This week, Nick and Goldy discuss the future of AI and its potential impact on labor markets and society with MIT professor and economist David Autor. While many pundits predict that AI will bring economic misery to working Americans, Autor optimistically argues that AI could empower the middle class by augmenting human expertise, unlocking new solutions to complex problems, and enabling individuals with fewer formal skills to excel in areas requiring advanced knowledge. Professor Autor also underscores the need for targeted investments, labor market supports, and thoughtful regulations to ensure the benefits of AI are widely and equitably distributed rather than concentrated among a privileged few. It’s a fascinating discussion about the future of AI that tackles the pressing questions about its ethical deployment, the risks of monopolization, and the societal shifts required to harness it for the greater good.
David Autor is a labor economist and professor of economics at the Massachusetts Institute of Technology who studies how technological change and globalization affect workers. He is also co-director of the MIT Shaping the Future of Work Initiative and the National Bureau of Economic Research Labor Studies Program.
Social Media
Twitter: @davidautor
Further reading:
NOEMA – AI Could Actually Help Rebuild The Middle Class
New York Times – How One Tech Skeptic Decided A.I. Might Benefit the Middle Class
Website: https://pitchforkeconomics.com
Instagram: @pitchforkeconomics
Threads: pitchforkeconomics
Bluesky: @pitchforkeconomics.bsky.social
Twitter: @PitchforkEcon, @NickHanauer, @civicaction
YouTube: @pitchforkeconomics
LinkedIn: Pitchfork Economics
Substack: The Pitch
Nick Hanauer:
The rising inequality and growing political instability that we see today are the direct result of decades of bad economic theory.
Speaker 2:
It’s time to build our economy from the bottom up and from the middle out, not the top down.
Nick Hanauer:
Middle out economics is the answer.
Speaker 2:
Because Wall Street didn’t build this country. Great middle class built this country.
Nick Hanauer:
The more the middle class thrives, the better the economy is for everyone, even rich people like me.
Speaker 3:
This is Pitchfork Economics with Nick Hanauer, a podcast about how to build the economy from the middle out. Welcome to the show.
Speaker 4:
We’re recording this about a month after the election, Nick, and obviously there’s a lot of things for me to feel really anxious about. Just one more thing. I just need to ask you, are you planning to replace me with AI?
Nick Hanauer:
If I could, I would, buddy. If I could, I would.
Speaker 4:
Spoken like a venture capitalist.
Nick Hanauer:
I think we’re a few years away though.
Speaker 4:
I’ll tell you what. I don’t know that AI can ever figure out how to do what I do, because I’ve never figured out how to do what I do, so I don’t know what it would learn from. But it’s a great way to lead into today’s conversation, because we’re talking about the potential future of AI.
Nick Hanauer:
Yeah. We get to talk to David Autor, who is one of the country’s most highly respected economists.
Speaker 4:
Labor economists.
Nick Hanauer:
Labor economist, but economist more generally. He’s an economist at the Massachusetts Institute of Technology, and he studies technological change and globalization and how it affects workers principally. But he’s a very consequential guy and has out a really interesting new paper arguing that AI, rather than decimating the middle class, may actually help them. It’s an interesting article. I would say there are a lot of assumptions baked into the argument.
Speaker 4:
Really, economists relying on assumptions? Never.
Nick Hanauer:
I’m not sure I agree with, but I think that AI is here, it’s here to stay, and we’re going to have to wrestle with what to do with it. It’s certainly a good thing that people like David are thinking about it and talking about it. With that, let’s jump into the conversation with David and find out what he thinks.
David Autor:
My name is David Otter. I’m the Daniel and Gail Rubinstein professor of economics at MIT, but basically I’m a labor economist and I work on things that affect jobs, skills, mobility, employment, income, and inequality.
Nick Hanauer:
That’s awesome.
Speaker 4:
Yeah. Our main question is how long before you’re replaced by AI? Is that where we’re going, Nick?
David Autor:
Yeah. Exactly. I would be delighted, man. I could really use a break.
Nick Hanauer:
You’ve got this fascinating new article out where you make this contrarian argument that AI, rather than being bad for the middle class, may in fact be good for it. Before we get into that, just help us understand why you think that AI is not just part of the tech industry hype cycle.
