I've Got Questions with Sinead Bovell - A Rational Conversation on AI and Jobs With Economist Avi Goldfarb

Starting point is 00:00:00 I want to get to the bottom of the question, are jobs going away or are they not? I believe that millions of white-collar workers are going to lose their jobs in the next 12 to 18 months due to AI. The AI job apocalypse is real. There's lots that's probably right, and yet at the same time, the overall narrative is probably wrong. A lot of the jobs he described are actually in the top 20 and not in the bottom 80. So if you see your job is 75% exposed to AI based on this latest study, That might not mean anything. You could actually be making more. Absolutely. If your job is 75% exposed, then you're going to be spending your time on the remaining 25%.

Starting point is 00:00:37 Could mean higher wages. I want to say what more thing about jobs, though, that's important. Jobs aren't good. Okay. Getting paid is good. And finding meaning is good. But a job per se is something we do, and it's part of our society, but it's not necessarily good. And I do think on professions and jobs, the long run looks good. There's risk in the short run. Are there skills that become quite non-negotiable? Yes, I think there's four things.

Starting point is 00:01:02 There's a growing argument that the way we are rolling out AI is economically self-destructive. The more people AI replaces, there's going to be fewer people who can actually afford anything in the economy. The economy self-destructs. And as an economist, do you agree with this thesis? No. Today I'm sitting down with my former professor Avi Goldfarb. He is an economist and the Rotman chair in artificial intelligence and health care. He's the chief data scientist at the Creative Destruction Lab.

Starting point is 00:01:31 and a professor of marketing at Rotman School of Management at the University of Toronto. And today, I want to get to the bottom of the question, are jobs going away or are they not? I'm Shane Bowell, and this is I've got questions. Professor Goldberg, do you mind if I show you a TikTok? I am terribly sad to record this. I believe that millions of white-collar workers are going to lose their jobs the next 12 to 18 months due to AI. AI is now able to do the work of a very, very smart human in minutes or even seconds. This is going to displace marketers, coders, designers, lawyers, accountants, call center workers, you name it.

Starting point is 00:02:13 The result of this is going to be an ever more extreme winter take-all economy where the top 20% or so are able to thrive. and then the bottom 80% are left behind. And when people lose their jobs, it affects dry cleaners, dog walkers, hairstylists, restaurants, all local businesses that see less people who are able to spend. Personal bankruptcies are going to surge. College graduates are going to have a very difficult time finding entry-level jobs. People are going to be very, very angry by the fact that this path to middle-class status and social mobility is going to be.

Starting point is 00:02:53 going to be broken. We should be doing much, much more, including a universal basic income. The AI job apocalypse is real, and it's underway right now. What are your thoughts on Andrew Yang's take? It's a popular one. What do you think? There's lots of, there's lots that's probably right. And yet at the same time, the overall narrative, I think, is probably wrong. But what I mean by that is, if AI does all the things that he says it will, that means it's going to lead to extraordinary abundance. It's going to, it only makes sense for AI to be replacing accountants and lawyers and coders if it is that much better, which means that there's going to be

Starting point is 00:03:45 abundance for the rest of us of the ability to access code, access the law, and the regulatory state and do accounting on the case of accounting, which means that for everybody who's not in those particular categories, they're going to be able to produce much more and consume much more. So the worry that I think he gets right is there are going to be some jobs. And I actually don't know if they're going to be those ones. I don't know what they're going to be. But there are going to be some jobs that are at meaningful risk because of what I can do

Starting point is 00:04:22 and maybe even in the short term. The things that I think gets wrong are if that happens, that means the people who aren't in those jobs are going to be much better off because they're going to have access to those services of much lower cost than they did before. Another part of that is the jobs he described, he said, you know, the top 20 and the bottom 80,

Starting point is 00:04:43 a lot of the jobs he described are actually in the top 20 and not in the bottom 80. And what we're seeing so far in the data is that the professions that are not experiencing rapid employment growth in the U.S. are those that are not AI exposed, which means they don't use typically writing, drawing, and coding. And writing, drawing, and coding jobs tend to be high wage. So what we're seeing so far, there's a recent paper called Canaries in the Coal Mine. And in that paper, there's a meaningful worry, which is 22 to 25-year-olds that are focused on those AI-exposed jobs are doing worse in the labor market.

Starting point is 00:05:31 But the hidden message in that paper is that everybody else is doing better. And there's been, in the U.S. economy at least, rapid employment growth well beyond, you know, for every category except for that narrow one. And that narrow one represents a small fraction of overall work. Okay, so there's a bunch there. So when people hear, it is maybe possible, and let's say it is legal services that randomly, let's just pretend that that's the job that AI end up doing. We're all better off who are not in legal services because those services become so cheap, we can use more of them. Yes. And then what happens to everybody in legal services?

