Transcription
All right, David, I am so excited because oh so many years ago, I was an economics major in college and and my life diverged and my my path not taken is being an economist and so you are a huge celebrity for me. So this is great.
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 uh in between college graduation and sort of what was next. You drove across the country and I'm going to I don't know anything about cars so I'm reading this right off the sheet in an eight Ford stick shift 1980 Dodge Colt RS that cost you $250. That's right. I spent onethird of what you spent on your car.
I know. Well, you were you were frugal. Uh so I want to ask what what did you learn about this uh cross country trip?
Oh uh actually it kind of is kind of set up the rest of my life believe it or not. Well great because uh I was um you know I had finished college I had studied psychology and I had done computer science separately just as an as a concentration done I had worked as a programmer and I wasn't very satisfied with either career. um uh you know I I like psych I like the question 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 I did when I when I finished college I really didn't know what I was going to do with my life uh and I went on a seven-week uh cross-country trip in this little roller skate of a car with my girlfriend and we were at the big top shiakqua in Minnesota uh and listening to NPR and they talked about this computer learning center opening up at a black Methodist church in San Francisco uh called Computers and You uh funded by kind of Silicon Valley folks who were trying to kind of bridge what they called the dig 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. Uh so, that was the main thing I learned uh uh on that trip.
I love it. That's awesome. That's very cool. Let's kind of cut to some of the very current um and very important things. Part of the reason why we've been looking forward to this podcast for, you know, some time now. Um so let's start with your China shock work uh which revealed how import competition hollowed out US manufacturing regions and over decades. Now you know we may be entering what some call might call an AI shock. Um so what are parallels and differences uh between a wave of lowcost goods flooding markets and a wave of cognitive automation reshaping tasks, jobs, industries.
So let me put this in context by talking a little bit about the China trade shock. So you know the China trade shock refers really to two different things simultaneously. One is China's incredible growth, you know, as a function of its own changes in policies. That starts in the 80s, continues through 1990s under Deng Xiaoing. And, you know, China just has this incredible productivity growth. They uh they adopt uh you know, 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 uh and so that is you know a tribute to their own uh you know uh you know talent and turnaround of a country that had been really in quite a bit of trouble for quite a while. Uh and then in 2001 they uh become a member of the world trade organization. Uh they also get uh permanent normal trade relationships with the United States in 2000 and that induces a a really a surge in exports to the United States. uh you know because of falling tariffs but it changed 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 uh you know had many benefits. Uh it lowered prices. It was great for China. It was great for many many countries. It had a very severe impact on US manufacturing on labor intensive US manufacturing particularly in the south uh and southeast. So, furniture manufacturing, uh textiles, uh clothing, uh you know, doll assembly, uh a lot of labor intensive, not the high-end manufacturing, not mostly cars or airplanes, electronics, and this in a very short order cause a loss of, you know, 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 like evenly across US counties, you know, it'd be a few thousand people per county, you wouldn't necessarily notice it. But that's not the way manufacturing works. It's very regionally concentrated. Uh and not just, you know, there's manufacturing here, but where manufacturing occurs, it's specialized, right? There was a town that was called, you know, that called itself the sweatshirt capital of the world and another town that called itself uh the furniture capital of the world, you know, Hickory, uh North Carolina. And uh so they concentrated in doing one thing and did it well and all of a sudden their work was just nonviable. Uh and uh many big factories closed in very short order. This 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 uh once we go into the great recession. And uh and so this was just a uh incredibly concentrated uh kind of uh loss of jobs and loss of the viability of entire towns and uh the industries they were built around and uh and so it was experienced uh you know as extremely scarring uh by the people in those places. And you know 20 years later those places have kind of rebuilt. Uh they're really quite different but the workers who were initially in those locations actually have not moved on. they've not kind of moved up or out. Uh and many of them are still in relatively low paid manufacturing or other low paid work. So it's been a very very uh you know un difficult uh challenging scarring transition.
Okay. So that's the China trade shock. Uh and 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. Uh so you know one thing they have in common of course is uh it's it could happen quickly. Uh it's not so much a policy choice although maybe China's trade uh WTO etc was but you know it's uh uh but uh it will affect a lot of people potentially really rapidly. I think there are three very important differences. Uh one is again that regional concentration. uh we don't expect the impacts of AI to have anywhere near that type of uh local impact right you know for example we've lost more clerical jobs uh over the last 30 years probably than we have lost manufacturing jobs but no one talks about the clerical shock uh why not well one reason is there was never a clerical capital of the United States right uh it wasn't that wasn't the way it worked because there were clerical workers in every industry uh and so it wasn't uh it so the time So similarly AI it will affect jobs and roles and occupations but it will not uh affect regions in the same with the same degree of of of concentrated impact. Uh so one is it's not going to have the same regional component. two, the tra trade shock really impacted the viability of industries themselves, right? You know, all of a sudden it just wasn't competitive to have a US uh you know, commodity furniture manufacturing industry that was making furniture for Walmart and Target that was now all moving overseas. And um AI will much more again affect specific occupations uh lines of work uh the way work is organized within occupations um but it won't wipe out entire industries uh or if it does I can't think of many that would be if in that category so it will not it will not have it will not be as kind of holistic. And then the third thing is um the time trade shock was experienced by US 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. Uh and so from a firm perspective this was just all bad. Uh from uh AI will be experienced by many firms as productivity increasing. So it may still lead to displacement of workers. I don't in fact it will I don't want to suggest it will not but it will have a very different you know texture. It will not be perceived as all bad news. Uh it will perceived as rapid change. Um, but some of it will be firms saying, well, we've got additional efficiencies. We can more offer more services. We can offer lower prices. They may still shed workers. I don't want to say they won't. Um, so I think there it will it will not be in any sense a repeat of the China trade talk.