David Autor:
Yeah. No. I think it is not. Let me clear. There’s lots of hype. There’s going to be lots of companies that fail, there’ll be a bubble, et cetera. But in the long run and the medium run, the technology will be extremely consequential, because it unlocks capabilities that we just didn’t have before.
AI can do things. Just to make the contrast, traditional computing followed rules and procedures. It could do what we could program it to do and nothing more. That was incredibly useful, but it never had new ideas. It didn’t improvise. It didn’t solve a new problem. It didn’t think of a better way of doing things.
We had to explicitly understand how to do something to write a program so the computer could follow those steps to do it, and AI learns a lot like we do. Not the same, which is it learns inductively, it learns experientially, it learns tacitly. Most of what we know, we don’t know formally.
We know formally how the structure of language works, how to make a joke, how to have a hypothesis. We don’t even know formally how to recognize a child in a photograph or ride a bicycle. These are all things that we understand tacitly, and for that reason, we couldn’t really teach computers to do them, because we didn’t know how to write the program.
AI can learn as we do from unstructured information, can recognize patterns that we’re not told to look for. It can produce new ideas that we didn’t program into it, and that makes it applicable to a whole host of problems that we couldn’t previously apply computing to.
Whether that’s in discovering the structure of proteins, or whether it’s in recognizing things in medical scans, or whether it’s helping people write and come up with ideas or make inferences from data. It’s really quite broadly applicable, though I will say we don’t really know what it’s for yet.
We’re still in the early age of experimenting and trying to figure out what is it good for and what is it not good for. A lot of what is not good for is what it’s being used for at present, because the first thing you do when you get new technology is you say, “Well, what does it allow me to do better, cheaper, faster that I was already doing?”
But there’s two problems with that. One is AI can do many things that you couldn’t do with the old technology, so you’re missing half the picture. The other is it’s not good at a lot of things that old technologies are good for. It can’t reliably do math or keep facts straight.
Think of that relative to traditional computers. There’s a lot of instinct to, “Let’s use it for this calculation. Let’s use it to write a law brief. Let’s use it to write a letter of recommendation.” It’s going to do those things badly. It’s going to confabulate. It’s going to not make a calculation correctly.
Nick Hanauer:
Yeah. I’ve actually been shocked by how bad the analytics are sometimes. You ask it a question and then you go look for the data and you’re like, “Dude, this is not close to the real data.” It’s like, “Ah, sorry.”
David Autor:
Even if it was right and you told it it was wrong, it would still say sorry, because it wouldn’t know. You can challenge it to rock paper scissors, which is a simultaneous move game. Then it will say rock and you can say paper, and then it’ll go, “Oh, you won. I guess you’re lucky.”
There are many ways you can convince yourself it lacks an understanding of the world, but that doesn’t mean it’s not incredibly powerful for many things that we need to do, because many things we’re looking at data and recognizing patterns and making predictions, and that’s a lot of what cognition is about. That doesn’t mean it’s better than us, but it can help us to do those things. Even when we’re good at it, we’re not consistent. We’re inattentive. We get tired.
Speaker 4:
Not good at math, but could be good for, I don’t know, enslaving humanity.
David Autor:
Sure. If that’s what you’re into.
Speaker 4:
Well, I look at some of the billionaires who are funding AI and I worry that that is in fact what they’re into. If Elon Musk could create Skynet, he would just to prove the point.
David Autor:
Actually, I agree with this point that a lot of AI research is driven by the idea of how do we replicate human capabilities in machines? The truth is we have lots of human capabilities, and I don’t think we’re running short of those. We want additional capabilities that we don’t have.
Flight was not a human capability. Penicillin was not a human capability. Telecommunications was not a human capability. What makes technologies transformative actually is not that they automate the stuff we already do, but they enable us to do things we couldn’t previously do, and that’s where AI has so much potential.
Nick Hanauer:
That’s a really good point. You make this broad argument that AI may in fact be good for working and middle class people. Because it will allow, if I may summarize your argument, it will allow people with less skills to apply themselves in areas which require a lot of skill, and therefore be more productive and ideally compensated better.
