Planet Money - How AI could help rebuild the middle class

Episode Date: May 17, 2023

For the last four decades, technology has been mostly a force for greater inequality and a shrinking middle class. But new empirical evidence suggests that the age of AI could be different. We speak to MIT's David Autor, one of the greatest labor economists in the world, who envisions a future where we use AI to make a wider array of workers much better at a whole range of jobs and help rebuild the middle class.This episode was produced by Dave Blanchard and edited by Molly Messick. It was fact-checked by Sierra Juarez and engineered by Katherine Silva. Jess Jiang is Planet Money's acting executive producer.Help support Planet Money and get bonus episodes by subscribing to Planet Money+ in Apple Podcasts or at plus.npr.org/planetmoney.Learn more about sponsor message choices: podcastchoices.com/adchoicesNPR Privacy Policy

This is Planet Money from NPR. It's been about six months since ChatGPT was released to the public. And basically, from the moment that happened, it felt like this seismic shift. Because all of a sudden, people everywhere realized just how powerful artificial intelligence already is. They began using this AI chatbot to do all sorts of things, to write raps, to take the bar exam, to identify bugs in computer code and fix them. I mean, all that stuff is pretty cool,

but at the same time, there's been all this doom and gloom about AI. Will it take our jobs? Will it derail democracy? Will it kill us all? And these aren't like off the wall questions, like serious people are asking these questions right now. Yeah, it's kind of easy to fall into this like doom spiral these days. But then a couple of weeks ago, I saw something that gave me like this little glimmer of hope. It was a study that looked at this customer service department

of a big software company, and they started using chat GPT to help workers get better at their jobs. And interestingly enough, it worked. Like, it made the less skilled workers at this company much more productive. And at the same time, it didn't do much for workers at the top. So basically, AI narrowed the productivity gap between lower skilled workers and workers with more skills. And Greg, I think it's fair to say you read a lot of economic studies. Probably too many.

And yet, you have been telling me, you've been telling all of us, that this finding felt really big to you because it's different from how we usually understand the way technology affects workers. Yeah, there's a whole generation of research looking at the effects computers have had on the labor market. And over and over again, what economists find is that for decades now, computers have been this major force for increasing inequality. What this study shows is that AI could be different. And when I saw that, I was like, you know what? I want to talk to David Otter.

David Otter, professor at MIT, widely regarded as one of the greatest labor economists in the world. Otter led a lot of that research that found computers were this force for a shrinking middle class. And I wanted to find out if he thinks maybe this new technological era we're in is going to be different. If maybe AI could be a force for greater equality. Right. So, hello and welcome to Planet Money. I'm Nick Fountain.

And I'm Greg Gruselski. And Greg, today's show is going to be a little different. We found your conversation with David Otter so interesting, so illuminating, so prescient, that we're just going to run it. Today in the show, the American middle class has been shrinking for more than 40 years. Could AI help reverse that trend? Today in the show, the American middle class has been shrinking for more than 40 years. Could AI help reverse that trend? When David Autor thinks about how AI will affect the future of work, he actually looks to the past.

He sees two major turning points when technology fundamentally changed our economy. The first turning point was a long time ago. We're talking about the industrial revolution, when machines began to replace work that had previously been done by hand. So prior to the industrial revolution, there was a lot of artisans, people who did all the steps in making a product, right? So whether it's a piece of clothing or, you know, building a house or a tool, the era of mass production created an alternative way of making things. And it was basically breaking things down into a series of small steps that would be, you know, accomplished in sequence, often by machines, managers and pretty

low skill workers, right? And so a lot of artisanal skill was displaced. I mean, the Luddites rose up for a reason. Otter says that at first, the factory jobs that displaced the artisans required less skill and also paid less. So kind of a bummer. But then machines got more complex and so did the things they could make. You know, we're talking automobiles instead of textiles. And so factory owners started to need workers with more skills. Over time, that work became more skill demanding because people had to follow formal rules. And if you're using a lot of expensive equipment and making precise products and using expensive inputs, you need people who are kind of can follow those rules well. So this created what you might think of as the kind of middle skill, what I would call

mass expertise, right? This is like the golden era that we hear a lot about in the United States. This time when people could graduate from high school with basic reading and math skills, and then go out and find gainful employment, you know, jobs on factory floors or jobs in offices where workers had to understand how to, you know, compile paper records or deal with basic financial transactions. For people who didn't have four-year college degrees, these were the relatively better paid jobs, right? They're better paid than, for example, food service, cleaning, security, and so on. And the reason is that food service, cleaning, security, they're valuable pursuits. They do important things in the world, but most people can do them.

