David Autor is Ford Professor of Economics and associate department head of the Massachusetts Institute of Technology Department of Economics. He is also Faculty Research Associate of the National Bureau of Economic Research, Research Affiliate of the Abdul Jameel Latin Poverty Action Lab, Co-director of the MIT School Effectiveness and Inequality Initiative, Director of the NBER Disability Research Center and former editor in chief of the Journal of Economic Perspectives. He is an elected officer of the American Economic Association and the Society of Labor Economists and a fellow of the Econometric Society.
Katya Klinova directs the strategy and execution of the AI, Labor, and the Economy Research Programs at the Partnership on AI, focusing on studying the mechanisms for steering AI progress towards greater equality of opportunity and improving the working conditions along the AI supply chain. In this role, she oversees multiple programs including the AI and Shared Prosperity Initiative.
Ioana Marinescu is assistant professor at the University of Pennsylvania School of Social Policy & Practice, and a Faculty Research Fellow at the National Bureau of Economic Research. She studies the labor market to craft policies that can enhance employment, productivity, and economic security. Her research expertise includes wage determination and monopsony power, antitrust law for the labor market, the universal basic income, unemployment insurance, the minimum wage, and employment contracts.
You can watch a recording of the event here or read the transcript below. Slides: David Autor – Katya Klinova
Welcome to all the human and artificial intelligences around the globe, who have joined us for today’s webinar on the governance and economics of AI. I’m Anton Korinek. I’m an economist at the University of Virginia and a Research Affiliate at the Centre of the Governance of AI, which is part of the Oxford Future of Humanity Institute and is hosting the event. Let me thank the Centre and in particular Markus Anderljung and Anne le Roux, for their support.
Our presenter today is David Autor, Ford Professor of Economics at MIT. David has earned so many honors and awards that I could not list them if I used the entire webinar, so let me just say that he is the world’s top authority when it comes to analyzing the effects of automation on the labor market. As discussants, we have Katya Klinova from the Partnership on AI and Ioana Marinescu from the School of Social Policy at Penn who will share their comments on David’s presentation. I will tell you a little more about them when they take the stage to give us their comments.
The topic of our webinar is “The Work of the Future: Building Better Jobs in an Age of Intelligent Machines” – and the title in fact reflects the title of a report that David released late last year at the MIT Task Force on the Future of Work, which he is co-chairing. The task force was formed by the President of MIT three years ago to analyze the relationship between emerging technologies and work, to help shape the public discourse around realistic expectations of technology, and to explore strategies to enable a future of shared prosperity. I will leave it to David to tell you more about the task force and the report.
But before handing over the mic to David, let me perhaps emphasize a point that we are particularly interested in at the Future of Humanity Institute and that I hope you can speak to, David:
Your report reflects a really comprehensive reading of the labor market developments that were triggered by automation in recent decades and, in some sense, you are suggesting that the coming decades will kind of continue that trend.
And indeed, with the narrow AI systems that we have in today’s world, that view may be well-justified and based on that, I believe that your report will be really an excellent guide for policy for perhaps the next decade.
But now let me do something that today’s narrow AI system cannot do, but we humans are very good at: to speculate about the future using just a tiny bit of data and a highly abstract meta model of the world.
So if we extrapolate based on our technological trajectory, our currently narrow and brittle AI systems are becoming broader and more robust, and in the next few decades, they may well reach a point where they surpass human capabilities in substantially all domains. So economically speaking, employing humans may be a dominated technology then, just like we don’t use horses for transportation anymore. Now this would be a marked rupture from the past, but it would not be the first time that human technology has fundamentally altered the course of history.
I am afraid that we may lull ourselves in a little bit of a false sense of security if we do not consider this possibility when we speak about the Future of Work. And so I would really appreciate it if you can include your thoughts on this possibility in your presentation.
And now without further ado, the floor is yours, David!
Thank you. So the question that you just asked is not one I exactly was set up to answer. So it’s not in my slides, but let me let me sort of circle back to it later, rather than taking on the sort of the ITF horse scenario. Let me start with the report itself, and I’m going to share my screen to do that. And thank you all. It’s pleasure to be here. Thank you for inviting me. Thank you to Ioana and to Katya for agreeing to discuss, and thanks for all of your attention. And if you guys all want to now tune me out and watch the Biden inauguration, I don’t fault you for that.
The title of the report, Building Better Jobs in the Age of Intelligent Machines and this was a taskforce set out by President Rafael Reif of MIT. It was co led by Professor David Mindell who’s both a historian and an engineer. He’s in AeroAstro [Aeronautics and Astronautics], the School of Humanities, and Arts and Social Sciences at MIT. And Elizabeth Reynolds, who’s the head of the MIT Industrial Performance Center.
So since Anton said some of this, let me not linger. The purpose of the taskforce was constructive, to sort of survey the landscape, ask what’s changing, and then ask how can we design and leverage innovations and institutions to maximise the benefits and minimise the harms of changes that are underway. Lots of people were involved. And I won’t say them by name. And let me just kind of start, I’m going to start with the economic context. Why? One I’m an economist, first and foremost. And second of all, I think it’s, it’s the thing that frames the debate and motivates the entire discussion. And then I’ll step back and talk about technological and institutional forces. But I want to start with the economics.
As many of you will be aware, we’ve been talking about the obsolescence of human labour for quite a long time, for a couple of centuries. And there have been periods, periodic moments of great concern. So you’re all familiar with the Luddites. And that’s not the only time. Even prior to the Great Depression, the US Secretary of Labour was talking about the notion that machines would be used for, quote, “scrapping men”, as opposed to scrapping machines we would be scrapping people. In the 1960s President Johnson of the United States, set up a Blue Ribbon Commission on automation and employment. And the concern at that time was that in the post-war period, productivity was rising so fast, that it would threaten to outstrip demand, or at least that was the concern. And so the outcome would be mass unemployment, because there’ll be insufficient demand to keep up with all of the supply.
Of course, none of those scenarios, those dreaded, dreaded scenarios have come to pass, at least in the form that people envisioned them. This figure just reminds you that the fraction of the adult population here in the United States – this has to be true in most advanced economies – has risen: the fraction [of the population] working has risen over the last 100 years. It’s risen because women have left unpaid, often highly constrictive, domestic unpaid employment to enter the paid labour force. The fraction of males working has come down over time, but generally, that’s a positive feature, of reflecting the fact that people don’t have to work until the point at which they expire. So we have not, despite decades, you know, centuries of concern about the possibility of work, we haven’t seen it. That doesn’t mean we can’t ever see it. But there’s no current evidence that we’re anywhere close to running out of jobs. In fact, prior to the pandemic, we were in the United States as close to full employment as we have been in quite a long time. And that’s true, as the Economist has reminded us, throughout the developed world, throughout the industrialised world.
So let me say, Why haven’t we run out of work so far in any case? So really several answers to that question. They go from the kind of the most prosaic to the most interesting. The most prosaic answer is that when we automate, we become more productive, that makes us wealthier, you consume more, and that creates work. So that’s one answer.
