Science, Innovation, and Economic Growth: OpenAI’s Ronnie Chatterji

**SPEAKER_1** (0:02)
How will AI enable the future of interdisciplinary collaboration? Find out on today's episode.

**Ronnie Chatterji** (0:10)
I'm Ronnie Chatterji, Chief Economist of OpenAI, and you're listening to Me, Myself, and AI.

**Sam Ransbotham** (0:15)
Welcome to Me, Myself, and AI, a podcast from MIT Sloan Management Review, exploring the future of artificial intelligence. I'm Sam Ransbotham, Professor of Analytics at Boston College. I've been researching data, analytics, and AI at MIT SMR since 2014, with research articles, annual industry reports, case studies, and now 12 seasons of podcast episodes. On each episode, corporate leaders, cutting-edge researchers, and AI policymakers join us to break down what separates AI hype from AI success.
Hey, listeners. Thanks for joining us. I think everyone listening by now knows that I tend to think all of our episodes are exciting, but today I especially do. Our guest today is Ronnie Chatterji, Chief Economist at OpenAI. Ronnie, great to have you on the podcast.

**Ronnie Chatterji** (1:08)
Sam, thanks for having me, and I think this will be an exciting one. I don't know if I can beat the standard of all your other episodes. We're going to do our best.

**Sam Ransbotham** (1:15)
Okay. A lot of pressure. So I read a stat that OpenAI has about 800 million weekly active users as of October. I think August it was 700 million, March 500 million, 600 billion tokens a minute. You know, you throw 100 million here, 100 million there, it starts to add up after a while. I suspect then that most of our listeners are pretty familiar with OpenAI. But can you give us a quick introduction to the company and what it does?

**Ronnie Chatterji** (1:40)
I can tell you the first time I ever heard of OpenAI was in late 2022, when I was working in government and a bunch of folks were reciting some poems that sound almost too catchy, too perfect to be written by a human. But there they were reading them and they were really funny. Sometimes poetry aligned around a particular individual or funny habits someone had. When I came back, they said, hey, it's this thing called ChatGPT. Most people know OpenAI through the ChatGPT product. But of course, you know that ChatGPT was an accidental consumer product, because the team at OpenAI had been working on an API for developers to build on top of AI models before that. But when it came time to release this consumer product, they weren't really sure how it would be received. And the quick adoption, almost 100 million users in just two months really was unprecedented, larger than any other consumer product that I'm familiar with. And really set the stage for the rapid pace of generative AI adoption we've seen over the last few years.

**Sam Ransbotham** (2:41)
Yeah, that's been huge, the ease of innovating. Ronnie is also a professor at Duke University. He knows diffusion of innovation theory.
Putting that chat interface out there really clicks all the relative advantage, complexity, trialability, observability, compatibility, all the things that we know from Everett Rogers. But practically, before, like you say, that fall of 2022, when I talked to people, they knew about AI, but they hadn't used it. Now practically everybody had used it. You've got a lot of people using it. And I think that free access got a ton of users. But it requires massive amounts of money to keep these things going. How is the economics going to start playing out with OpenAI? For context, you've just had the Buy Now button integrated and starting to integrate apps. How are the future economics going to play out here?

**Ronnie Chatterji** (3:30)
Well, because the adoption was so unprecedented, I think now we're trying to figure out where is AI going to actually deliver value both in the enterprise, which we can get to later, but also on the consumer side. And there's been so many other products released. If you think about Sora, OpenAI's recent video product. And so when you think about the economics of AI, we started at the very beginning when I got the job. I mean, they said, look, we need an economist to make sense of the economics of generative artificial intelligence. And I was a good choice, I think, because I worked a lot on these issues, as you mentioned, in my academic role at Duke University. I learned a lot from scholars around the world around how technology diffused across organizations. And so that was a big part of my research and my dissertation. At the same time, I had worked in government and thought a lot about technology policy and how government investments in critical infrastructure like semiconductors could be really important for national competitiveness. And finally, I was just really interested in business. And this gets to this point you're making here about the commercializability of AI. I taught in a business school for my entire career. I'm really interested in how my MBA students are going out in the world and going to make a career for themselves.