This transcript is generated with the help of AI and is lightly edited for clarity.
DAVID AUTOR:
Everybody knows AI could be used for all these great things. Everybody also knows it could be used for really terrible things. And your belief about what’s going to happen is not really a belief about AI, it’s a belief about what humanity will do with this opportunity. Will we squander it or will we make the most of it? The future is not a forecasting exercise, it’s a design exercise, right? We’re building it. It’s a matter of making good collective choices.
REID:
Hi, I’m Reid Hoffman.
ARIA:
And I’m Aria Finger.
REID:
We want to know how, together, we can use technology like AI to help us shape the best possible future.
ARIA:
We ask technologists, ambitious builders, and deep thinkers to help us sketch out the brightest version of the future—and we learn what it’ll take to get there.
REID:
This is Possible.
REID:
In the early 2000s, David Autor and his co-authors documented the “China Shock,” or the rapid influx of imports from China that reshaped U.S. manufacturing, hollowed out middle-skill jobs, and contributed to deep regional disparities in income and employment.
ARIA:
Today, we may face a new “AI Shock” where advances in machine learning promised to automate a broad swath of tasks from data entry to diagnostic imaging, potentially upending whole sectors. The pressing question is, will this shock exacerbate inequality, or can we steer it towards widespread opportunity?
REID:
To help us understand the parallels and the pivotal differences between those two seismic shifts, we’ve invited David as one of the foremost experts on technology and labor markets.
ARIA:
David Autor is the Ford Professor of Economics at MIT and co-director of its Work of the Future Task Force. His landmark research on the “China Shock” has become foundational for policymakers grappling with globalization’s labor impacts, and now he’s turning his analytical lens towards AI’s coming wave.
REID:
We sat down and talked with David about AI, jobs, expertise, automation versus collaboration, and labor markets.
ARIA:
Here’s our conversation with David Autor.
ARIA:
Alright, David, I am so excited because, oh so many years ago, I was an economics major in college, and my life diverged, and my path not taken is being an economist. And so you are a huge celebrity for me. So this is great. And we have another thing in common because when I was in high school, I used $700 to buy a 1983 stick shift Toyota Camry. And I have it on good authority that you perhaps in between college graduation and what was next, you drove across the country—and I don’t know anything about cars, so I’m reading this right off the sheet—in an eight-forward-speed stick shift, 1980 Dodge Colt RS, that cost you $250.
DAVID AUTOR:
That’s right. I spent one-third of what you spent on your car.
ARIA:
I know. Well, you were frugal. So I want to ask what did you learn about this cross-country trip?
DAVID AUTOR:
Oh, actually, it kind of has set up the rest of my life, believe it or not. Because I had finished college. I had studied psychology, and I had done computer science separately, just as a concentration. I’d worked as a programmer. And I wasn’t very satisfied with either career. I liked the question of psychology, but I didn’t really love the methods. And in computer science, I loved the methods, but I didn’t really like what I was working on as much. And so when I finished college, I really didn’t know what I was going to do with my life. And I went on a seven-week cross-country trip in this little roller skate of a car with my girlfriend. And we were at the Big Top Chautauqua in Minnesota and listening to NPR.
DAVID AUTOR:
And they talked about this computer learning center opening up at a Black Methodist church in San Francisco, called “Computers and You,” funded by Silicon Valley folks who were trying to bridge what they called the digital divide at that time. And I thought, “Oh, that sounds cool. I’m going to show up and volunteer there.” So I showed up and volunteered there, and then I ended up as the director of education for three years. And that kind of got me interested in technology, and work, and inequality. So that was the main thing I learned on that trip.
ARIA:
I love it. That’s awesome.
REID:
That’s very cool. Let’s cut to some of the very current, and very important things, part of the reason why we’ve been looking forward to this podcast for some time now. So let’s start with your “China Shock” work, which revealed how import competition hollowed out U.S. manufacturing regions over decades. Now we may be entering what some might call an “AI Shock.” So what are parallels and differences between a wave of low-cost goods flooding markets, and a wave of cognitive automation reshaping tasks, jobs, industries?
DAVID AUTOR:
So let me put this in context by talking a little bit about the China trade shock. So the China trade shock refers really to two different things simultaneously. One is China’s incredible growth, as a function of its own changes in policies. And that starts in the eighties, continues to 1990s under Deng Xiaoping. And China just has this incredible productivity growth. They adopt Western techniques and technologies. They allow foreign direct investment. They allow hundreds of millions of people to move from unproductive rural agriculture into export processing zones. And so that is a tribute to their own talent and turnaround of a country that had been really in quite a bit of trouble for quite a while. And then in 2001, they become a member of the World Trade Organization. They also get permanent normal trade relationships with the United States in 2000.
DAVID AUTOR:
And that induces really a surge in exports in the United States. Because of falling tariffs, but a change in investment environment in all kinds of ways. China actually became more competitive as a result of having to reform and open because of the WTO. And this had many benefits—lowered prices. It was great for China, it was great for many, many countries. It had a very severe impact on U.S manufacturing and labor-intensive U.S. manufacturing, particularly in the south, and southeast. So furniture manufacturing, textiles, clothing, doll assembly. A lot of labor-intensive, not the high-end manufacturing—not mostly cars or airplanes, electronics. And this in a very short order caused a loss of more than a million manufacturing jobs. Now, a million is not that large in a labor market of 150 million people. And so if this were distributed evenly across U.S. counties, it’d be a few thousand people per county.
DAVID AUTOR:
You wouldn’t necessarily notice it. But that’s not the way manufacturing works. It’s very regionally concentrated and not just—you know, there’s manufacturing here, but where manufacturing occurs, it’s specialized, right? There was a town that called itself the sweatshirt capital of the world. And another town that called itself the furniture capital of the world—Hickory, North Carolina. So they concentrated on doing one thing and did it well. And all of a sudden their work was just non-viable. And many big factories closed in very short order. The period from 2001 to 2007 was when this occurred. 22% of all US manufacturing employment was lost between 1999 and 2007, and then cumulatively about a third, once we go into the Great Recession. And so this was just a incredibly concentrated loss of jobs, and loss of the viability of entire towns and the industries they were built around.
