Ed Ludlow

Can we extend that to the Fed?

Now bear with me on that one.

Okay, I'm ready.

You talked about disaggregated data, but also improved measurement, citing Greenspan in that sense.

If AI is so good, can it process larger sets of data and make more accurate economic forecasts than traditional Fed models can?

It can't produce a more accurate neutral rate, for example.

This relates to the operations within the system.

I do have two questions relating to AI and monetary policy quickly, and I know we want to get to some audience questions as well, because I'm conscious there are students in the room who will go out into the workforce.

I think the main thing, reflecting back on the 90s, is that there are anticipated impacts from AI on the economy, and PCE is the preferred gauge of inflation running higher, beyond 2%.

How do you manage that?

Many would argue that those anticipated AI-driven productivity gains would justify lower rates, but they are that anticipated.

How are you thinking about the labor market now, particularly post-January jobs, which showed essentially the most hiring in more than a year?

It was an interesting data point.

So diversity in the economy is where I want to end it before we take audience questions.

One of the features on the show regularly is compensation in the field of AI, stock-based compensation, competitive salaries, the newly minted millionaires in the field buying property in San Francisco.

But within the 12th District, one of the things I always reflect on is if I drive from the Bay Area down to SoCal on the 5 or the 101, it's the agricultural sector, this state in particular, but you could expand that to the other regions of the district.

There's a big sort of contrast there.

Could you reflect on both?

You know, what you see at the high end of the tech sector and what you do or do not see in agriculture feeling benefit from AI?

We're going to take a couple of quick questions from the audience, but while we find the mic.