One of the things you talk about which I think is interesting is everyone's sort of obsessed with jobs. Like 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 has um sort of the it's been skill-based raising returns to education and so the question on AI like could AI usher in an era of task-based change instead where like the the premium flows to say emotional intelligence rather than former credentials like h how could AI reshape this and is there a positive scenario IO negative scenario because we don't want to have the inequality that we had in the past.
So, first let me agree with what you just said. Uh we're not running out of jobs. Uh we're much more running out of workers. And uh this is true in most industrialized countries. Uh where we have low population growth, uh 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 a decent uh standard, you know, the right to a decent standard of living in retirement, and you need workers to support that to provide that financing. Um, so that is not my concern. Um, uh and we've had, you know, we have very tight labor markets. We have low unemployment rate and we have, you know, for quite a while now, for more than a decade. Um, the, uh, the greater concern is the value of skills. People are paid to a substantial extent uh for expertise, their knowhow in specific activities, right? In the in, you know, rich industrialized countries, there just isn't much of a value to just pure physical labor anymore. Uh it's uh it's it's and by expertise, you know, I mean knowhow. And let me and I don't want to equate that with schooling. Uh because there's lots of expertise that's gained not through schooling. Some schooling hopefully some is gained through schooling as well. Uh but uh you know expertise means like you know 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. Uh but for expertise to have market value uh it needs to have two things. Uh one is it needs to be it needs to produce a service that people value right. So uh it's got to be you know data science not card tricks. Uh the second thing is it needs to be scarce. Uh because if everyone is expert no one is expert. uh it just won't pay very much. And and this is the threat that uh automation sometimes poses is it can devalue a skill set very quickly. Not because no one needs the skills anymore, but because the machine can do it, you know, better, chap, cheaper, faster, right? So, you know, we've seen that when uh phone based routing, you know, uh change the value of taxi services in London where people used to work from memory. Uh it used to be that, you know, um touch typing was a very valuable skill. Uh not so much anymore and a lot of uh mechanical uh skills used in manufacturing have been have lost value because either that work is done overseas or because it's automated and and this is the uh concern that one could have and 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 right there was a lot of skilled work uh following you codified rules and procedures that required expertise and knowledge and practice. And because those rules and procedures were well understood, it was uh 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 and nothing like it. But a lot of the people who would have been doing office work and manufacturing work 40 years ago now find themselves doing services uh food service, cleaning, security, uh entertainment, recreation, hospitality, home healthies and and that's socially valuable work. Uh no judgment there, but it's not expert work. Uh most people can be productive at that with very little training or certification and that means it won't pay well. So, you know, I like to give the example of, you know, you know, think of it like a crossing guard versus an air traffic controller. You know, at some fundamental level, those are the same job, right? The job is to prevent, you know, collisions between, you know, people in vehicles or vehicles and other vehicles. And yet, crossing guards make less than a quarter of what air traffic controllers do. And, uh, again, you know, they're protecting our children's lives when they go to school in the morning. So, they're doing valuable work. uh 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 you know if we suddenly ran short on crossing guards we could get the air traffic controllers to you know go uh do that in the morning. Uh 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. So expertise is really critical and so the threat that rapid automation poses uh to the degree it poses a threat. It's not running out of work but running but making the valuable skills that people have highly abundant so they're no longer valuable.
Have you um seen any work or speculation or theorization that you think is interesting around how AI can help people acquire those skills like like being adaptive to it like learning new skills um being in kind of new areas because you know most of the analysis that I've been running across has been well look you know um coding is an important thing and AI is going to be doing a bunch of coding and you take a zero sum amount to the amount of coding there is and you you kind of predict something, you know, uh challenging all of which strikes me as has a bunch of assumptions in it that are uh certainly not necessary um and maybe steerable. So I'm curious if you've seen anything that's you know things that we should be paying attention to, things that we should be trying to emphasize, things that we've been learning.