DAVID AUTOR:
And so it was experienced as extremely scarring by the people in those places. And 20 years later, those places have kind of rebuilt. They’re really quite different. But the workers who were initially in those locations actually have not moved on. They’ve not moved up or out. And many of them are still in relatively low-paid manufacturing or other low-paid work. So it’s been a very, very difficult, challenging, scarring transition. Okay, so that’s the China trade shock. So when we talk about the AI shock, it’s useful to draw that analogy, not because I think the analogy is exactly correct, but because it’s instructive to think about the differences. So, one thing they have in common, of course, is it’s could happen quickly. It’s not so much a policy choice—although maybe China’s WTO exception was.
DAVID AUTOR:
But it will affect a lot of people potentially really rapidly. I think there are three very important differences. One is, again, that regional concentration. We don’t expect the impacts of AI to have anywhere near that type of local impact, right? For example, we’ve lost more clerical jobs over the last 30 years probably than we have lost manufacturing jobs. But no one talks about the clerical shock. Why not? Well, one reason is there was never a clerical capital of the United States [laugh], right? That wasn’t the way it worked. Because there were clerical workers in every industry. Similarly, AI, it will affect jobs and roles and occupations, but it will not affect regions in the same with the same degree of concentrated impact. So one, is it’s not going to have the same regional component. Two, trade shock really impacted the viability of industries themselves, right?
DAVID AUTOR:
All of a sudden it just wasn’t competitive to have a U.S. commodity furniture manufacturing industry that was making furniture for Walmart and Target, that was now all moving overseas. And AI will much more, again, affect specific occupations, lines of work, the way work is organized within occupations, but it won’t wipe out entire industries. Or if it does, I can’t think of many that would be, if in that category. So it’ll not be as holistic. And then the third thing is, the China trade shock was experienced by U.S. firms as a pure negative competitive shock. All of a sudden, they couldn’t charge the prices they were charging. Someone else was charging much less. And so from a firm perspective, this was all bad. AI will be experienced by many firms as productivity increasing. So it may still lead to displacement of workers. In fact it will, I don’t want to suggest it will not. But it will have a very different texture. It will not be perceived as all bad news. It will be perceived as rapid change. But some of it will be firms saying, “Well, we’ve got additional efficiencies. We can offer more services, we can offer lower prices.” They may still shed workers. I don’t want to say they won’t. It will not be, in any sense, a repeat of the China trade shock.
ARIA:
One of the things you talk about—which I think is interesting—is everyone’s sort of obsessed with jobs. Will AI create jobs? Will it displace jobs? How will it be? And you’re saying, “Listen, we actually have a shortage of workers right now. We have plenty of jobs. The real question is actually about wages and income inequality, and what does technological change do to that?” And so historically, technological change, it’s been skill-based, raising returns to education. And so the question on AI—could AI usher in an era of task-based change instead? Where the premium flows to say emotional intelligence rather than former credentials? How could AI reshape this? And is there a positive scenario, negative scenario? Because we don’t want to have the inequality that we had in the past.
DAVID AUTOR:
So first, let me agree with what you just said. We’re not running out of jobs. We’re much more running out of workers. And this is true in most industrialized countries where we have low population growth, low birth rates—and now in the United States—heavily, heavily restricted immigration. And that creates real challenges, not just because it’s hard to find the workers, but it also means you’re going to have a large retired population that’s expecting—has earned the right to—a decent standard of living in retirement. And you need workers to support that, to provide that financing. So that is not my concern. And we have very tight labor markets, we have low unemployment rate, and we have for quite a while now, for more than a decade. The greater concern is the value of skills.
DAVID AUTOR:
People are paid to a substantial extent for expertise—their knowhow in specific activities, right? In rich industrialized countries. There just isn’t much of a value to just pure physical labor anymore. And by expertise I mean know-how. And I don’t want to equate that with schooling because there’s lots of expertise that’s gained not through schooling—some schooling, hopefully some is gained through schooling as well. Expertise means how to bake a loaf of bread, or code an app, or diagnose a patient, or remodel a kitchen. These are all valuable forms of expertise. But for expertise to have market value, it needs to have two things. One is it needs to produce a service that people value, right? So it’s got to be data science, not card tricks. The second thing is it needs to be scarce because if everyone is expert, no one is expert—it just won’t pay very much.
DAVID AUTOR:
And this is the threat that automation sometimes poses: is it can devalue a skillset very quickly. Not because no one needs the skills anymore, but because the machine can do it better, cheaper, faster, right? So we’ve seen that when phone-based routing changed the value of taxi services in London where people used to work from memory. It used to be that touch-typing was a very valuable skill, not so much anymore. And a lot of mechanical skills used in manufacturing have lost value because either that work is done overseas or because it’s automated. And this is the concern that one could have. And actually, let me put even say this more broadly. Over the last 40 years, computerization has led to a lot of hollowing out of both production work and office work.
DAVID AUTOR:
There was a lot of skilled work following codified rules and procedures that required expertise, and knowledge, and practice. And because those rules and procedures were well understood, it was feasible to turn them into computer code and have machines execute them. As it has happened, it’s not that we’ve run out of jobs in the interim—nothing like it—but a lot of the people who would have been doing office work and manufacturing work four years ago now find themselves doing services—food service, cleaning, security, entertainment, recreation, hospitality, home health aids—and that’s socially valuable work. No judgment there, but it’s not expert work. Most people can be productive at that with very little training or certification, and that means it won’t pay well. So, I like to give the example of, think of it like a crossing guard versus an air traffic controller.
DAVID AUTOR:
At some fundamental level, those are the same job. The job is to prevent collisions between people in vehicles or vehicles and other vehicles. And yet crossing guards make less than a quarter of what air traffic controllers do. And they’re protecting our children’s lives when they go to school in the morning. So they’re doing valuable work, but they’re not going to be paid a lot, unfortunately, because there’s almost no training or certification required to become a crossing guard. So if we suddenly ran short on crossing guards, we could get the air traffic controllers to go do that in the morning. But if we suddenly ran short on air traffic controllers, as we are now doing, we could not get crossing guards to do that job.