The second answer, which I think is less obvious, but more important, in fact, is that automation, we think of automation, or many of us think of automation as eliminating tasks that would be true for artificial intelligence that will be true for many forms of machinery and so on. But automation does do that. It absolutely does eliminate certain types of work. Simultaneously, it makes people more productive in the work that remains – often because what automation does is give us better tools. You can see this at all levels: roofers use pneumatic nail guns to hang shingles; doctors deploy batteries of tests to make diagnoses; architects rapidly render designs on the computers; teachers deliver lessons through telepresence; long haul truckers use route planning software to make sure they never carry an empty load. So a second reason that automation doesn’t simply eliminate work is that it makes us much more productive with the work that we do, and that increases our marginal product, it lowers the price of goods and services, again, boosts demand. And so it makes people more valuable. There’s no way that we could command the wages we do – have such high marginal products – if we didn’t have vastly improved tools that come from our machines and our computers and artificial intelligence.
And the final reason that automation has not eliminated work, in addition to creating wealth and complementing the work that we do, is that it leads to a lot of new work. This is a figure from an ongoing working paper, actually a working project, not a paper yet, that I’m doing with Anna Solomons at Utrecht University and Brian Segmiller, who’s an MIT PhD student. And what we do here is we look at employment across 12 categories of jobs, covering all of US employment. In 1940, for example – the blue bar is the fraction of all employment – in 1940, more than 25% of work was in production, was in manufacturing, almost 20% was in farming and mining, and everything else was much smaller. So those two categories comprised about half of all jobs. If you look in 2018, the height of the maroon and the teal bar together, shows you that in 2018, about 22% of employment was in professional work, about 14% in clerical administrative work, and a good amount in personal services.
So the composition of employment has changed enormously. Now I want to draw your attention just to the maroon or the pink, depending on how it looks on your monitor. This shows you the fraction of work in 2018 that exists in occupations that had not yet been invented in 1940. I’ll define what that term means in one second, but let me just say, if you add these up, what you see is that more than two thirds of all the jobs that people are doing in 2018 are jobs that did not exist in 1940. Let me give you some examples of that and then explain where that comes from. So here are jobs that didn’t exist, that were added to the US Census by decade. So in 1940 automatic welding machine operators were added. In 1950, airplane designers – let me just read through the list – textile chemists, computer application engineers, controllers, remotely piloted vehicles, certified medical technicians, artificial intelligence specialists, wind turbine technicians, pediatric vascular surgeons. So in this list, what you can see is these are primarily jobs where new expertise is demanded by the introduction of new technologies, right? You didn’t need airplane designers before we had the Wright brothers, you didn’t need computer applications of engines before we had computers. And all these medical specialties, of course, come from deepening knowledge. And so when we create new technologies, we create new demands for human expertise to service, to implement, to design, to advance, to apply those technologies.
Part of where your work comes from is that as we make the world more complicated and interesting, we create new work for ourselves within it. Now let me turn to the other side of this figure that I was covering up. Here are some other jobs that were added by decade: gambling dealers, beauticians, pageant directors, hypnotherapists, chat room hosts, sommeliers, drama therapists, these are all jobs that obviously they do not obviously have a technological component, but instead reflect rising incomes, creating demand for new luxury goods and services. Right so many of these things, mental health counsellors, sommeliers, drama therapist would not have been perceived as needed, some decades ago, but now are obviously demanded and paid for, supplied by people, primarily. And so what this suggests is that new work doesn’t just emerge, per se, from technology, but even from rising incomes and market scale themselves create these new opportunities that people jump into.
Let me just say, from where did we get this list? To do this, we’re building on work by Jeff Lin, who’s an economist at the Federal Reserve Bank of Philadelphia. And what we’ve done is we’ve taken historical census documents and used these kind of micro lists of occupations and industries and catalogued their changes over decades. And then we’re analysing where those come from. I’d be happy to talk more about that. But let me know.
The point I want to take away here is, as we change the world, we eliminate work, we create work, and we also increase wealth. And those processes have roughly tended to keep in balance over the course of decades. There’s not a law that says that they have to do that. But they have tended to do that. Okay, so if that’s the case, what’s to worry about? My goal here is not just to tell you not to worry about anything. I think there’s plenty to worry about. It’s just not obvious to me we’re worried about the right thing.
The concern that, really, I find very focal is the disjuncture between productivity and compensation growth, and many people call this the Great Divergence. This just shows in the US, after the mid 1970s trajectory of productivity growth, remains pretty steep, not as fast in the immediate post war period. That’s this purple line, right. So this is the post war period, and then it slows down in the 70s, picks up back up, again, not as fast. This is average compensation growth, what the average worker receives, it keeps pace with productivity until the early 2000s, when they diverged here, that’s the falling labour share. But then the real cause of concern, of course, is the flattening of median compensation after this period. And so what it says is, productivity is rising, average earnings are rising, but inequality is increasing so fast, that the median person is seeing almost none, the median worker is seeing almost none of this productivity growth. Now, let me say you can quibble, it’s reasonable to quibble: Well, is the median really rising by zero? Or is it rising by more? Are we understating the growth of real incomes? And it’s possible that we are, but that would also cause us to understate productivity growth and mean compensation growth. So in other words, that gap is real, even if you think the levels are more dramatic than they should be. So this is a really important phenomenon. And I think if you ask why are people so concerned about automation, if we know that automation or technological progress raises productivity raises national incomes? Well, this figure shows you a very good reason for concern, because the median person could correctly say, looking at the last four decades of economic history, I see that the country has gotten a lot richer, and yet the typical worker has really not gotten richer. And so it’s quite possible to have a lot of productivity growth without a lot of shared prosperity. And I think that is a huge concern.
Now, let me not just be US focused here, you can say, “well, what if I drew this graph for other countries?” Well, I don’t have a version of this graph for other countries. But it is the case that in most advanced economies, the median has not grown as fast as the average, has not kept pace with productivity reflecting the growth in inequality as well. But the US is an outlier, as in many things, in the degree of this gap. And if you look across countries, and the OECD does put together statistics on that, the US has done extraordinarily badly, in this respect, if you consider this gap a bad thing, as I do.
Okay, simultaneously as you’ll be aware, there’s been enormous growth of earnings differentials. You can see that a lot of that failure of median drives reflects the failure of incomes to rise for people without four-year college degrees. But what’s going on here? What are the causes? What is the cause of this juncture, if we had this much productivity growth, and yet, there’s so little compensation growth for the typical worker, what is going wrong?
I would say there are three different things that are going wrong. The first is technology itself. Although I don’t think technology has eliminated work per se, the digitalization of work has made highly educated workers much more productive and made less educated workers easier to replace with machinery. One way we see that is in this kind of barbell shape of occupational structure growth. So this figure, again using the US data, looks at these 11 occupations here, ranks them from low pay to high pay. And of course, these high paid jobs are our professional, technical, managerial jobs that require lots of education. They are obviously, at present, highly complemented by information technology. On the left hand side, we see a lot of these in-person service occupations, personal services including protective services, and these are numerically growing rapidly, but they’re not becoming much more productive. And the supply of people who do that work is highly abundant because they are not specialised. Those are growing. And many of these medium skill, occupations in production, administrative support, and sales are contracting as a share of employment. And I don’t think you have to introspect too deeply to see how that is related to computerization, not AI, really, that has made many of those codifiable tasks much more subject to automation. So technology itself has played a big role in changing the structure of occupations.