So first let me agree with you. You know there's a lot of thinking in the world that goes of the following. you know, let's look at people who are exposed and kind of if you're exposed, you're hosed, right? Like that's it. Uh your your work is going to shrivel and shrink and and die away. Um but of course that's that's we know that's not true from, you know, like think of the air traffic controllers versus the crossing guards. Who would you rather be, right? So uh in many many cases, you know, technology actually uh makes us more effective and valuable. We would be worthless without the technological tools that we have. I couldn't do my work. Uh air traffic controllers couldn't do their work. And you know if you're a doctor if you don't have a stethoscope you're you know much worse off. Um so in many ways our technologies are complimentary to our expertise and they are amplifiers or force multipliers to our expertise because they make us that they they they shorten the distance between intention and result. 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 you know do with our cognitively we couldn't compute them fast enough. you know, I do a lot of statistics. Uh, you know, I my my computer uh inverts matrices in milliseconds. Uh, it would take me months if I even remembered how to do it. Um, so uh I think so we should be thinking about uh where where does uh how how do you get to be on the right side of this equation, right? Uh does technology 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 uh those will have different answers and and and then let me go back to the question you asked Reed which is you know what about people acquiring expertise and and I think this is actually this is this is super central right so you know I the argument I've been making is work that pays well is decision-making work, right? Where you actually have to where the stakes are high. It's a one-off choice, right? How to land this plane, how to care for this patient, how to remodel this kitchen, right? Uh even, you know, how to season this, you know, meal at a restaurant. And there aren't simple rules. If there were simple rules, it' already be automated, right? Uh so it actually requires a lot of discretion. And uh the problem is a lot of that work is done by highly educated people, right? So you know the people with BAS and MDs and uh MBAs and so on they kind of monopolize the commanding heights of the modern economy whether in in medicine or in law or in design uh or in you know education uh technology and so on. And a lot of the people who used to do valuable work in offices and clerical offices and factories right they're kind of 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 use their 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 it's quite it's because the opposite can occur, right? if you rely too much on technology, you kind of won't bother. Uh, and you'll just say, "Well, it'll it'll do the job for me." And so I I like to distinguish between what I, you know, what I call automation tools versus collaboration tools, right? So automation tools are tools, you know, like, you know, like my the automatic transmission on your car or the elevator that gets between floors or the toll taker that when you drive through a highway toll takes takes money, right? These are examples of successful automation. Uh, and they didn't those all used to have be jobs, right? You used to shift your own car, right? You've probably I I know my Dodge Cult, the eight-speed car, uh, you know, that had a stick shift. Uh, there used to be elevator operators and there were lots and lots of toll takers. Uh, and this is successful automation. All of the specialized knowledge that was required is now fully encoded in machinery. It's done. We're happy with that. There's no problem. Uh, most tools are not that form. Most tools require you to bring some expertise to the table to use them, right? So, stethoscope, great thing. No use to me. I wouldn't know what I was hearing, right? Uh chainsaw, good for a lumberjack, not so good for my children. Uh and uh most tools uh require you they they're good because they allow you to take some knowledge and capability you have and do it faster or further or better. uh and uh and so if we use AI well, we'll be using it a lot as a collaboration tool uh to enable us to make better decisions uh to do harder tasks to solve problems or you know 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 you know pulls up the relevant information for you and helps guide you to do the work. You shouldn't do that if you don't if you're not an electrician. don't go opening up fuse boxes, you know, but if you have if you have the basic skills and then you have a a tool that can support you, you could probably go further with that. Um, and so that's the way that would be successful use uh for collaboration in many many cases. And again, I'm not morally opposed to automation. If you can automate something fully successfully, great. Uh, in most cases, we can't, right? There's actually an illusion, a kind of hubris that, oh, we have technology, it's superhuman, therefore expertise is dead. It can do whatever we want. You know, Jeffrey Hinton, you know, famously predicted about a decade ago that we would need no more radiologists. Now, AI is now used by radiologists. They love AI, but they're not fear radiologists. Uh they just do more of what they did. uh they they're nor more useful because they have better tools. Uh and a lot of their work is not just looking at scans, right? It's all of the communication with the patient and the other caregivers and so on. So, uh it's a mistake to think that everything because we have good technologies that we can automate everything. There's a problem with thinking you can automate when you can't. You're gonna you're going to design badly.
So, we can circle back that. So, let me actually let me see if I can pull all that together because that was too many things all at once. Um, so an 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, right? So I don't spend time inverting matrices. I can just work on what the statistics mean. Um, or it can descale your work by automating the expert parts and just leaving you with a sort of last mile, right? Uh, so both are possible. Um and uh so it's uh now it also often creates new work that requires new forms of expertise right but there's 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 we'll be displaced and how do we enable people to do expert work with better tools right people who might be shut out like if 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, right? That's a better world in my opinion. Um, and then finally, how do we create tools that enable people to get better at that stuff faster?
