# AI Is Not Improving Productivity: Nobel Laureate Daron Acemoglu

- **Show:** Me, Myself, and AI
- **Published:** 24 Feb 2026
- **Source:** https://sloanreview.mit.edu/audio/ai-is-not-improving-productivity-nobel-laureate-daron-acemoglu
- **Transcript:** https://podcasts.opennash.com/shows/me-myself-and-ai/ai-is-not-improving-productivity-nobel-laureate-daron-acemoglu-370189/
- **Provider:** OpenNash Podcasts (machine-generated)

## In brief

Economist Daron Acemoglu argues that technology has no preordained destiny: the direction AI takes depends on human choices. He says the industry is pushing toward automation and centralised information, which mainly benefits capital owners rather than workers. He proposes that AI be steered toward "new tasks" that make workers more capable, and that this will deliver real productivity gains.

The central tension is between what AI could do and what today's incentives reward. AI could help electricians, nurses and teachers do more, but big companies aren't investing in that, domain data is lacking, and current models aren't reliable enough. He suggests change can come from individuals, engineers, entrepreneurs and proactive regulation, while warning against heavy-handed rules that stifle innovation.

## Key insights

1. **Technology's direction is a choice, not destiny** Acemoglu says the Power and Progress thesis is that history shows technology can produce big losers or broadly shared gains depending on choices. Believing AI's future is fixed breeds helplessness and complacency.
2. **Automation helps capital owners more than workers** Automation removes tasks from workers and mainly benefits capital owners, he argues. New tasks, such as technical manufacturing jobs or podcasting, have historically raised productivity, wages and employment.
3. **Big AI firms aren't building pro-worker tools** He says none of the large companies put even a small fraction of investment into pro-worker AI, since that isn't their business model. Domain-specific training data doesn't exist without data property rights and markets.
4. **Reliability limits AI in high-stakes fields** Current language-model architecture may impose hard limits on accuracy. A one-in-a-thousand dangerous error would be an unacceptable casualty rate in medicine.
5. **Regulation should steer AI, not just restrain it** He calls for proactive regulation that tilts incentives toward pro-worker, decentralised AI, plus standards for health-critical uses and stricter scrutiny of acquisitions of startups. He also warns that Europe's regulation has hampered innovation.

## Questions this episode answers

### Is AI actually making the economy more productive?

Acemoglu says standard measures show slower productivity growth today than in the 1950s to 1970s, and no objective measures yet show huge gains from AI. He thinks measurement problems explain only part of this.

### What does Acemoglu mean by 'new tasks'?

New tasks are jobs or activities that technology makes possible and that make workers more capable, like making podcasts or doing more technical manufacturing work. He says they have historically raised productivity, wages and employment.

### Why does he say AI won't replace all humans?

He sees AI as a powerful information technology rather than something that thinks like the human brain, and says it lacks human judgment and creativity. He expects it to end up working alongside people.

### Why can't an electrician or nurse just use ChatGPT today?

Acemoglu says it wasn't designed for those jobs, isn't reliable enough, and hasn't been trained on expert, domain-specific data. In medicine even rare errors would be unacceptable.

### What can ordinary people or companies do to change AI's direction?

He says enough individuals changing their minds can matter democratically, and that engineers and scientists in AI firms and entrepreneurs have particular influence. He also urges stricter oversight of mergers and acquisitions.

