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James Manyika on Global Economic and Technological Trends
128 Plays3 years ago
James Manyika, Chairman and Director of the McKinsey Global Institute, joins us to discuss the rapidly evolving landscape of the modern global economy and the role of technology in it.
Topics discussed in this episode include:
-The modern social contract
-Reskilling, wage stagnation, and inequality
-Technology induced unemployment
-The structure of the global economy
-The geographic concentration of economic growth
You can find the page for this podcast here: https://futureoflife.org/2021/09/06/james-manyika-on-global-economic-and-technological-trends/
Check out the video version of the episode here: https://youtu.be/zLXmFiwT0-M
Check out the McKinsey Global Institute here: https://www.mckinsey.com/mgi/overview
Have any feedback about the podcast? You can share your thoughts here: www.surveymonkey.com/r/DRBFZCT
Timestamps:
0:00 Intro
2:14 What are the most important problems in the world today?
4:30 The issue of inequality
8:17 How the structure of the global economy is changing
10:21 How does the role of incentives fit into global issues?
13:00 How the social contract has evolved in the 21st century
18:20 A billion people lifted out of poverty
19:04 What drives economic growth?
29:28 How does AI automation affect the virtuous and vicious versions of productivity growth?
38:06 Automation and reflecting on jobs lost, jobs gained, and jobs changed
43:15 AGI and automation
48:00 How do we address the issue of technology induced unemployment
58:05 Developing countries and economies
1:01:29 The central forces in the global economy
1:07:36 The global economic center of gravity
1:09:42 Understanding the core impacts of AI
1:12:32 How do global catastrophic and existential risks fit into the modern global economy?
1:17:52 The economics of climate change and AI risk
1:20:50 Will we use AI technology like we've used fossil fuel technology?
1:24:34 The risks of AI contributing to inequality and bias
1:31:45 How do we integrate developing countries voices in the development and deployment of AI systems
1:33:42 James' core takeaway
1:37:19 Where to follow and learn more about James' work
This podcast is possible because of the support of listeners like you. If you found this conversation to be meaningful or valuable, consider supporting it directly by donating at futureoflife.org/donate. Contributions like yours make these conversations possible.
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Transcript
Revolutions in Human Labor
00:00:05
Speaker
Welcome to the Future of Life Institute podcast. I'm Lucas Perry. Today's episode is with James Meninka and is focused on global economic and technological trends as the agricultural and industrial revolutions both led to significant shifts in human labor and welfare. So too is the ongoing digital revolution driven by innovations such as big data, AI, the digital economy and robotics.
00:00:34
Speaker
also radically affecting productivity, labor markets, and the future of work.
AI's Societal Impacts
00:00:39
Speaker
And being in the midst of such radical change ourselves, it can be quite difficult to keep track of where we exactly are and where we're heading.
00:00:47
Speaker
While this particular episode is not centrally focused on existential risk, we feel that it's important to understand the current and projected impacts of technologies like AI and the ongoing benefits and risks of their use to society at large in order to increase our wisdom and understanding of what beneficial futures really consist of. It's in the spirit of this that we explore global economic and technological trends with James Meninka in this episode.
James Meninka's Credentials
00:01:17
Speaker
James received a PhD from Oxford in AI and robotics, mathematics, and computer science. He is a senior partner at McKinsey & Company, as well as chairman and director of McKinsey Global Institute. James advised the chief executives and founders of many of the world's leading tech companies on strategy and growth, product, and business innovation.
00:01:42
Speaker
and was also appointed by President Barack Obama to serve as Vice Chair of the Global Development Council at the White House. James is most recently the author of the book, No Ordinary Disruption, The Four Global Forces Breaking All the Trends. And it's with that, I'm happy to present this interview with James Meninka.
Global Challenges and Opportunities
00:02:12
Speaker
To start things off here, I'm curious if you could start by explaining what you think are some of the most important problems in the world today.
00:02:23
Speaker
Well, first of all, thank you for having me. Gosh, what are the most important problems in the world? I think we have the challenge of climate change. I think we have the challenge of inequality. I think we have the challenge that economic growth and development is happening unevenly. So I should say that the inequality question I think is most in inequality within countries, but to some extent also between countries. And this idea of uneven development is that some countries are surging ahead.
00:02:50
Speaker
and some parts of the world potentially being left behind. I think we have other socio-political questions, but I'm not qualified to talk about those. I don't really spend my time. I'm not a sociologist or political scientist, but I think we do have some socio-political challenges too.
00:03:06
Speaker
We have climate change, we have social inequality, we have the ways in which different societies are progressing at different rates. So given these issues in the world, what do you think it is that humanity really needs to understand or get right in this century given these problems?
00:03:25
Speaker
Yeah, but I should also before I dive into that also say, even though we have these problems and challenges, we also have incredible opportunities, quite frankly, for breakthrough progress and prosperity and to solve some of these issues and quite frankly, do things that are going to transform humanity for the better. So these are challenges at a time of, I think, unprecedented opportunity and possibility. So I just want to make sure we
00:03:52
Speaker
We acknowledge both sides of that issue. In terms of what we need to do about these challenges, I think part of it is also just, quite frankly, facing them head on. I think the question of climate change is one that is an existential challenge that we just need to face head on and, quite frankly, get on with doing everything we can, both to mitigate the effects of climate change and also, quite frankly, to start to adapt.
00:04:14
Speaker
how our society, our economy works to, again, address what is essentially a substantial challenge. I think what we do in the next 10 years is going to matter more than what we do in the 10 years after that. So there's some urgency to the climate change and climate risk question. I think with regards to the issue of inequality, I think this is one that is also within our capacity to address. I think it's important
00:04:38
Speaker
to keep in mind that I think the capitalism and the market economies that we've had and we do have have been unbelievably successful in creating growth and economic prosperity for the world in most places where they've been applied.
00:04:53
Speaker
Particularly in recent years, I think we've also started to see that, in fact, there's been growth in inequality, partly because the structure of our economy is changing and we can get into that conversation. So, in fact, some people are doing phenomenally well and others are not, and some places are doing phenomenally well and some other places are not.
00:05:13
Speaker
It's not lost on me. For example, Lucas, that even if you look at an economy like the United States, something like two-thirds of our economic output comes out of 6% of the counties in the country. That's an inequality of place, in addition to the inequalities that we have of people. So I think we have to tackle the issue of inequality quite head on. And unless we do something, it has the potential for getting to get worse before it gets
Economic Changes and Inequality
00:05:42
Speaker
better.
00:05:42
Speaker
The way our economy now works, and this is quite different by the way than what it might have looked like even as recently as 25 years ago, which is most of the economic activity, a function of the sectors that are driving economic growth, a function of the types of enterprises and companies that are driving economic growth, have tended to be much more today than they were 25 years ago to be fairly regionally concentrated and in particular places. So some of those places include Silicon Valley and other places.
00:06:12
Speaker
Whereas if you had looked, for example, 25 years ago, where you might have seen the kind of sectors and companies that were doing well, were much more geographically distributed. So you had more economic activity coming out of more places across the country than you do now. So this is just again a function of, not that anybody designed it to be that way, but just a function of the sectors and companies and the way our economy works.
00:06:38
Speaker
A related factor, by the way, is even the inequality question is also a function of how the economy works. It used to be the case that whenever we had economic growth and productivity growth, it also resulted in job growth and wage growth.
00:06:53
Speaker
That's been true for a very long time, but I think in recent years, depending how you count it, the last 25 years or so, when we have productivity growth, it doesn't lift up wages as much as it used to. Again, it's a function of the structure of the economy. In fact, some of the work we've been doing and other economists have been doing has actually been to look at this so-called declining labor share. I think a way to understand that declining labor share is to think about the fact that
00:07:23
Speaker
If you were to set up a factory, say 200 years ago, most of the inputs into how that factory worked were all labor inputs. So the labor share of economic activity is much higher. But over time, if you're setting up a factory today, sure, you have labor input, but you also have a lot of capital input in terms of the equipment, the machinery, the robots, and so forth. So the actual
00:07:47
Speaker
labor portion of as a share of the inputs is being grained down steadily. And that's just part of how our structure of our economy is changing. So all of these effects are some of what is leading to some of the inequality that we see. So before we drill more deeply into climate change and inequality, are there any other issues that you would add as some of the most crucial problems or questions for the 21st century?
00:08:17
Speaker
The structure of our global economy is changing, and I think it's now getting caught up also in kind of geopolitics. I'm not a geopolitical expert, but it's not lost from a global economy standpoint that, in fact, we now have and will have two very large economies, the United States and China, and China is a very large economy.
00:08:38
Speaker
It's not just a source of exports or things that we buy from them, but it's also intertangled with, say, the U.S. and other countries economically in terms of monetary and debt and lending and so forth. But it's also a large economy in itself, which is going to have its own consumption. So we now have for the first time
00:09:00
Speaker
two very large global economies. And so how that works in a geopolitical sense is one of the complications of the 21st century. So I think that's an important issue. Others who are more qualified to talk about geopolitics can delve into that one. But that's clearly in the mix as part of the challenges of the 21st century. We also, of course, are going to have to think about the role of technology in our economies, in our society.
