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Well, we talk about the big forces affecting our economy in 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 where the policy makers, let alone ordinary people, 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 twenty twenty four, 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 twenty twenty three,
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 twenty twenty three, 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. Darren, thanks very much for coming back on to this podcast.
Thank you. Stephanie's my pleasure to be here.
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 twenty twenty six. 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 eight and a half ago. So, especially with the recent developments in agentic AI, especially led by Anthropic, there is a real possible ability 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, and 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. If 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.
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, faster company is 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. And you highlighted in the twenty twenty three book, you know that 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 a 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. I think you said at most a few percent over ten 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 purebreductivity 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, 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, And 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 twenty twenty two. But even then a lot of people took issue at how I interpreted the data, etc. So you could boost some of the numbers that I have, which were that about five percent of the whole economy will be taken by AI within ten years, by twenty thirty or thereabouts, and that that would lead to about twenty five percent cost savings or relative to labor costs
that firms used to spend on the same tasks. Now you can boost my numbers by increasing either or both of these quantities. So you can say, no, no, it's not five percent, it's going to be twenty percent 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 twenty percent cost savings, but it's going to lead to thirty percent or forty percent cost savings.
After all, you know, comp lead to three hundred percent 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. That'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 away. And it'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 are not really focusing on what we can do best with these models. And 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 displaced twenty percent of the population from their jobs and they remain unemployed or they go to lower quality, lower
pay jobs, our democracy is not going to survive. We're already struggling to make our democratic system. 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 Suitrinian 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 twenty twenty eight. 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 timeframe is very unrealistic. 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 it a gent ki really both things.
I don't know. It's an open question, but my bet would be on the Cititrini side, not on the timeframe, 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. The whole agenda of all of the leading companies in the United States and now joined by deep
Seek 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 Catrini 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, but 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 Sutrini report has been said, and they themselves said, there is no search here that's original. But you know, we are living
in such fragile times in everything. You know, 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 justify if they make something like a trillion dollar revenues in the foreseeable future as an AI industry. I mean, that's just incredible. How are you going to get to a trillion dollar there?
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.
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 Citrey assessment. Though I absolutely know that you're I know you have your concerned about our capacity to to cape with this, but I thought I let.
Me give me out of the pushback as well. I mean, that's 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 or just let's repoliticplace all the workers.
They 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.
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 that 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 twenty percent of occupations, and obviously some people have high numbers. If one's thinking about different kinds of AI and the potential of different of AI,
how should we read those? Are they just going on your the way that businesses is looking at it now, as you pointed out as very much as a labor replacement technology, should we see that as exposure to replacement or should we see it 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 can lose your job with AI, or you could be exposed to AI because you could use AI to increase your contribution to your job.
And we see that in the company's exposure as well. And investors can't decide the difference between those two either.
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 Citrenia report, and I said, right now, we're going towards automation, so let me focus
on that second. Where does that twenty percent 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 construction, custodial work, manufacturing work, work that
involves home care, airdressing, 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 coosal 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. So 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 controlers. Everything's gonna 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? So 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 twenty percent comes from. Now what about companies. So companies are indeed going after those jobs. They'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 things. 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's spread slowly. That's us the basis of my numbers. And it's very difficult to integrate AI into what those companies do with the big
organizational change. And I think when actually push comes to shove, when they try the organizational change, but they're going to realize that you cannot really replace it security people with AI. You 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 twenty twenty six, I don't know. By twenty twenty seven, 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.
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 thirty two percent, but it's not necessary evident in any of the numbers. Let's get onto what policy makers could do about it, because that's something that governments everywhere are obviously very focused on.
And I noticed that you had recently done this report for Brookings. I think about a framework for thinking about pro worker AI. What are the main sort of policy areas that you would like people to focus on for that.
First of all, just two points I want to make before I talk about policy. The first one is just to clarify that by pro worker AI, I mean exactly the same thing that I was just talking about a second ago. Human complementary AIAI that helps workers do more, help workers become more expert in their jobs, perform new tasks, have better information for problem solving, travel shooting, judgment, and so on. That's what we're talking about with pro worker
and not just for office workers. We have a lot of examples in the paper showing how manual workers can benefit from AI. It cannot replace manual workers, but electricians, plumbers, nurses can hugely benefit from having the right kind of AI assistant. But it has to be the right kind of aassistant. It's not going to be chut GBT. So that's the first point. The second point is that my belief is that as important as policy actually is what
we're doing right now, Stephanie, is the public debate. Right now, we have delegated the future of this very very important technology. Some would argue, therefore the future of humanity to a handful of people who have no feedback from society, who have no accountability to society, and right now society is confused. So on our current path, this handful of people will decide what the future of AI is. And the best way to counter that is to have a vision that's
different and hopefully better for society. And that's what I hope the pro worker AI vision is. So the more people talk about that, the more the public pressure will grow, and the more of an alternative there will be. Look, my understanding from my limited experience is that anthropic Google Open AI are filled with people who are very well meaning. If they thought that there is a socially beneficial and still technically exciting area of AI, they would be much
more likely to take the plunge in that direction. It's just that we're not offering them an alternative, and society is not pushing back against some Outlin and his ilk's vision. So that's the point. Policy, in my view, is a supporting set of instruments. It can remove the stortions that exist that solidify the existing system, and it can give a notch to people, as policy has done in the past,
to try new things. So on the first bucket, there are many problems in our current system that would make a redirection of AI in a pro worker direction more difficult. I would single out two of them, but there are is more. The first one is that our tax code. That's true in the UK, that's true in the US. Our tax code encourages firms to replace workers because we tax capital essentially at zero percent, labor twenty five to thirty percent, especially in the US once you had the
healthcare costs and all the payroll taxes and everything. So that's a massive subsidy to capital that would make firms adopt automation even if automation wasn't better than humans, because they're getting this subsidy. Second, we know from historical evidence and current evidence that new things are done by new firms.