## Facts

- **Sam Ransbotham** [0:01:18]: "In 2024, he was awarded the Nobel Prize in Economics for this work."
- **Sam Ransbotham** [0:00:35]: "I've been researching data analytics and AI at MITSMR since 2014, with research articles, annual industry reports, case studies, and now twelve seasons of podcast episodes."
- **Daron Acemoglu** [0:02:45]: "why there has been a huge divergence in economic fortunes of different parts of the world since the sixteenth century or thereabouts."
- **Daron Acemoglu** [0:02:45]: "the fact that European powers colonized the rest of the world and shaped the institutional trajectories of very different nations around the world in very diverse ways."
- **Daron Acemoglu** [0:03:57]: "The main thesis of power and progress is that technology does, to some extent, what we want it to do."
- **Daron Acemoglu** [0:05:29]: "in 2021, 2022, when you were writing, it was impossible to foresee how rapid some of the advances in generative AI would be."
- **Daron Acemoglu** [0:07:31]: "It takes away tasks from workers. It is beneficial to capital and capital owners, and not so much for workers in general."
- **Daron Acemoglu** [0:09:00]: "many of the early hopes about computers were centered on decentralization. People could do in their garages things that IBM as a centralized organization couldn't do."
- **Daron Acemoglu** [0:09:00]: "Large language models are information centralization tools."
- **Daron Acemoglu** [0:12:57]: "None of the big companies are pouring even a small fraction of their investment into developing AI as a pro-human, pro-worker tool."
- **Daron Acemoglu** [0:15:06]: "That data doesn't exist and it won't exist unless we have property rights in data and we have proper data markets."
- **Daron Acemoglu** [0:19:03]: "we're having slower productivity improvements today than we did in the fifty, sixty, seventy, those boring pre-digital days."
- **Daron Acemoglu** [0:19:03]: "the Bureau of Labor Statistics is overestimating inflation."
- **Daron Acemoglu** [0:19:44]: "Life expectancy increased tremendously with antibiotics."
- **Daron Acemoglu** [0:21:21]: "Part of the reason why tech companies have so much power is because they have what Simon Johnson and I called in Power and Progress persuasion power."
- **Daron Acemoglu** [0:22:28]: "Right now, startups are aligned with the big companies because their dream is to be bought up by the big companies."
- **Daron Acemoglu** [0:23:47]: "Europe is so far behind in AI and many areas of tech because they've not been very conducive to innovation via their regulatory system."
- **Daron Acemoglu** [0:25:47]: "I've been working on automation for over twenty years."
- **Daron Acemoglu** [0:25:47]: "when AI models started making rapid advances in the mid 2010 s, I got worried about what that would imply from this aspect of the future of work"
- **Sam Ransbotham** [0:28:43]: "Me, Myself, and AI season thirteen premieres on March tenth."

## Transcript

**[0:00:02] Narrator:** Hi everyone, we're back with a bonus episode profiling another thought leader in the technology research space. MIT Institute Professor Daron Acemoglu is a Nobel Prize winning economist and the author of Power and Progress. He joins Sam today for a conversation spanning technological advancements, limitations, and regulation. We're back on March tenth with more new episodes. For now, we hope you enjoy this conversation.

**[0:00:28] Daron Acemoglu:** I am Daron Acemoglu, Institute Professor at MIT, and you are listening to Me, Myself, and AI.

**[0:00:35] Sam Ransbotham:** 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 MITSMR since 2014, with research articles, annual industry reports, case studies, and now twelve 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. Hi listeners, thanks again to everyone for joining us.

**[0:01:18] Sam Ransbotham:** I'm excited to be talking with Daron Acemoglu, professor of economics at MIT. Daron works extensively on economic development, labor economics, and the economics of technology. In 2024, he was awarded the Nobel Prize in Economics for this work. His insights on the interplay between institutions, technology change, and inequality are particularly relevant for today's businesses. Of course, our listeners will be most interested in Daron's thoughts on AI. Daron, great to have you on the podcast.

**[0:01:47] Daron Acemoglu:** My pleasure, thanks, Sam.

**[0:01:49] Sam Ransbotham:** Okay, so your work spans institutions, technology, and equality. Can you share some of the themes in general from your past research?

**[0:01:56] Daron Acemoglu:** I got into economics because I was fascinated by what I saw around me in my very young teen years about very divergent economic, political, and social outcomes across countries, huge disparities in terms of wealth, in terms of poverty. And those interests have framed my research and my focus on institutional factors, which determine the effects of history, the effects of how society is organized, the rules, the laws, the norms, and technology as the prime channel via which human ingenuity and human decisions impact economic productivity and economic well-being.

