Why AI Isn't Actually Boosting Productivity

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1:49Stephanie Flanders:I'm Stephanie Flanders, Head of Government and Economics at Bloomberg, and this is Trumponomics, the podcast that looks at everything in the economic world of Donald Trump. Today I wanted to dig into two seemingly conflicting stories about the global economy that are often embedded in the conversations on this show. I mean, one story that we hear often is that we live in an era of extraordinary innovation. New, more powerful AI models unveiled almost daily, and the US and China seemingly neck and neck in the race to dominate that transformative technology and reap the benefits. I mean, the challenge in that world is not that we have too little innovation, but too much and how we can absorb it into the economy without blowing up everything that we hold dear.

2:36Stephanie Flanders:But another story which we heard more often after the global financial crisis, but has definitely not gone away as one of stagnation, of declining competition in large chunks of the economy, more and more meddling by governments. And of course, we've heard that particularly in Europe. But I think if you think about the rise of monopoly power, more and more tariffs getting in the way of global trade, I mean, we've certainly seen all of that in Trump's American economy and not to mention the more direct involvement in the affairs of business that the president and his cabinet have engaged in. So I was interested to try and tease out some of these themes and try and get a sense of are we in a very innovative era that means we're now about to have loads of growth or actually potentially running the risk of something more negative.

3:28Stephanie Flanders:I wanted to discuss it with Carl Benedict Frey, who's the author of a book that came out last year, actually, How Progress Ends, Technology, Innovation and the Face of Nations. He's also an associate professor at Oxford University and often writes for the FT and other places. Carl, thank you very much for joining Trumponomics. Good to be with you.

3:56Stephanie Flanders:Before we get into this, I think it's probably helpful for people to understand what the argument of your book is, and specifically your thesis around how technological and economic progress happen and the patterns that countries have tended to go through. So the key theme of the book is that institutions need to adjust as technology moves along, and so you can grow for a long period of time just by adopting and scaling technology invented elsewhere. And so the Soviet Union did that quite successfully over four decades. It took ample advantage of the Ford Motor Company's open door policy and transfer that knowledge to the Soviet Union, build a vehicles industry of its own.

4:43And in the age of mass production, the Soviet system actually worked fairly well because when technology is mature, when production is fairly standardized, then you can hold factory managers accountable, essentially just by benchmarking performance. But as the returns to the mass production system petered out globally in the 1970s, and something new was needed for growth, that system did no longer work particularly well. Because when something is novel, when you're dealing with new technology, well, first of all, how do you benchmark performance? How do you hold factory managers accountable? And secondly, that new thing that was needed for further growth was the computer revolution to which Soviet contributions were essentially done.

5:33Why is that? Well, a big reason is that the Soviet Union provided very little room for decentralized exploration. And so if you were an engineer in the Soviet Union, you could go to the Red Army and ask for funding. If they decline, well, you maybe had two or three other options. If they decline, then your idea would die with you. And that's quite different from the American system of more decentralized finance, where Bessmer Venture famously declined to invest in Google back in 1999. They probably regret it today. But it also illustrates that Google was not a safe bet at the time. Alta Vista and Yahoo, they were dominating search.

6:15And so somebody needed to invest to show that Google would actually catch on. And the fact that Bessemer didn't invest, though, didn't mean the end of Google because others stepped in. And so when you explore new technology, you need more people exploring different potential technological trajectories. That's the exploration phase. But once you've settled on the prototype, then you need to scale that. And that scaling in the Soviet Union did reasonably well. But when that runs into diminishing returns, you need to get onto the new cycle. And so you need to move from more centralized consolidation to more decentralized system again.

6:59And it's those shifts in institutions that make progress very hard to sustain.

7:06Stephanie Flanders:If you just look at the title of your book first, anyone looking at that, particularly this year, How Progress Ends, Technology, Innovation and the Fate of Nations, you'd have to think, well, however it ends, it doesn't seem to be ending at the moment. So what's your take on the world we're looking at now? Are we at the end of progress or just the beginning of amazing progress? Well, the title of the book is not meant to suggesting that progress is inevitably about to end, it's more of a reflection of the fact that progress is unnatural, right? If progress was inevitable, it would not have taken 200 ,000 years to have an industrial revolution.

