallel search and shave off every bit of overhead.
Stockfish is a culmination of a lot of computer science research, chess knowledge and clever, meticulous design.
While what you mention is true, I'm not sure how it undermines the bitter lesson. Optimizing the use of hardware (which is what NNUE essentially does) is one way of "increasing compute." Also, NNUE was not a chess specific technique, it was originally developed for Shogi.