Starting point is 00:06:10 So if that starts to happen for a few professions, what happens to everybody in those professions? So a few things could happen. I can tell you what's happening so far and what might happen. So one version of it is we just simply need fewer lawyers. We don't need zero lawyers, but we need fewer of them, particularly at the entry level, if that's what the AI is doing. And the narrative, at least he's saying is, well, AI can do a lot of entry level legal work because it involves reading documents, redacting them, summarizing, et cetera,

Starting point is 00:06:45 which is the kind of thing that AI does well. And if that happens, then legal work becomes more top-heavy. The folks with 10, 15, 20 years experience for now are probably fine. But the new entrants are going to be less productive than new people in that industry a while ago, which means we're going to need fewer of them and or the new lawyers that we get are going to be paid less than current new lawyers. Now, because of equalizing, unequalizing, you know, junior lawyers getting paid less is, bad for those junior lawyers is sort of in terms of thinking about the overall income distribution, if the rest of us get better services, and that's effectively means we're better off,

Starting point is 00:07:31 that would be equalizing. I don't know that. Yeah. So from a net macro standpoint, we are all better off when law becomes better and it's cheaper, even though at the entry level, there will be some people that may be out of a job. Yes. So it's not, It's absolutely not all rosy. So as in there's reasons to worry about the folks who trained for years to do something, and then AI comes along and can do it as well or better. And so it's going to be hard for them to find jobs in that area. What we're seeing at least so far is that is in a handful of professions at the junior level,

Starting point is 00:08:10 and it's an open question whether that's going to affect the overall economy. So number one is we might need fewer lawyers. But there are other possibilities. Another possibility is we actually just need, you know, as lawyers get, as the AI takes some of the tasks that lawyers do, lawyers will spend more time on other tasks that relate to the law. And if they get good enough at those tasks, it's possible that there's no change in jobs at all. They just start doing different things. Okay. And if as law then becomes much more efficient, we realize that there's lots of opportunities to, you know, to use lawyers. Maybe lawyers aren't the best example, but to use lawyers than we had before, that would be great. So if imagine if right now a simple contract still costs $500, $500, $5,000, if AI makes the law much more accessible, and you can now write a contract for $3.

Starting point is 00:09:12 they're going to have a lot more contracts. And if we have that many more contracts, then maybe we could need a lot more lawyers to help with whatever the last things are in those contracts that make it so that we need a human loop. So then it seems like that thesis is kind of wrong from Andrew Yang or from anyone who kind of proposes that idea that we're headed for a draw apocalypse,

Starting point is 00:09:33 that they're not looking at the part two to that story. For it to be right, two very specific things have to happen. One is you have to, one possibility is you have to be able to automate an entire job, not just most of it. As long as there's a bottleneck in the job, then you still need somebody, people to do something. And second is as the price of things fall, do we actually want more of it? And there it's going to differ industry by industry. So as the price of food has fallen over the past 100 years, yes, we eat more food, but we actually, you know, we don't eat 100 times more food or 1,000. times more food. People, there's only, there's a satiation there. For other types of services,

Starting point is 00:10:14 maybe there won't be a cessation, like in healthcare. It's possible. We'll just keep wanting more and more and more and more. And so part of this is what we call elasticity of demand for the services as the price falls. And depending on what that elasticity looks like, we can end up with, with more people or less people as these things get cheaper. Andrew Yang talks about the top 20% are going to kind of have run away well. and then it's the rest of us in the 80% that are kind of left to the ditches. But you pointed out that it's actually the top 20% that AI, if anything, may be coming for? So far, that's what's happening.

Starting point is 00:10:56 Okay. Can't promise that's what will continue to happen. So you want to separate out skilled versus unskilled labor, like labor income relative to capital income. Okay. So, which is if you own stocks and or if you're owners of these companies and these companies start to do, AI companies start to do much better and make a lot of profits than, then the owners of those companies do better. Okay. On labor income, what we've seen so far is the things that AI tends to do well are things that relatively high skilled workers do. I think coding or aspects of it'll. law, like junior people in those professions, but they tend to be, at least within their age cohort, near the top of the income distribution in the 80th to 90th percentile. And the research we've seen so far is that those are the jobs that are most exposed to AI. So there's a paper called GPs or GPs, came out in science a couple years ago that identifies

Starting point is 00:11:58 those jobs as most exposed. Now, the reason I'm being cautious is exposed could be good or bad. Okay. So yes, they're more exposed. And the narrative that Andrew Yang had there is exposed means is bad. Okay. But exposed could actually be good, which is that some parts of your job get automated and you become super productive. And in that case, you do better. So if we take his narrative as given, which is if your job is exposed to AI, that's bad for you, then the second part doesn't follow. Because the second part being that then rich people are going to do even better. and poor people aren't, it's the rich people whose job, the relatively rich people whose jobs are exposed. Now, if we accept the second part of the argument and say, well, exposed is actually good and that means you're going to do better and better and better as you can take advantage of what AI can do, then the first part of the argument doesn't hold up because there it's, well, the narrative he had was AI is going to actually take jobs by doing things that humans do.

Starting point is 00:13:04 the opening was AI can do these things that humans do. And over time as they do that, those humans are going to be out of a job. So there's reasons to think high-wage people, that some people will be exposed to AI. And those exposures are going to be bad for them. It's going to be substitution. And in that case, some people will lose their jobs. And that's a real concern. And there's reasons to think that AI might increase inequality, okay, as it's superpowers.