**[0:02:45] Daron Acemoglu:** And throughout, I have been fascinated by the interplay between institutions and technology, and by how institutional factors and technological factors have evolved over time. So a lot of my research has focused on, for example, why there has been a huge divergence in economic fortunes of different parts of the world since the sixteenth century or thereabouts. It is very much related to, for example, the fact that European powers colonized the rest of the world and shaped the institutional trajectories of very different nations around the world in very diverse ways.

**[0:03:23] Daron Acemoglu:** And I've also been fascinated by the Industrial Revolution and how we started this process of using knowledge, science, and various skills in improving the way that we can actually start producing goods and services.

**[0:03:42] Sam Ransbotham:** That's all really salient for what's going on right now. You have a recent book, Power and Progress, and I think I was reading the preface of a revised edition, perhaps, where you noted that things sort of changed on you underfoot. How has the recent changes changed some of your thinking?

**[0:03:57] Daron Acemoglu:** Well, I think two things are worth noting there. The main thesis of power and progress is that technology does, to some extent, what we want it to do. It does not have a preordained destiny that will take us in one direction or another. We have a lot of agency, a lot of choice in shaping the future of technology. And different futures correspond to different winners and losers, different benefits, different costs, different productivities. We try to make that point by going into history, showing how critical periods during our recent history, like the last one thousand years, have led to sometimes big technological breakthroughs, but with huge losers.

**[0:04:51] Daron Acemoglu:** And sometimes those forces have been reversed, and gains from technological betterment have been shared more equitably. So that message, I think, is more relevant today than ever. AI is a particularly versatile technology. It provides so many different futures for us. And the narrative that there is a determined natural future of AI and we are all going there whether we want it or not. And and ultimately, we're all gonna become incredibly more prosperous out of that is just simplistic.

**[0:05:29] Daron Acemoglu:** And fighting against that narrative, I think, is very important today, because that narrative lulls us into a sense of helplessness and sense of complacence that could be quite costly. On the other hand, of course, in 2021, 2022, when you were writing, it was impossible to foresee how rapid some of the advances in generative AI would be. But those advances haven't really changed the basic trade-offs and the basic messages that we wanted to convey in the book.

**[0:06:04] Daron Acemoglu:** I talked at a high level about different directions of AI. What are they? I think simplifying it, you have a couple of poles that are pulling in different directions. I would single out in the production process automation, which is the dream of most AI models today, especially under the banner of artificial general intelligence AGI, which aims for large language models or other generative AI tools to reach levels of capabilities comparable to the best workers across a very wide range of domains.

**[0:06:48] Daron Acemoglu:** The reason why that is viewed as attractive is that, just like previous rounds of software that improved cognition in different domains, that can then be used for automating tasks. So AGI is very tightly interwoven with the automation agenda. Automation is great. It gets rid of some routine tasks, it can, you know, some boring tasks. When it's applied in the physical domain, such as with cranes or robots, it could remove the most dangerous tasks from the human work schedule.

**[0:07:31] Daron Acemoglu:** But automation also doesn't benefit workers by itself. It takes away tasks from workers. It is beneficial to capital and capital owners, and not so much for workers in general. So at the other pole, we have things that are complementary to humans, meaning that technology enables humans to do more things or better things, or completely new things. So then these new things is what I refer to as new tasks. So if you look at people around you, many of the occupations you'll see involve things that could not even be imagined fifty or sixty years ago.

**[0:08:17] Daron Acemoglu:** As a journalist, you're gonna be making video casts and podcasts and use technologies for research that require completely different skills than somebody sixty years ago going to the library and sifting through books. So that those are some aspects of new tasks. So are many of the physical occupations in manufacturing that involve much more technical work. Those have generally been very good for productivity and for worker wages and employment. So that's one dimension in which the future of technology could have very different effects, depending on whether we go on the automation or the new task direction.