7:45If progress was inevitable, most of the world would be rich and prosperous. If progress was inevitable, Britain, the country where I live, would not have suffered two decades of productivity stagnation. So progress is clearly not inevitable, and it's clearly not inevitable even despite the acceleration we've seen in innovation that you mentioned, right? So if you look at patenting, if you look at scientific publications, any measure of inventive output, all those indicators are up. Yet if you look at the economy, it's pointing in a very different direction and even measures of research productivity and breakthrough innovation are down.

8:24So we're getting more in terms of scientific and inventive output, but it seems that we're getting less transformational output. And I think that's important to keep in mind when we're looking at the potential impact of the economy on artificial intelligence as well. Because in many ways, the computer revolution was more transformative than the AI we have today, right? The computer and the internet connected the best scientists and inventors around the world. It streamlined the research the process enormously. It gave us access to the world's door knowledge, essentially in our pockets. And what do we get out of that?

9:06Basically, a decade-long productivity upsurge, mostly confined to the United States. Now, I do think that AI will show up in the productivity statistics eventually. But the question is, by how much and for how long? And so any productivity upsurge, you'll take it. But I think if you believe that we are entering a new renaissance for economic growth, you're likely to be mistaken. Because AI is actually likely to give less of a productivity boost, even the computer revolution. And the reason is this. With AI, you still need verification. So AI automates a lot of knowledge work, but in the end of that, you need to verify the output.

9:58And so it's the time saving minus the time for verification. The computer revolution was different, right? I could sit around and wait for hours, days, even weeks for new material to arrive for me to start my research. And the internet gave me access to that instantaneously. And so it automated downtime. AI is not automating that downtime, it's automating the production, and you still need a verification as well.

10:24Stephanie Flanders:I'm sure we'll get on to some of the implications of AI and also sort of the bearing of some of your research on that, because you sort of look back at thousands of years of what has produced innovation and then what's then and translated that into growth. But just what you said about that sort of disconnect around, we've seen more invention at some level, lots more invention, lots more patenting and everything else. And yet seemingly at the micro level, at the real level of the economy, still quite a lot of low productivity and lack of translation of that technology into productivity. And thinking about that contrast between the US and China, there was a story that was told for many years are probably still being told about the innate advantages of the US in technological progress that was always around.

11:17Stephanie Flanders:China might be very good at copying things, but when it comes to actual innovation at the frontier, the US is always going to have an advantage over a sort of centralized system, China. There's quite a lot of support for that in your book. But I guess if you look all around, you would have to sort of say, well, maybe AI is going to be the exception, because they seem to be getting pretty close to the frontier on AI without having the kind of decentralized system that the US has. Well, I think, first of all, there clearly is innovation in China. But China is also a country of 1.4 billion people.

11:55So it would be absolutely extraordinary if there was no innovation going on in China whatsoever. But in addition to that, though, China is not as centralized as is commonly believed. And so unlike the Soviet Union, which also had a one-party system where every industry was managed centrally from Moscow, whether it was railroads or steel, the Chinese economy is much more decentralized and provinces and provincial governors and mayors have much greater autonomy than the Soviet counterparts would have had. And so what you have in China is a system where provincial governors are competing against each other based on often growth targets for promotion inside the one-party system.

12:51And so what that creates is essentially a tournament of political competition. that is very hard to replicate anywhere else. And so where Europe does industrial policy, it often tends to be anti-competitive, right? Where German rearmament essentially means plowing funds into Rhinometal. In China, on the other hand, industrial policy can often be pro-competitive because you have provinces seeding new firms that are competing against each other. And so I think that is often underappreciated part of the Chinese economy. That said, though, when you look at the firms which are leading in innovation in China, it's mostly startups.