00:09:25
Speaker
Partly because technology can be a force of massive productivity growth, innovation and good and all of that. But at the same time, we know that many of these technologies raise new questions about privacy, about how we think about information, disinformation. So I think, you know, if you were to write the list of
00:09:45
Speaker
the questions we're going to need to navigate in the coming decades of the 21st century. It's a BT list. Climate change is at the top of that list, in my view. Inequalities is on that list. These questions of geopolitics are on that
Shifts in Global Economic Centers
00:09:59
Speaker
list. The role that technology is going to play is on that list. And then also some of these social questions that we now need to wrestle with.
00:10:08
Speaker
issues of social justice, not just economic justice, but also social justice. So we have a pretty rich challenge list even while at the same time that we have these extraordinary opportunities.
00:10:21
Speaker
So in the realms of climate change and inequality and these new geopolitical situations and tensions that are arising, how do you see the role of incentives pushing these systems and problems in certain directions and how it is that we come up with solutions to them given the power and motivational force of incentives?
00:10:46
Speaker
Well, I think incentives play an important role. So take the issue of climate change, for example. I think one of the failures of our economics and economic systems is we've never quite priced carbon and we've never quite built that into our incentive systems, our economic systems, so we have a price for it. And so that when we put up carbon dioxide into the atmosphere and all and so forth, there's no economic price for that or incentives, a set of incentives not to do that. We haven't done enough in that regard.
00:11:15
Speaker
So that's an area where incentives would actually make a big difference. In the case of inequality, I think this one's way more complicated beyond just incentives, but I'll point to something that is in the realm of incentives with regards to inequality. So for example, take the way we were talking earlier about the importance of labor and capital in our capital inputs. I don't mean capitalists and the moneyness of it, just the actual capital equipment and machines and so forth in our system.
00:11:42
Speaker
We've built a set of incentives, for example, that encourage companies to invest in capital infrastructure, capital equipment, how they can write it off, for example. We encourage investments in R&D, for example, and tax incentives to do that, which is wonderful because we need
Evolving Social Contract
00:12:00
Speaker
that for the productivity and growth and innovation of our economy. But we haven't done anything
00:12:06
Speaker
nearly enough or equivalent to those kinds of incentives with regards to investments in human capital. You can imagine much more the productivity and growth and innovation of our economy, but we haven't done anything
00:12:21
Speaker
nearly enough or equivalent to that, those kinds of incentives with regards to investments in human capital. You can imagine a much more concerted effort to create incentives for companies and others to invest in human capital and be able to write off investments in scaling, for example, to be able to do that at the equivalent scale to the incentives we have for other forms of investment like in capital or in R&D. That's an example of where we haven't done enough on the
00:12:51
Speaker
incentive upfront, and we should. But I think there's more to be done than just incentives for the inequality question, but those kinds of incentives would help. I should tell you, Lucas, that one of the things that we spent the last year or so looking at is
00:13:09
Speaker
trying to understand how the social contract has evolved in the 21st century so far. We actually looked at, for example, the roughly 23 of the OECD countries, about 37 or 38 of them, but we looked at about 23 of them in detail just to understand how the social contract had evolved. Here, because we're not sociologists, we looked at the social contract in
00:13:33
Speaker
in really three ways, right? How people participate in the economy as workers, because that's part of, you know, people work hard, and public exchanges, and they get jobs, and they get income, and wages, and training. So people participating as workers is an important part of the social contract.
00:13:49
Speaker
people participating as consumers and households who consume products and services, and then people as savers who are saving money for their kids or for their future, etc. When you look at those three aspects of the social contract in the 21st century so far, it's really quite stunning. Take the worker piece of that, for example.
00:14:13
Speaker
What has happened is that across most countries, we've actually grown jobs despite the recession in 2001, but also the bigger one in 2008. We've actually grown jobs. There are actually more jobs now than there were at the start of the 21st century. However, what has happened is that many of those jobs don't pay as well. The wage associated with that picture has actually shifted quite a bit.
00:14:40
Speaker
The other thing about what's happened with work is it's becoming a little bit more brittle in the fact that job certainty has certainly gone down. There's much more income and wage variability. So we've created more fragile jobs relatively to what we had at the start of the 21st century. So you could say for workers,
00:15:01
Speaker
It's a mixed story, right? Job growth, yes. Wage growth, not so much. Job certainty and job fragility has gone up. When you look at people as consumers and households, it also paints an interesting story. And the picture you see there is the fact that
00:15:20
Speaker
If households and consumers are consuming things like buying cars, or white goods products, or electronics, or basically things that are globally competed and traded, the cost of those has gone down dramatically in the 21st century. The 21st century, in that sense at least, globalization has been great because it's delivered these very cheap products and services.
00:15:45
Speaker
But if you look at other products and services that households and consumers consume, such as education, housing, health care, and in some places, depending which country or place you're in, transportation, those have actually gone up dramatically, far, far higher and faster than inflation, far higher and faster than wage growth. In fact, if you are
00:16:08
Speaker
in the bottom half of the social income scale, those things have come to dominate your income in terms of what you spend money on. So for those people, it hasn't worked out so well, actually, to the social contract.
Future Economic Growth Drivers
00:16:22
Speaker
And then on the savers side,
00:16:24
Speaker
people as savers are, very few people now can afford to save for the future. And one of the things that you see is that the growth of indebtedness in the 21st century so far has gone up for most people, especially middle-wage and low-wage households and people. Their ability to save for the future has gone down. What's interesting is it's not just that the loves of indebtedness have gone up,
00:16:48
Speaker
But it's the fact that the people who are indebtedness look a little bit different. They look, they're younger. They're also, in many cases, college educated, which is different than what you might have seen 25 years ago in terms of who was indebted and what do they kind of look like.
00:17:04
Speaker
And then finally, just to finish, the 21st century, in the social contract sense, also hasn't worked out very well for women who still earn less than men, for example, and don't quite have the opportunity as much as others, as well as for people of color. It hasn't. They still earn a lot less. Employment rates are still much lower. Their participation in the economy as any of these roles is also much less. So you get a picture that says,
00:17:31
Speaker
While the economy has grown and capitalism is doing great, so far in a social contract sense, at least by these measures we've looked at, it hasn't worked out as well for everybody in the advanced economies. This is a picture that emerges from the 23 OECD countries that we've looked at, and the United States is on the more extreme end of most of the trends I just described.
00:17:55
Speaker
Emerging from this is a pretty complex picture, I think, of the way in which the world is changing. So you said the United States represents sort of the extreme end of this, where you can see the largest effect size in these areas, I would assume. Yet it also seems like there's this picture of the global east and south generally doing better off, like people being lifted out of poverty.
00:18:19
Speaker
Yeah, it is true. So one of the wonderful things about the 21st century is, in fact, close to a billion people have been lifted out of poverty in those roughly 20, 25 years, which is extraordinary. But we should be clear about whether that has happened.
00:18:34
Speaker
Those billion people are mostly in China, and to some extent, India. What we say with lifted people out of poverty, we should be very specific about mostly where that has happened. There are parts of the world where that hasn't been the case, parts of Africa, parts of other parts of Asia, and even parts of Latin America. This lifted people out of poverty has been relatively concentrated in China primarily, and to some extent in India.
00:19:03
Speaker
One of the things about economics, and this is something that people like Bob Solow and others got Nobel Prizes for, if you think about what is it that drives our economic growth? If economic growth is the way we create economic surpluses that we can then all enjoy and lead to prosperity, right?
00:19:21
Speaker
At some level, the growth disaggregation models come down to two things. Either you're expanding labor supply, or you are driving productivity. And the two of those, when they work well, combine to give you economic GDP growth. So if you look, for example, at the last 50 years, both across the advanced economies, but even for the United States,
00:19:44
Speaker
The picture that you see is that much of our economic growth has come roughly, so far at least, has come roughly in equal measure from two things. One, you know, this is over the last 50 or so years. Half of it has come from expansions in labor supply. You can think about it as a baby boomer generation, more people entering the workforce, etc., etc. The other half has roughly come from productivity growth.
00:20:09
Speaker
the two of them have combined to give us roughly the economic GDP growth that we've had.
Labor Market Transformation
00:20:15
Speaker
Now, when you look forward from where we are, we're not likely to get much lift from the labor supply part of it, partly because most advanced economies are aging. And so the contribution that's going to come from expansions in labor supply, much less. I mean, you can think of it as kind of
00:20:33
Speaker
a plane flying on two engines. If one engine has been expansion of the labor supply and the other is in productivity, well, the labor supply engine is kind of dying out or slowing down or in its output. So we're going to rely a lot more on productivity growth. And so where does productivity growth come from? Well, productivity growth comes from things like technological innovation, innovating how we do things and how we create products and services and all of that. And technology is a big part of that.
00:21:02
Speaker
But guess what happens? One of the things that happens with productivity growth is that the role of technology goes up. So I come back to my example of the factory. So if you wanted a highly productive factory, it's likely that your mix of labor inputs and capital inputs or read that as machinery and equipment is going to change. And that's why your factory 100 years ago looks very different than a factory today. But we need that kind of
00:21:30
Speaker
technological innovation and productivity to drive the output. Then the output leads to the output in the sector and ultimately the output in the economy. All of that is to say, I don't think we should stop the technological innovation that leads to productivity. We need productivity growth. In fact, going forward, we're going to need productivity growth even more.
00:21:51
Speaker
The question is, how do we make sure that even as we're pursuing that productivity growth, that contributes to economic growth, we're also paying attention to how we mitigate or address the impacts on labor and work, which is where most people derive their livelihoods.