And so they're not going to be well remunerated. For work to be well paid, especially in an industrial economy, it needs to be expert work of some sort. By expert, I mean, one, you need a certain body of knowledge or competency to accomplish that a thing, that thing must be worth accomplishing, right? And not everyone can do it. And so it did the case that the kind of industrial era helped really grow the middle class. It created this tailwind where people with a reasonable amount of education all of a sudden made them highly productive in offices, highly productive in factories, highly productive in sales. And so, yeah, it created this huge rising tide that was relatively

equalizing. Now, I don't want to say it's only technology, right? There are institutions that went with this. There was democracy. There was obviously the system that educated people, but the technology helped. So, okay, the industrial revolution, it killed off jobs for skilled artisans, but then it created a whole bunch of new jobs for middle-skill workers, jobs that gave opportunities to Americans without a college degree. That's turning point number one. The second big turning point is computers. This is what a lot of David Otter's research has focused on. He finds that in the computer era, starting around 1980 or so, all of those middle-skilled jobs that emerged from the Industrial Revolution,

they started getting automated away. Think robots taking jobs on assembly lines, or computer software taking jobs from administrative office workers. At the same time, computers made higher skill workers much better at their jobs. This elite group benefited a bunch from using email, building spreadsheets, searching the Internet. I don't know, like trading stocks and information instantaneously all over the world. like trading stocks and information instantaneously all over the world. So if you're a highly educated worker, if you're a doctor or an attorney or a marketer or researcher,

those people are highly strongly complemented by this sort of automation of these information processing and routine tasks. On the other hand, if you are someone who does like dexterous manual work, like food service, cleaning, security, entertainment, recreation, there's really not much complementarity there at all, right? It doesn't make you much better. It doesn't make you worse. However, you have lots of people in the middle who are now being pushed out of those middle skill occupations.

And it's just not very easy to move up, right? If you're an adult and you're displaced from your manufacturing job, it's very unlikely you're going to get a law degree or medical degree. So you're going to more likely end up driving a truck, working in a restaurant, working as a security guard. And so the computer era actually devalued that mass expertise and massively amplified demand for elite expertise, which has been really not so great, right? It's just me- Not great if you're not an elite worker, because it's pretty great if you're an elite worker. It's true. It's been a great four decades for elite workers,

especially in the United States. But to put it in crude words, technological change over the last few decades has increased inequality. Sure. And now it feels like maybe, just maybe we're in a new era. I was already starting to think this. And then this new empirical study came out by Eric Brynjolfsson, Danielle Lee, and Lindsay Raymond, and that looked at what happened to a software company and its workers after the company adopted an old version of ChatGPT. And they basically find that this AI system makes their workforce much more productive. But more interesting to this conversation, they found that only some workers benefited from it.

And it was actually the less experienced, lower skilled workers that benefited from use of the technology. And the more experienced, higher skilled workers saw little or no benefit. And to me, that kind of, it seems to be like reversing what we've been seeing where it's complementing the bottom and not really doing much for the top. And I just want to get your reaction to those findings. not really doing much for the top. And I just want to get your reaction to those findings. Sure. And actually, my students, Shaked Noy and Whitney Zhang also have a paper where they did a sort of a related experiment working with people doing writing tasks. And these are people who are college educated and do like advertising copy and so on. And some use ChatGPT and some didn't. And basically, they found that using the large language model, it made everyone more productive by saving them a lot of time, but it pulled up the bottom very considerably. So the

people who were only pretty poor writers on this scale became average, and people who were excellent became a little better. And so it reduced productivity inequality. So it's very consistent with the paper by Brynjolfsson and Lee and Raymond. So that's interesting. I didn't know about that study. So now we have two empirical studies that are showing that it's pulling the bottom up and maybe doing a little for the top, but maybe not doing much. So there's a big implication there. Yes? big implication there. Yes. There's a big possibility there. So the good scenario is one where AI makes elite expertise cheaper and more accessible, right? So right now, if you want to do a lot of medical procedures, you need a medical degree. That takes a decade, right? And that makes

