AI goes economy-wide

0:01Patrick Coffey:Welcome back to the Barclays Brief podcast. It's Patrick here. Now, on this podcast series, we've talked about AI a lot. We've talked about robotics before. We've thought about the investment angle there, as well as use cases such as drones delivering pizzas into your back garden. But there's some new research out from the team as part of this year's Equity Guilt Study, which steps back and thinks about the much bigger picture, the macro and market implications of humanoid robotics as they start to enter the workforce. Now, I think it's pretty sci-fi. It's sort of weird to think about. And to put some numbers on it, our team frames this as a potential$200 billion market by 2035, with the possibility that humanoids could make up around 4 % of China's workforce.

0:52Patrick Coffey:So today we're going to think about AI and humanoid robotics through a pure economic lens, and there's no one better to talk about that than our chief economist, Christian Keller. Christian, thanks for coming back onto the Barclays Brief. Thanks for having me, Patrick. And let's hope it doesn't become too much of a theoretical economics lecture. Well, I'm sure our listeners would enjoy that, Christian. But let's start with a question about what you found most interesting. The most interesting thing I took away is that I believe that it's the combination of the generative AI together with that physical AI that we described in that study that John Hitz has done so much work of it, the humanoid robotics.

1:29That combination will actually allow us to really reap the full potential of productivity gains from AI, generally speaking.

1:38Patrick Coffey:Yeah. What I found so interesting is when I've looked at your work, but then I've looked across other pieces of research and the range is massive. How would you position your view in amongst all of the rest of the research that's been published on the impact of AI on productivity? Exactly. We didn't just want to add another point. I tell you, like you say, it's going from 0.1 % labor productivity, annual labor productivity increase, 0.1%, almost negligible from people like Acemolu, MIT professor, Nobel Prize laureate, all the way to 3%, 4 % or some even forecasting, an ever-increasing explosion of growth.

2:17And so our take here was, why is it so different? And could we identify what makes these studies so different? And not to go too far in the theoretical aspects, there are always different assumptions or so, but it seems that the key theory is that those who arrive with two very high aggregate productivity gains are the ones who take specific tasks where we already can see very high productivity increases from generative AI models and then just extrapolate them to the aggregate level. And the others who come up with lower ones are the ones who do general equilibrium models, meaning they see how the entire economy, the different sectors will adjust to productivity increases that may be quite uneven across sectors.

3:04Patrick Coffey:Yeah. So how do you think about it then? So what you do see is the first approximation approach. It's called the Hultons theorem, and it's done in economics. You look at certain sectors, you see what the productivity increase is from a task level, you build it to the sectoral level, then you weigh the sectors with their weight of GDP and you aggregate it. That's fair. And that gets you to the first round. The problem is if the productivity gains in sectors varies largely and you have an even distribution of these gains, will there be sectors that hold others back and how will they respond? So take legal, for instance.

3:41Patrick Coffey:What's the impact on the legal profession from AI? That's a good example. So you have legal where it's one of these sectors, very cognitive AI models have a very large impact. So we have a huge improvement in productivity, but that would reduce the price for legal services. What is a demand reaction to this? Will we increase our demand for legal services? And so it increases, or will it actually lead to a smaller share of legal services in GDP? That's relevant because if that share goes down in nominal terms, the productivity effect of that big improvement actually on an aggregate level goes down.

4:16And there are other sectors which may have very little impact from generative AI. Agriculture or something like that. Exactly. Like sectors that have the physical aspects of it that are not touched by generative AI. And they increase as a share in the economy, even though they're very low productivity. That phenomenon is real. It's called, you know, Baumol, an economist in the end 60s, basically documented this. That is held back productivity gains. It doesn't exterminate them. They're still there, but it slows them down quite significantly over time.

4:46Patrick Coffey:Yeah, that's Baumol's growth disease. Exactly. Okay, so that's a perfect time to think about humanoids, because we talked about agriculture, not really touched by Gen AI, although of course it is to a degree, but humanoids potentially could impact it in a much bigger way. How are you thinking about the impact of physical AI alongside Gen AI on productivity gains for the economy? We have a simple chart, a very good illustration, I think, in our paper, and that is a spiderweb chart. And it starts off with our fellow economists at Anthropic. They, with 22 occupation being on the chart, and they chart where they believe their models create the most exposure of these sectors.