Starting point is 00:13:34 hours people near the top of the income distribution. But those two things don't really work together. Okay. And so what, I give you an example from history, what we've seen with computers in the internet is, with computers in the internet, we didn't have like the same automation anxiety that we have today. But computers in the internet very clearly increased inequality. And they did it not by replacing humans, but by empowering skilled humans to do even more. And so what computers in the internet did is they help people at the top of the income distribution be much more productive. And while people who historically had been at the bottom of the income distribution didn't lose their jobs, they only did a little bit better. Well, everybody else did, while those near the top did a lot

Starting point is 00:14:22 better. With AI in the early data, we're seeing something that looks like the opposite, which is it's those most at risk are near the top of the income distribution, and those who seem to be helped are in the middle or toward the bottom. Now, that may be a long, you know, that may continue, but it might not. So their AI can potentially become a great equalizer, depending on which parts of the chain it automates. Absolutely. And so the key thing to remember is that one person automation ends up being someone else's augmentation. So if we automate, for example, what physicians do, what doctors do in diagnosis, that's going to be bad for doctors, but it probably will superpower nurses and pharmacists because now they have access to these tools that others

Starting point is 00:15:14 didn't. And so that would be an equalizing example because you're automating what somebody at the top does and empowering what people at the bottom do. Now, there's other worries in the other end. If we automate what taxi drivers do or Uber drivers do, then they're in the middle or the lower end of the distribution, income distribution, they're going to be worse off. And folks who now don't need to buy a car, buy car insurance and all this might be better off, but those people might be in the higher end of the income distribution. So really sort of depending on what gets automated and what gets augmented, or depending on what gets automated, the people who will benefit and the people will be hurt will change. So to summarize that, so if someone who says, okay, AI is coming for most

Starting point is 00:16:09 jobs, and especially in the knowledge economy, there's probably going to be a job apocalypse. Well, you have to actually first start to think, and if you're worried about income inequality, it's actually already higher wage earners that would be exposed to artificial intelligence. So There's a world in which we see a bit of equalizing. But there's also a world in which if AI doesn't end up automating everything within that role, we could see a bit more of a wealth divide because people, let's say, the lawyers, the doctors become more productive. But if it does end up automating some of the lawyers and the doctors, those tasks could be paired with somebody who theoretically doesn't make as much. We'll say the nurse compared to the doctor.

Starting point is 00:16:45 And then that nurse becomes supercharge. Yes. And we see more equality. Yes. And I'm not sure how much everyone can see your hand. but you had your top hand going down and your bottom hand going up. I think in many cases, the more likely scenario in the nurse doctor example is the nurse's wages go up, the doctors figure out other things to do with all that training.

Starting point is 00:17:07 And so it's equalizing where the folks in that case at the top are probably going to be stagnant, not doing worse, where folks who are relatively lower income, or aren't low income, but relatively lower income, do much better. And so then how do you think about a junior in that in that world? Because then how does somebody even step into the world of law? Or is it that the field itself also starts to change? So right now maybe a lawyer practices the way they've practiced for the last four years. But then we're going to have an agentic workflow in law.

Starting point is 00:17:41 And maybe it looks more like simulations and agents and all of these things practicing in trials, but the AI agents doing it. So then the junior doesn't jump on the ladder the way the senior would have, but they onboard in a different way. law also, because isn't the field also going to change? Absolutely. And I think we were just talking about medicine and law. And I hypothesize this, it was an interesting contrast there. So right now to become a doctor, you do a lot of post-secondary schooling. And to become a surgeon, you may not really be a fully fledged surgeon to well into your 30s, if not your 40s. Okay, it takes a long, it's a lot of training. Now, with an AI tool, potentially, um, the juniors who used to need to be in training for five, 10, even 15 years post-undergraduate,

Starting point is 00:18:32 now we'll have some skills right at graduation. And so we might have a lot more juniors. They'll look different. We may or may not decide to call them doctors. But as medical professionals who can take advantage of what AI diagnosis and AI recommended treatments can do. And so in medicine, depending on what, you know, if you're just, you focus on junior doctor, maybe, but if you focus on junior medical professional, there's

Starting point is 00:18:58 reason to think we're going to have a lot more juniors because just training to get up to the frontier is going to take less time. In contrast, law, the way we historically train lawyers is by having them do a lot of reading and summarizing and redacting to just have enough of a sense of what documents look like in a law so that they eventually get experience and become partners and can identify new ways to think about the law and ways to help their clients creatively. Now, in an AI world where, you know, as AI gets better at those tasks, the productivity, those junior lawyers goes down. They're just not as useful relative to AI. There might be some things for them to do, but they're almost surely going to be different. And so we may end up in a world where

Starting point is 00:19:49 those junior, where law ends up looking more like surgery, where to get the skills to truly be a lawyer that can help your clients, you need to be an apprentice for a very long time. And so junior lawyer's wages could go down with the promise of once they get the experience, it's going to be worth it, just like the way we currently treat our surgeons. Okay. So this means we have to stop thinking so linearly about jobs and professions because if we look at the role as you're either a doctor or you're not, well, then it seems like, okay, depending on which part that AI automates, we may not see as many people in the field there. But there's also a world in which we have a doctor, we have a nurse, and then we have something over here called Medical A. And

Starting point is 00:20:34 Medical A is somebody that goes to a health school. They're AI powered, and they do something maybe in between, and it's something different. And more people can reach for that job because it's not the 12-year having to get into med school. And so it actually brings the average wage up. And it may be harder to become a physician because you're also doing something different. So there's fewer people doing the higher earning occupation and more people that can do something in the middle. Absolutely. Technological change changes, you know, affects the system of professions. There's a book by a sociology book by Abbott where he talked about how over the course of the 20th century, We saw these battles for the definition of the boundaries between doctors and nurses and psychiatrists and psychologists and social workers.

