alt Hacker NewsAnd that's an issue why?
The issue is SNR: signal to noise ratio. Generating exponentially more mathematics, particularly if the process is indiscriminate or optimized for something other than usefulness or mathematical relevance (such as optimizing for machine-provability), does not imply that we get exponentially more applications. We may end up halting the progress of applications altogether as the entire capacity of the world's mathematical apparatus is consumed by the interpretation and investigation of machine-generated proofs.
You can already visit arXiv and find vast numbers of not-yet-published mathematical papers. Most should never be published. None of this junk is benefitting humanity in the slightest.
It could just as easily be the opposite: it could end up being far easier to reasonably direct and evaluate the research direction and output of AI systems than human mathematicians, who are forced to specialize over decades and essentially cannot pivot and often can't even meaningfully evaluate each other's work.
Moreover, the disdain you have for low-value output in mathematics is not unique to you. Talented mathematicians don't like it either. Your mistake is assuming that AI will cause math to be dominated by low-value outputs. In fact, the opposite is likely the case: the marginal value of proofs will fall so low that the bar for meaningful research will become dramatically higher, not lower. I expect the goals of research mathematics to become extremely ambitious relative to the past, organized around substantial and enormous goals, not mass-generated slop as you're imagining.
Of course, yes, there will still be lots of slop, just like GitHub is full of AI coding slop, LinkedIn is full of slop, etc. But that's a generalized issue of the AI era, not unique to math.