Terence Tao

It will be increasingly important to collect these really standardized data sets.

There are efforts now to create a standard set of challenge problems for AI to solve and not just rely on the AI companies to only publish their wins and not disclose their negative results.

So that will maybe give more clarity as to where we're actually at.

The progress is simultaneously amazing and disappointing.

It is a very strange feeling to see these tools in action and the

But it also acclimatized really quickly.

I remember when Google's web search came out 20 years ago, and it just blew all the other searches out of the water.

You're just getting relevant hits on the front page, like perfectly, almost exactly what you wanted.

And it was amazing.

And then after a few years, you just took for granted that you could just Google anything.

And yeah, so a lot of, yeah, I mean, 2026 level AI would be stunning in 2021.

And a lot of it, you know, face recognition, natural speech, doing, you know, college level math problems we just take for granted now.

Right.

Yeah.

Yeah, a trustworthy co-author if used correctly.

Yeah, so productivity, I think, is not quite a one-dimensional quantity.

Like, I'm definitely noticing that the style in which I do mathematics is changing quite a bit, and the type of things I do

So for example, my papers now have a lot more code, a lot more pictures, because it's so easy to generate these things now.

So some plot, which have taken me hours to do now, I can do in minutes.

But in the past, I just wouldn't have put the plot in my paper in the first place.