Competition is really important. The tech industry has become one of the least competitive industries in history, and moreover, business models that are new and different are likely to get crushed. So encouraging more competition via antitrust by enabling new companies to enter and try new things, I think that's a
very important part of way. Now there is a lot of energy in Silicon Valley, but it's all these startups that try to do exactly what open Ai and Anthropic and Google do so that they can be both by them. So that's not the kind of competition I'm talking about. And then in terms of nudging us to do new things, the government is horrible, in my opinion, at being an entrepreneur. It cannot be a venture capitalist, it cannot be an entrepreneur, it cannot be an innovator, but it has great potential
to be an aspiring leader. We have had so many examples where a small amount of money from the government has kickstarted industries in nanotechnology, in the internet, in robotics, it was the Robotics Challenge, a million dollar challenge that really focused people's attention to get robots that could actually play a game. So we could do the same with pro worker technologies. So we have given several examples of technologies that are very feasible but are not getting much investment.
A few of them are getting some investment from smaller companies. You can come up with another ten, fifteen examples and the government could have an easy competition in these kinds of technologies to focus the mind and show the demonstration effects that would then say to people, wow, you know, we could do this in other industries and other occupations as well.
In just thinking about what you've just said, and the paper you wrote for Brookings is trying to encourage us to think about AI policy in a different way. So I can't know what it was called, but it was the AI Action Plan or something that the Trump administration brought out lot and the way that we describe it generally, but particularly when we're talking about China in the US, AI policy is all about how to get there as fast as possible, how to make sure especially in the US,
how to make sure we win the race. And there's quite a lot of focus on sort of privacy and concerns around that, and maybe concerns about the pace of adoption, and that's obviously the gap that you're trying to fill, but it doesn't feel like there's much about how to
make this work for people. And I'm sort of struck because we had the Chancellor of Rachel Reeves, the UK Finance Minister, on the show a week or two ago, and you know, I think that's one of the things that she's thinking about is we're not going to lead the AI race in the UK, but we have said a lot about leading on the adoption and I guess the piece of that that you would add is you've got to adopt it in a pro worker way. I mean, what does that what would that look like for the UK.
Let me first say that the paper that you're referring to is actually co authored with David Arter and Simon Johnson, so let me give a shout out to them as well. Secondly, I think you're absolutely right. While you could give some credit to the Trump administration for emphasizing AI, they are really radardless their only shtick here is this has to be an American technology, and we have to race and we have to get rid of all regulations. That's not
a coherent AI policy. But I also fear it's even worse than that, and it's worse in the following way. This AGI winner take all framing is having truly pernicious effects on US China relations because once you are in this mindset that you are locked into this existential race for AI supremacy with China, it means that there's no room for collaboration with China on anything because they are your mortal enemy, because if they get to AI supremacy
before you, they are going to destroy you. That's completely false. Models are not going to be at a level that they can just give you global supremacy by themselves, And there are many other things that we can do with AI. In fact, now coming to the UK, China, Germany are doing more interesting things with AI than the US in
some domain. Sure, the US has the unrivaled leadership in large language models and foundation models, but I think the real gains from AI, as I hinted at the beginning of our conversation, will come from using AI in applications in manufacturing. Healthcare I think is huge, but manufacturing is going to be easier. And who is leading the efforts
to put AI into manufacturing is China. It's Germany even though they have no LLM industry, because they have the manufacturing know how, they have the data, and they are not beholden to this AGI race, so they're trying to do more practical things. I think that's the space in
which the UK has to be now. Unfortunately UK doesn't have much manufacturing left, but I think for the remaining manufacturing and other applications, I think that's where UK can have a leadership role because Germany is so far behind.
Germany shouldn't have a leadership role. China, of course is going to have a leadership role, but UK can have a leadership role once they have a broader scoping of what it is that we can do with AI, and if we actually manage that, it will have beneficial effects for global balances, because once you get out of this trap of winner take all, we cannot collaborate on anything
with China. We have so many global problems, global peace, all the societies that are aging, that require adjustment, climate change, pandemics, There is so much that we actually need to collaborate with China, and if in fact China makes breakthroughs in applying AI to manufacturing, US should learn from them. So there should be information sharing in AI as well.