And one interesting way, you can also see this, and this goes back to the work with Autor, Salomons, and Seegmiller is to look at the growth of new work over time. So this figure shows you the addition of work between 1940 and 1950. And what I want to draw your attention to is that a lot of the fastest growing categories of occupations are actually getting new titles, new jobs, not just more people, but new types of work are found in the middle: construction, transportation, production, clerical administrative support, and sales work. If I plot that same figure, for 2000 to 2018, what you’d see is all of the new work that’s being added is found really at the tails. On the one hand, in these highly paid specialised occupations and the other, in personal services. And so the direction of technological change is actually shifted in a way that it is not just displacing things in the middle but actually creating new activities at the edges.
And let me just link that to one other phenomenon. In the work with Anna and Brian, look at the relationship of new work creation to innovation. And innovation is measured by patents. And we show that you can predict where new work is appearing according to where innovation is appearing. And if you look at this figure, it divides patents across the 20th century into broad industry categories. And, over time, this amazing shift in the first part of the 20th century, from around 1900 to 1930, the largest categories of patenting are in manufacturing and transportation, so that’s the dark blue and the kind of maroon initially below it. In the next several decades that moves to chemicals and electricity, so that’s the brown to purple. And then if you look at the last 40 years, the majority of patenting has been just in two categories, instruments and information, which is the mustard coloured, and electricity and electronics. And so as the locus of innovation has shifted, the locus of new work creation has also shifted. And that’s very strongly tied to the growth of occupations at the top.
So now, let me say, technology is not the only factor that matters. Globalisation has been a huge positive for world welfare, but has placed a lot of pressure on manufacturing jobs, and manufacturing-intensive communities. In the United States, for example, we have what some would call the China Trade Shock, where when China joined the World Trade Organisation in 2001, its import penetration to the United States just accelerated remarkably, and US manufacturing employment fell pretty steeply. And although that only amounts to a couple of million jobs, in a labour market of 150 million workers, it was very, very strongly regionally concentrated and strongly felt. So a second factor that contributes to the type of changes in work and the divergence between productivity incomes, is trade pressure, or the way trade pressure has been managed.
The third factor, and I would say, what makes the US distinctive from other countries, is institutions. Weakened labour unions, historically low minimum wages and outdated employment regulations have been extremely harmful to the rank and file workers, to the median worker. You can see this in a variety of ways. For example, this just shows you the purchasing power adjusted hourly earnings of low-educated workers in 2015, according to the OECD. The US is here at $10.33. If you want to do a little better, just head to Canada, a little bit to the north, where wages for similar work are about a third higher. If you want to go lower, you would have to go to Portugal or Greece or the Czech Republic, to find low paid workers who are paid as little – and that cannot be a function of skills or differences in jobs. You would find McDonald’s workers in all of these countries doing basically the same work and yet wages vary dramatically among them. And I view that, especially when we look across these high income countries, as a function of institutional choice rather than underlying technology.
You can also see this in terms of collective bargaining. The US is an outlier in having extremely low and falling collective bargaining. The UK actually has a lot in common with the US labour market and has seen large declines as well, and a big opening up at the bottom. And then throughout the OECD, collective bargaining has been falling, but to a much greater extent, in some countries and others. Germany is another great example, and Germany is also a country that has seen a very rapid, a very sizeable increase in inequality, from the 1990s, to the present. Finally, I think I’ve already mentioned this and you will all be aware of it, the US minimum wage is remarkably low, almost meaningless. It actually in real terms, depending on how you deflate it is the same level of present as it was around 1950. And despite mass productivity growth, now, US states have taken the lead on this, we’ll see what happens in the Biden administration that is being sworn in as we speak.
I want to make sure that I leave enough time. So I’m going to stop speaking within 12 minutes and I want to leave time especially for Anton’s questions. So I had a section prepared on “are we getting a positive return on inequality”? Are we getting anything out of this? Let me just sort of say, if you look across countries, you will not find a positive correlation between labour force participation rates, economic mobility, and economic growth, you will not find those things to be positively correlated with high levels of inequality or high levels of divergence between productivity levels and the wages. In other words an argument was frequently made in the 1980s, and 1990s, that you could take your inequality in one of two forms, you could you could either have dispersed wages or you could have low labour force participation rates at the bottom, but you couldn’t have both high wages at the bottom and high employment. Or another way of saying that is, if you believe in the equity-efficiency trade off, that if you want more equity, you have to give up efficiency. And so if you want to have a more, a more egalitarian social system, you’d have to give up higher growth, you’d have to give up dynamism, and so on. There’s really no evidence, at least on a correlational basis that those patterns are visible.
Just to give you a couple examples, and not spend a lot of time on them, if you look across countries it is certainly not the case that more unequal countries have higher employment rates, which you might hope they would if they have very low wages, the US is a good example of having relatively low employment rates, especially among men. Um, if you look at economic mobility, this is the famous graph by Miles Corak, which Alan Krueger called The Great Gatsby curve. Just looking at the relationship between cross-sectional inequality and intergenerational mobility, more unequal countries have lower not higher intergenerational mobility, you might have hoped that high inequality would create a lot of rags to riches, but in fact, it seems to create a lot of permanent social stratification.
In the interest of time, that’s the economic structure or foundation I wanted to just lay out. And again, the main takeaway there is there has been an enormous divergence. It is not associated with falling employment, or the lack of creation of work. It is associated with divergence of incomes. And institutional factors, I believe, are as at least important in explaining the diversity of experience across countries as our differences in technology and globalisation. But now I was not talking about technology at all.
So let me spend a few minutes on that. And I can summarise the conclusions of our report on this in really a sentence, which is that the momentous impacts of technological change are unfolding gradually. What I mean by that is that new technologies themselves are often astounding. But it can take decades from the birth of invention to its commercialization, its assimilation into business processes, standardisation, widespread adoption, and broader impacts on the workplace.
There is often a line – a kind of headline – that is drawn from a laboratory invention to mass displacement of labour. And we have rarely, if ever seen that. I’m not aware of examples where we see that. And looking at the technologies we surveyed in our task force, we find a pattern consistent with this observation. So we look at autonomous vehicles, industrial robotics, intelligent supply chains, additive manufacturing, and artificial intelligence. In all cases, we came away saying they are remarkably important. Over the long run, they will do a great deal. In the short and medium run, they are often extremely limited. So autonomous vehicles will be one example. In the long run, it’s estimated that autonomous vehicles will displace 1.3, 2.3 million workers out of transportation jobs. This has strong regional implications. A lot of the people who drive for a living are located in the South although they drive all over the country. And this has potentially important consequences. But as all of you who’ve been reading the news will be aware, this is happening much more slowly than was predicted five years ago. So for example, here’s a headline I really like, from the Washington Post, “Shaken by hype, self-driving leaders adopt new strategy: Shutting up.”
And why is that the case? Well, there are two reasons. One is, of course, the technology itself was overhyped. Autonomous vehicles are just not as competent or reliable as they were initially claimed to be, although I think they eventually will be – I don’t mean to say they’re not amazing, they are. And eventually they’ll be much safer drivers than people and many good things will come from that.
