There is a bit of a difference between the likes of AlphaZero and what your average hobbyist programmer is going to cook up. Once your toolbox includes bits like 'custom hardware' the equations change. Also: that was still a wicked fast machine in terms of the number of positions evaluated by any normal standard.

Certainly AlphaZero was run on very fast hardware and lots of it. Nevertheless, I think it shows that the days of "speed is all that matters" is over. It did substantially fewer evals than stockfish and still crushed it. LC0 is the same way. Neural network chess proved that a higher quality, much more expensive, eval (with a very high quantity baseline, to be sure) is a viable strategy for a chess engine.

It still would lose to brute Force with minimax

That’s simply not true for the same amount of hardware, or else that would be all that chess engines do

Tell that to Stockfish NNUE :P. A simpler neural network running at Stockfish speeds, all on CPU, crushes Lc0 (Lc0 is probably stronger than AlphaZero IMO).

NNUE is slower than the classic evaluation function though, and much better.

But alphazero is still using some kind of min-max, right? It's not a net that you input a board into and get a move out, but a net you input a board into and get a score out. So in a stockfish context it's mostly replacing the heuristic/scoring function?

> It's not a net that you input a board into and get a move out, but a net you input a board into and get a score out.

It's both actually. The former helps guide the search (MCTS, not an Alpha-Beta minmax), and the latter helps truncate the search before an end state.