Starting point is 00:21:21 And here we're because of AI in many professions, we're about to have new battles over the boundaries of what makes a doctor and what makes a nurse, what makes a lawyer versus what makes a clerk and across the board. Now, there's this, Can you say more about that battle? Like, could you give an example of why you think that there will be soon a battle over territory of who does what and why? Well, so think about medicine. And just to read it, so AI is, depending on who you talk to, AI may already be outperforming many physicians on diagnosis. I think everyone agrees on many, but people disagree on whether.

Starting point is 00:22:08 it's the 10th percentile, the 50th percentile, the 90th. As AI gets better at diagnosis and conditional on diagnosis can figure out and recommend a treatment for most diagnoses, then so much of what the physician is currently trained for, memorizing information and being able to summarize it and getting a quick sense of how they can help a patient might be done by machine. that is going to mean other medical professionals like nurses and pharmacists or the soon-to-be-named medical professional we just talked about are going to start encroaching on doctors territory, physicians' territory.

Starting point is 00:22:51 Now, one thing that physicians could do is, say, is fight back and say, no, you can't diagnose those things. By law, we physicians have a monopoly on most diagnosis, and that'll continue. and there will be some regulatory battles and how that all plays out. There's small versions of that in pharmacists diagnosing certain simple things, but, you know, with an AI, maybe you don't have to limit them to the simple things. And, but the physicians may also then, in turn, go into other things and focus on the exceptions or focus on the exceptions. or focus much more on how to help patients navigate the stress of their health, in which case then this physician might be encroaching on another profession like the social worker.

Starting point is 00:23:46 And that would be a different skill set. So you can imagine that, and I'm using the word imagine on purpose because it hasn't happened yet, that as a technology gets better at one part of an occupation, that that occupation is going to adapt and the other occupations that could take advantage of it will also. and we're going to end up with a different rule set and a different skill set. So what do you say to people then who look at AI and look at robotics and say, well, it's only a matter of time before AI does all of this. So there's, sure, for the next little bit, AI makes mistakes.

Starting point is 00:24:18 We need humans in the loop. But after five, six, seven years from now and then we can bring in robots into that mix, there's not going to be anything left for anyone. AI will just, if it can be learned, AI can do it. Are they not understanding how technology and economies reshate? Yeah. So I think that's unlikely. But it's not necessarily wrong.

Starting point is 00:24:43 And what do I say unlikely? So the first version of this is we've seen technological change before. Maybe not quite at the same pace. But electrification happened fast. Steam power went fast. the productivity transformation and agriculture was was quick and what tends to happen is people find other things to do okay so and the reason there's an economic model behind the reason I can understand behind that reason which I don't think is going away with AI okay so there's historical

Starting point is 00:25:21 data but also this this time doesn't seem different to me and what that is is as long as there is something that you need the human for, where the human is more productive than the machine, then more and more of the human work will go into that remaining thing. So I should actually caveat all this under, I'm assuming a world where the machines are still tools and they haven't taken over the world in some meaningful way. So we have a bunch of tools and the worry is that these tools are going to replace human work as opposed to new. consciousness that tries to take over. Sure.

Starting point is 00:26:00 Okay. So as long as we're in that world. So as long as we're in a world of these being tools, as tools get better, as long as there's something that humans do with, this is called Bommel's Cost disease, which is as you get more efficient in some part of the economy, it's the part of the economy that's inefficient actually becomes much more important. And so what happened with agriculture is agriculture got more efficient. it moved from being what most people did to what almost nobody does.

Starting point is 00:26:29 But there's still, yes, there's still farmers, but farmers are well under 10% of the workforce. They used to be much closer to 50 or more. It's because as agriculture got more efficient, people moved into the professions that actually didn't have the productivity improvement and still had demand, like manufacturing. And then manufacturing started to get more efficient. And as manufacturing got more efficient, people started to move into services, which didn't have that increase in efficiency. And then in the last 20 to 30 years, as services have gotten more efficient, as many services have gotten more efficient, jobs have moved into the

Starting point is 00:27:05 services that have had less productivity improvement, or at least measurable, like health care and education. Now, so if we make those more efficient, as long as there's some parts of the economy, or more precisely, some things that people want, where the productivity of of the human workers isn't superpowered by the AI, then that's what more and more of us will do. Now, that sounds optimistic, okay, as in it's not going to take all the jobs, and I think that's right.

