AI Is Being Built to Replace You—Not Help You

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1:40Bloomberg Audio Studios. Podcasts. Radio. News. On our current path, this handful of people will decide what the future of AI is. And the best way to counterbalance that is to have a vision that's different and hopefully better for society.

2:08Stephanie Flanders:Welcome to Trumponomics, the podcast that looks at the economic world of Donald Trump, how he's already shaped the global economy, and what on earth is going to happen next. Well, we talk about the big forces affecting our economy and the broader world on this show, and there's no bigger topic in economic policy and general discussion these days than the impact of AI. We've talked at different times about the consequences for jobs, inflation, interest rates, and whether policymakers, let alone ordinary people, are ready for any of it. Well, my guest this week, Darren Asimoglu, famously takes the long view on these matters, a recipient of the Nobel Prize for Economics in 2024.

2:47Stephanie Flanders:He probably wrote the single most widely read book of economic history of recent times, Why Nations Fail. And more recently, he wrote, with Simon Johnson, Power and Progress, Our Thousand Years' Struggle Over Technology and Prosperity. And I talked to him about that book and the lessons for this AI revolution a while back in the summer of 2023. But given everything that's been going on, I wanted to have him back to see whether he saw anything in the new waves of AI that we've had since 2023, particularly in the last few months, whether he'd seen anything to make him change his view on either how fast this technology is going to change our economy or how well-placed we are to get the best out of it.

3:32Stephanie Flanders:Darren, thanks very much for coming back on to this podcast. Thank you, Stephanie. It's my pleasure to be here.

3:41Stephanie Flanders:We will get into some of the sort of key dimensions of this in a minute, but I should just get a sense from you. I mean, we're talking now mid-March, 2026. There's been so much chatter, and I suspect many people listening have had their own real-life experience now of the development of all the different forms of AI, and particularly the sort of agentic AI that we talk about. Are you in a kind of very broad sense reassessing how fast or how fundamentally this is going to change our world? Yeah, every day. I think the underlying technology is changing faster than what I would have predicted, what many would have predicted a year and a half ago.

4:25So, especially with the recent developments in agentic AI, especially led by Anthropic, there is a real possibility that these tools can be broadly useful in what people do. There is still a lot of uncertainty, however. First, we are not seeing any of the prepackaged, easy-to-use, reliable applications. Think of it as the Microsoft words or Microsoft offices of AI that can be used across a broad range of occupations or in some specific occupations. Those are not around yet. There is still uncertainty about whether there will be bottlenecks in reaching higher reliability and higher judgment. There is every evidence that there is a lot of rapid progress, but there are some weaknesses in these models that are still persistent.

5:28And I don't just mean hallucinations, but lack of a deep understanding. They don't seem to have a conceptual framework. They don't understand the context. And they cannot reason at multiple levels of abstraction about a problem yet. So those may be overcome. and I think many of those are going to be important in dealing with edge cases in many occupations. So wholesale automation of occupations is still not something we're going to see right away, but some people swear that we're going to see it in one year or two years, three years. So there's a lot of uncertainty. But let me make one thing clear.

6:09if we do not up our game about both how we regulate these models and how we actually develop them, there could be a huge amount of damage to society.

6:23Stephanie Flanders:I think that's very helpful because there's two elements of this where there's obviously, as you say, there's a lot of uncertainty and there's a wide range of opinion. If possible, I'm going to try and separate them, but obviously they merge into each other. One is this question of the pace of change, how fast are companies really going to be able to change their practices or capture those productivity improvements. And then the second is, which you've just highlighted, is how well are we positioned to make the best of this, not just to get all the productivity growth, but to make sure it's actually positive for most of the population, not just a few.

6:54Stephanie Flanders:And you highlighted in the 2023 book, you know, none of that was automatic in the case of the Industrial Revolution, and we may have to do it much faster this time. Just focusing on this speed question, there's the Citrini report, which there's been a little mini-industry in sort of debunking this research report, which sort of went viral because it captured some elements of this sort of faster pace that we were seeing. I think you wrote a paper, you know, the basic macroeconomics of AI. In the debate, I would say that you're fairly low-key about the pace of change, the extent of change in any given year.

7:26Stephanie Flanders:I think you said at most a few percent over 10 years, so maybe even a fraction of a percent of productivity growth, overall productivity growth a year. Would you stand by that basic assessment today, or do you think maybe the gains, just the pure productivity gains could be a bit faster? I think they could be a bit faster. There has been faster change of the capabilities of the foundation models. However, However, it would still require some big breakthroughs, especially at the application layer. So the bottom line of that paper was to point out how we can get a fairly simple understanding of the constituent parts of the contribution of AI to productivity and GDP growth.