You also make the point, I think, that’s very important in your piece, that the world is undergoing a demographic transition in which we’re very rapidly going to run out of people. The idea that we’re going to run out of jobs is something you do not subscribe to, which I’m in violent agreement with.
I think that as long as humanity faces problems, we’re going to have jobs. But what I want to understand from you is why you’re optimistic that these workers who are just more productive will share in the benefits of that productivity. How do you see this unfolding?
David Autor:
Sure. There are a lot of [inaudible 00:08:54] in there. Let me respond to a few of them. We’re definitely running into it, just on the demographic point, because we have low birth rates. Our populations are aging, so we have large and growing retired populations and actually numerically smaller number of people in prime working years to support them.
That labor scarcity, and that’s in the industrialized world, and we’ve seen that really intensively since the pandemic. Now, we should recognize that’s artificial. There’s a lot of people in the rest of the world who would like to come to these countries and work. There’s not really true labor scarcity worldwide. There’s labor scarcity in rich countries, but rich countries don’t seem that interested in bringing those people in.
Nick Hanauer:
But just to be clear, if you go out 25 or 30 years, you are seeing a massive decrease.
Speaker 4:
You go out 100 years, you’re going to see a massive decrease.
Nick Hanauer:
Massive.
Speaker 4:
Across the world.
David Autor:
The problem is there’s nothing wrong with having a smaller world population. It’s the transition between sizes that creates this demographic imbalance.
Nick Hanauer:
There’s always those pesky transitions.
David Autor:
Yeah. Exactly. About allowing what AI can enable. Rather than the word skill, which sounds one-dimensional. People have more education, have more skill. I like to use the word expertise. Expertise could be the expertise to code an app, but it could be to remodel a kitchen, to land a plane, to fix a washing machine, to do accounting, and expertise is really what makes labor valuable in the rich world.
It’s the capability to do valuable things, not everyone knows how to do them, requires specialized knowledge. Our rich world is dominated by experts, and they really are a choke point on everything, in education, in healthcare, in law, in design. More and more of our national income goes to highly educated people who are the tops of their fields and making these high stakes decisions, and we’re really beholden to their expertise.
I can point the finger at myself. I’m an academic, so I’m a paid expert. 50 years ago, we had a lot of people in the middle doing office work, clerical, admin, and also a lot of production and operative work. A lot of that work was automated by computing, because it followed well-understood rules and procedures that could be encoded in software and done by machines.
That has moved a lot of people who aren’t part of this elite, the 40% of people who have college degrees, for example, four-year college degrees, into services. Food service, cleaning, security, entertainment, recreation. Socially valuable work, but it’s poorly paid because it’s non-expert.
It doesn’t require specialized training or skills. Even some of those valuable jobs, being a crossing guard or a daycare teacher, these are life and death activities. They’re poorly paid, substantially, not entirely, but substantially because most people of able-bodied adults can do that work without specialized training or certification.
We’ve created this hollowed out middle and what we would like is a world in which more people who are not at the hyper elite educated could do more valuable work in software development, in medical technical work. Administering care. Doesn’t have to be doing surgery. Or in creating legal documents, or in designing, doing kitchen redesign and the engineering around that.
I think what AI is complementary to is the judgment and knowledge to do those things without having as much of the specific technical, formal frontier knowledge. The example I give in my article is, let’s say I wanted to change the breaker box, sorry, the fuse box in my 100-year-old house to a breaker box. Old technology, new technology.
I could go to YouTube and there’s hundreds of videos telling me how to do that, but imagine I have no electrical skills. I’ve never worn a pair of insulated gloves or used a wire stripper. I could watch one of those videos and I will surely electrocute myself, set my house on fire. Cannot work out well.
Nick Hanauer:
Probably produce another really great YouTube video in the process. Something that will go viral on TikTok.
David Autor:
Exactly. Of course, if I’m a master electrician, I’m not going to watch the video. What’s the point? But if I actually have some basic knowledge. I’ve done electrical projects. I have an O meter. I know some basic stuff. With this tool, I could extend my expertise further.
