Cal Newport

That's what you'd want to be announcing, not

oh, there's an interesting algebraic field in which if you move a standard, otherwise two-dimensional square lattice, you're able to squeeze something that is super linear in terms of points at unit distance, right?

That's not what you would announce.

If it was true that solving that problem meant that you now had genius-level AI, you would use that to solve all the economically useful things you could do with an automated genius-level artificial intelligent mind.

So I think the fact that this is what we're focusing on

just vindicates the idea that just being good at one thing doesn't make you better at other things, or we'd see more economically productive examples.

All right, question number four.

What does this tell us about the future of math?

The future of math is exciting.

Let me be really clear about this.

Just like how computer programming now has LLM based tools deeply embedded in it.

It's just, we haven't really figured out exactly how to use them.

Like we haven't stabilized that yet.

There's still a lot of nonsense of like agent supervising agents who supervise the agent supervisors.

And we look up at the end of the day and they have a broken hello world code or whatever.

Like, you know, we're still figuring it out, but like LLM tools are completely changing how programmers work.

Something similar has been afoot in professional mathematics for about the last year or so.

Computer-aided tools have always been there and very powerful, but they're too annoying to work.

But when you add LLMs into the picture, especially LLMs that are tuned for doing this type of mathematical reasoning, especially when you're in the very smart type of modular architectures that DeepMind is producing,

It really helps.