00:22:09
Speaker
So I don't think you want to set up a steady system of incentives that slows down the technological innovation and productivity growth, because otherwise we're all going to be fighting over a diminishing economic pie. I think you want to invest in that and continue to drive that, but at the same time, find ways and think about how to address some of the
00:22:30
Speaker
work implications of that or the impacts on work and workers. And that's been one of the challenges that we've had. I mean, we've all seen the hollowing out, if you like, of the middle class in advanced economies like America, where a big part of that is that much of that middle class or middle income workers have been working in these sectors and occupations where the impact of
00:22:52
Speaker
technology and productivity have actually had a huge impact on those jobs and those incomes. And so even though we have work in the economy,
00:23:02
Speaker
The occupations and jobs and sectors that are growing have tended to be in the service sectors and less in places like manufacturing. I mean, it's the reason why I love it when, you know, politicians talk about good manufacturing jobs. I mean, they have a point in the sense that historically those have been good, well-paying jobs. But manufacturing today is only what? Eight percent of the labor force in America, right, is diminished at its peak is probably at best close to
00:23:32
Speaker
you know, the mid-40s, 40% as a share of the labor markets back in the 50s, right? It's been going down ever since, right? And yet the service sector economy has been growing dramatically. And many, not all, but many of the jobs in the service sector economy don't pay as much.
00:23:50
Speaker
My point is you have to think about not just incentives, but the structure of our economy. So if you look forward, for example, over the next few decades, what are the jobs that are going to grow as a function of both demand for that work in the economy, but also as a result of
00:24:08
Speaker
was less likely to be automated by technology and AI and so forth. You end up with a list that includes care work, for example, and so forth. And even work that we say is valuable, which it is, like teachers and others that are harder to automate. But our labor market system doesn't reward and pay those occupations as much as some of the occupations that are declining.
AI and Job Evolution
00:24:34
Speaker
So those are some of what, when I talk about the
00:24:37
Speaker
changes in the structure of our economy in a way that goes a little bit beyond just local incentives, is how do we address that? How do we make sure as those parts of our economy grow, which they will naturally, how do we make sure people are earning enough to live as they work in those occupations? And by the way, those occupations are many of the ones that
00:24:58
Speaker
in our current or recent COVID moment or period here, many of where the essential work and workers are, by the way, people we've come to rely on, mostly in those service sector economies that we haven't historically paid well. Those are real challenges.
00:25:14
Speaker
There's that really compelling figure that you gave at the beginning of our conversation where you said 6% of counties account for two thirds of our economic output. And so there's this change in dynamic between productivity and labor force. And the productivity you're suggesting is what is increasing. And that is related to and contingent on AI automation technology. Is that right?
00:25:42
Speaker
Well, first of all, we need productivity to increase. It's been kind of sluggish in the last several years. In fact, it's one of the key questions that economists worry about, which is how can we increase the growth of our economic productivity? It hasn't been doing as well as we'd like it to do. So we'd like to actually increase it, partly because, as I said,
00:26:03
Speaker
We needed more than we've done in the last 50 years because of the labor supply pieces declining. So we actually would like productivity to go up even more. Mike Spence and I just wrote a paper recently on the hopeful possibility that, in fact, we could see a revival in productivity growth coming out of COVID. We hope that happens, but it's not assured. So we need more productivity growth. And the way you get productivity growth, technology and innovation is a big part of it.
00:26:31
Speaker
The other part of it is just managerial innovation that happens inside companies and sectors where those companies and sectors figure out ways to organize and do what they do in innovative but highly productive ways. So the combination of technology and those kinds of managerial and other innovations, that's what drives it. Usually in a competitive context, that's what drives productivity. Does that lead to us requiring less human labor?
00:27:01
Speaker
It shouldn't. One of the things about productivity is it's actually, in some ways, labor productivity is a very simple equation. It has on the numerator value added output divided by hours worked, or labor input, if you like.
00:27:18
Speaker
So you can have what I think of as a virtuous version of productivity growth versus a vicious one. So let me describe the virtuous one. The virtuous one, which actually leads to job growth, is when in fact you expand the numerator. So in other words,
00:27:35
Speaker
There's innovations, use of technology, the ways that I talked about before, that lead to companies and sectors creating more valuable output, more of it and more valuable output. So you expand the numerator. So if you do that and you expand the numerator much higher and faster than you're reducing the denominator, which is the labor hours worked,
00:27:58
Speaker
you end up with a virtuous cycle in the sense that the economy grows, productivity grows, everything expands, the demand for work actually goes up, and that's a virtuous cycle. The last time we saw a great version of that was actually in the late 90s. If you recall before that, Bob Solid kind of framed what ended up being called the solo paradox, which is this idea that before the mid and late 90s, you saw computers everywhere except in the productivity figures.
The Role of Re-skilling
00:28:26
Speaker
Right. And that's because we hadn't seen the kinds of deployment of technology, the managerial innovations do the kind of what I call the numerator driven productivity growth, which when it did happen in the mid to late 90s, you created this virtuous cycle. Now, let me describe the vicious cycle, which is the
00:28:46
Speaker
if you like the not so great version of productivity growth, is when you don't expand the numerator, but what you do is simply reduce the denominator. In other words, you reduce the hours worked. In other words, you become very efficient at delivering the same output or maybe even less of the output. You reduce the denominator. That can lead to productivity, but it's of the vicious kind because you're not expanding the output, it's simply reducing the inputs or the labor inputs.
00:29:14
Speaker
Therefore, you end up with less employment, fewer jobs, and that's not great. And that's when you get what you asked about, which is where you need less labor. And that's the vicious version of productivity. You don't want that either. I see. How does the reality of AI and automation replacing human labor and human work essentially increasingly completely over time factor into and affect the virtuous and vicious versions of productivity?
00:29:43
Speaker
Well, first of all, we shouldn't assume that AI is simply going to replace work. I think we should think about this in this context of what you might call complements and substitutes. So if our AI technology is developed and then deployed in a way that is entirely substitutive of work,
00:30:04
Speaker
then you could have work decline. But there's also other ways to deploy AI technology where it's complementary and complements work. And in that case, you shouldn't have to think about as much about losing jobs. So let me give you some specifics on this.
00:30:21
Speaker
We've done research and others have too, but let me discover at least what we've done. But I think the general consensus is emerging that is close to what at least we found in our research, which is that, so we looked at, so the Bureau of Labor and Statistics kind of tracks in the US, tracks roughly 800 plus occupations.
00:30:39
Speaker
We looked at all those occupations in the economy. We also looked at the actual particular tasks and activities that people actually do. This is because any of our jobs and occupations are not monolithic. They're made up of several different tasks. I spent part of my day typing or talking to people or analyzing things so that we were all in an algorithm of different tasks. We looked at over 2,000 tasks
00:31:06
Speaker
that go into these different occupations. But let me get to where we ended up. So where we ended up was we looked at what current and expected AI technology and automated technologies can do. And we came to the conclusion that at least over the next couple of decades,
00:31:23
Speaker
at the task level, and I emphasize the task level, not the job level. These are tasks. I'll come back to jobs. At the task level, these technologies look like they could automate as much as 50% of the tasks and activities that people do. And it's important to again emphasize those are tasks, not jobs. Now, when you take those
00:31:46
Speaker
highly automatable tasks back and map them to the occupations in the economy. What we concluded was that something like, at most, 10% of the occupations look like they have all of their constituent tasks automatable. That's a very important thing to note. 10% of the occupations look like they have close to 100% of their tasks that are automatable. In what timeline?
00:32:15
Speaker
This is all the next couple of decades. Okay. Is that like two decades or? We looked at this over two decades, right? We have scenarios around that because it's very hard to be precise because you can imagine the rate of technology development speeding up. I'll come back to that. But the point is,
00:32:31
Speaker
It's only 10% of the—in our analysis anyway—10% of the occupations look like they have all of their constituent tasks that are automatable in that rough timeframe. But at the same time, what we also found is that something like 60% of the occupations have something like a third
00:32:50
Speaker
of their constituent tasks that are automatable in that same period. Well, what does that mean? What that actually means is that many more jobs and occupations are going to change than get fully automated away. Because what happens is, sure, some activity that I used to do myself, now that activity can be done in an automated fashion, but I still do other things too. So this effect of the jobs that will change is actually a bigger effect
00:33:20
Speaker
than the jobs that will disappear completely. Now, that's not to say there won't be any occupations that will decline. In fact, what we found in our research, and we ended up kind of titling the research report, Jobs Lost and Jobs Gained, we probably should have fully titled it Jobs Lost, Jobs Gained, and Jobs Changed, because all three phenomena will happen.
00:33:43
Speaker
Yes, there'll be occupations that will decline, but there'll also be occupations that'll grow actually, and then there'll be lots more occupations that will change. I think we need to take the full picture into account. It's a bit like, I guess, a good example of the jobs change portion is the one of the bank teller, which is, if you look at what a bank teller did in 1968 versus what a bank teller does now, it's very different. The bank teller back then,
00:34:12
Speaker
spent all their time counting money, either to take it from you or to give it back to you when you went up to the bank teller, or the advent of ATM machines, or the ATM machine automated much of that. We still have bank tellers today. The majority of their time isn't spent doing that. They may do that on an exception basis, but their jobs have changed dramatically. But there's still an occupation called a bank teller. In fact, until about, I think the precise date is something like 2006, I think,
00:34:41
Speaker
What we actually had was the number of bank tellers in the US economy had actually grown since the early 70s to about 2006. That's because the demand for bank tellers went up, not on a bank basis, but on a economy-wide basis, because we ended up opening up so many more branch banks by 2006 than we had in 1968.