Second, adopting that technology at a very large scale doesn’t just mean people buying a new car, it means changing infrastructure. A lot of vehicle miles are driven by heavy machines, long haul trucks that have a service life of a couple of decades. And they work in a complex web of roads and warehouses and so on. And they will not overnight be replaced. Even if tomorrow someone introduced a truck with really great autonomous capabilities, it will take decades for the infrastructure to turn over. So that’s why it’s important to recognise that there’s an enormous gap between what is possible, and what is occurring at scale.
Another great example that we looked at – and I enjoyed learning about at the task force – was additive manufacturing. Additive manufacturing will be quite revolutionary. So these are just some examples of things that were additively manufactured. This is a very early example, the MIT dome just out of a plastic. This is a custom metal hip implant made for a patient. That’s an aircraft fuel nozzle. A faucet, you’ll notice the faucet is hollow, the water travels in those little channels on the side, or an orthodontic retainer.
Additive manufacturing is what some people call 3D printing, but really, additive manufacturing is probably a better term. Most manufacturing is subtractive, where you start with a raw piece of material, and you remove parts of it until you get what you want. Sometimes it’s formative where you put things in a mould. But additive is the idea that you put the pieces on layer by layer, and it has a potential to transform how products are developed and realized. It can eliminate the need for product specific tooling. It can make highly complex parts. They consolidate multiple materials in ways that were previously impossible. And it makes it possible to envision manufacturing as a mostly digital process where the actual turning of materials into final objects occurs only at the very end of the supply chain and most of manufacturing is in the design and engineering and prototyping, itself enabled by additive manufacturing. So this is a very big deal. But at the moment, it’s a very tiny part of the market. And it will take a very long time. In the long run, I do think it will reduce the amount of employment in people making things, increase the employment of people designing things. So like many of these technologies, people slowly devalue a lot of the physical skills and make the cognitive skills more consequential.
Just to summarise what I’ve said here, these AI robotic applications, they take time, often decades to develop and deploy, especially if you’re talking about safety and production critical applications. For example, it’s noteworthy that airplanes used to have three pilots 50 years ago, they’re down to two, but they’ve been at that for quite a long time, despite incredible advances in autonomy. The largest labour market effects of information technology that we’re seeing at present still from stem from maturing technologies of two decades ago, like Internet, like mobile computing, like electronic health records and e-commerce. We can see lots of glimpses of what’s going on, but it’s going to take a long time to fully roll out. And this time window offers opportunity for investment, investment in skills in particular. Let me wrap up, because I want to leave time for discussion.
The main argument of our report is that institutional innovation must complement technological innovation. And in particular, we argue that if the wave of technology that we’re seeing now deploys into the institutions that we have in place, we will have bad results, as we have arguably had bad results for the last four decades. Those institutions have not done enough to translate rising productivity into anything like shared prosperity, and that has had real social and political costs. But that was not necessary. And we can see from a diversity of experiences across countries using the same technologies, facing the same force of globalisation, that they have done very differently, and not obviously at great cost; they haven’t given up a lot to get better results. So we talk about three places where innovation is most needed. One is, of course, investing in innovation and innovating in skills and training. And any economist who woke up in his or her sleep would tell you that’s necessary. And that’s true. In the long run, human capital is critical. If we did not continue to raise our skills to keep pace with the technologies that demand those skills, we would have a problem. And we have done that successfully over a century, but must keep doing it. The second is really ensuring the productivity gains translate into better quality jobs. And the third is expanding and shaping innovation itself. So I’ll say just a minute on each of these and then I will stop.
Instead of walking through this long laundry list, I know Katya will say more about this in her remarks, I will just say that we talk about a variety of ways in investing, innovating and skills and training at scale. And one positive developments, you know, it’s boring to talk about education: education, one of the least sexy things you can do with technology. But it is the case that we are in a moment where the technology for education is suddenly much better, or at least potentially much better, although we have not found the kind of secret sauce for making online learning in every way as good as in person learning, in the long run, it will be better. Not only will it be better, it will be cheaper, it will be more accessible, and it’ll be much more immersive, when people can use tools like augmented reality and virtual reality, when they can do kind of hands on learning via simulation. And when they can do that, so when more of learning looks more like video games and Netflix and so on, it will be much more appealing to many more people and more broadly used. Although it is hard to improve education, and it’s especially hard to retrain adults – lots of research demonstrates that – I think the technology for doing this is going to help us a great deal both in terms of cost and even more so in terms of efficacy.
A second thing is really innovating institutions to go along with technological change. To use an example, in the US we would talk about modernising the unemployment insurance system, we would talk about making sure health care provision was not intimately dependent upon employment. Certainly restoring the real value of the US federal minimum wage and indexing it to inflation: all evidence suggests that this has benefits at low costs. And then a lot of what we talked about in the report that I won’t talk about here is strengthening and adapting labour laws, both enforcing existing protections, but also allowing for innovation in worker representation. The US has a highly atrophied form of collective bargaining but bringing it back in full strength in its current form is not necessarily desirable. It’s highly adversarial, and arguably not a good way to go about it. We talked about alternative models, but this is an area where experimentation is desperately needed. In addition, actually building legal protections for workers to organise without retaliation in non-traditional realms. Currently, US domestic and home-care workers cannot legally organise, nor can farm workers, and it’s unclear about independent contractors. So finding room for collective bargaining in an innovative way is critical.
Finally, we talk about expanding and shaping innovation itself. As you may be aware, the US has really slacked off in public investment in innovation. R&D in the US economy has stayed roughly stable as a share of GDP. But that’s because public sector has fallen enormously and private sector has risen. Now, you might say, well, isn’t that good enough? You know, are those substitutes, if the public sector doesn’t do it then the private sector does? Shouldn’t we be happy about that? And I would argue no: that these things are complimentary, that the public sector does a different type of innovation, earlier in the pipeline, more focused on public goods, it provides a lot of the fundamental science. And so these things go together. In fact, there’s nice research by my colleagues, Pierre Azoulay and Daniel Lee, showing that public sector R&D investment leads to private sector patents among other things.
So how do we do that? Again, you know, when I talk about policy, I’m constrained to speak about the US, or at least, it’s hard to speak about many countries simultaneously. One is not only to increase federal R&D spending but to use it to set the agenda. We forget, or many people forget, how important the government has been not just in paying for things but in deciding what things should be paid for, whether that was the telecommunications revolution, whether that was NASA – at one point, our space agency, was consuming more than 50% of all integrated circuits that were available in the United States. It’s the same with the internet, DARPA led the self-driving car, kind of initial catalytic moments. And so the federal government can set the agenda on innovation towards valuable things: towards education, towards things that increase worker productivity, and many others. A second is that innovation has a geographic element, and it can be used to bring prosperity to other places, not just to the coastal regions of the United States. And a third thing that we talked about the report which is quite controversial, and here we’re building on the work of Daron Acemoglu, Andrea Manera and Pascal Restrepo, is rebalancing our tax system, which at the margin subsidizes firms to eliminate workers and replace them with machines, which is not something that we think is desirable. Capital investment in general is good, but at the margin, could be counterproductive depending on where it goes.
