OpenAI’s intellectual cannon is so good at Math because Math can be expressed very precisely as a language, and training on Math can be made very effective through Lean formalization and proof checking. These factors are not there in the same way for medical research. It is unclear why being extremely good at Math should make these models equally good at curing cancer. This kind of consideration is probably bad for IPO. Proving the Riemann Hypothesis by Halloween 2026 would be fantastic PR and, at the same time, would tell us very little about curing cancer (or sending people to space, which seems to be another favorite of AI-enthusiasts these days). In the meantime, me and others are trying to make clear that modern Math is a very complex (and imperfect!) system, with tens of thousands of people involved in different roles, and years of training needed before someone is in a position to do creative work. This setup was not built because someone planned it. It grew over decades through changing traditions of mentoring and research. In 20 years of Math I have definitely seen huge changes in how research works, well before LLMs. Things can adapt, but with time to adapt. If you stop forming new people, the whole thing collapses. And once it has collapsed, you are not going to rebuild it at whim. You are just going to have very few people around who even know how human Math research used to work. We have no model for machines training humans to replace the human system that trained the machines. To me, it all seems like a very rushed and very risky bet.