**[0:09:00] Daron Acemoglu:** I would also like to add whether we use technology for information centralization or decentralization is also important. In that many of the early hopes about computers were centered on decentralization. People could do in their garages things that IBM as a centralized organization couldn't do. And personal computers enabled that to some extent, not anywhere comparable to the hopes of pioneers of computing in the sixties and the seventies. But today we are going in the opposite direction. Large language models are information centralization tools.

**[0:09:48] Daron Acemoglu:** They collect all of the information. They aim to collect all of the information of humanity, ultimately, and then centralize that and process it in a centralized manner that then gives you answers. And then so there is less for the decentralized human mind and human participation to do. The centralization and automation are two different poles, but they are complementary. So when I'm talking about new tasks, it is really about enabling the technology to go in a direction that can really help workers, help individuals, not just big corporations.

**[0:10:23] Daron Acemoglu:** So it's going back to those aspirations that were already present in the late nineteen sixties and nineteen seventies. And my work shows how new tasks, when they have been activated, have led to productivity gains and have led to wage gains and employment gains. So if we think about these new tasks though, what kinds of things should businesses be looking for? If people buy this argument and that they want to go down this path, what do they need to do?

**[0:10:55] Daron Acemoglu:** Actually, my thing is disarmingly simple. AI is really an information technology, a very powerful information technology. It's not an automation technology. AI is not thinking anywhere like the human brain. Instead, it has some truly impressive capabilities that the human brain doesn't have, and it lacks some of the judgmental and creativity related capabilities that the human brain naturally has. As an information technology, what AI is very good at is sifting through gargantuan data sets and find relevant context and information for some specific task or specific context or specific application.

**[0:11:41] Daron Acemoglu:** So if you're an electrician and you encounter an equipment that is behaving in a way that you haven't seen before, or a completely new equipment that you don't have experience with. And if you have the right AI tool, that can immediately and reliably give you information about why that sort of unexpected behavior is occurring, or what are the things you need to know about this equipment and how it interacts with the particular type of electricity grid or the environment that it is situated in.

**[0:12:14] Daron Acemoglu:** Those are the kinds of things that regular electricians would have to work decades to get the experience in an imperfect way. So we can significantly improve what electricians, what nurses, what educators, what journalists, what academics could do using AI in order to perform more sophisticated tasks or new tasks and acquire much better information. And I think while AI, generative AI, together with the right sort of scaffolding from good old fashioned AI that does pattern recognition, could provide that kind of ideal tool for human new tasks.

**[0:12:57] Daron Acemoglu:** That's not the direction in which AI is being developed. In fact, none of the big companies are pouring even a small fraction of their investment into developing AI as a pro-human, pro-worker tool. Well, let's connect these last two points a little bit. These are, as you say, being developed by big companies. When I think about the electrician in your scenario, wouldn't they naturally get recommended solutions that come from, let's say, advertising models that are built into the large language model?

**[0:13:33] Daron Acemoglu:** Right? So right now, today, you can as an electrician, you can take chat GPT with you and you can ask questions, but there are several problems with that. First of all, it has not been designed or optimized for that task. Second, it's not reliable, so a much higher degree of reliability is necessary. Third, it has not been trained on the domain specific information that all of the relevant electrical equipment and deep understanding of the electrical laws and electronics that would be necessary.

**[0:14:07] Daron Acemoglu:** And most importantly, it has also not been trained on use cases of best electricians dealing with similar problems from which AI could learn. So it is not designed for that task, and it hasn't been trained with high quality domain specific data. And all of those restrict your ability to use chat GPT or similar tools. And that's the reason why whenever employers are given a push towards using them, the first thing they want to do is just use them for automation.

**[0:14:40] Daron Acemoglu:** Because that just seems to be the path of least resistance. To think about that a little bit more, there's nothing that says that we couldn't train those models over those domain specific knowledge bases. Maybe we're just early days and that could come out. And I think that's plausible, but I'm not sure of the economic incentives for people to do that. The economic incentives are not there because this is not the business model of the leading corporations.

