Some important background for this is that, around 1900, mathematics got formalized to the point that there is a standard for whether a proof is valid or not that doesn't depend on humans thinking about it, just doing a bit calculation following an algorithm. However, it is such a large calculation that nobody wrote complete formal proofs or verified all of the steps (since they'd be spending huge amounts of time on obvious parts, and not the novel aspects). Then, in the past 20 years, people have made computer systems that work on complete formal proofs while showing you the interesting parts, with the rest in macros, and check these proofs automatically. This means that finding proofs has the form of things like what AlphaZero is good at: something to check if moves are legal and a win condition. However, only a relatively small portion of mathematical research has been put into Lean so far, and that doesn't include the sorts of obscure results that often turn out to be useful in other problems. So the current wave is using LLMs to find results in the literature that might be relevant and producing formal versions of them, game AI to put them together, and deterministic validation to check each of these as it goes along and then people can see at the end that the calculation shows the proof is valid. The only part requiring mathematicians is checking that what the proof proves is what they understood the open question to be.
