At first this was my take as well. That plus, well, maybe they are just on a serious PR kick with maths. But I am starting wonder if they have determined, or strongly suspect, that the road to exponential model improvement must first be paved with extraordinary improvements in math. Like in some sense this seems like a test case for where their true intensions might go: vast improvements in the efficiency / size / speed of models and their training. Hard to imagine trusting the models in all those spaces without first trusting them / training them to address new or unsolved math.

You can't really profit from proving theorems of applied problems (that are widely regarded to be true). Those who need to apply those theorems on real problems would have already done so (and if they don't work in some cases, well, congratulations... you found the counter example!)