13:39It's mostly privately funded firms. It's often foreign fund firms as well. And so in that sense, China is not that different from either Europe and the United States. What is different is that it doesn't have the rule of law. And so building political connections is more important in China because you need to have a seat on the table when priorities change, and priorities change from time to time. But perhaps somewhat paradoxically, people believed in the 2000s that China could become more like the United States. The opposite seems to be happening, and the United States today looks more like the political capitalism and that you have in China.

14:24And being on the right side of the current administration is important, which is probably one of the reasons that firms like OpenAI are saying that we are open to the government taking a 5 % stake in the company because if they are on your side, well, then you're on the right side of things from your perspective, at least.

14:46Stephanie Flanders:That's a quite important theme in your book, which is that ultimately has undermined in the past a country's position at the sort of cutting edge of innovation but also of growth and you have the example of the UK after the Industrial Revolution is that you have vested interests build up which slowly kind of resist disruption and competition and new entrants to industries which you then gradually lose your innovative edge. You seem to see scope for that happening both in the US and in China at the moment, but I guess for very different reasons. Yeah, and I think that's the natural order of things, right?

15:31So if you look historically, the leaders in bicycles didn't become leaders in automotive, although they tried, right? Legacy media companies did not lead the social media revolution. The legacy car companies did not lead in electric vehicles. The legacy retailers did not leading e-commerce. And so it's a clear pattern, the pattern that it's new firms that tend to develop new technology and new kinds of industries. And old industries have an incentive or every incentive to prevent that sort of competition. And so that's why we've seen the rise of killer acquisitions in the United States, for example, whereby incumbents buy up promising startups just to shut them down.

16:14That's why we see a revolving door between the US Patent and Trademark Office and some incumbents, whereby patent examiners grant them low-quality patents and then take on jobs for these firms in return. And that's all that is creating barriers to entry for new companies. And it helps explain why we've seen a decline in business dynamism, despite the fact that every technology from the personal computer, the internet, the cloud, and now AI, will have made it much cheaper to set up a company and operate a firm. And yet we're seeing less entry. And so those barriers are clearly at work in the US, and they were at work in China as well.

16:59Although in China you have the added component that the priorities of the CCP has changed over the past 15 years or so. from basically economic targets to targets that concern political means around self-sufficiency and national security, common prosperity, and so on. And what that means in the Chinese case is that that creates a greater reliance on state-owned enterprises because private firms are, for the most part, less keen on pursuing national, non -economic objectives. And by any measure, state-owned enterprises in China have been less innovative and less productive. And that, I think, is unlikely to change.

18:01Stephanie Flanders:This is the Bloomberg Tech Minute brought to you by ChatGPT. Now with ChatGPT work. I'm Carol Masser. Globetrotters hunting for airfare bargains are in for a rude awakening as the days of stumbling across a cheap seat on a popular flight could soon disappear. Bloomberg's Juan Ha reports that airlines from Delta to Virgin Atlantic are adopting artificial intelligence to change seat prices more quickly by weighing dozens of variables in real time, helping capture more revenue while shrinking pricing gaps that once allowed travelers to find bargain fares. Machine learning models can more accurately forecast demand by analyzing historical booking patterns, seed inventory, and seasonal trends, while also continuously tracking competitors' fares and capacity changes to update prices in near real time.

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19:25Stephanie Flanders:The system should just work better for everyone. That's exactly what the people at Optum are trying to do every day. They're a health care company linking patient care and pharmacy services and using data and technology to drive the whole system so care is connected, not complicated, for patients and providers. Things like making it easier to get care that looks at the whole person, from primary care doctors to mental health support and even in-home care, and then using technology to make sure they all work together. Technology designed to help doctors spend less time on busy work and more time with their patients.

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21:23Stephanie Flanders:One set of arguments against or points against your thesis would be in the realm of this time is different or that AI might be a different kind of technology. You make the example of the bicycles to cars and other sort of classic technological shifts. And, you know, one argument that you could make just looking at the way AI has evolved over the last few years and even the last few months is it seems like scale matters more than anything else in the development of this technology. And whereas in the past we might have thought that it was going to be about different innovations around different ways of doing things, the big developments in AI seem to be coming just from throwing a lot of resource at LLMs and that the gains go to the country that has the most energy and the most chips.

