"Some recognition is necessary and important, but yeah, it’s also important to not let these things take over your life and only be concerned about getting the next big award or whatever. So again, you see these people try to only solve really big math problems and not work on things that are less sexy, if you wish, but actually still interesting and instructive. As you say, the way the human mind works, we understand things better when they’re attached to humans, and also if they’re attached to a small number of humans. The way our human mind is wired, we can comprehend the relationships between 10 or 20 people. But once you get beyond like 100 people, there’s a limit, I think there’s a name for it, beyond which it just becomes the other."

"So this is one annoying thing about LLM-generated mathematics. So yeah, we’ve had human generated mathematics as a very low quality, like submissions who don’t have the formal training and so forth, but if a human proof is bad, you can tell it’s bad pretty quickly. It makes really basic mistakes. But the AI-generated proofs, they can look superficially flawless. And it’s partly because what the reinforcement learning has actually trained them to do, to make things to produce tech that looks like what is correct, which for many applications is good enough. So the air is often really subtle and then when you spot them, they’re really stupid. Like no human would’ve actually made that mistake."

"Yeah, I think all of the above. A lot of it is we don’t know how to use these tools, because it’s a paradigm that… We have not had in the past. Systems that are competent enough to understand complex instructions that can work at massive scale, but are also unreliable. It’s an interesting… A bit unreliable in subtle ways, whereas was providing sufficiently good output. It’s an interesting combination. I mean, you have graduate students that you work with who kind of like this, but not at scale. And we had previous software tools that can work at scale, but very narrow, so we have to figure out how to use, so Tim Gowers is actually, you mentioned he actually foresaw in 2000. He was envisioning what mathematics would look like in actually two and a half decades."