5:29And as you would expect, legal services, finance, management, engineering, et cetera, huge exposure. But the spiderweb is very asymmetric. There are certain sectors, like you mentioned agriculture, but also in care services and maintenance, all these functions have very little exposure. So then we take what our work and in particular Zonica's team has shown where we believe physical AI will in the near future already create some significant exposure. If we then overlay the two, you suddenly get a spiderweb chart that's much more asymmetric and filled out, so to say. And that makes me think that should reduce then the negative Baumol, the normal growth disease and other effects where you have this unevenness among sectors because you've suddenly had a much wider spread productivity gains across all occupations.

6:17Patrick Coffey:Yeah. So if you put it all together, you've got gen AI combining with physical AI, humanoids particularly, touching more parts of the economy, boosting more productivity overall, which is good for the economy. It's a pretty optimistic view. The nuance is the impact on the labor force. People have worried about this for centuries, from Aristotle to Keynes, all predicted in different ways that machines would eventually replace human work. Yeah, that's a question economists have devoted a lot of time to. Why each time we get afraid of a new technology and then we end up really having unemployment actually going down over decades.

6:54What is found is that there are really two aspects of a new technology. The one is automation, where really jobs are being replaced and substituted away. And then there's also the augmentation part where people become much more productive in what they do. And typically you have also related to that, the creation of new jobs. You can see this over decades. This is why today we still don't have the mass unemployment due to automation after decades and decades of automation.

7:21Patrick Coffey:Yeah. So automation comes along, new technology comes along, demand for new skills goes up. How do you think about humanoid robotics on the labor force? If you're worried about a humanoid taking over, what do we as humans have that the humanoids don't have? If you look at still issues like dexterity, if you look at emotional intelligence, you could actually imagine that in a society that gets richer from the productivity gains of AI, there'll be certain jobs that people want a human and humans are still better than machines. And therefore, we believe that that picture where human jobs, in a way, are being eliminated, we're still very far away from this.

7:58Patrick Coffey:Okay. But can you kind of go back to, I don't know, say the 70s and through to today and in the future and think about the relationship between income and capital and how that shifted with automation and how you think that might shift over the next 10, 15 years? That's a very good point because we may not get mass unemployment and we may even see wages continue to grow, but in relation to what capital earns, that could change. And you mentioned the 70s, we have data from the 40s. And what you do see since the 70s with the waves of automation, with the robotics, industrial robotics, and with computerization, we have seen the share of labor as part of total income falling.

8:39And that shift has been ongoing now for several decades. And with that powerful combination of generative AI and physical AI, that could even accelerate. Yeah.

8:49Patrick Coffey:Okay. So if we put it all together, we've got a picture where in the next 10 years, AI and physical AI could boost productivity across lots of sectors, therefore the overall economy, but more of the income goes to capital. and at the same time, demand for things like electricity and commodities will clearly increase to fuel AI. Do we end up in a world, therefore, where inflation is less about wages and more about inputs like energy? And if so, what would that mean for things like interest rates? Interest rates is certainly a topic du jour. And theoretically, I think it makes sense to look at it from two components.

9:27There's a real interest rate and then there's the nominal, which relates inflation. Now, what our research suggests is that if we are right about the CapEx needs, and we see it already, and they were from AI, and it would probably be increased from physical AI, you have such a demand for capital that we think the R star, at least over the next five years or so, is unlikely to go down, but rather go up. So, higher real interest rates. When it comes to inflation, it is less clear, but what seems quite obvious, and we see it already, that there is such a demand now for commodities that, you know, what you call a commodity super cycle or not, there'd be probably cost pressures, price pressures from commodities and probably less so from wages.

10:10I think that is also something that is likely to play out.

10:14Patrick Coffey:Yeah. So we're not returning anytime soon to very low interest rates pre-COVID then? That really does not look likely from many perspectives, including fiscal, but in particular also from what we found in our study. Yeah, well, it's a great note. Thank you so much for joining us on the podcast today. I've learned a lot sitting opposite you again. So thanks for being back on the Barclays Brief. Thanks. Great pleasure. Okay, so it's been great to have Christian with me today. A few takeaways for me. Clearly, the advance of Gen AI in combo with humanoid robots looks to expand automation and augmentation potential across a wide range of cognitive and physical tasks in the economy.

10:56Patrick Coffey:and the associated benefits across sectors could unleash a significant acceleration of economy-wide productivity growth. But there will be an impact. It will likely further increase the share of capital income in the economy at the expense of labour income. And so rising productivity combined with large capital expenditure needs points ultimately to a higher neutral real interest rate. Thanks a lot for listening to The Barclays Brief. do hit subscribe and we'll be back again at the same time next week.