So let me kind of conclude my canned remarks and then open this up. So I believe the work of the future is ours to invent. There is a palpable fear, and at least the way we perceive it, this could be a hypothesis but is not in fact proved, is that a lot of the fear comes from a consequence between advancing innovation and fairly stagnant labour market opportunity. And that will get worse if we don’t take countermeasures. But we do not think that there is a trade-off that we see between economic growth and strong labour markets as far as we can tell. These things are really either complementary or orthogonal. They are not at odds with one another. As I stress at the beginning, the majority of today’s jobs had yet to be invented a century ago. And a lot more are to come. The job of the President, as we understand it, is to build the work of the future, in a way and for a world that we all want to live and that is not a technology inevitability, it’s a matter, to an important extent, of social choice.
Let me talk very briefly to the question that Anton asked me. There is a scenario, the “horses” scenario, but of course, that has been with us for a long time. Many of the things that, you know, we used to do: dig ditches by hand, we used to pound tools out of wrought iron, we used to do bookkeeping using books, right? We have automated ourselves out of all kinds of work, right? If we were limited to things that we did 100 years ago, with employment in agriculture, we would be in big trouble. We have continued to complement ourselves with the machines we create, and this appearance of new work, which I’m arguing has been extremely important. Now, we don’t know that’ll stay in balance. There’s a nice theory paper by again by Acemoglu and Restrepo called The Race Between Man and Machine that talks about the forces that potentially counterbalance this and what do you need in equilibrium for labour to stay valuable, and what do you need for innovations that complement labour? So those are obviously hard to check – that’s true, there’s no evidence yet that we’re seeing what people are worried about. That we’re seeing vast displacement, what we’re seeing is some devaluation of skills. You’re not seeing an excess supply of labour, per se.
I perceive it as a challenge for at least for the next decade, maybe for next few decades as being distributional rather than pure human obsolescence. If we reach that point of pure human obsolescence, what we then have is a much bigger distributional problem. Because it’s not that we’re poor – at that point, we’re incredibly rich, labour is no longer scarce, and we own the machines – they don’t work for themselves. So we have a fabulously wealthy society with no obvious means of distributing income, in the sense that most of our income distribution systems are based on scarcity of labour. And where labour is no longer scarce, we must have income without really being clear who gets to make a claim on it. So I think that’s a huge social organisational problem, and I don’t look forward to that problem. I like the problem of scarce labour. I think it has many, many virtues. So I don’t see any early indications of that scenario. But I know that many will disagree with me. So let me let me stop there. And thank you very much for your attention – I look forward to the conversation that follows.
Thank you so much, David, for your deep insights! I find it very impressive how you managed to distill this huge wealth of data into high-level insights that are both really intuitive and also so policy-relevant.
Let me invite everybody in the webinar to submit questions through the Q&A field and to upvote the questions that have already been submitted and that you find particularly interesting. So we have two discussants now, and after that we will open the floor to the questions that you are all posing.
Our first discussant is Katya Klinova. Katya directs the AI, Labor, and the Economy Research Programs at the Partnership on AI. She focuses on studying the mechanisms for steering AI progress towards greater equality of opportunity and improving the working conditions along the AI supply chain. And in the interest of full disclosure, I have had the pleasure of working with Katya as part of the AI and Shared Prosperity Initiative that is dedicated precisely to this cause of sharing the economic benefits of AI broadly across society.
Katya, the floor is yours.
Thank you very much, Anton. I want to start by thanking David and his colleagues from the task force for creating this encyclopaedia of a report. You know, in the last three to four years, there’s been really a flurry of future work reports, and then just a number of them offer really valuable insights. But really, really, really few come even close to them on the mentality and comprehensiveness and level headedness of this report. So really, my biggest problem with that is that it’s the final one from the task force. I wish they kept going, because it’s just been a really great service to the future or community-shaping and directing this conversation. So I want to use my time to start really invite a conversation about implementing the report’s recommendations, which I think are really excellent. But we as a society, I guess, still have work to do figuring out how exactly to implement them and what are the guardrails that need to be put in place. And because our time is limited, I picked two of my favourite ones, which I think are incredibly important. The first one is about allowing innovation in the new forms of representation of workers in the workplace and corporate decisionmaker making. And the second one is about committing to an innovative agenda that is targeted towards augmenting rather than replacing workers. So let’s take these two in turn.
Firstly, about the worker representation. Let me begin by showing you this graph that David already shared, that is slightly depressing, that shows a decline in union membership across the OECD and US has declined the most proportionally big and has come to the lowest point out of all the countries. And I think it’s instructive to look at this chart back to back with the graphs that showed the discrepancy in the forking of the growth between the highest wage earners and the lowest wage earners in the OECD countries. And I just want to draw your attention to the Nordics that have not experienced the fork. And also the growth there has just been much higher, like it’s switching 60%, over almost 80% of Sweden. Of course, not all of that has to do with just union participation. And yet, I think it’s insightful to look at these graphs side-by-side and kind of regain our appreciation for unions and really appreciate that the report is talking a lot about that and calling for innovation there.
So one thing that unions can do when it comes to technology, and this is again, the story that the report tells as well. UNITE HERE, a few years ago, was the first union that was able to put clauses around technological development into their union contracts. And now Marriott is obliged to give 165 days notice, if they’re about to bring automation to the workplace, and workers are entitled to retraining. Which is a great provision, but of course we know that the technological disruption into worker’s welfare and life can come not only from their employer bringing in technology and deploying technology, it can come from Silicon Valley. This is what happened in the hotel industry as well – the biggest players in the hotel industry are being blown out of the water by Airbnb. And of course, it’s not only that industry, you can be putting clauses around technological adoption in retail brick and mortar stores, but the main disruption can come from the rise of e-commerce, for example. And that is not necessarily bad, disruption can be a sign of very healthy and dynamic economy. It’s also not new or unique to the digital age. So it’s definitely not something new for unions to deal with.
And yet, if we are thinking of AI as a force that can dramatically expand the variety and the range of human tasks that can be automated, then we can be entering an age in which for really most workers around the globe, you know, some faraway Silicon Valley company can be as relevant in terms of its influence, its ability to influence their well-being, as their own employer. And of course, that relationship is not covered by traditional union contracts cannot be covered because Silicon Valley does not employ all of these people, not in the direct way, not in an indirect way. And that’s why in addition to all the reasons that the report is listing, such innovation is so badly needed in unions.
So you can be thinking about who can be advocating for the aggregate labour demand to stay high and for human labour to stay relevant. We know that that can take a hit in aggregate. These are graphs from a seminal paper by Professors Acemoglu and Restrepo that show that while automation kept pace with creation of new tasks for humans in the four decades following World War Two, in the last three decades, that really has changed and automation is now outpacing the new task creation by far. So you could, of course think about the government and the policymaker (benevolent one) as the one who would be thinking about this aggregate picture. And this is what brings us to the second recommendations about shaping technology to augment as opposed to replace workers.
And the graphs that David already showed here, again, that are slightly depressing, or actually quite a bit depressing that showed the erosion of both employment and earning for the medium worker, make us think about what are the barriers like why the median worker clearly doesn’t seem to be sharing in on the productivity growth that the economy has been experiencing, even if that productivity hasn’t been growingas quickly as previously in history, but it has been, and that does not really spread evenly across education levels and wage levels. Clearly, technologies of late have been hugely complimentary to the knowledge workers, but not necessarily to everyone else. So how can we commit to boosting productivity of a typical worker to boost the demand for their labour and their wages in turn. So when we think about this productivity boosting technology, the recent examples of that do raise concerns, because we need guardrails that are not yet in place between making workers more productive and really exploiting them. In an example I’m going to give you a few years ago, technology was patented for wristbands for warehouse workers, which gives them haptic feedback, they basically buzz if you’re putting a wrong item into a wrong bin. And this just sounds like a great idea. And it’s something that can tell workers about the mistake they’re making, and overall raise their productivity. But of course, that same wristband can track every single movement of a worker and can tell their employer how many times they took a break, went to the bathroom, any of that sort of details. And people are obviously raising concerns about that.
