Jesse Zhang

They're building a foundational model.

So stuff like that, I think it's quite interesting.

Or even I would put Lockheed's company, physical and

I think those are very exciting.

It's just obviously as early to see how those will turn out.

But if I'm building a portfolio, I definitely want one of those in there.

It's a little bit more behind in being able to unlock a lot of the enterprise use cases, I would say, because of the non-determinism.

So two things that need to happen.

One, you need to reframe the way that people think about agents.

There's the Waymo effect that often happens where it will be objectively just way better than human drivers and human drivers make a lot of mistakes.

But because we're investing in new technology, the bar is a lot higher.

So it has to be near perfect.

So there's that dynamic that kind of needs to adjust in some folks' mind where instead of

evaluating AI in a way where you're just trying to find mistakes, you're evaluating holistically looking at the sort of success rate.

And then if you can frame it that way, well, the success rate is going to be way higher than humans, because again, humans are not perfect.

So I think that needs to happen.

And I don't think that's fully happened yet in the enterprise.

So that trend needs to happen.

And then on the AI use case side, I think the models need to get better in a lot of areas, right?

We were just talking about voice to voice earlier.