Math is an interesting case. Arguably a lot of the groundwork was already there. There’s a huge body of academic literature that’s historically been difficult for humans to draw on for various reasons. It’s relatively easier for LLMs to mine all of that for relevant prior work that it can recycle into proof segments to try. And we have theorem proving software that it can use to try out these proof segments and validate them. That creates a tight, self-contained, and relatively inexpensive feedback loop that lends itself well to a host of agents grinding away, as long as you have budget to pay for all that LLM inference.

I’m not sure the same conditions exist in other fields that deal with real world phenomena. Experiments to test hypotheses about biology or physics can take years and cost millions of dollars to run. They rarely scale down to the millipennies and milliseconds scale that makes letting a swarm of agents throw a bunch of shit at the wall and see what sticks a feasible option for finding mathematical proofs.

I posted the original comment, but there are multiple reasons why math success won't translate into drug development or materials engineering success. It is easy to create verifiable rewards environments for math and coding, and use the resulting data for RLVR post-training. For other domains you need machines or robots creating and running experiments. This is simply something that's never been attempted at scale.

Getting IPO level monies should enable the amount of capital needed to sustain such efforts, and if I were a betting man, that is going to be Anthropic's actual endgame given their nascent efforts in automating bio research.

There's not much money in math. The frontier labs massively outspent mathematics departments to achieve recent results.

The sort of mathematics that has been published recently is very particular. Which isn't really downplaying it but it is to say that there's a pretty massive gap between mathematics and semiconductors or wetlab work. I don't want to say anything totally deflationary like "AI will never effect research in biology" or something like that, but I think the contribution of AI in those kind of fields will be much more modest.

> Don't see why it shouldn't

Don't see why it should! Unless there's a harness like in math for other fields.

lol, no it's not, the machine that is built to find connections between concepts found connections, that's it.

What has evolved is that mathematicians are pearl clutching because they never thought it would happen to them and wondering if they can still pour all their ideas / thoughts / research into these tools after all is said and done.

That last point is the one that kills OAI / Ant going forward. Can they be trusted? Anecdotally no.