Was Go ever "safe from ML" as opposed to "[then] state of the art can't even play Go without a handicap"? Seems like exactly the sort of thing ML should be good

Was Go ever "safe from ML" as opposed to "[then] state of the art can't even play Go without a handicap"? Seems like exactly the sort of thing ML should be good at; approximating Nash equilibrium responses in a perfect information game with a big search space (and humans setting a low bar as we're nowhere near finding an algorithmic or brute force solution). Is it really magical that computers running enough simulations exposes limitations to human Go theory (arguably one interesting lesson was that humans were so bad at playing that AlphaGoZero was better off not having its dataset biased by curated human play)? Yes, it's a clear step forward compared with only being able to beat humans at games which can be fully brute forced, or a pocket calculator being much faster and reliable than the average humans at arithmetic due to a simple, tractable architecture, but also one of the least magical-seeming applications given we already had the calculators and chess engines (especially compared with something like playing Jeopardy) unless you had unjustifiably strong priors about how special human Go theory was. I think people are completely wrong to pooh pooh the utility of computers being better at search and calculations in an ever wider range of applied fields, but linking computers surpassing humans at more examples of those problems to certainty we'll synthetically replicate brain functionality we barely understand is the sort of stretch which is exactly why AGI-sceptics feel the need to point out that this is just a tool iterating through existing programs and sticking lines of code together until the program outputs the desired output, not evidence of reasoning in a more human-like way.