You ended with the word faster, which I think is really apppropo 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. Um, so I have a two-part question. one I thought you really sort of illuminated why speed can be tough uh 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 etc. And the second thing is sort of from hearing you talk about expertise it would make me think like okay the superstars are going to get even better like the you know the 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 like how can we think about that is it because we can retrain workers more quickly like how can this tool actually be good for the middle class okay great so let's first talk about the speed um so you know labor markets have a natural natural rate of adjustment. Uh if you think a career is 30 30 years, let's say that means kind of 3% of people will retire out of anything uh every year, right? So, you know, if you wanted to eliminate uh a third of people without laying anyone off, you just wait a decade and they'd all be gone. Uh and so and and most labor market change in for adults, it it actually doesn't happen mid-career, right? It's it's at the choice of of the entry point. And so it often occurs across cohorts, right? So in the areas that saw these big China shocks, right? 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, right? Right. Um so it it really does matter how fast this goes on. Like you know, we talk about autonomous vehicles all the time, right? If autonomous vehicles come Labor Day this year replaced all long-distance drivers, right, that would be a very serious problem because there are more than, you know, two million I I believe more than three million, you know, people who just do their living in driving vehicles. Um, that 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, right? That's kind of a manageable problem, right? People wouldn't enter the occupation. people retire out of it and it will happen actually much more slowly because even if this problem were solved tomorrow morning, right? It takes decades to replace all that capital, right? You're not just going to throw away all your trucks, right? You're going to replace them uh, you know, slowly over time. And so the concern with AI, I think that a lot of people have is it's just going to boom, you know, and and is absolutely the case that machines can acquire skills much more rapidly than people can, right? Once you have a machine that does something, wow, then you have a then you have a lot of machines that do the same thing. And uh and there are we will see this. I mean we should not be uh naive to think that won't occur, right? If you're a language translator, right? That's you know you're under threat. Uh if you're an illustrator, you're under threat. uh you know I think you know a lot of people who do just sort of uh workman software coding right there will be fewer I actually it's not completely certain but you know we do see a big decline in employment in computer coding right now in software development not or sorry I shouldn't say in the software engineering but in in the people who write programs right and so uh it's quite possible now I I think Reed alert alluded alluded to this earlier that it also depends on how much what demand looks like Right? So like if we you know got like really really good and cheap and fast at colonoscopies, people still wouldn't be lining up at their proctologist office to get more of them. Right? Uh but it it is the case that if we um you know if we if we get better, cheaper, faster coding, right, there is a lot of demand for software, right? Like you can't buy an appliance that doesn't have a microprocessor and is it doesn't have embedded software running in it. Uh you know that 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. Um, so uh it may be that we'll just get a lot more software coding, but uh it may also change what we use it for, right? So, you know, when people started developing websites, you know, back in the mid 90s, right? That was that was all about skill in HTML, right? Writing markup language, you know, they those websites, if you go back and look at them, they they're so incredibly horrible looking. It's hilarious how primitive they are. Now people, there's lots of people who build websites for a living, but it's not really a technical skill. It's design, right? It's how do you present information? It's actually has a different skill set that's involved. So, you know, that's that's kind of cool actually. So, the rate of change is a concern. It's absolutely is. And I, you know, now okay, so now come to the last part of your question. You say, well, how could this be good for anyone? Uh, well, this is not a given. This is kind of a good scenario. But you know as as I as I argued a few minutes ago a lot of valuable work in advanced economies is monopolized by elites right professors for example uh or lawyers or medical doctors or financeers or you know and uh we don't face that much competition right uh you know there's huge barriers to entry there aren't that many of us you know you can't print them that fast uh and and so you know we have this uh you know lock and you know that's fine for us like yeah things are expensive healthcare is expensive education's expensive legal services are expensive so but that's okay because I get paid a lot too because I do that right 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 you know software development more people to do medical services more people to be lawyers now it doesn't I don't mean that everybody can do anything and I don't mean that they you know 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, you know, tech the last wave of technology push 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 uh into new opportunities. That's the good scenario.
Now, have you seen anything that kind of is how other than just getting people into this, how they should start learning these tools in order to start kind of beginning to learn their own paths 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 kind of a macro thing um to say you know here is like a you know kind of call it a 30,000 foot or a 40,000 foot map by which people should start thinking about how these transformations are going to happen.
Learning to use AI well actually is a uh is an important skill in itself and it is you know the first like the first instinct that you have to develop. I mean, I remember, you know, I'm old enough uh where like I remember a world before Google and I remember the developing the instinct to Google things, right? You like you'd be at a dinner party or whatever and you'd say, you know, oh, I thought, you know, President Taft said this and I think someone would say that and then someone say, well, why don't we Google it? And everyone would be like, oh, Google it. And uh and then you know and we learned and then we sort of developed this kind of you know it's kind of a just a a sub routine or an instinct or whatever this habit to turn to it and and knowing actually when to turn to AI for things is also like not obvious. It's something you get better at. Like for example, I was talking with some of my research assistants and we were like 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're saying, well, what if you did this way? And then I say, oh, I know. And I like just took a picture of it. I stuck it into an AI and say, you know, make a bar chart of this. No, now rearrange it like this. And did it like that over course five minutes we all did on the screen and we said, yep, this won't work. Uh, but you know, that was a very timeefficient way to do it. Um and uh and so you know I always have a chat window open and I use it to sort of you know bounce ideas or look things up and so on. But another important thing to realize about AIS and this is goes to this distinction between collaboration versus automation. AI isn't really useful for things that you don't understand uh because it's not that reliable uh and it will misunderstand uh 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. Uh and uh 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 you know 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 kind of you know filter and interpret that knowledge. if you're trying to get it to do something for you that you don't, you know, write me a paper about, you know, the currencies of the Roman Empire or something like, you know, it might very well make up some Roman currency you've never heard of and you wouldn't know. Uh, so that also is part of learning to use AI is learning the instinct of when you know enough to know if it's doing something useful. uh and and when you can use it, you know, so you got to have, you know, it's like it's like I could use tell an AI, you know, tell me how to do a, you know, a surgical procedure on someone. Uh I shouldn't do that, right? If I'm a surgeon, right? And this is an extreme surge scenario and there's no other surgeon around and I need to do something, I can like look at the AI, give me instructions, and I could probably do it. But uh so you want to use it in the domains where it can collaborate with you and augment you but it it can't automate a way substitute for you know just fundamental lack of knowledge in some area that's a dangerous place to be.