GEMINI:
Google Gemini here. The US has faced a continual shortage of air traffic controllers over the last decade, a trend marked by persistent understaffing and declining numbers in many facilities. This ongoing problem stems from a confluence of factors, including past hiring constraints and disruptions like government shutdowns, and the COVID-19 pandemic, coupled with a lengthy and difficult training process that sees high failure rates. Additionally attrition due to mandatory retirement ages and the demanding nature of the job, along with inefficiencies in workforce allocation, have exacerbated the shortage, leading to concerns about controller fatigue and flight delays.
DAVID AUTOR:
So expertise is really critical. And so the threat that rapid automation poses, to the degree it poses a threat, it’s not running out of work, but making the valuable skills that people have highly abundant. So they’re no longer valuable.
REID:
I mean, there’s a whole set of different questions here that I think are really interesting. But one of the ones I actually want to start with is something I’ve been thinking a lot about, which is the question, exactly as you say, that it’s really important that we, for the quality and economics of work, is that it’s valuable to the market and that it’s relatively rare, uncommon at least, in order to command a premium in economics and wage. Have you seen any work or speculation or theorization that you think is interesting around how AI can help people acquire those skills? Like being adaptive to it, learning new skills, being in new areas. Because most of the analysis that I’ve been running across has been, “Well look, coding’s an important thing and AI’s going to be doing a bunch of coding,” and you take a zero-sum amount to the amount of coding there is, and you predict something challenging. All of which strikes me as has a bunch of assumptions in it that are certainly not necessary, and maybe steerable.
REID:
So I’m curious if you’ve seen anything that’s things that we should be paying attention to, things that we should be trying to emphasize, things that we’ve been learning.
DAVID AUTOR:
So first, let me agree with you, there’s a lot of thinking in the world that goes of the following—let’s look at people who are exposed and if you’re exposed your hosed, right? Like that’s it, your work is going to shrivel and shrink and die away. But of course we know that’s not true, think of the air traffic controllers versus the crossing guards. Who would you rather be? Right? So in many, many cases, technology actually makes us more effective and valuable. We would be worthless without the technological tools that we have. I couldn’t do my work. Air traffic controllers couldn’t do their work. And if you’re a doctor, if you don’t have a stethoscope, you’re much worse off. So in many ways our technologies are complimentary to our expertise and they are amplifiers, or force multipliers, for expertise. Because they shorten the distance between intention and result.
DAVID AUTOR:
They give us superhuman powers to see things, to do things, that we could not do with our bare hands or our naked eyes and so on. Or even do cognitively. We couldn’t compute them fast enough. You know, I do a lot of statistics. My computer inverts matrices in milliseconds, it would take me months if I even remembered how to do it. So we should be thinking about how do you get to be on the right side of this equation? Is your technology going to make your work more expert, or is it going to basically displace the valuable expertise that you have? And for different people in different roles, those will have different answers. And so let me go back to the question you asked Reid, which is what about people acquiring expertise? This is super central. So, the argument I’ve been making is work that pays well is decision making work.
DAVID AUTOR:
Where the stakes are high, it’s a one-off choice—how to land this plane, how to care for this patient, how to remodel this kitchen, even how to season this meal at a restaurant—and there aren’t simple rules. If there are simple rules, it’ll already be automated. So it actually requires a lot of discretion. And the problem is a lot of that work is done by highly educated people. So the people with BAs and MDs and MBAs and so on, they kind of monopolize the commanding heights of the modern economy. Whether in medicine, or in law, or in design, or in education, technology, and so on. And a lot of the people who used to do valuable work in offices and clerical offices and factories, they have been pushed into generic work that’s not expert. And the good scenario would be one where we were able to use AI to support people to do more valuable decision-making work, both to user expertise more effectively, and to acquire it more efficiently. And I actually think that’s one of the great challenges of our era, is to figure out how to create tools, AIs, that support people using their expertise better and learning faster. And I think that’s very hard, because the opposite can occur. If you rely too much on technology, you kind of won’t bother, and you’ll just say, “Well, it’ll do the job for me.” And so I like to distinguish between what I call automation tools versus collaboration tools. So automation tools, or tools like the automatic transmission on your car, or the elevator that goes between floors, or the toll taker that when you drive through a highway toll takes money.
DAVID AUTOR:
These are examples of successful automation. And those all used to be jobs. You used to shift your own car, right? I know my Dodge Colt, the eight-speed car, that had a stick shift. There used to be elevator operators, and there were lots and lots of toll takers. And this is successful automation. All of these specialized knowledge that was required is now fully encoded in machinery. It’s done. We’re happy with that. There’s no problem. Most tools are not that form. Most tools require you to bring some expertise to the table to use them. So stethoscope, great thing. No use to me. I wouldn’t know what I was hearing. Chainsaw—good for lumberjack not so good for my children. And most tools, they’re good because they allow you to take some knowledge and capability you have and do it faster or further or better.
DAVID AUTOR:
And so if we use AI well, we’ll be using it a lot as a collaboration tool. To enable us to make better decisions, to do harder tasks, to solve problems. And I don’t just mean like in research or whatever. I mean, like you’re an electrician, you go out to a site, you encounter an unfamiliar problem, you use your AI and it pulls up the relevant information for you and helps guide you to do the work. You shouldn’t do that if if you’re not an electrician—don’t go opening up fuse boxes—but if you have the basic skills and then you have a tool that can support you, you could probably go further with that. And so that’s the way that would be successful use for collaboration in many, many cases.
DAVID AUTOR:
And again, I’m not morally opposed to automation. If you can automate something fully, successfully—great. In most cases we can’t. There’s actually an illusion, a kind of hubris that, “Oh, we have technology that’s superhuman, therefore, expertise is dead. It can do whatever we want.” Geoffrey Hinton famously predicted about a decade ago that we would need no more radiologists. And because it’s so obvious that AI will just do this better. Now, AI is now used by radiologists. They love AI. But they’re not fewer radiologists. They just do more of what they did. They’re more useful because they have better tools. And a lot of their work is not just looking at scans, it’s all of the communication with the patient and the other caregivers and so on. So it’s a mistake to think that everything, because we have good technologies, that we can automate everything.