Starting point is 00:27:36 There's two caveats to that. Caviot number one is that's a long run story. I'm very confident about the long run. Okay. But the short run can be incredibly disruptive, as in if there are certain professions that suddenly we realize AI can do, and importantly, we figure out how to make that happen. It's not just that AI can do,

Starting point is 00:27:59 but we figure out what that new system looks like. Then there could be meaningful, large-scale job loss in certain areas that will take time to recover from it. So the long run is optimistic there, but the short run. So that's caveat number one. caveat number two is that I said as long as there's things that humans can do, then that will be what jobs are. I can't, I have no idea of those things, I should say, I'm pretty confident there'll be something. I don't know if those will be things that people like Andrew Yang think are meaningful jobs or are good jobs.

Starting point is 00:28:39 And to, you know, it might be that they're wonderful. about the caring professions and taking care of other humans or sort of some kind of intellectual pursuits or contests like sports and maybe that's wonderful or it might be that there is just something that the robot can't do that involves picking something up and putting something down that you need the human for in which case we could end up being something that seems a little more dystopian okay so it seems like what you're saying is we have to when we think about a technological wave, we have to also ask what becomes scarce in that. So with the agriculture example, and again with manufacturing, it wasn't that these sectors disappeared,

Starting point is 00:29:27 they became much smaller share of the economy. And so in a world where AI can do what we would consider cognitive labor today, isn't it that humans themselves technically become scarce or jobs where humans are the value proposition become something that's scarce. So you had mentioned the care economy or nursing or teaching, and those could be some of the occupations. And of course, it seems like, well, not everybody would want to go into that. And, of course, amazing professions and my mom's a nurse.

Starting point is 00:29:57 But maybe it's not for everyone. But isn't it also true that we can't predict, I mean, that's not the full menu of what we end up doing. Those are the care-focused jobs that we can see today. but isn't it also true that strange occupations and we find weird things to do that we call valuable? I mean, you were also a former professor of mine of marketing. And when you think about the origins of marketing, it came off the back of the second industrial revolution because we had way too many goods now that we could produce because of industrial capitalism. So we had to local demand couldn't absorb it.

Starting point is 00:30:32 And then we had the railroad. Now your market may be somewhere else. So we had to figure out ways how do we coordinate between finance and production? and maybe we shouldn't just make goods and try to get salespeople to sell them. We should think about things like consumer demand and consumer needs. So that entire part of the knowledge economy we call marketing, and we know so many people in it, came from a technology. So isn't it possible that AI lead to strange things that at the time were unforeseeable?

Starting point is 00:30:58 And I don't even mean that as an optimistic kind of view. I don't consider myself an optimist. I'm not a pessimist. I don't just subscribe to the title and then hope for the best. But that has technically been what we've done. And we invented this, the podcast, the whole thing that's now so important that cable news is going to be the beginning at the end of that. So it's possible we do strange things that are just as strange as marketing was. And now it becomes something that we think about.

Starting point is 00:31:24 But that may also not happen. So I think we should expect exactly what you just described, which is that we should expect that there's going to be new jobs, new professions, new industries that we can't even imagine. And the problem is it's really hard to anticipate. It's much easier to figure out what's going than what's coming. And so Ajay, Agrawal, Joshua Gans, and I have this paper that came out in 2019, where we were asked about the labor impact, labor market impact of AI. And the dominant model at the time said new jobs are going to come from the arrival of new tasks. So you worry about substitution.

Starting point is 00:32:04 Okay. You can identify which jobs are at risk from AI, and then you can anticipate the new tasks. And so our original assignment, as we were thinking about that paper, was to first identify where substitution was possible and people's jobs would be at risk, which we've started to talk about, and then to say, where were these new tasks? And what was both enlightening and frustrating about writing that paper is we realized we don't know what the new tasks are. We can't anticipate them.

Starting point is 00:32:30 They're going to happen. We think. but that's essentially, you know, that's a trust in the model and faith in human ingenuity, as opposed to empirically we can say, this job is meaningfully at risk. So where would I expect to see things? A lot of the places we just talked about. So if AI continues to get much, much better at intellectual work, then maybe there's going to be certain caring professions,

Starting point is 00:32:59 whether the same ones we have today or different ones, where there is just real value to the human consumers and interacting with other humans. There's going to be competitions, right? We still watch foot races, even though cars can go faster. But it's interesting, we enjoy watching that. And so a world of competition and a world of the arts where there's something about the human creation

Starting point is 00:33:23 that we enjoy consuming, there's likely a whole bunch of opportunities in that. Alex Imas, who's an economist at the University of Chicago, has talked about status-related work. So that if in a world of abundance, we humans are still going to want to compete with each other. And we do compete for status. And if the AI is making everything abundant, then the remaining things will be how we get status. Can we do something that the AI can't do? Can we interact with other humans or the world in ways that the AI doesn't?

Starting point is 00:33:58 And there's some things that are always going to be rare. Like, you know, coastline in California, even in a world of abundance, is still scarce. And so different versions of that are, you know, exactly how that plays out is unforeseeable, I think. But to anticipate that there's going to be something left for us, I think that's exactly what we should expect. And I do think on professions and jobs, the long run looks good. There's risk in the short run. The transition. Yeah.

