People used to be able to impress and intimidate reviewers with complicated proofs. This will change. In the age of AI inscrutable proofs are cheap. It is understandable proofs that are valuable. Opaque complexity is now it is a sign of laziness or lack of insight.
AI tools are very useful for mathematical research. But at present, producing good work requires a labor intensive step that I have been calling "deslopping" - making the thing readable. Please do not skip this step; even if the theorem is great an unreadable paper is worthless.
I sometimes see people trying to use complexity theory to argue that building "true" AI is impossible. I find this unreasonably annoying. It requires ignoring what is in front of your face and it ignores that worst-case complexity has been an awful guide in machine learning.
AI is getting good at math. What are our jobs as researchers now that we have proof machines? The raw proofs that come from LLMs are difficult to understand, even if correct. So its now easy to quickly write many badly written papers that nevertheless contain correct proofs of interesting theorems.
Right now we have "problem overhang" - lots of problems we as a community are interested in because smart and charismatic people thought about them and convinced us that these problems are important. So we are happy/interested to see them solved by AI.
An annoying part of the twitter/bluesky discussion of whether LLMs are "only" "next token predictor"/stochastic parrots is that "next token predictor" is contentless - all mappings from inputs to output strings can be factored into a sequence of "next token" distributions.