My judgment and knowledge is a complement to the tool, not a substitute. The good scenario is where we use AI as a tool that enables people to take on important problems, expert problems, without being at the frontier. What I mean the frontier, I’m not imagining a world where everyone can do everything.
That’s not even a good world actually from a [inaudible 00:13:21] point of view. But a world where someone who’s a registered nurse could do more care tasks, or someone who’s a junior law associate could do more legal work, or someone who didn’t do a four-year degree in CS, computer science from MIT, could do good coding.
AI is very supplementary or it supplements judgment, but it requires something we’ve seen from tons of research now in AI, and there’s lots and lots of case studies. AI is really good when you have enough judgment to know if it’s doing well, and it’s dangerous if you don’t.
It requires domain expertise and training, but it doesn’t necessarily require if you have that you can apply that tool to do things. I can code in a computer programming language I don’t know, because I know enough to know when it’s producing the right output, so I can use this tool.
Speaker 4:
What makes you confident that workers will actually reap the monetary benefits of using these tools rather than the owners of the intellectual property? Because I can see a dystopian future of just massive rent seeking from a handful of AI giants.
David Autor:
Right. This is a good question. I’m speaking of a possibility, not a certainty. I think there’s a case where we can do this. Whether we will is not clear. I think there’s two issues, both of which you’ve now raised. One is of course, how are the tools being designed?
Are they being designed to basically replace people or to leverage their expertise? That matters. We have a lot of choice about how we use. They’re incredibly flexible. There’s a million ways to use them. That’s one question, and then the next one is, well, is the market structure such that they get the returns for that productivity or not?
It depends, in my opinion, substantially to whether that expertise remains scarce and valuable. No one thinks that people with bachelor’s degrees, certainly PhDs are underpaid relative to what they do. Why? They have scarce expertise, and because of that, there’s a lot of competition for their services. A good example would be registered nurses. Four-year degree, and they make a very decent income in the United States. I think having expertise remains scarce.
Speaker 4:
I actually think a lot of adjunct professors would disagree with you.
David Autor:
Oh, you’re right. You’re absolutely right. But that’s again about competition. It’s about market structure, and the concern you’re raising, which is a valid one, is maybe this will all be monopolized by a few companies. I think that’s a risk that we have to be really vigilant about. I don’t want to take it as a given this all goes well.
Nick Hanauer:
The case you’re making, of course, is plausible or possible. What I’ve said a million times is that we cannot fear technology. What we have to fear is an economic system like neoliberalism that privatizes all the benefits of technology and socializes all of the disadvantages of technology, where a few people at the top get all the benefits and everybody else gets screwed.
I guess given history, take truck drivers. At one time, one person could move very little in terms of goods from one place in the country to another place in the country, because they would either have to carry it on their back or use a donkey or something like that.
Now a big truck can move a lot of goods from one place to another, but truck drivers are very poorly paid generally, even though they’re creating a huge amount of value relative to the alternative. That’s a consequence of the fact that truck drivers have no power.
Speaker 4:
We deregulated the trucking industry. In fact, truck drivers earned a middle-class wage before deregulation.
Nick Hanauer:
Yeah. Likewise,
Administrative assistant. Before computers, that job was X productivity. With a modern computer, you can obviously accomplish a heck of a lot more, do 10 times as much work really relative to what you could with a typewriter, but those folks don’t earn more today. They earn less than they used to.
Their wages have compressed down over the last 50 years. By the way, this is true of people with bachelor degrees, too. In general, people with bachelor degrees, their wages have either plateaued or decreased. This is the story of the last 50 years, is that the only people who really benefited from economic growth were the people it the top 1% or 2%.
David Autor:
I’m not agreeing with that, by the way.
Speaker 4:
Okay. Top 10%,
David Autor:
No. Basically, people who have a college degree on up, which is 40%, have seen real income growth, but there’s been a lot of stagnation.
Nick Hanauer:
But they have not maintained their same share of GDP.
David Autor:
Okay. This is a point I wanted to make. $6 out of every $10 in the US economy approximately is first paid to workers. It’s fallen somewhat, although it’s still pretty high. What’s actually remarkable, and I think many people will find this surprising, is you might think in the richest countries, the countries that have the most capital, the most technology, the most infrastructure, that labor share of economic activity would be relatively small.