those people scarce, expert, and expensive. But you can imagine that with the right tools, you could devolve some of those tasks to people who know something about medicine and healthcare, but they don't have to have that level of education. And then they could do much more. And we already have an example of that, right? So the nurse practitioner, nurse practitioner is a nurse who has an additional master's degree. My sister's a nurse practitioner. Okay, great. And so nurse practitioners are well-paid, right? The median pay is about $150,000 a year. And they do many of the things that only medical doctors were allowed to do, right? They diagnose, they prescribe, they treat, right? And how is that compatible? Partly it's a change in medical norms and scope of practice boundaries. Partly they're

enabled by technology, right? There's a machine that says, don't put those two prescriptions together. You know, that would be a problem. And, you know, this set of symptoms is associated with this constellation of diseases, check the following. And you can imagine many ways in which people with foundational skills in something could use AI to make that expertise go further. So the good scenario is basically where AI lowers the cost of elite expertise, makes it more available, and increases the value of basically the middle-skilled workers of the future.

That's my good scenario. So to translate potentially, potentially AI good for the middle class, good for rebuilding the middle class. That's like the hope. That's the good scenario. That's like the headline. And not just hope, we got to make it happen. That's the headline right there. Like David Otter hopes that AI is good for the middle class. No, no, no. Let's use AI to reinstate the middle class. What it will take to make that happen. And also the other scenario David Autor imagines, the one that doesn't go so well for workers.

That's after the break. Waylon Wong here with a plug for our latest bonus episode, where we take you insider reporting on social media influencers. There's kind of a magic number where it becomes, I can do this for a living, and that's less than 1%. And yet some Gen Zers say it's their dream job. I mean, that way I can earn money as I'm just like at home, just playing video games.

Follow us down that rabbit hole and hear how our Indicator series on the dark and mysterious economy of influencers came together. That's in our latest bonus episode, available now for our Planet Money Plus supporters. If that's not you, it could be. Learn more at plus.npr.org. So for years, David Autor has been looking at the effect of technology on the labor market, and he finds that computers made elite workers better at their jobs and much richer. But at the

same time, computers also made a bunch of good middle-class jobs disappear. Otter thinks maybe AI could help reverse that trend, lift a whole bunch of workers back into the middle class by helping them get better at writing or research or, I don't know, creating complicated legal documents. Basically, AI could allow them to do jobs currently reserved for the upper echelon of the labor market. That's what David Otter calls the good scenario. But even in this good scenario, no, there's going to be a disruption of people who are currently making, I don't know, $100,000 to $200,000 a year or something like that. All of a sudden, it doesn't make as much sense to pay those people as much anymore, because you have a whole pipeline of people who now do that job.

That's correct. It's possible that basically you will see some expensive expert work just less in demand, that you will need fewer expensive expert work just less in demand, that you will need fewer managers for certain types of decision-making, that more legal work will be done by machines as opposed to by lawyers, and that you'll have lawyers, but they're supervisory and there are fewer of them. So yeah, I think it's possible. But in the long run, that means fewer people have to go to college, which is expensive. And it also matters, right? This is not a zero-sum game, right? If it makes us all more productive, we're wealthier as a result of that, right? So even if it just places on what you do,

but then the rest of what you do, you do it 10 times as fast, that's a gain in productivity. So you're hopeful on the labor market thing contingent on smart government policy, essentially. Smart government, smart private sector, smart philanthropy, smart universities. And so maybe that will involve some disruption of people at the top, but you know what? They've been doing so well for so long that maybe you got to crack some eggs to make an omelet. That's right. And I don't think they're just going to be thrown out of the top. I do not think you're going to say, that's right. I love that though. I don't think they're all going to be thrown out of the top. I do not think you're going to say, that's right. I love that though. I don't think they're all going to be just like thrown out of the top floor of office buildings.

I, you know, this is, these things happen gradually. Yeah. So that's the optimistic scenario. I just want to bounce off the dystopian kind of, and maybe this isn't even, I mean, the truly dystopian is they become sentient and kill us all, but the dystopian economic. Or we actually, the more likely dystopian is they become sentient and kill us all, but the dystopian economic, or we actually, the more likely dystopian is some, we use it to kill one another. Yeah.

Okay. So putting that aside, putting human existence aside, I'm focusing on the economics. Other than that, how was the play Mrs. Lincoln? Yes.

