Azeem Azhar

So we would typically go off and use BLS data or similar data like that in the United Kingdom or Yale University has their budget lab and they run surveys.

What is the way that you look at to make sense of what workers are doing and what hirers are doing?

So give me a sense of the sense, the sensing that you're doing the data that you're gathering.

I mean, how do you do that?

What are you going to show us?

Are you going to show me because you've crawled LinkedIn and you've figured out how many customer service reps there are at United Airlines compared to field engineers?

And that gives us a number or is it not as granular as that?

Or is it more comprehensive than that?

So let's get a sense of then go back to what we really think is happening.

You talked about the Brynjolfsson paper.

The Brynjolfsson paper was something that came out in the summer.

It was in late August, early September with one of Eric's postdocs.

And they looked at a lot of hiring data over a couple of years, two or three years.

And they found that if you look to AI-exposed occupations...

computer science or programming and customer service, the level of hiring for entry-level jobs was much lower than in occupations that were not exposed.

But in those same occupations, mid and senior level hiring or employment levels

had not declined.

In some cases, it had increased.

And the argument was, and I think you put it earlier in one of your answers, it's challenging to hire somebody young because while they're cheaper, they need training.

They may not have decided that what they want to do is be a COBOL developer or a CSS jockey.