DAVID AUTOR:
There’s a problem with thinking you can automate when you can’t. You’re going to design badly. So automation, and it’s not just automation, can either increase the expertise of your work by eliminating the supporting tasks and allowing you to focus on what you’re really good at. So I don’t spend time inverting matrices, I can just work on what the statistics mean. Or it can de-skill your work by automating the expert parts and just leaving you with a last mile. So both are possible. Now, it also often creates new work that requires new forms of expertise. But it’s usually different people who are doing that work. So in terms of using the tool, we should be thinking about, well, where will expertise be needed? Where will be displaced? And how do we enable people to do expert work with better tools? People who might be shut out. In a world in which more people who don’t have a four-year college degree can do software development, can do some legal work, can do medical technical work, can do kitchen design—that’s a better world in my opinion. And then finally, how do we create tools that enable people to get better at that stuff faster?
ARIA:
You ended with the word faster, which I think is really apropos because the thing, I think—one of the things—that scares people the most about AI is time. They say, “it’d be one thing if this was happening over a hundred years, but it’s happening so quickly.” So I have a two-part question. One, I thought you really sort of illuminated why speed can be tough, for me, when you talked about the difference between an occupation disappearing over 20 years versus seven years. So I would love to sort of hear what that difference looks like in terms of unemployment, et cetera. And the second thing is, from hearing you talk about expertise, it would make me think, okay, the superstars are going to get even better. The people who are educated are going to be even more enhanced by AI. But you’ve talked about how AI could potentially be good for the middle class. How can we think about that? Is it because we can retrain workers more quickly? How can this tool actually be good for the middle class?
DAVID AUTOR:
So let’s first talk about the speed. Labor markets have a natural rate of adjustment. If you think a career is 30 years, let’s say, that means kind of 3% of people will retire out of anything every year. So, if you wanted to eliminate a third of people without laying anyone off, you’d just wait a decade and they’d all be gone. And most labor market change for adults, it actually doesn’t happen mid-career. It’s at the choice of the entry point. And so it often occurs across cohorts. So in the areas that saw these big China shocks. The adults who were in manufacturing have not primarily moved on to something else. Some have moved into lower paid services, it’s their kids who never enter manufacturing. So it really does matter how fast this goes on.
DAVID AUTOR:
We talk about autonomous vehicles all the time. If autonomous vehicles come Labor Day this year replaced all long distance drivers, that would be a very serious problem. Because there are more than two million, I believe more than 3 million, people who just do their living in driving vehicles. So that would be catastrophic. Not because autonomous vehicles wouldn’t be a good thing, but because that would be so much job loss all at once if it happened over 25 years. That’s kind of a manageable problem. People wouldn’t enter the occupation, people would retire out of it, and it will happen actually much more slowly. Because even if this problem were solved tomorrow morning, it takes decades to replace all that capital. You’re not just going to throw away all your trucks, you’re going to replace them slowly over time.
DAVID AUTOR:
And so the concern with AI, I think that a lot of people have is it’s just going to boom. And it’s absolutely the case that machines can acquire skills much more rapidly than people can. Once you have a machine that does something, wow, then you have a lot of machines that do the same thing. And we will see this. I mean, we should not be naive to think that won’t occur. If you are a language translator you’re under threat. If you’re an illustrator, you’re under threat. I think a lot of people who do just workman software coding, there will be fewer—actually, it’s not completely certain, but we do see a big decline in employment in computer coding right now, in software development.
DAVID AUTOR:
Or sorry, I shouldn’t say in the software engineering, but in the people who write programs. And so it’s quite possible. Now, I think Reid alluded to this earlier, that it also depends on what demand looks like. So if we got like really, really good and cheap and fast at colonoscopies, people still wouldn’t be lining up their proctologist office to get more of them. But it is the case that if we get better, cheaper, faster coding, right, there is a lot of demand for software. You can’t buy an appliance that doesn’t have a microprocessor and it doesn’t have embedded software running in it. I mean, I’m sure my toaster oven has that. I know my coffee pot does because it regulates the temperature and tells me what it is. So maybe we’ll just get a lot more software coding, but it may also change what we use it for.
DAVID AUTOR:
So, when people started developing websites back in the mid nineties, that was all about skill in HTML—writing markup language. Those websites, if you go back and look at them, they’re so incredibly horrible looking. It’s hilarious how primitive they are. Now, there’s lots of people who build websites for a living, but it’s not really a technical skill. It’s design. It’s how do you present information. It’s actually has a different skillset that’s involved. So, that’s kind of cool actually. So the rate of change is a concern. It’s absolutely is. Okay, so, now come to the last part of your question. You say, “Well, how could this be good for anyone [laugh]? Well this is not a given, this is kind of a good scenario. But, as I argued a few minutes ago, a lot of valuable work in advanced economies is monopolized by elites.
DAVID AUTOR:
Professors, for example. Or lawyers, or medical doctors, or financiers. And, we don’t face that much competition. There’s huge barriers to entry. There aren’t that many of us. You can’t print them that fast. And so we have this lock and that’s fine for us. Yes, things are expensive,—healthcare is expensive, education’s expensive, legal services are expensive—but that’s okay because I get paid a lot too because I do that. But for most people, they don’t do that. It’s just expensive. So if we can enable more people to compete in those domains—if we can enable more people to enter software development, more people to do medical services, more people to be lawyers. Now I don’t mean that everybody can do anything. And I don’t mean that we don’t need doctors anymore because people have AI. But you could imagine more supporting roles for people who don’t have as much elite education to do that type of valuable work. In other words, just instead of the last wave of technology, pushed so many people out of the middle and down towards low-paid services, it would be great if we could use this technology to enable more people to move up into new opportunities. That’s the good scenario.
REID:
Now, have you seen anything that is how—other than just getting people into this—how they should start learning these tools in order to start beginning to learn their own path on what the new contours of how jobs will be transforming, how markets will be transforming, how industries will be transforming? And is there separately anything that you’ve been looking at as a macro thing to say, “Here is like a 30,000 foot or a 40,000 foot map by which people should start thinking about how these transformations are going to happen.”
