His argument proves too much.

You could easily transpose it for the time when Checkers or Chess programs beat professional players: back then the "keystone, foundational aspect" of intelligence was thought to be the ability to do combinatorial search in large solution spaces, and scaling up to AGI was "just" a matter of engineering better heuristics. Sure, it didn't work on Go yet, but Go players were not using a different cortical algorithm than Chess players, were they?

Or you could transpose it for the time when MCTS Go programs reached "dan" (advanced amateur) level. They still couldn't beat professional players, but professional players were not using a different cortical algorithm than advanced amateur players, were they?

AlphaGo succeded at the current achievement by using artificial neural networks in a regime where they are know to do well. But this regime, and the type of games like Go, Chess, Checkers, Othello, etc. represent a small part of the range of human cognitive tasks. In fact, we probably find this kind of board games fascinating precisely because they are very different than the usual cognitive stimuli we deal with in everyday life.

It's tempting to assume that the "keystone, foundational aspect" of intelligence is learning essentially the same way that artificial neural networks learn. But humans can do things like "one-shot" learning, learning from weak supervision, learning in non-stationary environments, etc. which no current neural network can do, and not just because a matter of scale or architectural "details". Researchers generally don't know how to make neural networks, or really any other kind of machine learning algorithm, do these things, except with massive task-specific engineering. Thus I think it's fair to say that we still don't know what the foundational aspects of intelligence are.