The particular techniques used to make progress on go do not seem to help much with mathematics. While we might figure out how to get computers to prove deep theorems requiring the introduction of new mathematical ideas later, most of the work has yet to be done.

Neural networks are reasonably good at regression problems, and most games can be expressed as regression problems. This means coming up with a good encoding of the game situations as vectors (say, as elements of $[0,1]^n$ or $\{0,1\}^n$) and then finding an approximation of a function of those vectors to $[0,1]$. In a game, we predict things such as the probability of winning from that position using perfect play or randomized strong play.

As far as I know, we don't have a good way to encode a mathematical situation (say, a partially written proof) as a regression problem. We could use something like ASCII, or better an encoding of a formal mathematical language, but this naive representation is of a type that would be expected to perform poorly. Further, what value would we associate to such an encoding? The probability that a slightly randomized brilliant mathematician can complete a correct proof from there within the next few pages? It would be difficult to get the evaluations of situations for training data.

If we could get a huge database of well-written formal proofs, this would help. This might let a deep neural network find its own internal encoding (using unsupervised pretraining). However, while we can generate a huge amount (billions) of reasonable game positions rapidly through self-play, it's not clear what the analogue of this could be for mathematical proofs. If you use lots of minor variations on a short proof of the Pythagorean Theorem, or proofs of trivial facts, you would not prepare the network to find a medium-length proof of Fermat's little theorem or the prime number theorem, much less a longer proof of something open.

There was a lot of warning before computers became strong chess players, and before they became strong go players. Computers made steady progress of roughly 100 Elo points per year in chess from 1976 through 1986, for example, and computers have been steadily climbing the ranks on internet go servers. So far, we haven't gotten any such warnings about doing mathematics in general. We can automate calculations, integration, and combinatorial telescoping but those don't generalize easily. Further, we do a lot more than prove things in mathematics.