Is that really true? Demis stated that the distributed version of AlphaGo beats a single machine version only 75% of the time. That's still stronger than virtually all human players, and probably would still beat Lee Sedol at least once.
Now was a sweet spot in time. This algorithm ten years ago would likely have been pathetic and in ten years time will likely superhuman in the same manner that chess is.
None of these constitute a general advance in AI however.
[1]: http://www.milesbrundage.com/blog-posts/alphago-and-ai-progr...
"Facebook’s darkfmcts3 is the only version I know of that definitely uses GPUs, and it uses 64 GPUs in the biggest version and 8 CPUs (so, more GPUs than single machine AlphaGo, but fewer CPUs). ... Darkfmcts3 achieved a solid 5d ranking, a 2-3 dan improvement over where it was just a few months earlier..."
But when you're pitching it at a top class opponent in a match getting global coverage, you want all the incremental improvement you can get.
By a single machine winning many games relative to the distributed version, really it's just saying that the value/policy network is more important than the monte carlo tree search. The main difference is the number of tree search evaluations you can do; it doesn't seem like they have a more sophisticated model in the parallel version.
This suggests that there are systematic mistakes that the single 8 GPU machine makes compared to the distributed 280 GPU machine, but MCTS can smooth some of the individual mistakes over a bit.
I would suspect that the general Go-playing population of humans do not share some of the systematic mistakes, so you likely won't be able to project these win/loss distributions to playing humans.