00:35:04
Speaker
So the collective demand for banking actually drove the growth in the number of bank tellers, even though the number of bank tellers per branch might have gone down. So that's an example of where a growing economy can create its own demand for work back to this virtuous cycle that I was talking about, as opposed to the vicious cycle that I was talking about. So this phenomenon of
00:35:28
Speaker
jobs changing is an important one that often gets lost in the conversation about technology and automation and jobs.
Augmenting Human Work with AI
00:35:37
Speaker
And so to come back to your original question about substitutes, we shouldn't just think of technology substituting for jobs as the only thing that happens, but also that technology can complement work and jobs. In fact, one of the things to think about is, in particular for
00:35:54
Speaker
AI researchers or AI people who develop these automation technologies. I think on the one hand, while it's actually certainly useful to think of human benchmarks when we say how we build machines and systems that match human vision or human dexterity and so forth, that's a useful way to set goals and targets for technology development and AI development.
00:36:17
Speaker
But in an economic sense, it's actually less useful because it's less likely to lead to technologies that are more substitutes because we've built them to match what humans can do. Imagine if we said, let's build technology machines I can see around corners or do the kinds of things that humans can't do, then we're more likely in that case to build more complementing technologies and substituted technologies. I think that's one of the things that we should be thinking about and doing a heck of a lot more to achieve.
00:36:48
Speaker
This is very interesting. So you can think of every job as basically a list of tasks and AI technology can automate, say, some number of tasks per job. But then the job changes in a sense that either you can spend more time on the tasks that remain and increase productivity by just focusing on those tasks or the fact that AI technology is being integrated into the job process will create a few new tasks.
00:37:16
Speaker
The tension I see, though, is that we're headed towards a generality with AI where we're moving towards all tasks being automated. So perhaps over shorter timescales, it seems like
00:37:32
Speaker
we'll be able to spend more time on fewer tasks or our jobs will change in order to meet and work on the new tasks that AI technology demands of us. But generality is a movement towards the end of human level problem solving on work and objective related tasks. So it seems like it would be increasingly shrinking. Is that a view that you share? Does that make sense?
00:38:00
Speaker
observation makes sense. I don't know if I fully share it, but just to back up a step. Yeah, so if you ask me over the next few decades, I mean, our research has looked at the next couple of decades. Yeah. If journalism, because others have looked at this too, by the way, and come up with
00:38:17
Speaker
know, obviously slightly different numbers and views, but I think they're generally in the same direction that I just described. So if you say over the next couple of decades, what do I worry about? I certainly don't worry about the disappearance of work, for sure, but that doesn't mean that all is well, right? There are still things that I worry about, so I still worry about
00:38:37
Speaker
Well, we're going to have work because I think what we found, for example, is the net of jobs lost and jobs gained and jobs changed. The net of all of that in the economies that we've looked at is still a net positive in the sense that there's more work gained net than lost.
00:38:56
Speaker
That doesn't mean we should all then be less restaurant laurels and be happy that, hey, we're not facing a jobless future. So I think we still have a few other challenges to deal with. I want to come back to your future future AGI question in a second. So what are the things to worry about even in this
00:39:12
Speaker
state where I say don't worry about the disappearance of work. Well, there's still a few more things to worry about. I think you want to worry about the transitions, the skilled transition. So if some jobs are declining and some jobs are growing and some jobs are changing,
00:39:27
Speaker
All of that is going to create a big requirement for skilling and reskilling, either to help people get into these new jobs that are growing, or if their jobs are changing, gain the new skills that work well alongside what the tasks of the machines can do. So all of that says reskilling is a really big deal, which is why everybody's talking about reskilling now, although
00:39:51
Speaker
I don't think we're doing it fast enough or at scale enough at the scale and pace that we should be doing it. But that's one thing to worry about. The other thing to worry about are the effects on wages. So even when you have enough work, if you look at the pattern of the jobs gained, most of them, not all of them, but most, many of them, many of them are actually
00:40:12
Speaker
jobs that pay less, at least in our current labor market structure, right? So care work is, you know, hard to fully automate because it turns out that, hey, it's actually harder to automate somebody doing physical mechanical tasks than, say, somebody doing analytical work. But it turns out the person doing analytical work where you can probably automate what they do a lot easier also happens to the person who's
00:40:36
Speaker
earning a little bit more than the person doing the physical mechanical tasks. But by the way, that person is one that we don't pay much in the first place. So you end up with physical mechanical activities that are hard to automate, also growing and being demanded, but then we don't pay much for them. So the wage effects are something to worry about. And even in the example I gave you of complementing work,
00:41:00
Speaker
That's great from the point of view of people and machines working alongside each other, but even that has interesting wage effects too. Because at one end, which I'll call the happy end, and I'll come back to the challenged end, the happy end is when we automate some of what you do, Lucas, and the combination of what the machine now does for you and what you
00:41:23
Speaker
you still do yourself as a human. Both are highly valuable, so the combo is even more productive. This is the example that's often given with the classic story of radiologists. Machines can maybe read some of those images way better than the radiologist, but that's not all the radiologist does. There's a whole other value-added activities and tasks that the radiologist does that the machine reading
00:41:49
Speaker
that's understanding that MRI doesn't do, but now you've got a radiologist partnered up with a machine, the combination's great. So that's a happy example, probably the productivity goes up, the wages of the radiologist go up, that's a happy story. Let me describe the less happy end of that complementing story. The less happy end of that is when
00:42:11
Speaker
The machine automates a portion of your work, but the portion that it automates is actually the value added portion of that work. And what's left over is even more commoditized, commoditized in the sense that many, many, many more people can do it. And therefore, the skill requirements for that actually go down as opposed to go up because the hard part of what you used to do is not being done by a machine.
00:42:36
Speaker
The danger with that is that that then potentially depresses the wages for that work, even the way you're complementing. So even the complementing story I described earlier isn't always in one direction from a wage effect and its impact. So all of that to step back is to say the second thing to worry, if the first thing is rescaling, the second thing to worry about are these wage effects.
00:42:59
Speaker
And then the final thing to worry about how we think about redesigning work itself and the workflows themselves. So all of that is to say, even in a world where we have enough work, as in the next few decades, we still are going to have to work these issues. Now, you are posing a question about what about in the long, long future, because I actually think it's in the long future that we're going to have AGI.
Strategies for Workforce Adaptation
00:43:24
Speaker
I'm not one who thinks it's as imminent as perhaps others think.
00:43:28
Speaker
Do you have a timeline you'd be willing to share? No, I don't have a timeline. I just think that there are many, many hard problems that we still seem like a long way from. Now, the reason I don't have a timeline is that, hey, we could have a breakthrough happen in the next decade that changes the timeline. So we haven't figured out how to do causal reasoning. We haven't figured out how to do what Kahneman called System 2 activities. We've solved System 1 tasks, but we have System 2. So there's a whole bunch of things that we haven't solved issues of.
00:43:58
Speaker
how we do high-level cognition or meta-level cognition. We haven't solved through how we do meta-learning, transfer learning. There's a whole bunch of things that we haven't quite solved. Now, we're making progress on some of those things. Some of the things that have happened with these large language universal models is really breathtaking. But I think that in my view, at least the collection of things that we have to solve before we get to AGI,
00:44:25
Speaker
There's too many that still feel unsolved to me. Now, we could have somebody break through in a day. That's why I'm not ready to give a prediction in terms of timeline. But these seem like really hard problems to me. And many of my friends who are working on some of these issues also seem to think these are hard problems, although there are some of them who think that
00:44:44
Speaker
we're almost there, that all we need to do, deep learning will get us to most places we need to get to, and reinforcement learning will get us most of what we need. So those are my friends who think that, think that it's more imminent. Decade or two away sometimes, they say.
00:45:00
Speaker
Yeah, some of them say a decade or two. So there's a lot of real debate about this. In fact, you may have seen one of the things that I participated in a couple of years ago was Martin Ford put together a book that was a collection of interviews with a bunch of people. This is his book, Architects of Intelligence.
00:45:20
Speaker
and he had a wonderful range of people in that book. I was fortunate enough to be included, but there are many more people and way more interesting than me. People like Demisis Arbus and Yoshua Bengio and a whole bunch of people. It was a really terrific collection. One of the things that he asked that group who are in that book was to ask them to give a view
00:45:41
Speaker
as to when they think AGI would be achieved. And what came out of it is a very wide range from 2029, and I think that was Ray Kurzweil, who stuck to his date, and all the way to something like 500 years from now. And that's a group of people who are deep in the field, and you get that very wide range. So I think
00:46:03
Speaker
For me, I'm much more interested in the real things that we're going to need to break through. And I don't know when we'll make those breakthroughs. It could be imminent. It could be a long time from now. But there just seem to me to be some really hard problems to solve. But if you take the view, right, to follow your thought, if you take the view, the thought experiment to say, OK, let's just assume we truly achieve AGI in all its sense in both in the
00:46:30
Speaker
AGI in the, in some people say, in the oracular. I mean, it depends what form of the AGI it takes. If the AGI takes the form of both the cognitive part of that, coupled with the embodiment of that of physical machines that can physically participate, and you truly have AGI in a fully embodied sense as well, in a digital cognitive sense. What happens to humans at work in that case?