That’s not the only example. Now there are startups that literally advertise themselves by offering you to tell apart efficient and inefficient workers and build a map of who, where, when, and where is doing what kind of activity. And that might sound wonderful if you’re an employer, I don’t know if that’s compelling to many of them, but if you are a worker, you might be quite concerned about that, because you are human and there are days on which you might need to have more breaks or fewer breaks. And there are obvious concerns around that information become fully transparent.
Really, the workplace and the labour market is a market in which employers have way more power than workers. And the little power that workers have comes from the information asymmetry in their principal agent relationship with their employer. So when that information asymmetry goes away, all bets are off, in some sense. And then employers no longer need to worry about offering incentives or offering bonuses and carrots to try to induce higher performance, they can just limit their tools to the sticks and the punitive measures, if someone doesn’t meet their sometimes really exaggerated performance thresholds. And then lastly, of course, there is a lot of uncertainty, when we’re trying to anticipate the impact of new technology on labour demand. If you’re a central planner, or just the policymaker, thinking how much you want to incentivize or disincentivize the development of a certain technology, there are a lot of uncertainties that you’re dealing with and your calculation would look very different if you’re making it for a single country, especially, you know, a tech maker country that might have ageing workforce and might be in need of robots, or if you’re making the calculation for the world as a whole, in which millions of young people are entering the workforce every year in need of formal sector jobs. And then again, the very same technology that can be used to automate the jobs of truck drivers can be also used to automate going to the grocery store, which, you know, in many countries is still done by households that sell themselves and this is unpaid labour that would be converted into creation of new jobs for people who pack those groceries and send them to people’s home. So the very same technology — a lot of different ways to apply it, which creates additional uncertainty when we try to predict what would be the impact on labour demand. And that is just when we think about the labour demand, but there are of course, all kinds of different effects that we might or might not want to encourage with self driving cars. There’s, of course, first of all the considerations of safety and saving lives on the road.
So, I want to wrap to give it back over to Anton but just by saying that, we are trying to think about some of these questions. If you have advice for us or just want to get involved, please get in touch. My Twitter is here. And you can also sign up at the Shared Prosperity website, partnershiponai.org/shared-prosperity. Thank you very much.
Thank you so much, Katya. Our second discussant is Ioana Marinescu. Ioana is an assistant professor at the School of Social Policy & Practice at the University of Pennsylvania, and a Faculty Research Fellow at the NBER. She studies the labor market to craft policies that can enhance employment, productivity, and economic security. Her research expertise includes wage determination and monopsony power, antitrust law for the labor market, the universal basic income, unemployment insurance, the minimum wage, and employment contracts.
Hello, everybody. David, I really enjoyed your presentation. And it touched upon many policy issues that my work and my thinking has also been concerned with. So I want to make here two points. The first one is very much already in the perspective of what David was talking about. But I want to point out for our audience, the paradox in a way of the institutions and the technology, in the sense that in the US, as David already mentioned, we have one of the lowest minimum wages among OECD countries. And you might think that, you know, if technology is skill-biased in favour of more skilled workers, and it’s going to put downward pressure on the wages of the less skilled, you might think in a free market, that it’s good to have a low minimum wage, that that will allow the economy to create more jobs. And so therefore, the US should be in a better position to weather those issues as compared to other countries, like the country I was raised in, in France, where the minimum wage is much higher.
And paradoxically, that’s not been the case. And employment rates are higher for prime wage workers in France than in the US. And it’s also the case in many other OECD countries, and yet they have higher minimum wage. So what gives, you know, why is it that by making workers more expensive, you know, we have higher employment and certainly not low employment. So here I want to introduce the idea that that’s possible because in many cases, workers are underpaid relative to their productivity, which we call monopsony power. Just by analogy with monopoly power in the product market, you have firms with market power who overcharged consumers relative to cost. So in the case of the labour market, firms with employers with market power will underpay workers relative to these workers’ productivity. And if that’s the case, then a minimum wage rather than decreasing employment as would be the classic prediction and the basic model, where if you increase the price of something, the demand for that decreases with monopsony power, as you increase the minimum wage, employment can in fact increase since firms can afford to pay more. And by paying more, they’re able to attract additional workers. So basically, if at baseline workers are underpaid, there is a margin to increase the minimum wage without destroying employment. And as we increase wages, we make jobs more attractive, which attracts more workers into this these jobs. And in fact, my work in the US shows that that’s exactly what happened across US states, that when there was less competition for workers, increasing the minimum wage has tended to increase employment. So that kind of solves a bit the paradox of the cross-country pattern, where you see countries with institutions that make labour more expensive, seemingly doing better in the face of technological developments that seem to go against the demand, especially for low-skilled labour.
So how do we solve this? Of course, as David said, by increasing the minimum and also by increasing or helping worker unionisation as well as other forms of worker bargaining power. Because unionisation in the recent research has been shown to be able to counteract firm’s monopsony power. So therefore resist pressure by firms to underpaid workers. And also, of course, law and legislation in particular, some of my work looks very much into antitrust law. So if we already have issues with competition in the labour market, we want to act to prevent behaviour by firms that could further diminish competition, including things like mergers, by which firms become bigger and more powerful, as well as things like no poaching agreements, wage fixing agreements where employers collude between themselves to keep wages low.
So that’s, that’s my first point explaining for the audience hear some of the reason I think is behind the paradox that countries with more expensive, seemingly more expensive labour have often been doing better than the US when facing these technological changes. And then the second point I want to talk about is the universal basic income. So as Anton said in his question to David, you know, it’s true that in the past, the labour market has been able to adapt for all the mechanisms that David has explained so well. But obviously, you know, we cannot be sure of what will happen in the future in 20, 30 years. And there is a huge corner of uncertainty. And it seems to me definitely possible that at least some workers will go the way of the horse so that their labour is simply not valuable anymore, in the face of, new technologies. And so, if that’s the case, then something like a universal basic income is one interesting policy innovation to think about. First of all, there’s already growing interest in something like a universal basic income, with what we’ve seen during this crisis with the stimulus checks, which have gone to almost everybody, up to 90% of US households without any conditions, they have, you know, allowed people to weather this crisis. And more generally, in the US, we have a social welfare system that is much less protective of people than in other ECD countries, there’s a lot of holes and a lot of the benefits you can only get if you work. Now imagine what would happen if technology were in fact massively killing jobs. And as they would say, we’re not there yet. But what if that were the case, then, you know, these people without a reform of our social protection system would no longer be able to make ends meet. So particularly in the US something like a universal basic income, which is cash for all, without questions asked, could be quite an interesting solution.