**[0:15:06] Daron Acemoglu:** That data doesn't exist and it won't exist unless we have property rights in data and we have proper data markets. The current architecture of large language models may create hard limits on reliability, whereas in situations like this, reliability could be a very important constraint. So, for example, imagine we do this with nurses, and one in a thousand time they give you the complete opposite of what they should do and you poison the patient. I think one in a thousand seems very small, but actually in medical applications, that would be an unacceptably large casualty rate.

**[0:15:43] Daron Acemoglu:** So it's really a different architecture and different sort of preparation training of these models that may be necessary. Yeah, I think your error rate's an interesting one because I'm not really sure what I think about that. Half the nursing students graduate in the bottom half of their class. That's just how averages work. But as a result, we don't allow nurses to make those decisions at the moment. So, except in a few cases where you have highly trained, licensed practitioner nurses, nurses cannot prescribe drugs, they cannot make emergency decisions.

**[0:16:18] Daron Acemoglu:** And when a patient is having problems, they have to wait for a physician to come. So that's the margin that we're talking about. The nurse complementary technology would expand what nurses do in those domains. And no, you couldn't do that unless all of the nurses become even better trained than licensed practitioner nurses, or the AI models get much better. Let's push on the nursing example a little bit more. My daughter has recently learned how to drive.

**[0:16:37] Daron Acemoglu:** I mean, I'll make you nervous. I think we both live in the same area, so no, she's a good driver though. But she hasn't seen millions of almost wrecks yet, and I would love for her to have that experience. You know, by analogy, the nurses may not have seen these esoteric cases in a way that we were just talking about. These AI models are are fabulous at storing lots and lots of information and recalling that. I think there are many things that can be done.

**[0:17:17] Daron Acemoglu:** The future of technology is rich. If you integrate AI with virtual reality, you can have personalized experiences where, you know, your daughter could experience very dangerous situations sitting in front of a computer. And I can tell you from my own experience, you know, when you get behind a wheel, you think you know, and you don't.

**[0:17:36] Sam Ransbotham:** We talked a little bit about incentives. Let's talk about measurement a little bit. I think the issues we've always had is we can measure the number of widgets. We have a lot of trouble measuring the outputs of our knowledge economy. How important is measurement, and is there anything that we can do to try to improve that?

**[0:17:56] Daron Acemoglu:** Well, I think measurement is very important, and there are some puzzles that we should bear in mind. And I think these puzzles do feed into my concerns and also skepticism about some of the claims. We definitely do live in an age of innovation, according to many measures. If you look at the number of patents at the USPTO, they have quadrupled over the last forty years. We get an incredible array of new apps every day on our phones.

**[0:18:30] Daron Acemoglu:** We have much faster turnover of electronics in quite a significant way. I mean, when I use my iPhone that's a couple of years old, everybody says, Wow, you're really missing out. And you know, when people were using rotary phones, dial phones, you know, you could use the same model for thirty years and nobody would bat an eye. So there is a sense in which we are getting a lot of innovations, but using the standard measures of economists, we don't see much improvement in productivity.

**[0:19:03] Daron Acemoglu:** In fact, we're having slower productivity improvements today than we did in the fifty, sixty, seventy, those boring pre-digital days. What's up with that? Well, the people from Silicon Valley and economists who are sympathetic to that perspective would say that's all measurement problem. You're just not making allowance for how high quality some of the products you're getting now is. And the Bureau of Labor Statistics is overestimating inflation. You have in the middle of your palm a super computer, super powerful machine that allow you to access information like never possible before.

