Podcast Episode

Ronnie Chatterji - How The Labor Market Looks From Inside Open AI

Who’s talking

2 speakers
Ronnie Chatterjee35 min · 79%
Ben Zweig9 min · 21%

Transcript

57 turns · 44 min
BZ
Ben Zweig0:02

Lots of people have opinions about what AI will do to jobs and growth. My guest today is the one building a research agenda to find out. Ronnie Chatterjee is the chief economist at OpenAI and one of the pioneers of the economics of AI. We get into what AI means today and what that means for workers and policy.

RC
Ronnie Chatterjee0:16

Here's our talk.

BZ
Ben Zweig0:17

Welcome to the Economics of Worth. I'm excited to welcome Ronnie Chatterjee, Chief Economist at OpenAI. Maybe you've heard of it. Ronnie, welcome to the show.

RC
Ronnie Chatterjee0:23

Oh, thanks so much for having me, Ben. It's exciting to be here.

BZ
Ben Zweig0:26

For sure. So you've got the coolest job in the world, I think. How do you set a research agenda for a company like OpenAI? How do you decide what not to work on?

RC
Ronnie Chatterjee0:35

It's a great question, and I really do agree that I have the coolest job in the world. I recently wrote my first Substack post out of a series that we're coming out with that said I think I have the coolest job. I like it, by the way, and

BZ
Ben Zweig0:45

welcome to Substack. Better than Twitter.

RC
Ronnie Chatterjee0:47

Thank you. No, no, I enjoy the long form part of it. It's also why I enjoy the podcast. I did describe it as the coolest job in the world. And I don't know if it would be true for everybody, but for people like you and I who are economists interested in new technologies and how they impact society and the economy as a whole, I couldn't think of a cooler job in the world. And one of the biggest challenges in the job is setting a research agenda. And so when I started this job about two years ago, There really wasn't a playbook in an AI lab for a chief economist. I was the first one. There were chief economists at other tech companies, though, and I sought them out. I asked them how they prioritize, how they built their team. Who did you talk to? I talked to everyone from Susan Athey to folks who had worked at Google, people like Jed Kolko, who'd done stuff at Indeed, and just a great network of folks across many, many different companies. I also, when I had been chief economist at the Commerce Department in a different kind of role inside government, I talked to a lot of chief economists at banks and manufacturing firms. And so I had a little bit of sense of how we might set this up. But a lot of it was new, Ben. I mean, just being in the middle of this AI revolution, a lot of the assumptions that might have guided my thinking in the past went out the window. And I needed to kind of rewrite the script in terms of what we were doing. I'll tell you a couple of things that guided me. I mean, one was, what are the questions I'm getting asked? I mean, as an economist, I really believe that Some of the best research questions and getting a sense of the vibes come from reading the newspaper and thinking about what people are talking about on the front pages of The New York Times, The Wall Street Journal, The Financial Times, The Economist magazine. These are some of the biggest issues. I also think you can do a straw poll at your kid's soccer game and see what people are asking you about. And I realized early on people were going to ask me about questions around labor, which I know you've made a lot of big contributions, as has Rebellio. And they were going to ask me about

Episode notes

What does it look like to do economics from inside the AI revolution with access to data nobody else has, and questions nobody has figured out how to answer yet?

In this episode, Ben sits down with Ronnie Chatterji, chief economist at OpenAI, to pull back the curtain on how economic research gets done at a frontier AI lab.

Topics covered:

  • Why the right time horizon for AI economics is a mix of real-time, medium-term, and scenario planning
  • Why you can't afford to be late to recursive self-improvement even when it feels far away
  • What OpenAI's own labor data is showing
  • The four-category job framework: which roles are at risk, which will be reorganized, which will expand as prices fall, and which are largely insulated
  • The Codex paper: what OpenAI's internal data on agentic coding tool adoption reveals about how long it takes different functions to catch up with one another
  • Extensive vs. intensive margin: why 2025 was about getting firms to adopt AI at all, and why 2026 is about measuring how deeply they're using it
  • AI and entrepreneurship: whether the falling cost of starting a business is producing more solo firms, thicker startup markets, or just more noise
  • Whether AI favors small firms that can now punch above their weight, or large firms with the data, governance, and training capacity to go deeper
  • B2B Signals: what OpenAI's enterprise data set reveals

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