Now, many will say this is not targeted, right? Because at least in the pure system, everybody gets the same amount, no matter their circumstances and their income. But that feature allows it to really make sure that nobody falls through the cracks. So everybody gets it, definitely, there’s no need to apply, or it’s really minimal. So that’s the big advantage. And I want to say that even though it seems untargeted, if you add to that the financing side of it, so you have a basic income, then you have a conduit for financing this, which could be many things, including a carbon tax, sales taxes, additional income tax, wealth, tax, you name it, many possibilities. So these extra taxes, almost any tax you can think of is progressive, meaning that rich people end up paying more so that on net, even though everybody gets the same basic income, through the tax system, it turns out that, you know, rich people pay a lot more taxes to pay into this system relative to what they’re getting. And depending on your tax, you can make it as progressive as you want, by using a more progressive tax.
So therefore, if labour was going to go the way of the horse, I think that the universal basic income is one of the innovative ideas that’s very much worth thinking about in that context, and it already has many advantages today. So this is, you know, what I had to say for now, and I’m looking forward to the discussion.
Let me perhaps also start with a follow-up on our discussion on human replacement, and that also weaves in two of the questions that were posed by members of the audience:
David, you observed that the main problem if we automate away all labor at some point in the future will be distributional. Ioana has spoken about one potential solution, the universal basic income. And you said it’s a distributional problem, but at that point, we will be incredibly wealthy, except of course, I suppose, if the automation mainly took the form of what Acemoglu and Restrepo call so-so technologies.
Now one interesting observation is that this distributional problem that we would be facing in that future, it’s in some ways just a continuation of the distributional problems between high and low skilled workers, that you have emphasised at the beginning of your talk. So there really is no fundamental difference between what we may face in the future, and what we have already been facing over the past decades, it’s just going to be more extreme, it’s a difference in degrees.
Now, let’s say that we may, indeed, in a couple of decades, be at that point where humans are no longer economically useful. Let’s say it is cheaper to pay robots and AI than to pay humans to buy the basic food and the amenities that we need to live. So in that world, it’s not that there is no work, it’s just that your competitive wage, your marginal product is worth very little, let’s say two cents an hour, and it costs you $1 a day to keep alive.
Now, there will, of course, be a transition between today and that future. And some may argue that we are already in that transition in the US, based on precisely your work on skill premia, and the growing inequality in the labour market. But of course, many may also disagree. But, David, my question to you is, if we want to prepare for the possibility of that future, what would be concrete policy measures that would make sense anyways, that would help us for the transition? And then let me also invite you to respond to all the broader points that were brought up by Katya and Ioana.
Okay, there’s so much to talk about here. And I really appreciate the observations from Katya and Ioana. And I agree that the policies are extremely hard to implement, you know, it’s hard to figure out how do we bargain over these things? What is the way? What is the form of collective bargaining that is not too restrictive, that doesn’t have too many loopholes simultaneously? And then, you know, how do we shape technology in the direction that we want it as well? Those are both very important and difficult, but important questions and difficult questions to answer. And then, of course, I very much agree with you Ioana saying about, you know, for too long, you know, sort of assume the labour market, somehow there are perfectly competitive markets for toothbrushes, cereals… of course they’re not and now people are sort of re-examining that presupposition. But let me try to tie this together a bit.
You know, Anton, you’re talking about a future which I view as relatively distant. But I agree that it connects, you’re right. And since if what we have done is made labour less scarce in some domains and more scarce in others, you’re talking about a future where labour is not scarce, right, where there’s no such thing as labour scarcity. I don’t think we are in that world at the moment, we’re in a world with extreme inequality in labour scarcity.
And so the policies that I’ve been advocating, are all ones that do some forms of redistribution, but not through post-market redistribution, through your tax and transfer, but through what you would call pre-market or within the market really, which is changing the quality of jobs. And I don’t think that skills, just simply skill-ing is going to be sufficient.
And so you know, this a point that Rodrik and Blanchard made in their very nice conference a year ago, about, look, there are three ways you can do this, you can do some supply side, by building better workers, you can do this through post market tax and transfer, or you can directly intervene to affect the quality of work. And I think the second one is the least exploited and the most direct.
And so how do we do that? One is, of course, through minimum wages. Another is through collective bargaining. And another is for focus on labour standards. And I agree that the technological headwinds are against us, right, in a way that they were not. So if we talk about the post war period to the 1970s, it’s clear that the new work creation was very much in the middle. And so technology was sort of helping create the middle class, even as regulation and norms and so on were complementing that. And now I don’t think that’s occurring. I think that the technology is creating new work at the very top and some of the bottom, and so we have to push harder against it. But on the other hand, if you think about the figures that content brought up, you know, product from economic growth and so on, countries have had remarkably different trajectories, facing these same set of forces. And it doesn’t seem they paid a high price for that. In fact, I would say the US has paid a very high price for not, you know, stepping in, the price of non-intervening has been much higher than the price of intervening.
So, if you say, well, how do you know well, how to prepare for the future? Well, one way is, we take action now on whatever manifestations of it we’re already seeing, basically start to build this social compact that invests in people that cares about the quality of jobs and that uses rising productivity to create rising prosperity, right? That’s the only way we’re going to be able to do this, if we wait till the day that comes and all of a sudden, Mark Zuckerberg owns everything. And then we all come after Mark Zuckerberg, right? Nothing against him personally, that’s not going to be a good system of distribution. So we have to basically create — and it’s politically hard, right, it’s not economically hard, it’s politically hard.
And that’s the problem. And it needs to be palatable in some way. So I’m not a big fan of UBI myself. First of all, I think it’s the answer at the moment to a problem we don’t have, which is the lack of work. I also think it’s not very politically palatable, at least certainly in the United States, people want other people to work if they if they’re getting money, they don’t want to give money to people who they don’t perceive as working for that money in some way. And so now, that could change, but the norms and projections of what is fair and reasonable will affect what types of tax policies you make.
And I finally would say, and then I’m going to stop, I think work is an intrinsic good. The whole economic model that people do work – which causes disutility – to get income to afford consumption, which is the only thing they enjoy, it’s completely backward. Work is incredibly important, because it gives people identity, it gives them a structure, many people enjoy the tasks they do, it gives them social esteem, and a set of relationships, and a way of life. And so I would like to – and I think people prefer to get income for their work relative to having a lousy low paid job and getting a supplementary check. I don’t think most people find that as appealing. And so, in my mind, there’s still plenty of room for improving work, rather than preparing for its demise. And so as long as work is a viable system, as long as there’s a lot of it, I think there is a lot of it, then working on improving equality, such that we get better distribution through employment for people who are capable of working, I think is much more socially palatable, much more psychologically healthy, and moves us in the direction of a kind of social compact that is more robust. I’ll pause there.
Thank you, David. Let me maybe give Katya and Ioana an opportunity to jump in here. I think Ioana, you looked like you were about to contribute something?
Yes. So you know, I don’t think that the two are contradictory. And, you know, that was the sense of my remarks. First, I said, you know, let’s raise the minimum wage, improve collective bargaining and so on and so forth. So I think that there should be policies out there that improve the quality of jobs as well as worker position in this bargain. So I don’t see this as a substitute.
