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Nobel Economist Daron Acemoglu WARNING: AI's Payoff Is NOT What You Think artwork

Nobel Economist Daron Acemoglu WARNING: AI's Payoff Is NOT What You Think

Into the Impossible With Brian Keating

October 5, 2026

Nobel laureate Daron Acemoglu says AI's real productivity gain is an order of magnitude smaller than the industry claims. The bigger danger is not the machine. It is that a handful of people are deciding how it gets built.

Speakers Brian Keating, Daron Acemoglu, Ryan Reynolds

TopicsNatural SciencesSciencePhysics

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Brian Keating (1:00)

Great, Daron. It's great to welcome you on. I will only try one phrase in Turkish that I know which is banditurkum.

Yeah, an old friend told me that at Brown University here. All his Turkish friends get together for lunch as you know the Turkish will, and they all thought I was Turkish, I guess, for some reason.

Daron Acemoglu (1:18)

So they taught you the sentence.

Brian Keating (1:20)

They taught me that sentence. Okay. Great. So this book has so much in it.

We need multiple podcasts to talk about it. I really loved it. I love the audio book, and I love your tweets. And I think we're going to start with just the most basic foundation. You teach at MIT, and you undoubtedly have a great facility with science, and science plays a huge role in this book. Is science possible without liberal democracies?

Daron Acemoglu (1:48)

Yes, a little bit, but I think liberal democracy provides a much better foundation for science, and also a much better platform for us to decide which scientific ideas are a better basis for further inquiry and better for applications. And it also enables them to spread in society in a healthier way. In particular, I think liberalism, at its best, celebrates inquiry, experimentation, and admits the fallibility of everything, including scientists, and established truths. So that's very important because science sometimes, often, faces resistance from authorities.

So you have to take those authorities with a grain of salt. Sometimes science faces resistance from previous scientific advances. And again, the fallibility, the recognition that we are continuously learning and adjusting to our environment is a great basis for making further scientific advances.

Brian Keating (3:09)

Well, of course, you qualify that by stating that it's not maybe a sufficient condition because the counter example is, of course, China. And your recent fellow Nobel Prize winner, Omari Agdi, of UC Berkeley, he actually left the UC system to go to China. Is that a mistake or is that driven by economic considerations or political?

I know you can't get into his mind, but what do you think would cause someone to leave a liberal democracy to go to an illiberal autocracy?

Daron Acemoglu (3:39)

Well, I think I would not be comfortable in China because of constant surveillance and restrictions on what you can say, including in universities where these are actually quite severe. In Hong Kong, they have become even more severe.

But if I was working, say, on RNA, and suddenly my funding was cut here, I could see how it would be attractive to go to China when much better conditions are being promised. Promise is the important word. I'm not sure when all of that will be delivered, but they're being promised. The other thing that's very important is, I've always been very critical of the Chinese regime, and I still think that their growth trajectory, so impressive, has many problems. But right now, they are really investing in AI, robotics, manufacturing technologies, and they have built a tremendous engineering workforce that is so important for further progress in many of these fields. So China is not staying idle. It's not just copying the United States.

Brian Keating (4:59)

I study for a lot of the research I do in astronomy. I try to follow in the footsteps of my great mentor, Galileo Galilei. He's a finger puppet of him. Now, he worked in an extremely illiberal autocracy. Also, the Catholic Church was every bit as totalitarian as any of their worst fears, and yet science flourished in the Renaissance. In fact, he had many permissions that were granted by these illiberal forces. As long as he didn't teach it, he could study it.

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