I can still imagine like a narrative or potential future where it's actually ai is inequality increasing so one one scenario obviously like companies who own these systems will get insanely rich but then there's also like the downstream effects where there's a whole bunch of industries where a bunch of people used to do the job, but now only you need one or two people to do it. So what do you think about that sort of pessimistic potential? I don't want to rule it out. I mean, you can, so you can imagine a world where, you know, you just need a few super

experts overseeing everything and everything else is done by machines, right? So that's one possibility. Another possibility is one where like no one's labor is scarce, right? So that's one possibility. Another possibility is one where like, no one's labor is scarce, right? That's not a good world because then we have lots of productivity, but nobody who owns it, just the owners of capital, right? Then we have to have a revolution and blah, blah, blah. It's not going to work out well, right? Those things never work out well. So I don't view those scenarios as highly likely. One thing to recognize is that we are actually in a period of sustained labor scarcity because of demographics, right? We have very low fertility rates. We have large populations who are retiring and we have radically restricted immigration. And so the U.S. population

is growing at its slowest rate since the founding of the nation. And most industrialized countries and China as well, by the way, are facing this problem of they're getting smaller and older or their populations are not growing. That's a world where we need a lot more automation actually to enable us to do the things we need to do, including care for the elderly. So I'm not worried about us running out of work and running out of jobs, Stu. I am worried about the devaluation of expertise. Just for clarity though, because you just said, I am concerned about the devaluation of expertise, but also though it sounded like you were excited about

the devaluation of expertise because then anybody could do it. I'm worried about a world where no one's labor is scarce. But let me give you like an example of what I mean by this. Like, for example, you might say, oh, Waze makes everyone an expert driver, right? But no, actually it doesn't. It doesn't make anyone an expert driver. It has the expertise, right? So there was a time when London taxi cab drivers needed to know all the highways and byways of London, which is, it took years to master, right? It was an incredible feat of memorization. And then that made them really expert. They could get you around London better than any other drug. Well, now you don't need to know that. You just need a phone, right?

And that's good for passengers. It's good for consumers. But it devalues the expertise that those drivers have. Would you call that de-skilling? Essentially de-skilling? I would say it devalues the expertise. So I realize I didn't meet you halfway there. But what I mean is. It sounds like de-skilling to me because they used to have

a skill that was, oh, I guess it was a valuable skill and now they still have that skill. They still have the skill, it's just not needed. It's not scarce, right? So the expertise of, you know, being a London cabbie has, you know, has been substantially devalued. Okay. So yeah, that's one of Otter's big worries, that what happened to London cabbies kind of happens to the entire labor force, that AI makes human expertise kind of irrelevant. It devalues it.

But David Otter doesn't actually think that will happen, at least not for all workers and not anytime soon. He says, people still have all of these advantages over AI. Like we're more adaptable. We have more common sense. We're better at relating to other people, not to mention we have bodies, we have arms and legs and we move around in the world. Like there's a bunch of things about being a human that still have advantages in the marketplace. So AI raises all of these different possibilities, some more promising, some kind of scary, some very scary. And I just want to end by getting

sort of the big picture gut check from David Otter. I'm curious where your head is at, like, because where are you? Because you seem hopeful that it could rebuild the middle class if we channel it, but then it's also like there's all these other who knows where this is going. Yeah, I mean, I think there's an optimistic, there's a positive scenario for the labor market, but that's the labor market side. in all kinds of other ways, right? From misinformation to control of critical systems to surveillance and monitoring and coercion to very, very dangerous weapons, smart weapons that can autonomously do all kinds of terrible things.

So I think there's lots of reasons to be scared about how it can be used. The irony is the labor market is the least scary part of this at the moment in my mind. Well, thank you very much. I really appreciate it. Sure thing, Greg. It was a pleasure speaking with you.

Thanks. This was really a lot of fun. If you enjoyed this episode, we've got more AI content on the way. Next week, we will be launching a three-part series where we try to figure out whether we can replace all of planet money with AI. Yeesh. If you just want more insightful planet money content about the economy from a real-life

human being, subscribe to the newsletter that I write. You can find it at npr.org slash planet money newsletter. This episode was produced by Dave Blanchard and edited by Molly Messick. It was fact-checked by Sierra Juarez and engineered by Catherine Silva. Jess Jang is Planet Money's acting executive producer. I'm Greg Rosalski. This is NPR. Thanks for listening.

And a special thanks to our funder, the Alfred P. Sloan Foundation, for helping to support this podcast.