But at the same time, I think that, as I mentioned before, the social protection system in the US is quite a bit less generous than in other countries. And so it’s worth thinking about how you could improve that. And you know, it’s not like basic income is yet highly popular, but I think it’s becoming more popular. And I think one of the things that it has going for it is the universal part of the universal in it, which, which means that it can, you know, no longer be perceived as a handout for someone. Why are they getting it, and I’m not getting it? We’re all getting it anyway. So that kind of potentially changes the perception of this, and therefore potentially can a little bit expand our budget possibility through politics. Now, if people really want this, then they might be willing to spend you know what it takes to, to get something like that. And then the final quick point I wanted to make is that, yeah, work is very important to many people. And it’s not just about the disutility of work. But it doesn’t necessarily always have to be a classic paid market work. And I think what something like UBI enables is for people, first of all, as I said, we should make more classic market work available that’s good work. But also people having the opportunity to do other types of non-market work, caring and volunteering and whatever else they want to do and artistic activities that are nonremunerated, and that having something like a basic income could enable people economically to engage in those sorts of activities that are work-like, but that are not necessarily remunerated by the market.
Thank you, Ioana. Katya?
Yeah, I think Anton your question is very important, like, is there a discontinuity between the distribution problem that we’re facing now and the distribution problem we might be facing if labour is not, is not scarce anymore. And I think like I am with David all the way that I don’t want to face the problem. It seems like a qualitatively different problem. Because it’s a very fragile setup, it’s like much more fragile setup, right, then the one that we’re facing right now, in even if, for example, UBI is providing enough for people to live on and pursue their artistic and other interests, which, like I would be all in for, but it does rely on whoever is possessing all these productive, you know, forces to be willing to generation after generation continue to redistribute that, while they don’t really need the people to keep producing. And people don’t have the political power. So this political setup is what I worry the most about, in that, you know, hypothetical scenario that you did for us.
It would be like the resource curse, right, only for everything, right? So we know countries that have basically one source of income, right, they tend to be terribly governed, because it’s so easy to monopolise that, right, whether that’s oil or diamonds or something, then we would use the machines, where we would worry.
Thank you, Katya. And also, David, let me maybe bring up one more question from the Q&A that relates to education. And let me maybe broaden it a bit. So David, what types of education would you advocate the most? And how much should public sector be involved versus a private sector? And let me perhaps also ask the question, is there going to be a limit to the human capacity to being educated? Like, let’s say, I’ve worked very hard to get a PhD degree, how much more will I have to educate myself to still be relevant in the labour market? Three decades from now? Will I be mentally able to process that?
Yeah, excellent question. So I really think the fundamental needs of education have not changed. But it’s not about learning specific skills. It’s about being able to read, it’s being able to think logically and analytically, right, and quantitatively, but that doesn’t mean math that means analytically, to be able to present and communicate, like people’s writing skills have actually gotten worse over time by the way. To communicate with group and to lead and work in a team and so on. And these things are incredibly foundational. Now, then you say, Well, what will people do for work beyond that? You know, I don’t know, I think that, you know, it’s very, very likely that they sort realm in which humans will maintain competitive advantage is in things that continue to require flexibility and interaction with others, but draw on a base of expertise that interacts with technology, right? You can’t make a living just being empathetic. But you can’t make a living just, you know, adding raw columns and numbers. One of those is not scarce because of human capacity and the other is not scarce because of machine capacity. You need to be in the place where those things are complements, not substitutes.
And now on the finite capacity of humans to learn, right, certainly there’s got to be a limit. However, it’s not clear how close we are to it. For one, during the high school movement in the turn of the 20th century, you know, the end of the 19th century, you know, there was this concern about sending kids to high school. One, isn’t that expensive, you know, all these teachers, all these books. Two, the opportunity costs are really high, they can’t work on the farm. But three, is it really reasonable to think that all these cretins could actually achieve this level of education because a high school diploma, right, which was considered elite, and we had this belief that maybe people, we just already hit the capacity of most people, right, in that era, that healthy era of eugenics, right, like, we knew who wasn’t gonna be able to do that.
And so it’s not, you know, we’ve gone a lot beyond that. And more than that, we’re getting more efficient at learning, I would argue, so I do think we’ll eventually hit a limit. But one way we deal with that limit is we specialise, right? There are, you know, hundreds of types of doctors now, whereas, you know, a century ago there was a dozen. And they’re more specialised because the technology has deepened, expertise has deepened, but people’s capacity for expertise is finite. And so they specialise, right? You see this in our field as well. There was a time when someone like Paul Samuelson could do all of economics. Now only Daron Acemoglu does all of economics, everyone else has to specialise. And so it’s possible that we will specialise and remain complementary to the tools we create through this specialisation.
Katya, Ioana, would you like to add anything?
I am excited about the potential capability of AI to facilitate and make more scalable teaching at the right level. I’ve definitely you know, I’m sure that my fellow panellists like, we’ve never been in this situation, but like, I’ve definitely sat in class where I was not able to follow as well as some of my classmates. And if there wasn’t a system that could catch me up to what I’m missing out on, I think that can really improve the quality of education and scale it to a lot of the countries where it’s scarce.
Actually, I did have a comment about Katya’s potential political dystopia. And of course, this is a bit distant. I agree with David that even if all this is a threat, it’s like quite a few decades afield, but you know, I think this also speaks to thinking about modes of property, you know, who owns what, how, and, you know, I’m very committed to a market economy, but that doesn’t necessarily mean that we have to have a few people, you know, owning these machines. And so sort of trying to think about a potential transition again, from here to there, I think, you know, through perhaps, wealth taxes, which over time, if they’re high enough, would draw down the wealth inequality, you know, things like that, I think are very much worth thinking about in connection with technology. If we think that it’s a serious concern that in the longer run a few, you know, will own this productive capacity for everything, which indeed, I think is extremely politically dangerous for society.
We are already towards the end of our webinar. David, would you like to make any concluding statement before we wrap it up?
Well, first of all, thank you. Thanks to all of you. It’s been a great conversation, it could go on for hours and it was a lot of fun, at least for us, I don’t know about the audience. I guess I think that you know, sort of something that Katya said, is this question: is it a discontinuity or is it a continuity? And I think that’s a really important question. And I think, you know, I view the singularity view, which has been around for a while, as being not realistic. And the notion that there just comes a day, there’s a crossing point, and boom, I actually think we hit diminishing returns on most things, not increasing returns. But I do think it’s useful to say, well, maybe we are seeing some manifestations of that. And if that’s true, in some sense, that’s good, because it means that there’s a transition path that we can work with, right, as opposed to arrives on Monday, like the last thing we want, is the whole economic system collapses on Monday. Right? Much better, that this occurs over a long period of time. So I think it’s a really important question to ask and sort of a very focal question for thinking about AI and the future of humanity: is this going to be a big bang or is this going to be a continuation and create these specific challenges, and maybe it’s more the latter. And hopefully we can shape that as well. And I think that’s probably something that we overlook, is our capacity, not only our capacity to shape where the technology goes, but the degree to which we have already done that. The degree to which the world in which we live in is the one we created, not the one that technology created. And in many ways, we did it intentionally.
Thank you, David, for that uplifting conclusion that I hope we can all strongly agree with. And thank you all for your contributions. I also hope that the new administration that has been sworn in while we were having this webinar will listen carefully to everything that we have learned from David, Katya, and Ioana during this webinar, and I hope to welcome you all soon to the next GovAI webinar. Thank you.