Yeah, but that's an embarassingly parallel task, so you can recreate it with sufficient resources. If there's a sufficiently lucrative application, businesses will happily pay that.
And it's already been done for the chess version, in a community effort[0]. Well, "recomputing the weights" isn't plausible for a neural network of this size, but computing weights that give the network a similar level of performance.
One thing I think that will be critical to the future of software is community efforts to build deep neural nets that match the AI power of the big companies.
There are newer approaches where specialising for the problem domain (Go) gets you a 5-100x speedup: https://github.com/lightvector/KataGo/
"Recomputing the AlphaGo Zero weights will take about 1700 years on commodity hardware."
[1] https://github.com/leela-zero/leela-zero