But in fact, it’s larger. The world average is about 50%. In parts of North Africa, it’s 45%. One of the amazing things actually about the industrialized countries is even as we’ve gotten all this technology, we’ve made our labor more valuable, not just in terms of we pay higher wages. A larger share of all that activity has gone to workers.
Now, we should be concerned about that declining, but it’s still actually relatively high here. I would argue in the case of truckers, this is part of the reason wages haven’t risen with productivity. You can make the same argument about cashiers or many activities, is because the labor is not scarce.
The people who can do it or the capability to do it is abundant, and that’s why I really focused on this notion of what makes expertise valuable. The problem that we have is that people who do not have, or a problem that we have, is that the majority of people who do not have a college degree have been pushed downward into non-specialized work.
Clerical work, production work that required real skills and knowledge and training that made it valuable over long periods of time. In a world in which people are doing work that many, many others can do, it’s very difficult for that to pay well. Now, I don’t want to make the case that it can’t pay well. Obviously, it pays more in some countries than others, and that’s a function of the way they structure their economies.
Nick Hanauer:
Well, power. Some people who work at McDonald’s make $20 an hour. Some people who work at McDonald’s doing exactly the same job make $7.25 an hour. That is not a reflection of rarity. That’s a function of we somehow delivered more power to the people who were earning $20 than the people who earn $7.25.
David Autor:
I’d say in almost all countries, the ranking of earnings across occupations activities is very similar, but the disparities very enormously. In Scandinavia or even in Canada, wages are substantially higher for people doing that same work, and that is not an economic inevitability. That’s a choice of power, as you say. How is the social system organized?
Again, going back to these two questions. One is can we augment the value of expertise? Can we enable more people to compete in these high paid activities? It won’t necessarily be good for doctors or lawyers or software coders, but if more people who are not really able to do that work, it would be better for them and it would also bring down the price of goods and services.
It would improve the quality of jobs for some, it would improve the availability of a lot of expensive stuff for others. But then there is the question that was just asked. Well, what’s to prevent firms from monopolizing that? It depends on healthy labor market competition. It absolutely does.
That means the way we structure regulations, the ability, the worker voice and institutions to protect that, labor standards, and social norms about what we view as tolerable. I don’t think it’s either or. I think we need to be productive. Ultimately, how much we can distribute depends on how much we produce.
We want to use these tools well, but we don’t want to use them in a way that just makes human labor unnecessary or superfluous. We want use it in a way that makes more people’s judgment and knowledge and expertise more applicable across valuable things.
Speaker 4:
Earlier when we were talking about the impending demographic, I don’t want to use the term crisis, collapse, decline, whatever. It certainly doesn’t have to be a crisis. You emphasize it’s the transition that’s the difficulty. Not the smaller global population, but the transition to it. As a student of history and technology, can broadly say that new technologies in the long run have proven beneficial. We have a much higher living standard now than we did 200 years ago, and we’re all better off for that transition.
David Autor:
Incommensurately.
Speaker 4:
But in the moment that transition immiserated people. It was very difficult. We all live in the moment. The people who are going to live through the AI revolution, a lot of them risk being displaced and immiserated. What are the policies that we need to focus on to get through the transition without, I don’t know, undermining democracy?
David Autor:
Just to put a fine point on it. Yeah. First, let me agree with you that even if the long run outcome is a good one, the transitions are often really costly.
The transition into the industrial revolution. Working class incomes did not rise in Britain for the first 60 years of the industrial revolution. People were immiserated, and even when they started to rise, there’s lots of people who were displaced.
People who are on the upside are usually not the ones on the downside. Pittsburgh has reconstituted itself after losing steel and coal, but the people in Pittsburgh who are doing well now are people in education, people in healthcare. They’re not former steel workers or former coal workers.
Even if you think it’s all going to work out great, that doesn’t mean every person is going to benefit. But then there’s an even harder problem. Let’s say it’s all going to be great for people in the long run, but not in the first 20 years. I think that’s a very, very serious challenge.