00:46:56
Speaker
I don't know. I think that's where presumably those machines allow us to create enormous surpluses and bounties in an economic sense. Presumably, we can afford to pay everybody to give everybody money and resources. Then the question is, in a world of true abundance, because presumably they will help us solve these machines, AGIs will help us solve those things. In a world of true abundance, what do people do in that?
00:47:24
Speaker
I guess it's kind of akin, as somebody said, to the Star Trek economy. What do people do in the Star Trek economy when they can replicate and do everything? I don't know. I guess we explore the universe. We do creative things. I don't know. I'm sure we'll create some economic system that takes advantage of the things that people can still uniquely do.
00:47:46
Speaker
even though they probably have a very different economic value and purpose. I think humans will always find a way to create either literally or quasi-economic systems of exchange of something or other.
00:48:00
Speaker
So if we focus here on the next few decades where automation is increasingly taking over particular tasks and jobs, what is it that we can do to ensure positive outcomes for those that are beginning to be left behind by the economy that require skill training and those whose jobs are soon to have many of the tasks automated?
00:48:27
Speaker
starting now, actually, in the next decade or two, I think there's several things. There's actually a pretty robust list of things we need to do, actually, to tackle this issue. I think one is just re-skilling. We know that there's already shortage of skills. We know, even before we think about
00:48:43
Speaker
We've had skill mismatches for quite a while before any of this fully kicks in. So this is a challenge we've had for a while. So this quest of reskilling is a massive undertaking. And here, the quest is really doing it at pace and scale. Because while there are quite a lot of reskilling examples, one can come across. And there are many of them that have been very successful. But I think the key thing to note about many of them, not all of them, but many of them,
00:49:10
Speaker
is that they tend to be small. One of the questions one should always ask about all the great re-skilling examples we hear of is how big is it?
00:49:19
Speaker
How many people went through that program? I think you'll find that many of them, not all of them, many of them are relatively small, at least small relative to the scale of the rescaling that we need to do. Now, there have been a few big ones. I happen to like, for example, you know, Walmart has had these Walmart academies. It's been written about publicly quite a bit. What's interesting about that is it's one of the few
00:49:42
Speaker
really large-scale rescaling, retraining programs through their academies. I can't remember for reading this, but they've put something like 800,000 people through those academies. I like that example simply because the numbers start to sound big and meaningful. Now, I don't know, I haven't evaluated the programs, but I think the scale is about right. The rescaling and scale is going to be really important, number one.
00:50:11
Speaker
The other thing we're going to need to think about is how do we address the wage question? Now, the wage question is important for lots of reasons here. One is, if you remember early in our conversation, we talked about the fact that over the last two decades, for many people, wages have been gone up in relative wage stagnation, relative compared to rates of inflation or the cost of living and how things have gone up.
00:50:36
Speaker
our wages haven't gone up. So the wage technician is one we already have before we think about technology. But then as we've just discussed, technology may even exacerbate that even when there are jobs. And the continuing changing structure of our economy will also exacerbate that. So what do we do about the wage question? So one could consider raising minimum wage, right? One could consider ideas like UBI. I mean, we can come back and talk about UBI
00:51:04
Speaker
I have mixed views about UBI. What I like about it is the fact that it's at least a recognition that we have a wage problem, that people don't earn enough to live. I like it in that sense. Now, the complication with it in my view is that
00:51:23
Speaker
Well, of course, one of the primary things that work does for you, for the vast majority of people, that's how they derive their livelihood, their income, so it's important. But work also does other things. It's a way to socialize, it's a way to give purpose and meaning, et cetera. I think UBI may solve the income part of that, which is an important part of that. You may not address the other pieces of the other things that work does. But we have to solve the wage problem.
00:51:51
Speaker
I think we also have to solve this geographic concentration problem. We did some work where we looked at all the counties in America. At the time that we did this, because the definition of what's a county in America changes a little bit year from year. But at the time that we did this work, which was back in 2019, I think we looked at something like 3,149 counties across America.
00:52:15
Speaker
And what we're looking at there was a range of factors about economic investment, economic vibrancy, jobs, wage growth. We looked at like 40 different variables in each county, but I'm just going to focus on one, which is job growth. So when we looked at job growth across those counties,
00:52:36
Speaker
While at the national level, we're all celebrating the job growth that had happened coming out of the 2008 recession and over the last between 2008 and 2018 was the data set we looked at. First of all, at the national level, it was great. But when you looked at it at the county level, what you suddenly found is that a lot of that job growth was concentrated in places where roughly a third of the nation's workers live.
00:53:05
Speaker
The other two-thirds of the place where people live either saw flat or no job growth or even continued job decline. All of that is to say, we also have to solve this question of how do we get more even job growth and wage growth across the country in the United States. We've also done similar work. We've looked at these microregions in Europe
00:53:29
Speaker
And you see similar patterns, although maybe not quite as extreme as the US, but you see similar patterns where some places get a lot of the job and wage growth and some places get less of it. It's just a function of the structure of our economy. So we'd have to solve that too. And then the other thing we need to solve is the classic case of the hollowing out of the middle class. Because if you look at the pattern of, you know, mostly driven by technology to some extent, the
00:53:56
Speaker
A lot of the job declines or the jobs lost as a result of technology have primarily been in the middle wage, middle class jobs. And a lot of the job growth has been in the low wage jobs. So this question of the hollowing out of the middle class is actually a really particular problem which has all kinds of
00:54:16
Speaker
social political implications, by the way. But that's the other thing to figure out. So let me stop
Challenges for Emerging Economies
00:54:22
Speaker
there. But I think these are some of the things we're going to need to tackle in the near term. And this is mostly made that list most in the context of, say, an economy like the United States. I think if you go outside of the United States and outside of the advanced economies, there's a different set of challenges. So I'm talking about places outside of the OECD countries and China.
00:54:44
Speaker
So you go to places like India and lots of parts of Africa and Latin America where you've got a very different problem, which is.
00:54:52
Speaker
demographically young populations. China isn't, but India and most of Africa is, and parts of Latin America are. They're the challenges a huge number of people are entering the workforce. The challenge there is, how do you create work for them? That's a huge challenge. When you look at those places, the challenge is just, how do you create enough jobs in very demographically young countries?
00:55:18
Speaker
And the picture's now gotten a little bit more complicated in recent years than perhaps in the past, because in the past, the story was, if you were a developing country, a poor developing country, your path to prosperity was to join the global economy, be part of either the labor supply or the cheap labor supply often, and go from being an agrarian country to an industrialized country,
00:55:43
Speaker
And then ultimately, maybe someday you'll become a service economy, which most advanced economies are. So that path of industrialization is less assured today than it used to be for a bunch of reasons. Some of those reasons have to do with the fact that advanced economies now no longer seek cheap labor abroad as much as they used to.
00:56:04
Speaker
Right. They still do for some sectors, but not less so for many other sectors. I mean, so we're less likely to do that. Part of that is technology, the fact that in some ways manufacturing has changed. We can now, going forward, do things more like 3D printing and so forth. So the industrialization path is less available to poor countries than it used to be. In fact, economists like Danny Roderick have written about this and called it this kind of
00:56:31
Speaker
premature de-industrialization challenge, which is facing many low-income countries. So we have to think about what is the path for those countries. And by the way, these are countries, if you think about it from the point of view of technology and AI in particular,
00:56:47
Speaker
The AI technological competition globally rapidly seems to have come down to be a race between the US, led by the US, but increasingly by China, and others are largely being left behind. That includes, in some cases, parts of Europe, but for sure, parts of the poor developing economies. The question is, in a future in which capacity for technology is developing at different paces, dramatically different paces for different countries,
00:57:17
Speaker
and the nature of globalization itself is changing, what is the path for these poor developing countries? I think that's a very tough question that we don't have very many good answers for, by the way, where people will think about developing economies and developing economies themselves. I think that's one of the tough challenges, I think, for the next several decades of the 21st century.
00:57:40
Speaker
I think that does a really good job of explaining some of these really significant problems. I'm curious what the most significant findings of your own personal work or the work more broadly being done at McKinsey are with regards to these problems and issues. I really appreciate some of the figures that you're able to share. So if you have any more of those, they're really helpful, I think, for painting a picture of where things are at and where they're moving.
00:58:06
Speaker
Well, I think on the question of these kind of left behind countries and economies, as I said, these are topics we're trying to research and understand. I don't think we have any kind of path, simple solutions to them. We do know, though, that in fact, some of the things, if you look at the pattern, a lot of our work is very empirical. We're typically looking at what has happened and what is actually happening on the ground. One of the things that you do see for developing economies is that
00:58:34
Speaker
the developing economies that are part of a regional ecosystem, either because of the value chains and supply chains. Take the case of a country like Vietnam. It's kind of in the value chain ecosystem around China, for example. It benefits from being
00:58:53
Speaker
a participant or an input into the Chinese value chain. When you have countries, and you could argue that's what's happened to countries like Mexico and a few others, there's something about being a participant in the value chains or supply chains that are emerging somewhat regionally, actually. That seems to be at least one path.