So sort of thinking about the the the possible future we want to shoot for like you're talking about AI being complimentary and that's one of the visions that obviously Reed spoke about in super agency. It is okay. How can we have more people sort of 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 it's actually we we work a lot with this organization opportunity at 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 sort of 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. How fantastic would that be to add you know $20,000 to each job? Another sort of positive vision actually for AI that some people talk about is that less people will be doing jobs like we will 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 sort of freedom and only working 10 hours a week which sounds a little fantastical. So I would love you to to comment on sort of those two visions both of which are sort of positive as positive visions from sort of the new AI future.
Yeah. So the first vision of course I'm all behind uh and and it doesn't need to be by the way that everybody is doing middle or high you know skill work right. So if we just extracted half the people who were doing, you know, uh leisure and hospitality and janitorial services from that work, the the half who remained would get a big pay increase, right? Because firms would have to compete for the more. The problem is there's too many doing it and that's why wages are so low. So you know, and this is a well-known, you know, kind of economic parable, right? Like 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, right? So if there's you know in the long run if productivity rises and there's you know there aren't lots of people and you if you need to convince someone to do you know food service or cleaning whatever and you know 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, uh, you know, we don't, it doesn't have to be everybody. Just more opportunity benefits a lot of people, um, including not every and they don't have to all take that opportunity to benefit on the the notion of, you know, 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. Uh, you know, we are already an incredibly wealthy society, right? 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 health care uh and uh don't have a safe housing, don't have safe neighborhoods, don't have good schools, right? 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. Uh 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 sort of you know uh you know make us you know take care of all of us. I just don't know if that's if that's a reliable thing to do. Uh I don't you know it generally doesn't work that well. Uh you know the US US is not getting more generous as a society even as it's getting wealthier. we seem to be getting less generous. Uh and um so that so you know I I like to compare two scenarios. What I call the um the uh the Wall-E and the Mad Max scenario. So you've all seen the the movie Wall-E, right? Ara, you've probably seen it with your kids recently. Absolutely. Uh and you know it's a future where basically people, you know, sit around on, you know, kind of hovercraft armchairs, uh watching, you know, holographic TV, uh drinking big gulps, and they all weigh 300 lb, right? And this is supposed to be some sort of future dystopia uh because there's no work to do and everyone's bored. But I view that as the good scenario uh because the more likely scenario to me looks much more like Mad Max Fury Road where everybody's competing over you know a few remaining resources that aren't controlled by uh you know some warlord somewhere. Uh so you can have a world that's very wealthy and yet uh most people don't have anything. Uh and so that's why I so I like to think about work because I actually think the labor market uh has so much going for it. Uh 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, right? They just own themselves. And so we all start off, you know, at a kind of a at a relatively even starting point. Uh and so and so, you know, 60% of the income in the United States is labor income that goes first to workers and so it just creates uh you know, as you know, much more shared uh resources. Uh work also has a lot of virtues, I think. You know, it gives people identity, it gives them structure, it gives them meaning. I mean, not all jobs are good. So that's a luxury for me to say, you know, work is all great. Um so I I don't I don't want to say that for a minute but the other thing is in a democratic society it g if most people are working then it's easy for people to say well these are people all contributors to our society of course they have a vote right you know they of course they we're the we're the co-owners of society whereas if we're in a world where we say well like all the money you know comes out of a fountain uh in you know in San Francisco you know next to the uh you know open AI headquarters or something then it's much harder to say that everybody deserve deserves their share. I mean, I might I might agree to that, but I don't know that everyone else will. So, that's why I'm I don't uh I'm not excited about a world in which uh the resources are mostly coming from machinery and capital and everybody expects to be supported. Now, let me say important to emphasize, you know, we do much much much less work than we used to, right? So, at the beginning of the 20th century, you know, 120 years ago, 120 years ago, people worked on average in the United States about 3,000 hours a year. uh now we work on average about 1,900 hours of the year right we now you know we've invented the weekend uh you know we have vacation and so on additionally you know people used to enter the workforce as soon as they were physically able right you know 10 years old uh and they would work until they died right and now you know our people enter the labor force you know 16 18 20 25 if they're PhD students 40 45 uh and and then they retire when they have, you know, 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. Uh, 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. Uh but um but that's very different from there being no need for people to work and that they just you know hope that the society will care for them. That I'm I'm not as confident in.