22:23Stephanie Flanders:And at the moment, certainly the most energy is China, doesn't currently have the most chips, but doesn't seem to suggest that you need a lot of innovation or sort of new entrants, because actually the new entrants can't necessarily afford to put in all these enormous fixed costs. So what's the chance that this is just a different kind of technology? It's going to be innovating itself if it's given enough resource to do it. So if what you said is correct, then clearly what I've written and said is wrong, right? And so I don't believe that that is the case, though. And so, you know, if the world was just a static distribution of events, you could probably, you know, brute force things, right?

23:01So more compute, more data would eventually get you there, right? But the world is not just a static distribution of events. It's changing all the time, right? And so my job today may be very different from my job tomorrow. Not all work is like that. And so there's a lot of things that's static, and you can automate that. But a lot of things also require some degree of resilience, right? And so if you use AI to manage supply chains, that might work quite well. And then all of a sudden you have a pandemic and you don't know what on earth is going on. And we see this even in more confined spaces, right?

23:39So most people know of AlphaGo that beat Lizzie Dole 41 back in 2016. And so by that time already, AI had achieved superhuman performance in Go. Few people know that actually just a couple of years ago, human amateurs using standard computers beat the best available Go programs quite easily by exposing them to new positions, new concepts that they would not have encountered in training. And so that raises a sort of fundamental question of, you know, even in cases where we achieve superhuman performance, we cannot be sure if that's actually going to be true tomorrow when circumstances change. And so humans, we are capable of learning from just a few examples.

24:32We are very, very data efficient. And I think you will have to get to AI that is capable of that, and that will need some innovation. And so right now, we don't really know what the path forward in AI looks like. It may be that large language models is the future of AI. It may be that small language models. It might be world models. It might be something entirely different. And so I think what we need is greater data efficiency. And that's clearly not something we're going to get through just through scaling. AI is still waiting for what I call the separate condenser moment because during this first industrial revolution, early on, steam engines, they were tremendously energy inefficient.

25:19They were basically just used to drain coal mines. They couldn't really be used for anything else. And it took the separate condenser to make them energy efficient, for them to be applied to transportation later on, railroads and steamships, etc. And I think AI is still waiting for that moment. And that's a question of further innovation, not just scaling.

25:39Stephanie Flanders:It's also a very good documentary about the AlphaGo experience. And it was this sort of classic example that stayed in people's minds of like when the humans have lost against this new technology. I was surprised to read in the book the idea that you actually had had a fight back by people who were using techniques that AlphaGo had not been able to surmise from what it was learning from. And it's sort of interesting to me that people are determined to think that humans are going to lose this race rather than jumping on those kind of examples. So it seems like you would, again, I mentioned at the start, there's this kind of dizzying array of new models of AI and then debates about how close the US is to China.

26:29Stephanie Flanders:when you're trying to think about AI's impact on the world and whether the US is ahead of China or behind China is that even the right way to think about it when we look at the sheer power of these models as a gauge to that or should we be looking at other aspects of their economies? So unless we get to a stage where some firm, you know, some company gets onto a curve where it really just pulls away from the rest, I don't think whether the US or China or OpenAR or Antropic or Google is three months ahead or not, if one place really pulls ahead, that could have a meaningful impact, but it's not clear that that's going to be within the space of large language models.

27:22It might be something else. And then there's the question of adoption. And obviously, in the end of the day, the use of a technology is what drives productivity. But there also the question is adoption for what? If people adopt it for email, it's not going to drive growth in a meaningful way, but it might look good in the sense that the adoption rate is high. But if people adopt it for scientific research in ways that develop new products and new technologies, that's obviously a different matter. And I think there, you know, incentives matter a lot. And I think that has some bearing on why we're in this sort of global productivity stagnation since the computer revolution, really.