Nobel Economist: The Biggest AI Opportunities We're Overlooking | Daron Acemoglu

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Daron Acemoglu (1:00)

I could definitely see a future, very scary future, in which a lot of people would not have meaningful work.

Marina Mogilko (1:06)

This is Daron Acemoglu, an MIT economist and winner of the 2024 Nobel Prize in Economics. He thinks we're overlooking some of AI's biggest opportunities. So where should we be looking and what will keep us valuable?

I've seen a lot of posts where people say, oh, instead of hiring someone, I deployed an agent.

Daron Acemoglu (1:24)

Automation is great. The problem is the only thing we do is automation. That will eliminate too many jobs without creating meaningful, high-paying, high-productivity jobs to create new opportunities for people.

Marina Mogilko (1:36)

So what should people be doing? What skills should they be acquiring?

Daron Acemoglu (1:40)

If anybody wants my opinion, I would say go and study.

Marina Mogilko (1:45)

So you said something in 2024 that AI would add about 1% to US GDP in the next 10 years, with only 5% of tasks being fully automatable. What do you think about this now in 2026?

Daron Acemoglu (1:57)

Well, first of all, it's my pleasure to be here. And I do not claim that I have any unique insight into AI. AI is like an elephant. Everybody is feeling a different part of it. But I think I try to bring a little bit of basic economic calculus to the discussions of productivity. And this was more than two years ago.

There's a huge amount of uncertainty, and there is even more today. But there also been some very rapid developments, especially this year, with agentic AI.

Probably now I would have put that number a little higher than 5 percent. But I still think that a lot of the discussion exaggerates how quickly we're going to see automation, because automation is hard. Integrating automation into organizations is hard. Diffusion of new technologies, especially when they require lots of complementary investments and adjustments, is hard and slow. That doesn't mean it's not a transformative technology. It doesn't mean that we cannot get benefits from it, but we have to be grounded in thinking about how quickly and how thoroughly the economy is going to change. And we still have the same discussion that we should have about every technology. What's the best way of using it?

How it could be misused? And who will benefit from it?

Marina Mogilko (3:33)

You mentioned agentic. Is there anything when you see it, you'll think, okay, now I can expand my figure to like two digits when it comes to the impact of AI?

Daron Acemoglu (3:46)

I think the advances in coding have been really, really rapid. What is it about coding that makes it so conducive to have the most rapid advances of any occupation?

I think it's the fact that coding is obviously done in offices. You don't have any interaction with the real world. The people who are supervising coding are very, very fluent in AI and technology in general. But also, and this is a very important part, just like math, just like chess, coding has rules, something either runs or doesn't run. Reinforcement learning is very effective when you have a ground truth that says, no, that's wrong. I think in many occupations, we lack that.

Customer service, I think most people would have said, customer service is going to be the first occupation to be fully automated by AI. And we're seeing some of it, but it's turning out to be very slow and very difficult. And most of the organizations that I deal with, their AI system is so bad, it's like a nightmare when I call customer service and I get AI, oh my God, it's going to be another 10 minutes before somebody can actually help me. And that's because there's a social element. Many of the things that you ask from customer service change over time. They are person specific, different people use different language for making the same questions.