00:59:15
Speaker
The other path that we've seen is that when you've got developing countries that tend to have large and competitive private sectors and emphasize competitive, that actually seems to make a difference. So we did some empirical work where we looked at something like 75 developing countries over the last 25 years to see what are some of the patterns of
00:59:37
Speaker
which ones of those have done well to the growth and development and so forth. And some of the factors that you see we found in that research is, in fact, when the countries that happen to have, as I said, one is proximity to either participants in the global value chains of
00:59:54
Speaker
other large ecosystems or economies did well. Second, those that seem to have these large and vibrant and very competitive private sector economies also seem to do better. Then also those that had resource endowments did well, so that oil and natural resources and those kinds of things also seem to do well. Then we also find that those that seem to have more
01:00:21
Speaker
mixed economies. So they didn't just rely on one part of their economy, but they had two or three different kinds of activities going on in their economy. Then maybe a little bit of a manufacturing sector, a little bit of an agricultural sector, a little bit of a service sector. So the ones that had more mixed economies seemed to do well. And then the other big thing was the ones that seem to be reforming their economies seem to do well. So at least those are some patterns. I don't think those are
01:00:50
Speaker
guaranteed in any of them to be the recipe for the next few decades, partly because much of that picture around global supply chains is changing, and much of the role of technology and how it affects how people participate in the global economy is changing. So I think those are useful, but I don't know if they're an assured recipe going forward. There certainly have been the patterns for the last 25 years, but maybe that's a place to start if you look forward.
Global Economic Dynamics
01:01:17
Speaker
To pivot a bit here, I'm curious if you could explain what you see as the central forces that are currently acting on the global economy.
01:01:28
Speaker
Well, I'll tell you some of the things that are interesting, that we find interesting. One is, in fact, the fact that more and more and more, the role of technology in the global economy is getting bigger and bigger and bigger, in the sense that technology seems to become way more general purpose in the sense that it's foundational to every company, every sector, and every country. And so the role of that is interesting. And it also has these other
01:01:55
Speaker
outsize effects because we know that technology often lead to the phenomenon of superstar firms and superstar returns and so forth. So you see that quite a bit. So the role of technology is an important one.
01:02:10
Speaker
The other one that's going on is what's happening with globalization itself. And by globalization, I just mean the movement of value and activity related to the global economy. So we did some work a few years ago that we've tried to update regularly where we looked at all the things of economic value. So we looked at, for example, the flow of products and goods across the world, the flow of money, finances and other financing and other things.
01:02:36
Speaker
the flow of services, the movement of people, and even the movement of data and kind of data related activities. What was interesting is that one of the things that has changed is that the globalization in the form of the flow of goods and services, it actually kind of slowed down actually.
01:02:53
Speaker
And so that's why one of the reasons people are questioning is globalization dead, does it slow down? Well, it certainly looks that way if you're looking at it through the lens of the flow of products and goods, but not the case if you're looking at necessarily the flow of money, for example, not necessarily if you're looking at the flow of people. And for sure, not the case if you're looking at the flow of data around the world. One of the things that I think is underappreciated is just how digitized the global economy has become
01:03:21
Speaker
and just the massive amounts of digital data flows that now happen across borders between countries, and how much that is tied into globalization works. So if you're looking at globalization through the lens of digitization digital data flows, nothing is slowing down. In fact, if anything, it's accelerated, actually.
01:03:42
Speaker
That's why you often hear people who are looking at it through that lens say, oh, no, it's even more globalized than ever before. But people looking at it through the flow of products and goods, for example, might say, oh, it looks like it has slowed down. So that's one of the things that's changing. And it's also the globalization of digital data flows is actually interesting because one of the things that it does is it changed the participation map quite significantly. So we did some work where if you look at through that lens, you suddenly found that you have
01:04:12
Speaker
many more countries participating and many more kinds of companies participating, as opposed to just a few countries and a few companies participating in the global economy. You have much more diversity of participation. So you have very tiny companies, a two- three-person company in some country.
01:04:32
Speaker
plugged into the global economy using digital technology and digital platforms in ways that wouldn't have happened before if you were a two or three person company 30 years ago. So this digitization of the global economy is really quite fascinating. The other thing that's going on in the global economy is the rebalancing, if you like.
01:04:51
Speaker
where with the emergence of China is a big economy in its own right, that is changing the gravitational structure of the global economy in some very profound ways. In ways that we haven't quite had before, because
01:05:09
Speaker
Sure, in the past you've had other large economies like Germany and Japan and others, right, as large as you could, but none of them were ever as big as the United States. And also, all of them, whether it's Japan or Germany or any of the European countries, largely operated in a framework, a globalization of a global framework, that was largely kind of Western-centric in a way.
01:05:32
Speaker
But now you have this very large economy that's very different, is very, very large, will be the second largest economy in the world. That is quite different, but yet is tied into the blue. So that gravitational structural shift is very, very important.
01:05:49
Speaker
And then, of course, the other thing that's happening is what's happening with supply chains and global value chains. And that's interesting, partly because we're so intertwined with how supply chains and value chains work. But at the same time, it changes how we think about the resilience of economies. And we've just seen that during this COVID last year, where
01:06:11
Speaker
All of a sudden, everybody got concerned about the resilience of our supply chains with respect to essential products and services like medical supplies and so forth. So I think people are now starting to rethink about how do we think about the structure of the global economy in terms of these value chains.
01:06:28
Speaker
We should have at some point also mentioned other kinds of technologies that are happening because it's not all AI and digital technologies as much as I love that and spent a lot of time on that. I think other technological developments that are interesting include what's happening in biosciences or the life sciences. We've just seen spectacular demonstrations of that with these
01:06:52
Speaker
the mRNA vaccines that were rapidly developed. But I think a lot more has been happening with just amazing progress that we're still at the very early stages of with regards to the biotechnology and the life sciences. I think we're going to see even more profound, societally and profound impacts from those developments in the coming decades.
01:07:17
Speaker
So these are some of the things that I see happening in the global economy.
AI's Impact on Competitiveness
01:07:23
Speaker
Now, of course, climate change looms large over all of this as a thing that could really impact things in quite dramatic and quite existentially concerning ways. So in terms of this global economy, can you explain what the economic center of gravity is, where it's been and where it's going?
01:07:46
Speaker
Well, undoubtedly, the economic center of gravity has been the United States. If you look at the last 50 years, it's been the largest economy on the planet, largest in every sense, either as a market to sell into, as its own market. Everybody around the world for the last 50 years has been thinking about how do we access and sell into consumers and buyers in the United States. It's been the largest market.
01:08:13
Speaker
It's also been the largest developer and deployer of technologies and innovation. So in all of those ways, it's been the United States as the big gravitational pull. But I think going forward, that's going to shift because current course and speed, the Chinese economy will be as large and you now start to have even other economies becoming large too, like India. So I think
01:08:40
Speaker
You know, economic historians have created a wonderful map where they showed the movement of the gravitational center of the global economy. I think they went back a thousand years. And while it's been in the Western Hemisphere, primarily the United States, I think somewhere in the mid-Atlantic, it's been kind of been shifting east, most as the Chinese economy primarily, but also India and others, come to grow. So that's clearly one of the big changes going on in the global economy, so its structure and its center of gravity.
01:09:10
Speaker
With this increase of globalization, how do you see AI is fitting into and affecting globalization?
01:09:18
Speaker
You know, the impact on globalization, I don't think that's the way I would think about the impact of AI. Of course, it'll affect globalization because any time you, anything to do with products, goods, and services, because AI is going to affect all of those things. So to the extent that those things are playing out in the global economy landscape, AI will affect those things. I think the impact of AI, in my mind, is first and primarily about
01:09:45
Speaker
any economy, whether it's the global economy or a national economy or a company, right? So I think it's how it's profoundly going to change many things about how any economic entity works, right? Because we know it'll affect the capital labor inputs. We know it'll affect productivity. And we know it'll change the rates of innovation. Because imagine the kind, because, you know, while we, you know, in this conversation, at least we talked, I think, mostly about
01:10:15
Speaker
AI's impact on labor markets, we should not forget AI's impact on innovation, on productivity, on the kinds of creation of products, goods, and services that we can't imagine and how hopefully it's going to accelerate those developments. Deep mind deals with AlphaFold, which is cracking a 50-year problem that's going to lead to all kinds of hopefully biomedical innovations and other things.
01:10:43
Speaker
One of the big impacts is going to be how AI affects innovation and ultimately productivity and the kinds of things we're going to see with its products, goods and services that we're going to see in the economy. And so, of course, any economy that takes advantage of that and embraces those innovations will obviously see the benefit to the growth of their economy. And of course, if on a global scale, on a global stage in the global economy, we have some countries
01:11:10
Speaker
do that more than others, then of course it'll affect who gets ahead, who's more competitive, and who potentially gets left behind. So one of the other things we've looked at is what is the rate of AI participation, whether in terms of developments or contributing to developments or just simply deploying technologies or having the capacity to play technologies or having the talent and people who can either both
01:11:38
Speaker
contribute to the deployment or the development and also embrace it in companies and sectors. And you see a picture that's very
01:11:46
Speaker
different around the world. Again, you see the US and China, we're ahead of everybody. And you'll see some countries in Europe, and even Europe is not uniform, right? Some countries in uniform, in some countries in Europe, doing more of that than others. And then a whole bunch of others who are being left behind. So again, AI will impact the global economy in the sense of how it impacts each of the economies that participate and each of the companies that participate in the global economy.