Starting point is 00:34:36 I want to say one more thing about jobs that's important. Jobs aren't good. Getting paid is good. And finding meaning is good. But a job per se is something we do. And it's part of our society, but it's not necessarily good. And to the extreme that in Econ 101, when we teach labor economics, we say people have a tradeoff between work and – or between leisure and consumption. So if you have more time off, then you get less income and you can consume less.

Starting point is 00:35:14 And so that means that in the standard economics framework, jobs are bad. We'd rather get paid a lot and not have to work. And we can see that in the world, right? So the victories of the 20th century labor movement weren't that people worked more. They were that we got weekends and 40-hour weeks and we got to retire at 65 and all these sorts of things.

Starting point is 00:35:39 It was more leisure time was the win. And at the other extreme, in the movie The Matrix, see The Matrix. That's a dystopian movie, as I understand it. And every single human works from the day they're born to the day they die. They're batteries, but they work. And no one, to my knowledge, thinks that that kind of work is what we want.

Starting point is 00:36:01 And so if to the extent that there are folks who are thinking about, you know, what does the optimistic, exciting future with AI look like? The goal of jobs per se only makes sense if we think that's the best way to distribute the wealth, that AI is going to create, or that's the best way for at least some subset of the population, if not most of us, for us to have meaning. When we think about the worry that jobs are going away, in the short run, I want to be, again, I want to stick with a short run and Lauren. In the short run, if somebody is working and their job goes away or someone expects to be working and their job goes away, that is really hard, and that is something that is absolutely a first order importance. In the longer run, if we get to retire at 50,

Starting point is 00:36:53 Or we get to only work a few days a week. Or there are other opportunities to spend our time that are even more meaningful than work. And we have enough wealth as a society and distributed fairly that we can benefit from it. That sounds wonderful. So there's an economist at Michigan, Betsy Stevenson, who's talked about these ideas. I just want to give credit to me. So she's framed it as if AI gives us the abundance as claimed. so that it creates extraordinary productivity gains and extraordinary economic growth,

Starting point is 00:37:28 then the worry isn't jobs per se. It's whether we can find a fair distribution of income that our society accepts. And two, whether for those people who get meaning from their lives through jobs, can we find other ways for them to find meaning? And if the answer to those questions is yes, then the job issue per se isn't. the right focus. Right. So there's jobs, there's work, there's income and purpose. Sometimes those things are bundled and often they're actually not. And so if you were to ask someone,

Starting point is 00:38:10 if you were to design the workforce from scratch, would you keep it this way? Would you fight for it to be this way? Or if you had an opportunity, if this was a design space, what would you do differently. And would you do what you're doing even if you didn't get paid? Is that the thing that you would want to be doing? And for some people, the answer is still yes. And then for some people, it's, no, I would rather not be contributing to somebody else's dream. I'd rather be pursuing my own. When you ask somebody is work the most important thing in your life, they think, oh, no, and I would never want to answer that. But then when you think, okay, we might take that part away, that also feels very violating and scary. So we know it's not the thing that really brings

Starting point is 00:38:48 us meaning per se. We also don't want to lose it. So in a world where you get income and you can pursue different things, that's actually potentially on the table if we design it right. But then I guess there's also the fear of what the heck do we do with our time. Because when you look at what we've done with the free time, we have more free time today than we probably did 200 years ago. And we're scrolling. We are yelling in the comment section. We are buying nonsense and some nonsense or being sold But we are also living longer and better educated. Yes. Well, the structure we all exist within right now, including our education system, came out of the

Starting point is 00:39:26 Industrial Revolution, right? The factory model. The idea of the job became because of specialization and in the factory. It wasn't some long-loss quest that we pondered upon and thought, okay, let's all run to the factory, invent the firm, and then go work in it nine to five. I don't think that's how most people would have designed society. And it actually goes further than that. How would you design society if you could redo it?

Starting point is 00:39:47 And we might be at that design moment. Yes, we might be. And what I'm good at as economist is talking about the tradeoffs. Okay. And if we move to fewer hours of work, then to what extent are we still motivating people to produce in a way that allows society to keep giving us more of what we want, whether it's health care or education or food or clothing or other things. If the AI's tools are providing extraordinary abundance, then that's wonderful. But there still needs to be some kind of incentive system to grab that abundance and distribute that abundance in a way that makes society's work.

Starting point is 00:40:34 We're sitting at the University of Toronto. There's a lot of smart people here and around the world thinking deeply about what those new systems look like. and some of that involves actually thinking about policy changes. Like, does a universal income make sense? Should the retirement age be older or younger? Should we move to a three-day week versus a five-day week? These are incremental, but with enough of them, it could be meaningful. But each of those has a handful of assumptions embedded in them that you, before you grab and say,

Starting point is 00:41:11 this is what we want, you need to think really carefully about. As in, once that system comes into place, how will us humans, potentially superpowered by AI, be able to game it for some people's advantage and not others? I think, and what does agency even look like in that system? Absolutely. Because I think that that's the kind of the real question. People may be able to say, you know what, I like my job, or maybe I do, maybe I don't. I like having the agency to pursue something that I at least think maybe interests me. And then it can be rewarding to see growth in that field and to earn something from it. And yes, I don't know if it's human nature.

