Modern engines would crush Deep Blue with fewer resources and even without NNUE, and this is because of specific search techniques and search improvements that we've discovered. For example: formulas that work well for late-move reduction; continuation history tables and the update formulas that work well for them; internal iterative reductions; refinements to futility pruning, reverse futility pruning, delta pruning, multi-probcut, and so on. None of these were found by meta methods and none of these emerge from scaling computation. But it's also the case that none of them resemble anything that a human does when they play chess.
So I don't think I agree with the first point of the bitter lesson, that general methods beat specific methods, and that scaling beats efficiency. But I do agree with the second point, that performant systems often behave in ways that don't resemble human thinking, and we're often hopeless to personally understand performant systems' complexity.