**[0:19:44] Daron Acemoglu:** So all of these things they think are the reasons why you shouldn't look at macroeconomic data, you should ignore all of the economists' core data sources. There's some truth to that, but I think it can be exaggerated. You know, we did not measure the benefits from antibiotics that well either, but you still got amazing improvements on many directions. Do in terms of GDP, in terms of output of the pharmaceutical sector and life saved. Life expectancy increased tremendously with antibiotics.

**[0:20:17] Daron Acemoglu:** Well, life expectancy is not increasing. We're not seeing any of the AI facilitated pharmaceuticals do anything yet. So perhaps time will change that, but but we just don't have objective measures that show huge gains from AI as of now. I don't think that's just a measurement problem, but measurement can help understand where the bottlenecks are, and also improve perhaps certain assessments of what the impact of AI is in different sectors. But I think a lot of it again comes down to what I was talking about.

**[0:20:52] Daron Acemoglu:** If you overdo automation, if you overdo information centralization, you're not actually gonna get all that promised productivity boom. So if you know I'm bought in, what do we need to do here? I mean, as an individual, what does an individual need to do, given that I can shake my tiny fist against the fang companies? What should individuals be doing here? So I think a lot. At the end of the day, society consists of individuals. If a lot of individuals change their mind, that has an effect.

**[0:21:21] Daron Acemoglu:** Part of the reason why tech companies have so much power is because they have what Simon Johnson and I called in Power and Progress persuasion power. They have persuaded the rest of society that their intentions are benign, their technology is good, and they will not misuse it too much. There's a lot of counter evidence to that, but we still sort of believe it. We still believe the leading AI companies when they say we have this amazing godlike technology, believe that it's godlike and that it will be used just in your service, your own personal God.

**[0:21:58] Daron Acemoglu:** So, you know, absolute power corrupts absolutely. I don't know that we should really believe those claims. So different individuals will have to reach their own conclusions, but enough individuals, a critical mass of them changing their views would have an effect through a democratic process. And you know, who are individuals who have a lot of say? Hundreds of thousands of people, perhaps more, who work as engineers and scientists in these corporations. They determine the direction of research.

**[0:22:28] Daron Acemoglu:** If they decided next year that they want to work not on automation and AGI, but developing more pro-worker, pro-human technologies that will help workers and human decision makers and decentralization, that's what we would get. So that's an individual decision. Another individual decision is entrepreneurs, you know, a lot of new ideas come from startups. Right now, startups are aligned with the big companies because their dream is to be bought up by the big companies. That's the way you become a billionaire right now.

**[0:23:00] Daron Acemoglu:** Well, again, that's a choice. Different values, different priorities, different regulatory systems. Perhaps we should really be much more vigilant in mergers and acquisitions, then that could lead to very different dynamics.

**[0:23:14] Sam Ransbotham:** Good, I hope our students are listening, because I do think that most of our students are out there trying to come up with startups with their goal of being an acquisition by one of these large companies. And you

**[0:23:24] Daron Acemoglu:** And if I wanted to be rich, that's what I would too, too.

**[0:23:25] Sam Ransbotham:** hm, hm, yeah. So maybe our measurement problem extends both to our productivity as well as to our incentives, if that's how we're measuring success. You mentioned regulation, let's touch on that a minute. I tend to think, well, we can have a market forces perhaps do a better job of aligning incentives than regulation. What can regulation do here, particularly when we are dealing with goods that are not physical goods?

**[0:23:47] Daron Acemoglu:** well, I would like to say three things about regulation. First of all, regulation is always tricky. And look at Europe, and Europe is so far behind in AI and many areas of tech because they've not been very conducive to innovation via their regulatory system. Too many organizations, too much interference, that can be very bad. So you you have to balance things. Second, some regulation on health critical, information critical, democracy critical things is absolutely necessary. You cannot let AI models pretend to be doctors without having some sort of assessment that they are actually giving adequate information.

**[0:24:33] Daron Acemoglu:** We apply tremendous barriers to anybody becoming a quack doctor, while we should apply the similar standards to AI models. But most importantly, we may need a change in the philosophy of regulation. Regulation should be not a reactive thing where, oh, we try to stop whatever AI companies are trying to do. I think we need proactive regulation that helps the AI industry move in a more socially beneficial direction. And that starts by recognizing what the socially directional is.

