When learning to play Go, you go to a number of steps, where sometimes you have to unlearn something you learned in a previous step. I am a weak amateur player (around 10k) and I only have a vague idea about aji (taste in Japanese) and no idea what good aji and bad aji is. I even don't master life and death. When AIs started to beat professional Go players, it seemed that they had a good understanding of aji, because they created good aji and avoided bad aji. They also played a very balanced game, overseeing the whole board, something that non-professional players often fail to do, and now the adversary strategy reveals that the AIs do not even have a good understanding of life and death and even fall for 'tricks' that I would have been able to counter.
For Go there is no such possible outcome, and deep reading (calculation) will always be in the machine's favor. The architecture of AlphaGo (policy and evaluation networks plus MCTS) is sufficient to recreate and surpass any kind of human-practical strategy. The era of human dominance in Go is simply over.
Of course, current AI programs will never be perfect either, so there may continue to be such adversarial attacks, but they will necessarily become harder to use (and quickly impossible for humans). AI go programs probably have nearly infinite possibility to continue marginally improving against each other, though.
This is an assertion without evidence. Leela Zero uses the architecture of AlphaGo and yet this guy just beat it with a human-practical strategy. I have no doubt that this can be fixed, but exactly how to fix it is not obvious and it may require more compute, and that's interesting.