Starting point is 00:41:57 I guess we'll have to talk to a biologist about that, but our tendency to game things or tendency to be power seeking, there probably will be winners and losers in any new system. Absolutely. And how, so would you say that? then that when you look at something like software engineering today, is software engineering the canary in the coal mine for what's about to happen to the knowledge economy as something that's going to become a smaller share? I mean, this is for the first time in 15 years, after 15 years of strong growth in computer science,

Starting point is 00:42:31 there's been a drop in enrollment. And we've seen, of course, we talked about the fewer probably junior hires doing software engineering. So would this be a rational attempt by students to avoid the psychological and economic suffering of an AI-dominated field? Or are we, again, misreading what is happening here? I think software engineering is a super interesting area to talk about. So first, because there is a sense that there's fewer new job openings, at least fewer new job openings at the wage expectations of new graduates.

Starting point is 00:43:09 my understanding is graduates of software engineering are still doing relatively well compared to other 22-year-olds, both in wages, certainly in wages conditional on getting a job. And then you've got to think through because, you know, sort of wages adjusts, you know, is if employment, if unemployment is high, is that because wage expectations were sort of set earlier. So it's, like, it's an interesting profession to think about because they, in some objective ways, software engineers are still doing very well, even though I think there's evidence they're doing worse than they used to. Now, the second point is if AI is enabling us to code at scale, do we now have a system professions point about software engineering? So I suspect you don't remember having to code an HTML in the late 90s. Yeah, okay. So if you wanted to build a website in the late 90s, you had to do it yourself and code in a relatively easy to understand coding language,

Starting point is 00:44:15 but it was still code. And so there were a number of people who developed that skill and were web developers, where web developer meant something like today's software engineer. You actually needed to know how to code. Now, you have a website. You may even be able to edit your website yourself. Okay. But you don't think of yourself as a software engineer.

Starting point is 00:44:43 Not at all. Okay. And so what happened is as no code web design came along is a whole bunch of people who didn't used to be software engineers or web designers are now web designers. Some of them identify as web designers who use no code and don't think of them as programmers, but they themselves as programmers, but they do think themselves as web designers. Some of them are more like you who, I don't think you put that in your... It's not in my bio.

Starting point is 00:45:07 It's not in your bio at all. but it is a part of what you do. And so what I think is going to happen with software engineering, it's not that we're going to have less code. As I understand it, we're going to have more code. And more of us, especially once tools are developed to help everyone take advantage of what AI-based coding can do, many more of us are really going to be coding,

Starting point is 00:45:34 but we're not going to think of ourselves as software engineers. But the real consequence there was the whole system of what it meant to be a web developer changed. And I think we're in the first step where we can see the risk. And those of us who are going to benefit from being able to code haven't really experienced what that means yet. It seems like computer science, those who are going to be in the very top percentile are going to be kind of systems, designers, sociotechnical architects, software engineering, those who remain in it, it's actually going to become much more complex. If you think about what's coming at us, right, the robots that were going to be sinking with the different applications people are building connected to your drive-less car and maybe

Starting point is 00:46:19 some AI wearable and you create some sort of immersive movie that way, picture what it would mean to be some form of a software engineer, wherever we call that, some system architect in that world. So I think it's eventually software engineering, whatever we call it, will be even more complex, those who remain in the top of it. And then there will be a cohort of people who kind of do the, I did for my company website, I hired somebody. And then for my personal one, I just did it. And that can be kind of the rest of software engineers.

Starting point is 00:46:45 I think that's a really interesting vision. I have no idea of that vision is three years away, 10 years away, or 40 years away. And so I'd be hesitant to say to a recent grad or someone starting college saying, okay, you know, this is, that's where things are going to land at the time you graduate. So you've got to prepare for that now. Instead, maybe even to the extent that you have conviction on that is where the future is going at some point, then what's useful perhaps today is to find one piece of that. There's only so much you can learn in a year. So one piece of that that you think you're excited about and that you're relatively good at and focus on learning that piece. And then next year, it'll be something else.

Starting point is 00:47:35 And maybe one of the macro lessons of how we can dissipate the change with AI on all sorts of jobs is that whatever it means to be a software engineer in 2026, I'm confident that it will be different from 2031. And so what you want to be able to do is learn enough different pieces of it. it and get exposed to enough different opportunities that as the world changes, you're capable of changing along with it. The adaptability piece as a skill, which is something that we all need to be doing. I was at an AI roundtable dinner a couple weeks ago, and there was a woman there. She's in the CEO level or C-speed level at a financial services firm. And she was saying that their organization is designing AI systems that intentionally keep

Starting point is 00:48:27 humans in the loop in the workflow. And so the thesis for her was we want to make sure employees stay engaged and their skills don't atrophy. We also want to make sure we have a succession pipeline so as our, so our juniors can become managers and then can become partners. So we're not, so the lifespan of our firm isn't, you know, 20 years or something. Yeah. So she's designing, they're designing AI systems where it checks in with the human. The human has to have this core part. Is that the right way for companies to be thinking about AI right now?