DAVID AUTOR:
Learning to use AI well actually is an important skill in itself. The first instinct that you have to develop—I’m old enough where I remember a world before Google, and I remember the developing the instinct to Google things. You’d be at a dinner party or whatever and you’d say, “Oh, I thought President Taft said this.” And someone would say that, and then someone would say, “Well, why don’t we Google it?” And everyone would be like, “Oh, Google it.” And then we learned. And then we’d develop this sub-routine, or an instinct, or whatever—this habit—to turn to it. And knowing actually when to turn to AI for things is also not obvious.
DAVID AUTOR:
It’s something you get better at. For example, I was talking with some of my research assistants and we were looking at this table and say, “Could this be made into a figure?” And, and we’re trying to say, “Well, would it work as a figure?” And we would say, “Well, what if you did this way?” And then I say, “Oh, I know!” And I just took a picture of it and I stuck it into an AI and say, “Make a bar chart of this. No, now rearrange it like this.” And did it like that over the course of five minutes—we all did it on the screen—and we said, “Yep, this won’t work.” But that was a very time efficient way to do it. And so I always have a chat window open and I use it to bounce ideas or look things up and so on.
DAVID AUTOR:
But another important thing to realize about AI is—and this goes to distinction between collaboration versus automation—AI isn’t really useful for things that you don’t understand, because it’s not that reliable. And it will misunderstand for you and lead you astray. So if you’re using AI to do something you really don’t get, you’re kind of out over your skis. And that’s not a good place to be. So that’s why I say it’s a good collaboration tool, because it’s complimentary to. If it’s something you know about, then you can adjudicate, “Oh, this makes sense, this doesn’t make sense.” You can ask the right question and you can filter and interpret that knowledge. If you’re trying to get to do something for you that you don’t know—write me a paper about the currencies of the Roman Empire or something like that, it might very well make up some Roman currency you’ve never heard of and you wouldn’t know.
DAVID AUTOR:
Part of learning to use AI is learning the instinct of when you know enough to know if it’s doing something useful, and when you can use it. It’s like I could use, tell an AI, “Tell me how to do surgical procedure on someone.” I shouldn’t do that. If I’m a surgeon, and this is an extreme scenario and there’s no other surgeon around, and I need to something, I can like look at the AI, give me instructions, and I could probably do it. So you want to use it in the domains where it can collaborate with you and augment you. But it can’t automate away, substitute for just fundamental lack of knowledge in some area. That’s a dangerous place to be.
ARIA:
So, thinking about the possible future we want to shoot for, you’re talking about AI being complimentary. And that’s one of the visions that obviously Reid spoke about in Superagency. It is, okay, how can we have more people moving up and being able to do. Before they could be a nurse, now they can be a doctor. Before they were a radiologist assistant, now they can be a radiologist. Because they have these additional skills. And actually, we work a lot with this organization Opportunity@Work, which is trying to increase the returns to skill-based work as opposed to just degrees. If you have the skill, you should be able to get the job. So that’s one vision. That in the future, everyone who perhaps is in a low-wage job right now—or not everyone, but some of those people—can move up into middle-wage jobs.
ARIA:
How fantastic would that be to add $20,000 to each job? Another positive vision actually for AI that some people talk about is that less people will be doing jobs. We’ll have so much money, our government will have it, or the big AI companies will have it, or someone will have it, that these jobs are not necessary. And we’re able to give out a UBI, or we’re in this new world of freedom, and only working 10 hours a week—which sounds a little fantastical. So I would love you to comment on those two visions, both of which are positive as positive visions from the new AI future.
DAVID AUTOR:
Yeah. So the first vision, of course, I’m all behind. And it doesn’t need to be, by the way, that everybody is doing middle or high skill work. So if we just extracted half the people who were doing leisure and hospitality and janitorial services from that work, the half who remained would get a big pay increase. Because firms would have to compete for them more. The problem is there’s too many doing it, and that’s why wages are so low. And there’s a well-known economic parable: why do the wages of barbers rise over time? They’re not getting any faster cutting anyone’s hair, right? And the answer is, well, they have to be compensated to be barbers as opposed to being something else. So if there’s in the long run of productivity rises and there aren’t lots of people, and if you will need to convince someone to do food service or cleaning—whatever—and they may want to, they may not. But if you want to in a competitive market, if there aren’t that many people available, you’ll have to pay them more. So it doesn’t have to be everybody, just more opportunity benefits a lot of people, and they don’t have to all take that opportunity to benefit.
DAVID AUTOR:
On the notion of we’ll have all this income and therefore have all this leisure. The concern I have with that is not that we won’t have wealth, but that we’ll have a lot of trouble distributing it equitably. We are already an incredibly wealthy society. We’re arguably the wealthiest society humanity’s ever seen. And we don’t really have any real scarcity here. And yet we have a lot of people who are quite poor and don’t have access to healthcare, and don’t have a safe housing, don’t have safe neighborhoods, don’t have good schools.
DAVID AUTOR:
That’s not because those resources don’t exist. It’s that we don’t have a system where people that we really want to distribute that much to people who don’t somehow earn it on their own through the labor market. And I don’t know that more wealth is going to solve that problem. And I really worry about a world in which so much income is concentrated. The notion that we are sort of reliant upon the generosity of strangers through—the tax and transfer system—to take care of all of us, I just don’t know if that’s a reliable thing to do. It generally doesn’t work that well. The U.S. is not getting more generous as a society, even as it’s getting wealthier. We seem to be getting less generous.
DAVID AUTOR:
And so I like to compare two scenarios, what I call the WALL-E and the Mad Max scenario. So you’ve all seen the movie WALL-E. And it’s the future where basically people sit around on hovercraft, armchairs, watching holographic TV, drinking big gulps, and they all weigh 300 pounds. And this is supposed to be some future dystopia because there’s no work to do and everyone’s bored. But I view that as the good scenario, because the more likely scenario to me looks much more like Mad Max: Fury Road, where everybody’s competing over a few remaining resources that aren’t controlled by some warlord somewhere. So you can have a world that’s very wealthy, and most people don’t have anything.