8:20And that comes from realizing that the GDP contribution of AI is nothing other than what fraction of tasks are going to be taken over or completely transformed by AI in the economy times the average productivity gain or average cost savings in these tasks. So that's the calculation that I did with the available evidence in 2022. But even then, a lot of people took issue at how I interpreted the data, et cetera. So you could boost some of the numbers that I have, which were that about 5 % of the whole economy will be taken by AI within 10 years, so by 2030 or thereabouts. and that that would lead to about 20-25 % cost savings or relative to labor costs that firms used to spend on the same tasks.

9:16Now, you can boost my numbers by increasing either or both of these quantities. So you can say, no, no, it's not 5%, it's going to be 20 % of the economy that AI is going to take over, in which case you would quadruple my numbers. Or you could say it's not 20 % cost savings, but it's going to lead to 30 % or 40 % cost savings. After all, you know, comp lead to 300 % cost savings because, you know, labor wasn't very expensive anyway. But you see the elbow room to do that kind of thing, but you're not going to come up with revolutionary numbers here. And part of the problem here is that we are right now, and that was the case two years ago and continues to be the case, we are right now focusing on AI as an automation tool, as a tool to replace workers.

10:09That's not the best way of using AI. The best way of using AI is to try to complement workers so that they can do new things. They can perform new tasks. They can increase their sophistication level and also respond to challenges in the world economy from globalization, from aging, from climate change by creating new goods and services, new organizations, and so on. If we do that, I think I would be more optimistic about the future of AI. And that's one of the aspects of the wisdom gap that we have right now. We don't know how to regulate existing models, and we're not really focusing on what we can do best with these models.

10:48And that's both for productivity and social consequences. So I've just made the productivity case that we could actually get better productivity consequences. But actually, the social consequences are even starker. If we displace people, if we say displace 20 % of the population from their jobs and they remain unemployed or they go to lower quality, lower paid jobs, our democracy is not going to survive. We're already struggling to make our democratic system work.

11:13Stephanie Flanders:It's not doing that well already. Yeah, we're not doing that well. And if we put another huge shock on top of that, I'm not very optimistic. So beware. And just thinking about the economics of what you're saying and also thinking about what captured people's imaginations about that Citrini report. They say themselves, this is a thought experiment. There was something kind of gripping about the fact that it was claiming to be a memo written in 2028. It was basically the claim was you would have an extraordinary amount of change in business models in a very short period of time. I assume you would say, given real world frictions and just the way things tend to happen, that a two year time frame is very unrealistic.

11:53Stephanie Flanders:But there was another basic assumption built into that, that the change that we will most immediately see and will have the biggest impact will be simply to replace labor, not to augment it. And that to the extent that it's creating other stuff, that's going to be far outweighed by the job destruction. Even if you don't accept the time frame, do you think there is an emphasis on replacement relative to augmentation? Or is agentic AI really both things? I don't know. It's an open question. But my bet would be on the Citrini side, not on the time frame, but on the path that we are following. There is so little that these companies are doing in order to understand what work humans do and try to be useful to humans.

12:44The whole agenda of all of the leading companies in the United States and now joined by DeepSeek in China is AGI, artificial general intelligence. That is a banner for saying these models are going to do everything better than humans, which, of course, then leads to the corollary that a lot of companies should just throw away their humans and use these companies. That is an automation agenda. That is exactly what Citrini banked on. Now, they then made a number of other assumptions and steps about how that would work out, what its consequences would be, how quickly those would be. Those I don't agree with.

13:22But credit to them, they said this was a scenario. They weren't even making a prediction. I don't know why the markets went haywire given that there was no new information in there. Everything that was in the Citrini report has been said. And they themselves said there is no research here that's original. but we are living in such fragile times in everything. The valuation of these companies is all based on very fragile assumptions about what they're going to be able to achieve in the future. If you look at the amount that they're spending and their valuations, this can only be justified if they make something like a trillion dollar revenues in the foreseeable future as an AI industry.

14:05I mean, that's just incredible. How are you going to get to a trillion dollar? They're hardly struggling to make a couple of billion dollars right now as a whole industry. So there is something, you know, glass in this house.