Part of it depends on how fast it changes. This is very important. If you told me 20 years from now all trucks will be self-driving, I’d say, “Well, we can manage that. 20-year transition. People will retire. People won’t enter.” If you told me that’s going to happen on New Year’s Day, I would say that’s a crisis.
Part of it’s how fast it happens, but then the other of course, is the support systems that we have to enable people to thrive and rebound. That’s just something where the US falls uniquely short among developed countries. We spend one tenth as much per capita or per GDP as does for example, Denmark on retraining people and getting them back in the workforce.
We used to believe that we had a national job security program. It was called get a new job, and we’ve learned that that just isn’t such a good program, and that’s why I worry the most about the US. I think the US does so little to buffer against the damaging forces of inequality and the scars they leave that it actually inhibits economic mobility, inhibits opportunity.
It’s not just cruel. It’s also inefficient. We have a thriving meritocracy from the median on up. Then for everybody else, we under-invest in their skills and their security and their healthcare and their safety and their education. I think that’s the danger that we run.
Survey evidence says that US workers are more nervous about technology than people in other countries. You say, “Why? We’re creating this technology. Why shouldn’t we love it?” The answer of course, is what you said earlier, because they recognize the economic system.
It’s not they’re afraid of the technology. They’re afraid of the cost they’ll personally pay. The science fiction writer Ted Chiang said, “Fear of technology is not fear of the machines. It’s fear of capitalism.” It’s the fear of I don’t win when we get more productive, because something else takes my job.
Nick Hanauer:
That’s what I said. Yeah. It’s not the technology. It’s the neoliberalism that kills you.
David Autor:
That’s right. Exactly. I think we’re at a transitional period. There is incredible [inaudible 00:25:43] simultaneously be fearful and optimistic in the sense that there’s a lot to be worried about. The things outside the labor market are arguably more worrisome than even what’s going to happen in the labor market.
But there’s also enormous opportunity, and there’s no question that we will enhance the rate of technological progress. We’ll be able to do better things with addressing some of our most challenging problems, and if we do it well, we’ll enable more people to do valuable work. Even that’s good over a while, it doesn’t mean it’s good for everyone [inaudible 00:26:12]-
Speaker 4:
Are you suggesting that labor markets do not inevitably return to a Pareto optimal equilibrium, that we can just trust the invisible hand? You’re an economist. I don’t know what you teach in your classes.
David Autor:
Look, I think well-functioning labor markets are central to democracy. You cannot have a well-functioning democratic system where most people don’t perceive themselves to be stakeholders and aren’t perceived to be productive contributors. A society where everybody is seen as a ward of the state is not politically stable, because the people who perceive themselves as the income generators will say, “Why are we supporting all these people?”
The best case scenario is a country like Kuwait where you have no taxation and no representation, but at least the dictatorship is relatively benevolent. But we have a real stake in maintaining labor’s value, making sure that work pays well, and that people have economic security that is tied to it. Work is not just about the displeasure of going to a job. It’s about community. It’s about identity. It’s about esteem. For many people it’s also a source of meaning and fulfillment.
Nick Hanauer:
By the way, I think one of the things that we can be most optimistic about is that a greater and greater proportion of humans on planet Earth every year have jobs from which they can derive meaning.
David Autor:
This has been the best 30 years in the history of humanity in terms of elevating people out of poverty. From the perspective of the west, we say it’s been kind of mid. We haven’t grown that much, but billions of people have been lifted out of poverty actually by China’s rise, and the prosperity that’s created in Sub-Saharan Africa and Central and South America. Yes, it absolutely is the case. China, it’s not a democracy. It is a market economy, and that has helped.
Nick Hanauer:
Yeah. For sure. Just a couple more questions, David. If you were in charge of the world. We have a benevolent dictator question. Political constraints aside, what would you do with respect to AI to lead to the promised land that you described?
David Autor:
A couple things. First of all, the first thing I would do just because it’s easy is I would regulate the use of intellectual property. I actually think there’s enormous IP theft occurring at industrial scale. Intellectual property law was not meant to protect people against machines that would read everything and regurgitate it.