Well, one of the things that I think this discussion highlights in a really interesting way is there's there's kind of two issues that I think get combined in the inequality discussion. uh one of which I'm extremely sympathetic to and I think is very important to to navigate and one of which is complicated. The complicated one is um is like the well no I want there not to be that much of a gap between me and you right like I just think that the a gap is an issue like you know you have two cars I have one and I think that's a real big deal deal whatever that kind of thing is and I tend to be a little bit more
You know, how do we really figure out what the right, the right mechanism and the gap is? Um, but a lot of people, that's their real driving issue in their rhetoric, but I think it's more complicated.
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, you know, everybody. And increasing and quality of life isn't just like, "Oh, look, I can, you know, I can afford the cheeseburger." That's great. But it's also kind of like, "Do I have meaningfulness and and control and respect and personhood and, you know, place within community and society?" And that kind of breaks down into two components.
One component is, you know, kind of what are you learning to do that gives you kind of a unique position in your community, in your workforce, you know, in your, you know, your kind of your tribal group. Um, and, you know, that's part of where I, you know, super agency trying to embrace the future, learn, become AI curious, you know, step into it, don't get forced into it, but like jump into it, uh, first and foremost.
But the other one's also reducing, this is the abundance thesis, is reducing the cost of a whole bunch of things. So, like, for example, part of how you, um, increase quality of life is you say, "Well, one of the things that AI can create is a 24-hour, you know, 7 days a week medical assistant," where, you know, very, very few people, even amongst, except when you get to super wealthy, actually have that in the US.
Has anyone gotten good kind of sense of of both this, not just the the the thing we're talking about, like the jobs and the transformation in the industry, but also on the kind of quality of services and quality of life based on, you know, because with AI can see not just a medical assistant that can essentially one for free for everyone on a smartphone, but a tutor, a legal assistant, a set of things none of which I think will take away actually jobs. I think there will be a whole bunch of of of coordination, you know, kind of amplification there. Um, I mean, they'll take away some jobs, but but they'll also create a bunch, I think, in this, but in that kind of quality of life that can possibly come from AI based on, uh, helping people in all these ways.
I mean, for sure. I mean, look, we, you know, even even people who are, you know, much, much less affluent in the United States, you know, in terms of the sort of physical quality of life, or is much higher than it was, you know, even 40 years ago. You know, people, most people didn't have air conditioning, right? They didn't have private transportation, uh, they certainly didn't have all the things that come from mobile telephony and the ability, you know, and mobile phones aren't just about entertainment, right? They're about communication. They're about access to services, uh, and information. Um, 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, you know, we've gotten better technology for doing it. You know, so people live in larger houses, they have more indoor plumbing, they have more electricity, they have bigger TVs. Uh, and again, that, you know, more importantly, they have, you know, cars, air conditioning. And I do think, yes, AI will make a lot more services can be used to make a lot more services less expensive. And and, you know, we use the web this way all the time, right? You know, the amount of time we spend, you know, waiting on phone queues or trying to buy airline tickets or, uh, or dealing with banks and so on, it's, you know, it's actually a lot more convenient than it used to be.
Okay. I, I remember buying plane tickets, uh, on the phone. Um, the, uh, or, you know, waiting in line, uh, to see a bank teller. Um, so that's, uh, I think that's very important.
But, you know, I, I don't think low prices are sufficient because, you know, what are the things that are really defining for, you know, kind of a good quality of life, in addition to the work you do? It's, "Do your kids have opportunity, right? Are your neighbors' neighborhoods safe? Are, are these good schools?" And, and also equally important, like, "Do people get a fair shake when they start, right?" The problem, like my problem with inequality, I, I'm with you. I don't, I don't care if, you know, if I have two cars and, you know, some people have 20 cars. I, I'm, I'm still okay with that. Uh, but it is the case that, uh, kind of inequality can lead to dynasticism where the, you know, the next generation is like, one set of kids starts so far ahead of the others that, you know, sure that, uh, even if they're, you know, their family got wealthy on the merits, then then there's no longer a kind of of, you know, anything close to fair chance in the next, in the next round. And so I think that's something we, uh, we, and those things are harder to deliver just through low prices, right? You know, just, you know, good schools, good opportunity, safety, you know, 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, um, but so, uh, that's the, that's the key thing. I mean, I think those things matter so much.
So, what people matters, people is of course economic security, like from day to day. Can I pay my bills? Right? Am I going to eat? Do I have a roof over my head? But then, like, "Do I have a job that provides me stability?" And then, "Do I have a way to ensure that my family, uh, is going to, you know, is going to do at least as well as I have done and get good opportunity commensurate with their, um, their own hard work?"
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 you would go into medical debt and you'd declare medical bankruptcy, like those aren't really sort of 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's certainly, um, some negatives, uh, about unions. Sometimes they lock in workers that we don't want, etc. But they do sometimes provide that certainty.
So my question for you is, as we want sort of gains to be broadly applied, are there institutions, policies, uh, collective bargaining, like what are the things that we can put in place to make sure that these gains are broadly applied?
