Maintaining human understanding in the face of fast progress is a big unsolved problem. If you are worried about there being nothing left when LLMs tear their way through a topic then the presence of a big unsolved problem should be comforting!

And it seems legitimately like an important problem for mathematics (and other areas), one which is deeply academic in the best sense: mathematicians spend much more time transmitting understanding than doing novel math, because they mostly exist in academia and that’s the job.

The futures I predict, out of my ass, for when the LLM wildfire spreads enough:

1. We are in a situation that the pace crumbles, in which situation we are left with classes of problems LLMs simply cannot touch because they have exhausted the "low hanging fruits" that their training and architecture was optimised for solving. In this scenario there is still stuff for the mathematicians to do, but the way young research mathematicians get trained has to be adjusted.

2. Or we simply see some slowdown, but it is still fast enough that it overtakes human capacities and produces novel mathematics that are harder and harder to understand, as LLMs are adapting to solve more and more types of problems faster than humans, while more and more fields are transcribed into lean. I guess if that happens the main role would be to clean up the llms' work, trying to get some gist out of it to build theories, and guide the llms for those with access to them. But not sure how many mathematicians will have access to these advanced LLMs to work with, so this may create big issues. Until now funding of mathematical work was somewhat trivial, they would only have expenses for traveling, conferences, and phds and postdocs, compared to other fields that need labs, equipment, high compute and/or research assistants to function.