I think people should be paid for their creations, and that’s a first-order problem. But more at a deeper level, I think we should be investing in these good use cases. We should say, “How do we make healthcare a place where more people can use expertise, where it’s cheaper and more available? How do we make education more effective and accessible?”
Actually, AI is a tool that we can use to make AI, sorry, to make learning more like simulation. More like a flight simulator, less like a chalkboard. I would be investing for good use cases and then I would build lots of labor market supports that are not really around AI. They’re just around insecurity.
We have trade production programs in the United States where people’s jobs are displaced by trade, but we don’t have technology displacement protection programs. Why not? If I lose my job, I lose my job. I don’t care if it’s because I lost it because of a robot or I lost it because of a competitor in another country.
I just need economic security, and there’s where we have models of countries that do this much more effectively than we do. Now, it’s hard. We can’t just become Denmark, but we shouldn’t think that it’s impossible to do, because we know it is possible to do.
Certainly even during the Obama administration, they stood up what was called a wage insurance program for people displaced from trade. Essentially, it said, “If you take a new job at a lower wage and you’ve been displaced by trade, we’ll make up some of the difference for a while.”
That got people back into the labor market faster. It didn’t help them recover earnings per se. It didn’t get them back to where they were, but it made that transition at least more successful. Longterm unemployment is devastating for people, not just economically, but psychologically.
Nick Hanauer:
One final question. Why do you do this work?
David Autor:
It just seems the most natural thing in the world to care about work and how people make their living. I think that the labor market is the most central social institution. More than the church, more than the government, more than the bowling alley that’s talked about by people who study social capital.
It’s constantly changing. It’s at the center of these tectonic forces of globalization, of trade, of technology, and I see how it affects people. I worked for several years in nonprofits. I did education for poor kids and adults. I did volunteer work in South Africa, and I saw how the world was changing around them and how difficult it was people to adapt and how the institutions were not in place to enable them to thrive.
I feel like the first thing we need to do is understand so we can respond appropriately. I do hope that my work and my colleagues and many other people in my field have helped us better understand both how computerization has changed the set of jobs and the skills we need. How labor institutions shape opportunity, shape wages, shape allocation fairness, and how globalization has been such a mixed bag.
[inaudible 00:31:22] economists were really rah-rah about my work, and my co-authors really suggested that it does lead to economic growth, but the direct cost to individuals are enormous, and this will be the same with AI in some ways. It will create growth, but there will be huge displacement, and the beneficiaries will be pretty diffuse and the people who are on the losing side will be very concentrated.
They’ll know why they lost their job, and we should invest in them. Both for their sake, because it’s morally just, it’s the right thing to do, and collectively it’s really in our interest to have a society where people feel that their opportunity is protected and preserved and invested in. We would want them to do the same for us and we need that for political function and social harmony and for human wellbeing.
Nick Hanauer:
Democracy.
David Autor:
Democracy.
Speaker 4:
Weird. Social cohesion is efficient.
Nick Hanauer:
Well, David, thank you so much for being with us. It’s obviously a fascinating and timely topic. I do hope you’re right.
David Autor:
I hope I’m right. I want to say, as I say in the end of the article, I’m painting, I think, an intellectually and morally coherent idea. It’s not really a prediction.
Speaker 4:
It’s not a prediction.
David Autor:
It’s a possibility. Saying this is something we could attain and we should try. It’s not out of our reach. It’s actually more within our reach than it has been.
Nick Hanauer:
I’m 100% in agreement with that. The future you describe is absolutely possible. It will just take a remarkable amount of political and economic leadership to get us there.
Speaker 4:
Yeah. My guess, Nick, it’s not what your buddies in venture capital are hoping for when they invest in these-
Nick Hanauer:
That is not what Marc Andreessen is thinking.
Speaker 4:
No.
David Autor:
Even not knowing him personally, I’m sure that that is correct.
Nick Hanauer:
Anyway, thank you so much for being with us. It’s been fantastic.
David Autor:
Thank you. Thank you both very much. Great show and appreciate the opportunity to speak with you.
Speaker 4:
I think it’s important, Nick, to reiterate what David said, that he’s not predicting the future. He’s not predicting this utopian future. He’s just painting a portrait of a possible future in which AI benefits everybody.