Certainly collective or, you know, kind of worker representatives are, you know, there might have been a time when we had too few, too many, but now we definitely have too few. And, uh, the decline of collective bargaining at this point, I think, has left workers very vulnerable. So I would like to see more of that. Um, I would like to see more investment in our schools, uh, in our children. Those things would make a big difference. And then I do think, you know, people in the United States have a much greater level of economic insecurity than than do other people in in less affluent economies working the same jobs. And we tend to tend to think it's a, it's a necessary fact of life, and it just isn't. If you work in a McDonald's in, you know, uh, in Norway or Denmark or even in France, you're going to have vacation, you're going to have healthcare, uh, you're going to have, you know, sick leave. And, uh, we, this does not have to be, uh, inaccessible or only accessible to people who, you know, are, you know, middle and upper class. And that's a choice that we make. But we tend to think it's inevitable, and it's not.
Uh, you know, the US, we, we're so impressed with ourselves, uh, that we forget to make the right comparisons about, you know, and, and this actually, and, and, neg, and the other way too. People don't realize how affluent we are. Like, you know, people think that America's been in decline, but, you know, 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, you know, everyone's talking about making America great again, but, you know, actually, we're pretty, we, there's, we had a lot done, a lot of great things. Uh, and we should recognize that. I mean, we're also, you know, the US is an incredibly innovative country, right? You know, AI comes from here. So much of the computing area, the internet era, but so much, you know, we, we have an incredible culture of innovation that has been so important to us. So, we should recognize both our strengths and, and our good fortune, but also recognize we can learn a lot from, uh, other models. And it doesn't, not everything that is bad is inevitable. Uh, school shootings are not inevitable. Uh, and, you know, people not having adequate healthcare, not inevitable at this level of income. Uh, those are choices that we're making. I don't think we're making the right choices.
So, one of the things that I think, uh, you know, is a kind of question that we're going to, we're going to see happening that'll be a little bit different within the AI universe, um, is, you know, previously, kind of workforce transitions and so forth have happened, what I would think of as industrial time frames, which is, you know, part of like, when you look at our educational system, it's like, you know, train, then deploy for a lifetime, uh, versus, you know, kind of a constant recycling. And I actually think that part of what happens as we get into the 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, Startup 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, you know, how to do that. Um, and obviously, I, you know, part of my optimist is think about how AI can help with that. Is there any kind of, uh, good work, good lenses, good good principles for people thinking about now these transitions are, call it within, as opposed to within a 30-year time frame, you know, they're within a 10-year time frame. And so that that kind of that that that kind of moving along and adjusting is going to be one of the things that's going to be important throughout, kind of a human life, a human career, and so forth. And has, has, has any, have any principles or work been done so far in this? I, I'm convinced it's going to be very important.
Yeah, I'm convinced as well. Um, there, I would say the work is early. Like, you know, I, like, 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, uh, in the last millennium or so. And even, you know, if we walked into a classroom today from, let's say, you know, we all left school 30 or 40 years ago or 20 years, we wouldn't be surprised by a single thing in there, right? Or the way things are done. It's really not different. So I think we, we need to figure out how to use these tools to get better at education, at teaching ourselves and learning new skills. And I, and I think, you know, one of the things we know about, especially for adults making transitions, is they learn much more successfully, kind of experientially, than they do going back to classrooms, right? It's shocking to me as a professor, but not everybody loves being in a classroom. Uh, and, uh, and, you know, we sort of forget this lesson over and over again, right? So, uh, remember, uh, like MOOCs, massively online open courseware, right? Supposed to just everyone was now going to be a, you know, a botnet herder or, you know, an ethnomusicologist or whatever they wanted to be. And they really weren't very successful. Uh, and, you know, people don't talk about them much anymore. Why aren't they that successful? Well, they were like, they were like simulated classrooms, right? And it's like everything you hated about a classroom and worse, right? Like, who wants that, right? 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 make what do better what makes education effective. Uh, and that is kind of engagement and immersion.
So, I certainly think that there's a lot of skills that people can learn in simulated environments, right? So, you know, like, we, we do this, right? If you, if you want to fly a plane, you're going to spend a bunch of time in a flight simulator. If you, if you're, uh, you know, learning to do medical procedures, you're going to start off on animatronic dummies that bleed and scream, right? Why don't we do that for, you know, plumbing and electrical work? Well, it's clear, you know, we do it in a few places because it's so damn expensive. We don't do it everywhere else. Uh, even though we could, but we can now. That will be possible. So I, I think that's that's an important lesson.