01:12:14
Speaker
in their products and services and their innovations and outputs.
AI Governance and Safe Deployment
01:12:18
Speaker
There are other things that will play out on the global stage related to AI, but from an economy standpoint, I think I see it through the lens of the participating companies and countries and economies and how that then plays out on the global stage.
01:12:32
Speaker
So there's also this facet of how this technological innovation and development, for example, with AI and also technologies which may contribute to and mitigate climate change, all affect global catastrophic and existential risk. So I'm curious how you see global catastrophic and existential risks as potentially fitting into this
01:12:57
Speaker
evolution of the global economy of labor and society as we move forward.
01:13:05
Speaker
Well, I think it depends whether you're asking whether AI itself represents a catastrophic or existential risk. Many people have written about this, but I think that question is tied up with the view on how do we think about how close we are to AI or how close we are to AGI and how close we are to superhuman AI capabilities. And I think we're not
01:13:32
Speaker
As we discussed earlier, I don't think we're close yet, but there are other things to think about even as we progress in that direction. These include some of the safety considerations, the control considerations, and how we make sure we're deploying and using AI safely. We know that there's particular problems with regards to things like even with narrow AIs, it's sometimes called how do we think about
01:13:59
Speaker
reward and goal corruption, for example, how do we think about how we avoid the kind of interference, you know, catastrophic interference between tasks and goals? How do we think about that? So there are all these kind of safety-related things, even
01:14:17
Speaker
on our way to AGI that we still need to think about. So in that sense, these are things to worry about. I also think we should be thinking about questions of value and goal alignment. And these also get very complicated for a whole bunch of both philosophical reasons, but also quite practical reasons. That's why I love the work, for example, that Stuart
01:14:39
Speaker
Russell has been doing on how we think about human compatible AI and how do we build these kind of the kind of the value alignment and goal alignment that we should be thinking about. So these are even on our way to AGI of the safety control and these kind of value alignment and somewhat normative questions
01:14:59
Speaker
about how we think about normativity and what does it even mean to think about normative things in the case of value alignment with the AI. These are important things. Now, that's if you're thinking about catastrophic or at least existential risk with regards to AI, even way before you get to AGI, then you have the kinds of things that at that point that Nick Bostrom and others have worried about.
01:15:24
Speaker
I think because those are non-zero probability concerns, we should invest all effort into working on those existential, potentially catastrophic problems. But I'm not super worried about those anytime soon, but that doesn't mean we should invest and work on those kinds of concerns that
01:15:45
Speaker
that Nick and others write about. But there are also questions about AI governance in the sense of we're going to have many participating entities here. We're going to have the companies that are leading the development of these technologies. We're going to have governments that are going to want to participate and use and deploy these technologies.
01:16:05
Speaker
We've got to have issues around when to deploy these technologies, use and misuse. And many of these questions become particularly important when you think about the deployment of AI, especially in particular arenas. So imagine if once we have AI or AGI that's capable of
01:16:23
Speaker
manipulation, for example, or persuasion, or those kinds of things, or capabilities that allow us to detect lies, or be able to interfere or play with signals intelligence, or even cryptography and number theory. Cryptographic systems rely on a lot of things in prime number theory, for example, or if we think about arenas like autonomous weapons. Questions of governance become
01:16:51
Speaker
ever more important. I mean, they're already important now when we think about how AI may or may not be used for things like deepfakes and disinformation. The closer we get to the kinds of areas that I was describing here, it becomes even more important to think about governance and what's permissible to deploy where and how and how do we do that in a transparent way and how do we deal with the challenges with AI about attribution. One of the nice things about
01:17:21
Speaker
other potentially risky technologies or developments like nuclear science or chemical weapons and so forth. At least those things, they're easy to detect when they happen, and it's relatively easy to do attribution and verify that it happened and was used. It's much harder with AI systems.
01:17:45
Speaker
So these questions of governance and so forth become monumentally important. So those are some things we should think about.
01:17:53
Speaker
How do you see the way in which the climate change crisis arose given human systems and human civilization? So what is it about human civilizations and human systems that has led to the climate change crisis? And how do we not allow our systems to function and fail in the same way with regards to AI and powerful technologies in the 21st century?
01:18:18
Speaker
I'm not an expert on climate science, by the way, so I shouldn't speculate as to how we got to where we are. But I think the way we view certain technologies and fossil fuels, I think is a big part of that. The way our economies have relied on that is our only mode of energy and is part of that. And the fact that we've done that in a relatively
01:18:41
Speaker
costless way in terms of pricing the effects on our environment and our climate, I think is a big part of it. And the fact that we haven't had very many as effective and as efficient alternatives, historically, I think is a big part of that. So I think all of that is part of how we got here in some ways. But I think others more expert than me should can talk about that. I think if I think about AI, I think one of the things that
01:19:07
Speaker
is potentially challenging about AI, if in fact we think there's a chance that we'll get to these superhuman capabilities and AGI, is that we may not have the opportunity to iterate our way there. I think quite often with a lot of these deployment technologies, I think a practical thing that has served us well in the past has been this idea that
01:19:31
Speaker
you know, well, let's try a few experiments. We'll fix it if it fails or if it doesn't work and we'll iterate and do better and kind of iterate our way to the right answer.
01:19:41
Speaker
Well, if we believe that there is a real possibility of achieving AGI, we may not have that opportunity to iterate in that same way. And so that's one of the things that's potentially different, perhaps, because we can't undo that, as it were, if we truly get to AGI.
01:20:03
Speaker
think about these existential things. So maybe in that sense, there's maybe something of a similarity or at least analog with climate change is that we can't just undo what we've done, at least in a very simple fashion. Look at how we're now thinking about how do we do carbon sequestration? How do we take carbon out of the air? How do we undo these things? And it's very hard. It was easy to go in one direction. It's very hard to go in the other direction.
01:20:32
Speaker
And in that sense, at least, it's always dangerous with analysis, but at least in that sense, AI on its way to AGI may be similar in that sense, which is we can't always quite get to undo it in a simpler fashion.
Balancing AI's Economic and Social Impacts
01:20:50
Speaker
Are you concerned or worried that we will use AI technology in the way that we've used fossil fuel technologies such that we don't factor in the negative effects or negative externalities of the use of that technology? So if with AI there is this deployment of
01:21:06
Speaker
single objective maximizing algorithms that don't take account all of our other values and that actually run over and increase human suffering. For example, the ways in which YouTube or Facebook algorithms work to manipulate and capture attention. Do you have a concern that our society has a natural proclivity towards learning from mistakes, from ignoring negative externalities until it reaches sort of a critical threshold?
01:21:34
Speaker
I do worry about that, and maybe this to come back to one of your central concerns back to the idea of incentives. I do worry about that in the sense that
01:21:45
Speaker
They're going to be such overwhelming and compelling incentives to deploy AI systems for both good reasons and for the economic reasons that go with that. So there are lots of good reasons to deploy AI technology. It's actually great technology. Look at what it's probably going to do in the case of health science and breakthroughs we could make there in climate science itself.
01:22:10
Speaker
scientific discovery and materials. So there's lots of great reasons to get excited about AI and I am, because it'll help us solve many, many problems, could create enormous bounty and benefits for our society. So people are going to be racing ahead to do that for those reasons, for those very good and very compelling reasons. There are also going to be a lot of very compelling economic reasons, the kinds of
01:22:39
Speaker
innovations that companies can make, the kind of contributions to their economic performance of companies, the kinds of
01:22:48
Speaker
economic benefits in the possibility that AI will contribute to productivity growth, as we talked about before. So there's lots of reasons to want to go full steam ahead, and a lot of incentives will be aligned to encourage that, right? Both the breakthrough innovations that are good for society, as I said, the benefits that companies will get from deploying and using AI into the innovations, the economy-wide productivity benefits, so all good reasons.
01:23:16
Speaker
And I think in the rush to do that, we may in fact find that we're not paying enough attention, not because anybody's...
01:23:23
Speaker
is out of malice or anything like that, but we just may not be paying enough attention to these other considerations that we should have alongside considerations about what does this mean for bias and fairness? What does it mean for potentially for inequality? We know these things have scale superstar effects. What does that mean for others who get left behind? What does this mean for the labor markets and jobs and so forth? I think we're going to need to find mechanisms to make sure that
01:23:53
Speaker
there's continued but substantial effort at those kind of other sides of the side effects of, you know, of AI and some of the unintended consequences. That's why at least I think
01:24:05
Speaker
Many of us are trying to think about this question of what are the things we have to get right? Even as we race towards all the wonderful things we want to get out of it, what are the other things we need to make sure we're getting right along the way? And how do we make sure these things are, people are working on them, they're funded, there's support for people working on these other problems. I think that's going to be quite important and we should not lose sight of that. And that's something I'm concerned about.
Ensuring Fairness in AI
01:24:34
Speaker
So let's pivot here then into inequality and bias. Could you explain the risk and degree to which AI may contribute to new inequalities or exacerbate existing inequalities?