**[0:25:12] Daron Acemoglu:** And I've argued it's pro-worker, new tasks, more decentralization. It then recognizes why the current playing field is tilted against it and tries in a soft way, without, you know, stopping or killing the market process, trying to correct those distortions and provide a living chance to the alternative directions.

**[0:25:38] Sam Ransbotham:** That's a moment of hope there, that's good. Let me switch a little bit. Our show is Me, Myself, and AI. Let's let people get to know you a little bit. How did you get interested in these things?

**[0:25:47] Daron Acemoglu:** I've been always interested in technology as the engine of the industrial revolution, of the rapid growth process, and that brought me together with my studies of labor markets to focus on automation. So I've been working on automation for over twenty years. And then when AI models started making rapid advances in the mid 2010 s, I got worried about what that would imply from this aspect of the future of work, what it would imply for wages and employment.

**[0:26:26] Daron Acemoglu:** And that made me invest more time and resources into AI and understanding AI, understanding its societal implications, but also understanding the technology. And I think it's fascinating, it's super promising, but also super scary.

**[0:26:41] Sam Ransbotham:** I think that's a nice way to wrap up that balance that you keep coming back to. One of the things we like to do on the show is ask you a bunch of rapid fire questions, you know, top thing, top of your mind. What did you want to be when you grew up? When you were a kid? What did you want to be when you first were thinking about a career?

**[0:26:56] Daron Acemoglu:** I wanted to become a social scientist.

**[0:26:58] Sam Ransbotham:** Ah, so well, okay, that worked out for you then. What's the biggest misconception that people have about artificial intelligence?

**[0:27:05] Daron Acemoglu:** That it will somehow completely replace humans. I think at the end, AI will be something that works alongside humans, and the better we understand that and how to achieve that, the better we will be in shaping the future of work and the future of humanity.

**[0:27:24] Sam Ransbotham:** How do you personally use these tools?

**[0:27:26] Daron Acemoglu:** I use it just like other people. I sometimes ask questions to ChatGPT, and, you know, most of the time, I am both surprised by how good it is and disappointed that, you know, if I really trusted everything I got from it, I wouldn't be doing so well.

**[0:27:45] Sam Ransbotham:** Yeah, I have to push back a little bit there. I find that if I know something about a subject and I ask a question, I'm disappointed in the results. And if I don't know much about the subject, then I'm impressed with the results.

**[0:27:54] Daron Acemoglu:** That's it.

**[0:27:55] Sam Ransbotham:** That ought to worry me because

**[0:27:57] Daron Acemoglu:** But even when I know about the subject, I am impressed by, you know, how good it is able to synthesize the basic knowledge there. But it's just it always pretends to know more and gives answers that are really incorrect because it's extrapolating too much.

**[0:28:19] Sam Ransbotham:** what has moved faster than you expected with artificial intelligence?

**[0:28:22] Daron Acemoglu:** Oh, the uh, large language models, I mean, their capabilities, their reasoning capabilities are truly impressive.

**[0:28:29] Sam Ransbotham:** All right, it's been great talking to you. This has been a fascinating conversation. I love your balance of both optimism and concern, and I think that's a nice way to wrap up this session. Thanks for taking the time to talk with us.

**[0:28:40] Daron Acemoglu:** Thank you, Sam. This was a lot of fun.

**[0:28:43] Sam Ransbotham:** Thanks for listening. Me, Myself, and AI season thirteen premieres on March tenth. Please join us.

**[0:28:52] Narrator:** Thanks for listening to Me, Myself, and AI. Our show is able to continue in large part due to listener support. Your streams and downloads make a big difference. If you have a moment, please consider leaving us an Apple Podcast review or a rating on Spotify and share our show with others you think might find it interesting and helpful.