Cal Newport

And it was pretty cheap to run.

These are small models.

These are not 20 trillion parameter general reasoning LLMs.

So that probably is the way to do math.

This doesn't discount the importance of the problem.

I'm just saying the fact that it's a pure LLM is new, but maybe not that important.

Okay.

The second thing that made this OpenAI result novel is the fact that it was an important problem.

And that's by far its biggest distinguishing factor is like people knew this problem.

If you're a combinatorial geometrist, as I know so many of you in my audience are,

This was a well-known problem.

And so this is like the first cool problem that was solved.

This technique of using LLMs and computer tools to help find proofs or just proofs, this has been going on for a little while now, but this is the first major problem that was solved.

So that is another, like a feather to put in OpenAI's cap.

Again, there's another caveat here, as Bloom was kind of emphasizing earlier,

This may have been a little bit of luck in the sense of this well-known problem happened to have a relatively easy counterexample that existed in the type of space that these type of tools are really good at, right?

Not that...

we now can solve all these type of open problems as shown now by the, the, you know, alpha proof nexus, which like is a, a, probably a higher power, better application of this type of thinking, much more automated, much more systematic.

And it could only solve nine of the 353 similar style of problems they pointed at, right?

So again, most problems,