01:24:51
Speaker
Well, I think on the inequality point, it's part of what we talked about before, right? Which is the fact that even though we may not lose jobs in the near term, we may end up with a creating jobs or complementing jobs in a way that have these wage effects that could worsen the inequality question. So that's one way in which AI contribute to inequality. The other way, of course, is the fact that
01:25:17
Speaker
Because of the scale effects of these technologies, you could end up with a few companies or a few entities or a few countries having the ability to develop and deploy and get the benefits of AI when the other companies or countries and places don't. So you've got that kind of inequality concern. Now, some of that could be helped, by the way, as it is, because as you know, it has been the case so far that
01:25:45
Speaker
The kind of compute capacity needed to develop and deploy AI has been very, very large, and the data endowments needed to train algorithms has been very, very high. But we know the talent of people who are working on these things has been up until now relatively concentrated. But we know that that picture is changing. I think the advent of cloud computing, which makes it easy for those who don't have the
01:26:09
Speaker
The compute capacity is helping that. The fact that we now have ways to train algorithms, either with pre-trained algorithms or other universal models and others, so that not everybody has to retrain everything every single time. So these capacities and these kind of scale constraints, I think in those particular ones, will get better as we go forward. But you do worry about those
01:26:31
Speaker
inequality both in a people sense, but also in a entity sense, where entities could be companies, countries, or whole economies. I think the questions of bias are a little bit different. I think the set of questions of biases just simply have to do the fact that up until now, it is so far anyway, most of the data sets that have been used to train these algorithms often come with societally derived biases.
01:26:56
Speaker
I emphasize the society to drive by just because of the way we collect data and the data that's available and who's contributing to it so often you start out with.
01:27:06
Speaker
training data sets that reflect society's existing biases. Not that the technology itself has introduced the bias, but in fact these come out of society. And so what the technologies then do is kind of bake these biases in into the algorithms and probably deploy them at scale. So that's why I think this question of bias is so important.
01:27:28
Speaker
But I think often it gets conflated with the fact that, well, proponents of using these technologies will say, but humans already have bias in them anyway, right? We already make biased decisions, et cetera. So, of course, that's a two-sided conversation. But at least in the case, the difference that I see between the biases we have already as human beings versus
01:27:52
Speaker
the biases that could get baked into these systems is that these systems could get deployed at scale. In a way that if I have biases that I have and I'm in a room and I'm trying to hire somebody and I'm making my biased decisions, at least hopefully that only affects that one hiring decision. But if I'm using an algorithm that has these things baked in and hundreds of millions of people are using the algorithm, then we're doing that at scale.
01:28:20
Speaker
We need to keep that in mind as we have the debate about people already have biases and exaggerated biases. That's true. So we need to do work on that. But one of the things I like about the bias question, by the way, that these technologies are forcing us to confront is that
01:28:36
Speaker
It's actually forcing us to really think about what do you even mean when we say things are fair, quite aside from technology. I think they're forcing us, just like the UBI debate is forcing us to confront the question that people don't earn enough to live, the bias question is also forcing us to confront the question of what is fair?
01:28:55
Speaker
Because what counts as fairness? I think all too often, I think in our society, we've tended to rely on proxies for fairness. So when we can't define it, we'll say, well, let's constitute the right group of people, a diverse enough group of people, and we will trust the decision that they make because it's a diverse group of people. So yeah, if that group is diverse in the way we expect, then gender or racial or any other social income terms,
01:29:25
Speaker
and they make a decision, we'll trust it because the deciding group is diverse. That's just a fairness by proxy in my view, right? Who knows what those people actually think and how they make decisions that's all simple matter, but we trust it because it's a diverse group. The other thing that we've tended to rely on is we trust the process, right? So if we trust the process that, hey, if it's gone through a process like this,
01:29:50
Speaker
we will live with the results because we think that a process like that is fair and unbiased. Who knows whether the process is actually fair and that's how we're typically done with our legal system for the most part. If you've been given due process and you've gone through a jury trial, then it must be fair, we'll live with the results. But I think in all of those cases, while they're useful constructs for us as society, they still somewhat avoid defining what is actually fair.
01:30:20
Speaker
And I think when we start to deploy technologies where in the cases of AI, the process is somewhat opaque because we have this kind of explainability challenge with these technologies. So the process is kind of black boxy in that sense. And if we automate the decisions with no humans involved, then we can't rely on this constituent group that, hey, a group of people decided this so it must be fair.
01:30:46
Speaker
This is forcing us to come back to the age-old or even millennia-old question of what is fair? How do we define fairness? I think there's some work that was done before with somebody who's trying to come up with all kinds of definitions of fairness, and they came up with something like 21. So I think we're now having an interesting conversation about what constitutes fairness. I think
01:31:09
Speaker
Do we gather data differently? Do we code differently? Do we have reviews differently? Do we have different people develop the technologies differently? Do we have different participants? So we're still grappling with this question of what counts as fair. I think that's one of the key questions of this technology as we rely more and more on these technologies to assist, in some cases, eventually take over some of our decision-making, of course, only when it's appropriate,
01:31:39
Speaker
These questions will continue to persist and will only grow on how we think about fairness and bias.
Inclusion in AI Development
01:31:45
Speaker
So in terms of fairness, bias, equality and beneficial outcomes with technology and AI in the 21st century, how do you view the need for and path to integrating developing countries' voices in the use and deployment of AI systems?
01:32:05
Speaker
Well, I don't know if there's any magical answers, Lucas. At some level, at a base level, we should have them participate. I think any participation, both in the development and deployment, I think is going to be important. And I think that's true for developing countries. I think it's true for parts of even US society that's often not participating in these things.
01:32:33
Speaker
striking to me how the lack of diversity, in diversity in every sense of the term, there is in who is developing AI and who's deploying AI, whether you look within the United States or around the world.
01:32:48
Speaker
entities and places and communities and whole countries that are not really part of this. So I think we're going to need to find ways to do that. And I think part of doing that is, at least for me, starts out the recognition that capability and intelligence are equally distributed everywhere. I don't think there's any one place or country or community that has a
01:33:07
Speaker
natural advantage to capability and intelligence. So on that premise, we just need to get people from different places participating in the development and deployment, and even the decision making that's related to AI, and not just go with the places where the money and the resources happen to be, and that's who's racing ahead, both within countries, e.g. in the United States itself,
01:33:32
Speaker
or in other countries that are being left behind. So I think participation in these different ways, I think it's going to be quite important. If there's anything you'd like to leave the audience with in terms of perspective on the 21st century, on economic development and technology, what is it that you would share as a takeaway?
Opportunities and Challenges of Technological Advancements
01:33:56
Speaker
Well, I think when I look ahead to the 21st century, I'm in two minds. On the one hand, I'm actually incredibly excited about the possibilities. I think we're just at the beginning of what these technologies, both in AI and so forth, but also in the life sciences and biotech. I think the possibilities in the 21st century are going to be enormous. Possibilities is for both improving human life
01:34:23
Speaker
improving economic prosperity, growing economies. The opportunities are just enormous, whether you're a company, whether you're a country, whether you're a society. The possibilities are just enormous. I think there's more that lies ahead than behind.
01:34:41
Speaker
At the same time, though, I think alongside pursuit of those opportunities are the really complicated challenges we're going to need to navigate through. So even as we pursue the opportunities that AI and these technologies are going to bring us, we're going to need to pay attention.
01:34:57
Speaker
to some of these challenges that we just talked about, these questions of potentially inequality and bias that comes out of the deployment of these technologies, some of the superpower effects that could come out of that. Even as we pursue economic opportunities around the world, we're going to need to think about what happens to poor developing countries who may not keep up with that or be part of that. So in every case, for all the things that I'm excited about about the 21st century, which there's plenty,
01:35:25
Speaker
There are also these challenges along the way we're going to need to deal with.
Balancing Growth with Social Justice
01:35:31
Speaker
And also, the fact that society, I think, demands more from all of us. I think demands for a more equal and just society are only going to grow. The demands or desires to have a more inclusive and participative economy are only going to grow as they should.
01:35:50
Speaker
So we're going to need to be working both sets of problems, pursuing the opportunities, because without them, these other problems only get harder, by the way. I mean, try solving inequality when there's no economic surpluses, right? Good luck with that. So we have to solve both. We can't pick one side or the other. We have to solve both. At the same time, I think we also need to
01:36:13
Speaker
deal with some of the potentially existential challenges that we have and may grow. I mean, we we are living through one right now. I mean, we're going to have more pandemics in the future than we have perhaps in the past. So we just need to be ready for that. We've got to deal with climate change. And these kind of public health climate change issues, I think, are
01:36:35
Speaker
global. Therefore, all of us, these are not these are no chances for any one country or any one community. And we have to kind of work on all of these together. So that set of challenges, I think, is for everybody, for all of us. It's on planet Earth. So we're going to need to work on those things, too. So that's kind of at least how I think about
01:36:56
Speaker
what lies ahead. We have to pursue the opportunities. There's tons of them. I'm very excited about that. We have to solve the challenges that come along with pursuing those opportunities. And we have to deal with the things that these collective challenges that we have. I think those are all things to look forward to. Wonderful, James. Thank you so much.
Accessing James' Research
01:37:16
Speaker
It's really interesting and perspective shifting. If any of the audience is interested in following you or checking your workout anywhere, where are the best places to do that?
01:37:25
Speaker
If you search my name and search the McKinsey Global Institute, you'll see some of the research and papers that I referenced. And for those who love data, which I do, these are very data rich kind of fact-based perspectives. So just look at the McKinsey Global Institute website. All right. Thank you very much, James. You're welcome. Thank you.
01:38:08
Speaker
Thanks for joining us.