It's also the question of, you know, how do we get where there is a lot of research now on how should we interact with AIs? What should they tell us, uh, 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, right? That's not how we learn. Uh, and, uh, in some, they need to interact with us in a way like, say, you know, there's one theory, uh, that says like, well, they should, you know, if you're coming to a choice, you know, there's A and B, right? The the AI could say, "Do A." But it could say, "Well, you could take A, and this would happen, or B, and I predict this would happen." The contrast pass 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. I'm, you know, 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, do they not, do they just help them get better results? Probably, they, they will. But do they help them acquire judgment faster, right? Because in so many, you know, if you're a lawyer, if you're a doctor, uh, if you're a researcher, if you're an actuary, if you're a carpenter, right? You develop judgment over time. That's part of the expertise that makes you so valuable, right? It's not just like what you learn from a book. You learn how to make the right decisions at the right time, at the right moment, you know, and, and so we, I would like to understand better what are the tools that enable people to do that faster, get those skills sooner, because of course, it takes a long time. Expertise, you know, it's great, but, you know, it takes a long time to get expertise. It's, it's slow, it's expensive, and even the best experts are fallible, and most people are not the best experts. So, uh, you know, so that, 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, and that makes the, that makes people smarter. And, and I think, kind of the key principle for me is automation versus collaboration. That, you know, automation is when you tell people what to do, uh, and that takes out expertise. And collaboration is when you harness expertise, that you give people the information. And, you know, and collaboration is, you know, like 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 sound totally that they weren't considering, right? 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, uh, I think this, this principle of collaborative design is, is going to be, is to me seems like very essential for thinking about, uh, how we help people learn, uh, and acquire skills more efficiently.
We'll now move to, uh, a rapid fire. Is there a movie, song, or book that fills you with optimism for the future?
So, I gave this one a lot of thought, and I, 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 watched Tiny Desk. But, oh my, have you watched Tiny Desk? Arya? I have not. Oh my god. Oh, jeez. Okay. So, yeah, let me just explain. Great Tiny. So, you know, this has started like 20 years ago. Bob Boland, who was their music editor, started inviting bands to come play at his desk, uh, and they would sort of film those. And over time, this has become an institution. So, like major acts, you know, so like, you know, Taylor Swift and, uh, not Bob Dylan, but, you know, almost everybody else you've ever heard of shows up. And some of the, 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, like, you know, I go see Anderson Pack's Tiny Desk concert or go see the Tiny Desk concert of the Buena Vista Social Club. These like, you know, 15, 20 minute productions, but I look forward to this like so much. And the amount of music that I've encountered I never would have heard of that gives me so much joy. So, Tiny Desk.
David, what is a question that you wish people would ask you more often?
You know, I wish people would ask more often about, and I ask people this all the time, like, "What is your counter-life?" What is the thing you would be doing if you weren't doing what you're doing? Uh, and that often tells you about something else that they're really passionate about. And, you know, 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, uh, 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 what, what is my? David? Okay. Yeah, let's hear it.
I, you know, I think there's another world I could have, I could have been a sailor. Uh, I love to sail. I still sail. In fact, I'm in a place where I sail. Uh, and I could have imagined, you know, doing round-the-world sailing, doing racing, uh, crewing. Uh, so that would have been okay.
Where do you see progress or momentum outside of your industry that inspires you?
People in, you know, in the West, certainly 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, you know, half-century." But actually, this has been the best 40 or 50 years that humanity has ever experienced, right? The amount of people brought out of poverty, right? We've never had a world, a global middle class until now. And, and, and partly this is China itself, right? China has, you know, reduced its poverty level from 70% to, you know, effectively a couple percentage points. And that, 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, you know, growth in Sub-Saharan Africa has been really strong, and livelihoods have improved. Like, you know, it's still very poor, but it's much less poor, and health is better. Uh, and so it is, um, I think we overlook how much progress there has been, uh, to in so much of the world. And so, you know, we, uh, 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.
All right, our famous final question. Uh, 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?
So, if everything broke our way, if we really did this right, uh, you know, 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 kind of improve welfare in so many dimensions. Not just, you know, in terms of material standard of living, not just in comfort, but investing in our kids, uh, creating opportunity for the next generation. Uh, so I think that would be great. And that, 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 I think that is, you know, and that's the kind of sad thing. Like everybody knows AI could be used for all these great things. Everybody also knows it could be used for really terrible things. Uh, 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, you know, I don't think anyone thinks we'll absolutely make the most of it. Uh, I'm sure many people think we'll totally squander it. Uh, but I think, but I think many people are really deeply, deeply uncertain. Uh, so, you know, in terms of breaking our way, this is, you know, you know, as, uh, um, my friend, uh, Josh Cohen, a philosopher, you know, likes to say, you know, the future is, is not a forecasting exercise, it's a design exercise, right? 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. And that's extremely hard to do. Uh, and so that is what's feasible, uh, but not easy.
If we were going to do, do that, like where would we start? I would say look, healthcare and education. Two activities we, you know, in the United States has 20% GDP, a lot of it is 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 like more treatments for rare diseases, it means things that make healthcare more available to everyone, so people have, you know, longer lives and higher quality lives. Uh, so that's what I would, that's where I'd like to get started.
David, I love that so much. Thank you for being here. Thank you both so much for taking the time. Uh, it was really a delight.
Possible is produced by Wonder Media Network. It's hosted by Araf Finger and me, Reed Hoffman. Our showrunner is Sha Young. Possible is produced by Katie Sanders, Edy Allard, Tanasi Doss, Serish Sched, Vanessa Handy, Aaliyah Yates, Paloma Mareno Jimenez, and Malia Aguello. Special thanks to Sura Yalaman Chile, SA Sepeva, Ian, Alice, Greg Biato, Parth Patil, and Ben Relis.