14:18Stephanie Flanders:Okay, I have to say that I'm slightly depressed by that answer because I thought you were going to push back more heavily against the Citrini assessment. I absolutely know that you're concerned about our capacity to cope with this, but I thought I was interested. Let me give you the pushback as well. I mean, that's the point I want to make. throughout. There is the potential to use AI, not for automation. That's what I keep emphasizing. But I also want to push very hard against the assumption that either we are going there already, no, we're not, or that we can get there automatically. No, we cannot, because all of these companies have this business model of just let's replace all the workers.

15:03They haven't even put into their calculations much of a revenue stream that they can get from complementing workers, because that's just a very difficult thing to monetize. So I think that's where our wisdom gap is. We are not even wisely thinking about what we should be doing with these very capable models, and the industry is going in its own direction.

15:28Stephanie Flanders:You have just done a paper with two of your colleagues for Brookings that is trying to give some concrete advice to policymakers in this area to answer specifically that question. I do want to get to that. I just want to quickly, one of the things, just to make sure coming out of this conversation, one of the things we see a lot in this is, and particularly if you look at the research studies in this area, they tend to look at the range of occupations and talk about their degree of exposure, quote unquote, to AI. And there's a whole range. I think in your original assessment, and a lot of people use this, they sort of thought it was about 20 % of occupation.

16:05Stephanie Flanders:And obviously, some people have high numbers. If one's thinking about different kinds of AI and the potential of AI, how should we read those? Are they just going on the way that businesses is looking at it now, as you pointed out, is very much as a labor replacement technology? Should we see that as exposure to replacement, or should we see it as something more sophisticated? Great set of questions. There are really three questions you're asking here, Stephanie. Let me answer each one of them in turn. One is, what does this AI exposure mean? And in general, it is an ill-defined concept because you could be exposed to AI because you're going to lose your job with AI, or you could be exposed to AI because you could use AI to increase your contribution to your job.

16:50Stephanie Flanders:And we see that in the company's exposure as well. And investors can't decide the difference between those two either. 100%. That's why they fluctuate between devaluing software companies and giving them a huge boost. So that's the first problem with AI exposure. So when I wrote my paper, I took a position similar to the Citroen report. And I said, right now, we're going towards automation. So let me focus on that. Second, where does that 20 % number come from? So roughly speaking, think of it this way. Right now, and I think in the near future, AI is pretty useless in jobs that involve a huge amount of interaction with the physical world.

17:34Construction, custodial work, manufacturing work, work that involves home care. Hairdressing. Hairdressing. The reason being that we are very far behind in robotics, but also AI models themselves don't have a good conceptual understanding of spatial causal relations, that even if we had fantastically flexible robots that could cut your hair or hold your hand, AI models would continuously make mistakes about spatial causal relations, and those unreliabilities would end up breaking your neck. So let's eliminate those jobs. I've also eliminated, again, based on other people's coding, any jobs that include a high degree of judgment.

18:21So we wouldn't want AI to run air traffic control. So Stephanie, think about it yourself. If the Manchester airport said, from now on, we're not going to have any air traffic controllers, everything's going to be done by AI. It might hallucinate, it might make some mistakes, but that's fine. It's cost savings. Would you fly to Manchester Airport? We don't want that. So those jobs are out, and any job that involves a high degree of social interaction is out as well. So that leaves essentially a range of office cognitive jobs. So that's where the 20 % comes from. Now, what about companies? So companies are indeed going after those jobs.

19:01They're going after IT jobs. They're going after back office jobs. But there are several new papers that have come out over the last few months, and they all find the same thing. The companies are talking a big game about AI. They say, oh, we have a lot of AI being used. But it has so far zero impact on the companies. Zero impact on employment, zero impact on productivity. Because it's actually like just other technologies. It spreads slowly. That was the basis of my numbers. And it's very difficult to integrate AI into what those companies do without a big organizational change. And I think when actually push comes to shove, when they try that organizational change, what they're going to realize is that you cannot really replace IT security people with AI.

19:49You need to use IT security people together with AI. And that might actually give us a boost towards more human complementary, more pro-worker AI. but we're not there yet because they're not trying to do that in big scale yet. Now, of course, code, that's a big advance. Will that change things in 2026? I don't know. By 2027, I'm sure there will be more companies that have attempted to do things and perhaps we'll have a rude awakening. And this is not going to work in the way that we're trying to do it. Perhaps we'll find a new direction. But this is where both policy and public debate are really important.

20:22Stephanie Flanders:To your point, I think Goldman Sachs added up, If you just listen to all the earnings sort of calls that companies are doing and chief executives are giving, just to your point about all these companies claiming, I think the average productivity growth that they're claiming is 32 percent. But it's not necessarily evident in any of the numbers.

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