Azeem Azhar

The second challenge was simply the idea that sometimes, especially if you're having to sail uncertainty, you may have overhired in previous years during COVID, you may have cost concerns.

It's much easier to not hire a 24-year-old than it is to fire a 44-year-old.

So as a manager, you like water, you'll take the path of least resistance.

I suppose the third argument was really what are we seeing here?

Because a few weeks later, the Yale Budget Lab or Yale Economics Lab came out and said, well, we've looked at widespread data and we can't see these effects at all.

Now, of course, Eric and Bharat's effects, as they described in Canaries and a Coal Mine, was highly, highly plausible, right?

It makes it lots of mental models we might have had.

But equally, there are mental models and arguments that I've put forward that might be as credible.

So when you look at that through your lens, how would you disentangle

What was really going on?

And what does AI exposure mean?

I mean, who's got an AI exposed job?

Does a graphic designer have an AI exposed job?

Does a paralegal have an AI exposed job?

What is the measure by which you assess AI exposure and how do you control for factors?

as relating to the industry or the sub-industry or the firm itself?

We have this ad that goes out around Christmas, which is a dog is for life and not just for Christmas because lots of kids get bought puppies and they have to keep them for a long time, right?

So a young grad hire is for three years, not just for three weeks.

And your argument here is that even if AI can't do

What people say it might be able to do, the fact that the managers believe it might be able to do that has them looking at these long, long-term decisions, which is hiring a person, making a commitment to them, being willing to work with them for two, three, four years.