Cal Newport

We can close this gap.

We're finding a lot of counterexamples to existing like conjectures.

And a lot of it's not going to be that interesting.

And there's going to be a problem of too many results to be refereed.

But I think longer term, medium term, let's say, we're just going to see the average quality of the high-end math results is going to jump up.

Because I just know... I produced a few interesting results in my time.

I think they could be more interesting with these tools.

So I think the future for mathematics here is exciting.

And it kind of makes sense.

Math and computer programming.

That's what we were told from the beginning.

LLM sweet spot.

And we're getting these custom tools...

Custom tools and programming, which are these like complicated coding harnesses and custom tools and mathematics, which are these modular architected sort of proof exploration systems.

And they're making a difference.

And I actually think that's exciting.

It also, by the way, vindicates my vision.

of what you could call either distributed AGI or narrow AGI.

This idea that the only people who believe that we're going to just keep scaling these massive models, these singular LLMs until they're HAL 9000, that you can just build lightweight harnesses on top of them to do everything.

are the AI companies that want that to be their moat.