DAVID AUTOR:
And that’s why I like to think about work because I actually think the labor market has so much going for it. Two things. One is it’s intrinsically a lot more equitable or equal than the capital market because everybody in a society that doesn’t have slavery and doesn’t have labor coercion, everybody owns no more than one worker. They just own themselves. And so we all start off at a relatively even starting point. And so 60% of the income in the United States is labor income that goes first to workers. And so it just creates much more shared resources. Work also has a lot of virtues. I think it gives people identity, it gives them structure, it gives them meaning. I mean, not all jobs are good.
DAVID AUTOR:
So that’s a luxury for me to say, “Work is all great.” I don’t want to say that for a minute. But the other thing is, in a democratic society, if most people are working, then it’s easy for people to say, “Well, these people are all contributors to our society. Of course, they have a vote. We’re the co-owners of society.” Whereas if we were in a world where say, well, all the money comes out of a fountain in San Francisco, next to the OpenAI headquarters or something, then it’s much harder to say that everybody deserves their share. I mean, I might agree to that, but I don’t know that everyone else will. So that’s why I’m not excited about a world in which the resources are mostly coming from machinery or capital and everybody expects to be supported.
DAVID AUTOR:
Now, let me say—important to emphasize—we do much, much, much less work than we used to. So at the beginning of the 20th century—120 years ago, 125 years ago—people worked on average in the United States about 3000 hours a year. Now we work on average about 1900 hours of the year. Now we’ve invented the weekend, we have vacation, and so on. Additionally, people used to enter the workforce as soon as they were physically able—ten years old. And they would work until they died. And now, on our side, people enter the labor force, 16, 18, 20, 25—if they’re PhD students, 40, 45—and then they retire when they have 20 years of health remaining. So we work a much smaller percentage of our healthy lives than we used to. So we actually have much more leisure. So we’ve handled that well. It’s not that we’ve just become this kind of overworked society—that’s kind of a myth. So I’m in favor of that. I’m in favor of us all working somewhat less, or at least those who want to work less. I’d like to work more, but that’s very different from there being no need for people to work, and that they just hope that the society will care for them. That I’m not as confident in.
REID:
Well, one of the things that I think this discussion highlights in a really interesting way is there’s two issues that I think it combined in the inequality discussion. One of which I’m extremely sympathetic to, and I think is very important to navigate. And one of which is complicated. The complicated one is the, “Well, no, I want there not to be that much of a gap between me and you. I just think that a gap is an issue. Like, you have two cars, I have one, and I think that’s a real big deal,” whatever that kind of thing is. And I tend to be a little bit more, “Eh, how do we really figure out what the right mechanism of the gap is?” But a lot of people, that’s their real driving issue in their rhetoric, but I think it’s more complicated.
REID:
Now, the one that’s not complicated, that I think you’re highlighting, that is extremely important that we solve, is a notion of increasing quality of life across everybody. And quality of life isn’t just, “Oh, look, I can afford the cheeseburger.” That’s great, but it’s also do I have meaningfulness, and control, and respect, and personhood, and a place within community and society? And that breaks down into two components. One component is what are you learning to do that gives you a unique position in your community, in your workforce, in your tribal group. And that’s part of where Superagency, trying to embrace the future, learn, become AI curious, step into it.
REID:
Don’t get forced into it, but like jump into it, first and foremost. But the other one’s also reducing—this is the abundance thesis—reducing the cost of a whole bunch of things. For example, part of how you increase quality of life is you say, “Well, one of the things that AI can create is a 24 hour, seven days a week medical assistant.” Where very, very few people—except when you get to the super wealthy—actually have that in the US. Has anyone gotten good sense of not just the thing we’re talking about—like the jobs and the transformation of the industry—but also on the quality of services, and quality, of life based on—because with AI can see not just a medical assistant, essentially one for free for everyone on the smartphone, but a tutor, a legal assistant, a set of things, none of which I think will take away actually jobs. I think there’ll be a whole bunch of coordination of amplification there. I mean, they’ll take away some jobs, but they’ll also create a bunch, I think, in this. But in that quality of life that can possibly come from AI based on helping people in all these ways?
DAVID AUTOR:
I mean, for sure. I mean, look, even people who are, you know, much, much less affluent in the United States—in terms of the sort of physical quality of life—is much higher than it was even 40 years ago. Most people didn’t have air conditioning. They didn’t have private transportation. They certainly didn’t have all the things that come from mobile telephony. And mobile phones aren’t just about entertainment, they’re about communication, they’re about access to services and information. So there are many, many ways that there are a lot of things that our material standards of living have improved because we’ve gotten better technology for doing it. So people live in larger houses, they have more indoor plumbing, they have more electricity, they have bigger TVs. And again, more importantly, they have cars, air conditioning.
DAVID AUTOR:
And I do think, yes, AI can be used to make a lot more services less expensive. And we use the web this way all the time. The amount of time we spend waiting on phone queues, or trying to buy airline tickets, or dealing with banks and so on. It’s actually a lot more convenient than it used to be. I remember buying plane tickets on the phone, or waiting in line to see a bank teller. So I think that’s very important. But I don’t think low prices are sufficient. Because what are the things that are really defining for a good quality of life. In addition to the work you do, it’s do your kids have opportunity?
DAVID AUTOR:
Are your neighbors neighborhoods safe? Are these good schools? And also equally important, do people get a fair shake when they start? My problem with inequality—I’m with you. I don’t care if I have two cars and some people have 20 cars. I’m still okay with that. But it is the case that inequality can lead to dynasticism, where the next generation is like one set of kids starts so far ahead of the others that even their family got wealthy on the merits, then there’s no longer anything close to fair chance in the next round. And those things are harder to deliver just through low prices.
DAVID AUTOR:
Just good schools, good opportunities, safety—a lot of that depends on basically having a level of affluence in your family, and in your neighborhood, that supports those things. Maybe we’ll get better at that. But that’s the key thing. What matters to people is, of course, economic security. Like from day to day, can I pay my bills? Am I going to eat? Do I have a roof over my head? But then, do I have a job that provides me stability? And then do I have a way to ensure that my family is going to do at least as well as I have done, and get good opportunity commensurate with their own hard work?