I'm going to run out of time, but I had a couple of more and one is following on from what we were saying about how a country could position itself that's not trying to be in this kind of
existential race that the US has positioned itself. In the other conversation, you hear a lot in the UK is and I mentioned it to the Chancellor the other day, is you know that professional services that are successful in the UK, we'd still have some advanced manufacturing, but our strongest categories tend to be along with creative industry, professional services,
legal services, accounting, finance, all those things. They seem to be particularly in the frame when it comes to AI, at least in the discussion, and there has been I know a government concern. That means, if we want them still to be leading sectors, they have to be leading in adoption and we have to make sure there are no regulatory or data privacy obstacles in the way of that.
I mean, I guess that raises the question in the race to A do you could actually be making the institutional set up worse, not just failing to make it better.
Right, you've given me an opening to talk about another one of my topics, which is data. So yes, Indeed, if your objective was pour as much money into AI as possible and get rid of all short term obstacles to AI, you would get rid of privacy and you would allow AI companies to capture as much data as they want freely. That would be and that has been the worst idea you can imagine. It's actually worse for the industry. The future of the industry depends on data.
Data is going to be more important for our future as an economy, as a society than land. Can you imagine that if we said any piece of land you want, you can take it. That would be just chaos. But that's how we treat data, and that is actually bad for the industry because it creates a tragedy of the commons where everybody's exploiting data and nobody is investing in data. Especially if you want to do useful things with AI, like the pro worker AI that I was mentioning, you
need a lot of high quality use cases. We can do pro worker AI to help teachers, to have nurses, to help electricians. How are you going to do that? Well, you need to train these models on basic knowledge, but you also need to train them on use cases by the most experienced workers in that field, working with edge cases, difficult cases, and they're not going to produce that data unless you pay them. So the current environment where we say privacy doesn't matter data, you should give it as
much data to these companies because they're data hungry. That's actually destroying the future of the industry because these models are going to run out of high quality data, they're going to be produced, they're going to be trained on low quality data, and they're going to be more likely to create AI slope rather than the kind of high quality, reliable AI that we need across a range of occupations.
I guess just coming back to sort of where we started in the sense of the perspective of your twenty twenty three book, and one of the features of that you and Simon's book was the comparison with the Industrial Revolution and making the point that although we tend to say, oh, it was fine, we ended up with the productivity, and it made everyone better off. You were just pointing to how long the transition lasted, how incredibly costly that was for people, and how much it required active effort to
manage it and have a better outcome for people. One of the big differences, it seems, between that Industrial revolution and what we may see now in the next few years with AI is that the workers in the frame fundamentally are white collar workers. And in fact, Dario Medea's talked about half of entry level white collar work. It's quite a sort of safe number because he talks about this and then you could change you could change all the definitions, but half of the entry level white collar
jobs will be gone in five years. Does it fundamentally change the challenge for policymakers and even the sort of short term macroeconomic impact If the main workers affected are also white collar workers, they're possibly some of the better paid, greatest consuming members of society.
Well, first of all, yes, indeed, there's a lot of uncertainty about what that impact is going to be, but it's true that it's going to be on white color workers more than manufacturing workers. For the reasons that we talked about that these models cannot do physical work or
cannot be combined with physical work. Yet now white color workers are college educated, our leaders are college educated, so their plight might have a bigger impact on the political system than the plight of say, high school graduate or high school dropout workers did in the United States or the UK in the nineteen eighties, for example, So that's
a possibility. The second important issue is that the Industrial Revolution. Indeed, and this is very important because you hear this sort of grossy view over the Industrial Revolution from Silicon Valley all the time that everything worked that well, it took about one hundred years of pain and suffering before things started getting better. Well, we don't have that kind of time.
Our democracy wouldn't survive, and AI is advancing far too rapidly, so our political system needs to be much better and much faster at redirecting things and adjusting to things. So I think those are very important points for us to remember. But finally, I think it's also very important to recognize that the impact will not stop with white color workers, because if college graduates cannot get the jobs that they want to get, they're going to go and compete for
other jobs. They're not going to stay at home, they'll create wage downward, wage pressure and job displacement risk for other people, or they will all be pulled into sort of gig work, which then creates all sorts of other problems for the economy and for the labor market. So it's a systemic problem for the labor market as well.
Okay, I'm not sure that that was the most uplifting place to end, but it's been embracing but profoundly illuminating to me and clarifying conversation. Darren A Samoglu, thank you so much.
Thank you, Stephanie, it was great to talk to you.
Thanks for listening to Trumponomics from Bloomberg. It was hosted by me Stephanie Flanders. Trumponomics was produced by Samasadi and Versus, and sound design was by Blake Maple's and Aaron Casper. To help others find the show, please rate and review it highly wherever you listen.