Extrapolating Deep Blue's 11GFLOPs supercomputer to today with Moore's law would be equivalent to a 70TFLOPs cluster. AlphaGo is using 1+PFLOPs of compute. While they likely aren't actually achieving that compute throughput, to put this in perspective this is the compute scale used to model huge geophysics simulations covering a 800km x 400km x 100km volume with 8+ billion grid points around the San Andreas.
At the very least, it's interesting to see how much more accessible computation has become. Back when I was in school I could only dream of having a cluster of 280 GPUs. When sometimes the dream would come true and you had access to a cluster you would have to wait your turn in the job queue and hope you had enough compute in our resource quota to prevent your job from being terminated.
Now I could spin up a 280 GPU cluster on AWS (after dealing with pesky utilization limits) for only $182/hour. If researchers at Google have been doing this non stop for the past year they have "racked up" $1.6M on just compute. This is a drop in the bucket for a marketing department and the publicity they have achieved. I don't think normal Go AI developers have access to those resources :)
Don't underestimate algorithmic improvements. Today's chess engines running on DeepBlue hardware outperform DeepBlue running on today's hardware.
Modern chess engines are built on a testing infrastructure that makes it possible to measure how each potential change affects the playing strength. This "Testing revolution" has brought massive improvements in playing strength.
For AlphaGo, it's probably the training that requires the most computational resources. The 'distilled knowledge' could perhaps run on a desktop PC. The program would search fewer variations and would be weaker, but if AlphaGo improves further, that version might still be stronger than any human.