ARIA:
I do think what is important actually about a lot of this is uncertainty. So if you didn’t have the uncertainty that your rent would go up next year. If you didn’t have the uncertainty that someone would get cancer, and so you would go into medical debt, and you’d declare medical bankruptcy—those aren’t really price issues. Those are more uncertainty issues. And so one of the things that I think a lot of people talk about is declines in unions and collective bargaining. They leave workers more vulnerable to past shocks. Obviously there’s also more uncertainty about your wages, are you going to be fired. There are certainly some negatives about unions. Sometimes they lock in workers that we don’t want, et cetera, but they do sometimes provide that certainty. So my question for you is, as we want gains to be broadly applied, are there institutions, policies, collective bargaining? What are the things that we can put in place to make sure that these gains are broadly applied?
DAVID AUTOR:
Certainly collective or worker representatives, there might have been a time when we had too many, but now we definitely have too few. And the decline of collective bargaining at this point, I think has left workers very vulnerable. So I would like to see more of that. I would like to see more investment in our schools, in our children. Those things would make a big difference. And then I do think people in the United States have a much greater level of economic insecurity than do other people in less affluent economies working the same jobs. And we tend to tend to think it’s a necessary fact of life, and it just isn’t. If you work in a McDonald’s, in Norway or Denmark—or even in France—you’re going to have vacation, you’re going to have healthcare, you’re going to have sick leave.
DAVID AUTOR:
This does not have to be inaccessible, or only accessible to people who are middle and upper class. And that’s a choice that we make, but we tend to think it’s inevitable and it’s not. The U.S, we’re so impressed with ourselves that we forget to make the right comparisons. And the other way too. People don’t realize how affluent we are. People think that America’s been in decline, but our productivity has risen 30% relative to Europe over the last 20 years. We’re much, much better off. Our labor markets actually work really well. So everyone’s talking about making America great again. But actually we had a lot done a lot of great things and we should recognize that.
DAVID AUTOR:
I mean, the U.S. is an incredibly innovative country. AI comes from here. So much of the computing era, or the internet era. But we have an incredible culture of innovation that has been so important to us. So we should recognize both our strengths and our good fortune, but also recognize we can learn a lot from other models. And not everything that is bad is inevitable. School shootings are not inevitable. And people not having adequate healthcare, not inevitable at this level of income. Those are choices that we’re making. I don’t think we’re making the right choices.
REID:
So one of the things that I think is a question that we’re going to see happening, that’ll be a little bit different within the AI universe, is previously workforce transitions and so forth have happened to what I would think of as industrial timeframes. Which is part of when you look at our educational system, it’s like train, then deploy for a lifetime. Versus a constant recycling. And I actually think that part of what happens as we get into the new world of AI is that you’ll need to have more constant adaptation. It’s part of the reason why my very first book that I wrote, The Start-up of You, was how we’re all going to have to be more entrepreneurial, and how we think about our work and our careers. Doesn’t mean start businesses, it means how to do that. And obviously part of my optimist is thinking about how AI can help with that. Is there any good work, good lenses, good principles for people thinking about now these transitions are as opposed to within a 30 year timeframe, they’re within a ten year timeframe? And so that moving along and adjusting is going to be one of the things that’s going to be important throughout a human life, a human career. And so have any principles or work been done so far in this? I’m convinced it’s going to be very important.
DAVID AUTOR:
Yeah, I’m convinced as well. I would say the work is early. For example, I think everyone who looks at AI says, “Oh my God, there’s so much potential here for education.” It’s amazing how little education has changed in the last millennium or so. And even if we walked into a classroom today—let’s say we all left school 30 or 40 years ago, or 20—we wouldn’t be surprised by a single thing in there. Or the way things are done, it’s really not different. So I think we need to figure out how to use these tools to get better at education, at teaching ourselves, and learning new skills. And I think one of the things we know about, especially for adults making transitions, is they learn much more successfully experientially than they do going back to classrooms.
DAVID AUTOR:
It’s shocking to me as a professor, but not everybody loves being in a classroom. And we sort of forget this lesson over and over again. So remember MOOCs—Massive Open Online Courses. Everyone was now going to be a botnet herder, or an ethnomusicologist, or whatever they wanted to be. And they really weren’t very successful. People don’t talk about them much anymore. Why aren’t they that successful? They were like simulated classrooms. And it’s everything you hated about a classroom and worse. Like, who wants that? At least a classroom is a social environment, whereas watching a video is not. So if we want to use these tools to make education better, we need to do better what makes education effective, and that is engagement and immersion.
DAVID AUTOR:
So I certainly think that there’s a lot of skills that people can learn in simulated environments. We do this. If you want to fly a plane, you’re going to spend a bunch of time in a flight simulator. If you’re learning to do medical procedures, you’re going to start off on animatronic dummies that bleed and scream. Why don’t we do that for plumbing and electrical work? Well, it’s clear we do it in a few places because it’s so expensive. We don’t do it everywhere else, even though we could. But we can now, that will be possible. So I think that’s an important lesson. It’s also the question of—there is a lot of research now on how should we interact with AIs?
DAVID AUTOR:
What should they tell us to help us learn? And one thing—the consensus literature is—what they should not do is just sit around telling us what to do. That’s not how we learn. And, sometimes they need to interact with us in a way. There’s one theory that says if you’re coming to a choice, there’s A and B. The AI could say, “Do A,” but it could say, “Well, you could take Am and this would happen, or B, and I predict this would happen.” The contrast between those things actually very instructive for learning. So if you want to help people learn, it’s not sufficient to tell them what to do. You need to give them information that supports that choice and enables them to reason about it. So there’s a lot more work going on now about what are the ways, and I’m doing some experimentation this myself.
DAVID AUTOR:
I’m a Google Tech and Society visiting fellow, and one of the things we’re trying to do is stand up experiments on expertise. And to ask whether tools that are built to support experts, not that they just help them get better results—probably they will—but do they help them acquire judgment faster? Because if you’re a lawyer, if you’re a doctor, if you’re a researcher, if you’re an actuary, if you’re a carpenter—you develop judgment over time. That’s part of the expertise that makes you so valuable. It’s not just what you learn from a book. You learn how to make the right decisions, at the right time, at the right moment. And I would like to understand better what are the tools that enable people to do that faster.