Nick Hanauer:
Yeah. I think that it is absolutely true that that future is possible. I just think it’s not probable.
Speaker 4:
Well, not probable under our current institutions and norms.
Nick Hanauer:
Yeah. I think that we have had tens of thousands of years of technological advancement. It’s certainly true that over time that technological advancement has benefited people broadly, but as we spoke about in the chat, it’s those pesky transitions that usually are pretty terrible.
Speaker 4:
Right. Our lives are so much better for the millions of people who were mutilated in the mills of the 19th century and enslaved in the cotton plantations.
Nick Hanauer:
Such nice fabric.
Speaker 4:
Because of all that suffering, the modern world is made possible. The genocides, all that, we’re better off for it, and I think the important thing is one could imagine that we could have gotten to this future in less awful ways.
Nick Hanauer:
Yeah. That’s right. I really truly do believe that that is true, that there is a way to go towards the future ambitiously without the horrific downsides that these transitions generally generate. I do think it’s all about recognizing the downsides. But I guess if I have to quibble with David, it’s just that I think that there’s this view among economists generally that talent and ability is connected to benefits, and that the most expert people are most highly rewarded.
In some abstract sense, some of that may be true, but this narrow ability, for example, to trade stocks on Wall Street or some other obscure financial thing, which produces an income of $100 million a year, against somebody who teaches high school calculus. It’s very hard to make the argument that the person trading financial derivatives or something like that is more expert than the person who is teaching high school calculus.
Speaker 4:
Or more meritorious. David did make this comment that we basically have a meritocracy from the median up. We’ve talked about this before. We don’t believe it is a meritocracy.
Nick Hanauer:
I just think that the real danger going forward is to pretend that it’s not about power.
Speaker 4:
Right. That the market will handle this, that the Marc Andreessens and Elon Musks and Peter Thiels. You look at their comments and they actually think we need the immiseration. Musk talking about we need a big economic collapse, there’s got to be a lot of pain and suffering coming up. He thinks that’s a good thing, and maybe that’s because he looks at history and thinking all those people who suffered in his family’s emerald mines created a much better world in the future. But there’s this mean spirited ideology among the super rich.
Nick Hanauer:
Or Larry Summers.
Speaker 4:
It’s not all of you.
Nick Hanauer:
No. Or Larry Summers thinking that the only way out of inflation is to put 7% of Americans or 8% of Americans out of work, that that’s the only solution.
Speaker 4:
It turns out there was another path.
Nick Hanauer:
Yeah. There was. Well, there were a lot of other paths, but making the little people suffer seems like to a lot of people the only path, which is, I think, the problem.
Speaker 4:
I don’t see a problem in the technology itself other than the possibility of Terminator style, the AI becomes self-aware and says, “Oh, my God. Humanity does what,” and decides to enslave or destroy us. I guess that’s a possible future, too. But to me, it’s ownership.
It always comes down to ownership. Are we going to allow the owners of the IP, of the intellectual property, of the AI to extract all of the benefits for themselves or most of the benefits for themselves, or are we going to regulate it in a way that broadly benefits society?
If we do the latter, things quite possibly will be okay. The vision that David put out here. If we allow Elon Musk and Mark Andreessen and Peter Thiel and so forth to determine how it’s used and who it will benefit, it’s not going to be such a good outcome. That transition is going to be very, very bad.
Nick Hanauer:
Yeah. Well, it’s a great conversation
Speaker 4:
We will provide a link in the show notes to David’s piece, AI could actually help rebuild the middle class.
Speaker 6:
Pitchfork Economics is produced by Civic Ventures. If you like the show, make sure to follow, rate, and review us wherever you get your podcasts. Find us on other platforms like Twitter, Facebook, Instagram, and Threads at pitchfork Economics. Nick is on Twitter and Facebook as well at Nick Hanauer.
For more content from us, you can subscribe to our weekly newsletter The Pitch over on Substack. For links to everything we just mentioned, plus transcripts and more, visit our website, Pitchfork Economics.com. As always, from our team at Civic Ventures, thanks for listening. See you next week.