One way of looking at the significance of this is that it might tell us that relatively simple machine learning algorithms can capture key aspects of the versatile human cortical capacity to learn things like Go using sheer pattern recognition. (It's amazing that human visual cortex can do that.) If the human brain were more mystical in its power, then human-level ability to recognize Go patterns wouldn't have been penetrable at all to a comparatively simple neural algorithm like deep learning.
From another standpoint, this could show that we're reaching the point where, when an AI algorithm starts to reach interesting levels of performance, a much-encouraged Google dumps 100,000 tons of GPU time and 5 months of a few dozen researchers' time to improve the algorithm right past the human performance level. In N years from now when it's a more interesting AI system doing more interesting cognition, we could see a more interesting result materialize when a big organization, encouraged by some key milestone, invests 5 months of time and massively more computing power to further improve an AI system.