Nilay Patel

And by the way, this is not just Kevin speaking.

Professor Ethan Malek wrote, I'm hearing similar things in economics and the social sciences.

Not autonomous work, but expert-directed AI is absolutely helping academics do novel research in significant ways.

One example that got a lot of conversation came from back in August.

Sebastian Bubeck, a researcher at OpenAI, posted an academic mathematics problem to GPT-5, and it appeared to come up with a novel result.

The problem was an extension of existing work, which Bubeck explained as, In smooth convex optimization, under what conditions, on the step size eta and gradient descent, will the curve traced by the function value of the iterates be convex?

It's totally fine if that's gibberish to you, it is absolutely gibberish to me.

The fact that you need to understand is that the original paper on Arvix found a general result if the eta is larger than 1.75 divided by L, where L is the smoothness of the curve.

The paper also provided the result below 1 divided by L, so there was a remaining gap between 1 and 1.75.

GPT-5 Pro appeared to produce a general result for 1.5 divided by L, reducing the lower bound of the solution.

Bubek commented that this was, quote, However, he continued, the only reason I won't post this as an Arvix note is that the humans actually beat GPT-5 to the punch.

Namely, the Arvix paper has a V2 with an additional author, and they closed the gap completely, showing that 1.75L is the tight bound.

Still, he pointed out that GPT-5's proof was completely novel, commenting, The fact that it proves 1.5 divided by L and not the 1.75 divided by L proof also shows that it didn't just search for the V2.

Also, GPT-5's proof is very different from the V2 proof.

It's more of an evolution of the V1 proof.

Shortly after Bubek published his results, others at OpenAI chimed in that this wasn't the only aval academic work that GPT-5 was capable of.

Chief Research Officer Mark Chen posted, GPT-5 Pro is starting to develop new mathematics.

I'm hearing similar stories in other scientific domains like physics too.

Now, what's interesting about these math results is that as much as we are talking about AI's ability to generate new knowledge by synthesizing old knowledge as a pathway for medical and scientific discovery, this math result seems to be an emergent capability of reasoning models.

In coming up with the proof, GPT-5 Pro thought for 17 minutes and then presented work that wasn't previously published.