DAVID AUTOR:
Get those skills sooner. Because of course it takes a long time. Expertise, it’s great! But it takes a long time to get expertise. It’s slow, it’s expensive, and even the best experts are fallible, and most people are not the best experts So I think that’s the question we should be asking, and that’s where the research is that I’ve seen. Is about how do you do these interactions in a way that makes people smarter? And I think the key principle for me is automation versus collaboration. That automation is when you tell people what to do, and that takes out expertise. And collaboration is when you harness expertise that you give people the information and, and, you know, and collaboration is if two people, two experts, look at the same problem and reach different answers, and then they put their heads together. The answer they come up with may not be the average of A or B, it may be C. It may be something they weren’t considering. Two people collaborating could actually come up with a different answer than either of them thought originally, which is not something that can happen when a machine tells you what to do. So I think this principle of collaborative design to me seems very essential for thinking about how we help people learn and acquire skills more efficiently.
REID:
We will now move to a rapid fire. Is there a movie, song, or book that fills you with optimism for the future?
DAVID AUTOR:
So I gave this one a lot of thought, and I came up with an answer that’s not quite in any of these categories. But it would have to be the Tiny Desk series of concerts on NPR. I don’t know if you guys watch Tiny Desk, but, oh my God. So, this started like 20 years ago. Bob Boilen, who was their music editor, started inviting bands to come play at his desk. And they would just sort of film those. And over time, this has become an institution. Major acts that you know,—Taylor Swift and not Bob Dylan,—but almost everybody else you’ve ever heard of shows up. But the greatest thing about Tiny Desk is they’re in an intimate space. So it’s a small group, and it’s often people you haven’t heard of, and the range of music that you’ll encounter is so incredible. Things you would not see. I go see Anderson .Paak’s Tiny Desk concert. Or go see the Tiny Desk concert of the Buena Vista Social Club. These like, 15, 20 minute productions. I look forward to this so much, and the amount of music that I’ve encountered I never would’ve heard of that gives me so much joy. So Tiny Desk.
ARIA:
David, what is a question that you wish people would ask you more often?
DAVID AUTOR:
I wish people would ask more often about—and I ask people this all the time—what is your counter life? What is the thing you would be doing if you weren’t doing what you’re doing? And that often tells you about something else that they’re really passionate about. And we, in America especially, we always ask people, “What do you do for a living? What’s your work? What’s your job?” And that so much summarizes people’s identity, but kind of too much. So yeah, asking people their counter life, I think—wish people would ask me. Oh, so now you’re going to ask me.
ARIA:
Yeah! David, okay, let’s hear it.
DAVID AUTOR:
You know, I think there’s another world I could have been a sailor. I love to sail. I still sail. In fact, I’m in a place where I sail. And I could have imagined doing around-the-world sailing, doing racing, crewing. So that would’ve been okay.
REID:
Where do you see progress or momentum, outside of your industry, that inspires you?
DAVID AUTOR:
People in the west, certainly in the United States, if they look back over the last 20, 30, 40 years and they go, “Eh, it’s been mid. It’s not been a great half century.” But actually this has been the best 40 or 50 years that humanity has ever experienced. The amount of people brought out of poverty. We’ve never had a global middle-class until now. And partly this is China itself. China has reduced it’s poverty level from 70% to effectively a couple percentage points. And that’s more than a billion people. But it’s also created prosperity in Central and South America, in Sub-Saharan Africa. And in fact, growth in Sub-Saharan Africa has been really strong, and livelihoods have improved. It’s still very poor, but it’s much less poor and health is better. And so I think we overlook how much progress there has been in so much of the world. And we don’t appreciate our good fortune, but we also don’t appreciate the good fortune of others. So I think that is overlooked progress.
ARIA:
Alright, our famous final question. Can you leave us with a final thought on what you think is possible to achieve if everything breaks humanity’s way in the next 15 years? And what’s our first step to get there?
DAVID AUTOR:
So if everything broke our way, if we really did this right, we would make—it’s not that we put ourselves out of work, but we would give people more secure and fulfilling work. We would give them more access to education and access to better healthcare, everywhere. And those things alone would improve welfare in so many dimensions. Not just in terms of material standard living, not just in comfort, but investing in our kids, creating opportunity for the next generation. So I think that would be great. And that’s feasible! I mean that’s the thing, many of these things are feasible. If we think we’re not going to do them, it’s not because we couldn’t do them, it’s because we’re somehow not delivering on what is feasible. And that’s the sad thing. Everybody knows AI could be used for all these great things.
DAVID AUTOR:
Everybody also knows it could be used for really terrible things. And your belief about what’s going to happen is not really a belief about AI. It’s a belief about what humanity will do with this opportunity. Will we squander it or will we make the most of it? And I think most people—I don’t think anyone thinks we’ll absolutely make the most of it. I’m sure many people think we’ll totally squander it, but I think many people are really deeply, deeply uncertain. So, in terms of breaking our way, as my friend Josh Cohen—philosopher—likes to say, the future is not a forecasting exercise. It’s a design exercise. We’re building it. And so breaking our way is not just a matter of luck, it’s a matter of making good collective choices.
DAVID AUTOR:
And that’s extremely hard to do. And so that is what’s feasible, but not easy. If we were going to do that, where would we start? I would say, look, healthcare and education, two activities. In the United States, that’s 20% GDP. A lot of it’s public money actually. And this is where there’s such great opportunity where AI could be a tool that could be so helpful to us in a way that other tools have not been. And that’s where we could be really investing. And investing doesn’t just mean more treatments for rare diseases. It means things that make healthcare more available to everyone so people have longer lives and higher quality lives. That’s where I’d like to get started.
REID:
Possible is produced by Wonder Media Network. It’s hosted by Aria Finger and me, Reid Hoffman. Our showrunner is Shaun Young. Possible is produced by Katie Sanders, Edie Allard, Thanasi Dilos, Sara Schleede, Vanessa Handy, Alyia Yates, Paloma Moreno Jimenez, and Melia Agudelo. Jenny Kaplan is our executive producer and editor.
ARIA:
Special thanks to Surya Yalamanchili, Saida Sapieva, Ian Alas, Greg Beato, Parth Patil, and Ben Relles. And last but not least, a big thanks to Abby Abazorius.