Starting point is 00:49:00 Now, are they better off to intentionally only try to augment workflows? Or is there something that companies that are having or that are taking that lane are missing? If there's an opportunity to automate something that will make your company much more productive, it's hard for me to understand how that would not be a good idea. But that if at the beginning was very important, which is it turns out it's very hard to fully automate a job. There's a study by Dan Gross looking at telephone operators. So in the 1920s and 1930s, perhaps the most common profession, certainly for young women in the United States, was telephone operator. Today there are pretty much none.

Starting point is 00:49:50 But it took the automated switch was invented in the 1890s. And it took until 1978 for in the U.S. the last sort of traditional. telephone operator to be out of work. It just took a long time to figure out how to automate that job fully. And so if companies do see an opportunity to automate, then that does meaningfully impact the bottom line. I don't see why they wouldn't. But there's a barrier because it's hard to automate.

Starting point is 00:50:24 And there's another barrier, which is that even if you automated, it has to be worth it for your bottom line. And a lot of what we talk about in our book Power and Prediction is this difference between point solutions and system solutions, right? So a point solution is you look at your existing workflow, you take out some human process, drop in the machine, and keep the workflow the same. And in that scenario, typically the best you can do is what you're already doing, but a little bit better. And that might not be worth it for the disruption to your workers, the disruption to your pipeline, to just do a little bit better. But if you can reimagine your system so that you're becoming much more productive, serving your customers much better than you did before, reducing costs in some significant way that allows you to do things differently, then for a company to say, I just don't want to do that for these other reasons. It's hard for me to understand. Another sort of economic concept in what you just described I think is really important, which is it's wonderful that company is training their employees and the way they are.

Starting point is 00:51:28 The question is, are they training their employees to have what we call general human capital or specific human capital? So general human capital means that the things they train their employees for are useful at every other company. And specific human capital mean they're only useful at that company. If they're training their employees for things that are only useful at that company, then the pipeline you just described sounds great. if they're training their junior employees with skills that are going to be useful everywhere, then if I were the leadership of that company, I would worry that as we train employees after three, five years, and they're ready to thrive in an AI world,

Starting point is 00:52:09 that all of their competitors are going to poach their employees. And so what I imagine they're doing is thinking three, not just training people, but training people to thrive in their company. And that makes a lot of sense, which is to figure out for your new employees as they get tooled up, how do you train them to thrive in your company in that environment to be good at what you do so that they can be the next generation? And doesn't that, don't you have to first ask, is this how our company is going to continue to compete in the future? Because if you're assuming, that your business model can stay the same.

Starting point is 00:52:54 And you just need to have more efficient, faster employees. And so, yes, they're going to be AI powered. And we've kept them in the workflow. But we're not actually changing anything else. We're just kind of trimming a few things here and there. If you haven't first done the business model work to make sure this is the best way to compete, couldn't you be calling into the question, the longevity of your firm in its entirety anyways?

Starting point is 00:53:15 Yes. So I was, that's a great point. So the, the senior leadership of every company should be doing the work to understand what the competitive threats are from AI, what the opportunities are, and what does the future of their firm look like. And the training I just, the training processes that they're going to develop in-house, if that's what they want to do, have to be consistent with that future.

Starting point is 00:53:46 Sure. Yeah. There's another possibility. which I hope is already happening, which is, yes, companies need to train their own employees, absolutely. But we are in higher ed have an important role to play in getting people ready for the workforce where AI is ubiquitous. One of the reasons why in the first years after ChatGPT's launch, employment for some young workers was, lower than they might have anticipated employment rate than we might have anticipated is because in the middle of their education, chat GPT arrived, and we in higher ed weren't ready for that.

Starting point is 00:54:30 And one, it made it easier to write essays in ways that we couldn't detect. But two, our curriculum wasn't designed to teach people how to use this tool and use it well and take advantage of it. I think now three and a half years later, we've made real progress, both here at University of but also at other universities around the world in what does it mean to train people to work in a world where they have access to machine intelligence.

Starting point is 00:55:00 And because of that, I'm hopeful that the work you just described the company having to do, so you're going to have to do something. But the students will graduate and on day one they'll be able to contribute in a way that students from three years ago couldn't because new students will be used to working with AI. and we'll know how to use that effectively.

Starting point is 00:55:22 So you could actually theoretically be doing a disservice to your employees. Because the reality of the world is AI will be ubiquitous, and soon AI agents, everything will be ubiquitous. And so if you were trying to engineer a workflow with the assumption that AI isn't, and a human has, and of course this is within finance and certain, we're assuming the workflow doesn't have something dire where human should oversee it, something kinetic or something physical. in the world of, let's say, finance, if you're designing a workflow and you're saying, okay, every 15 minutes we've designed in a section where the human comes in and they do some review and then it goes on, you're actually designing for a world that no one's going to actually be living in because AI will be ubiquitous. And they'll either be a new grad that has learned these tools from scratch or a company

Starting point is 00:56:11 that is built in a different way that assumes you're streaming AI the way you stream electricity. And you're potentially doing a disservice to the people in that company. if it's simply you have your existing workflow and you've just randomly picked a point in that workflow to make sure the human stays there, then absolutely. There's another possibility. I have no idea what this company's doing. So there's another possibility, which is they've actually thought very carefully about each of their workflows. Instead, we can redesign the entire workflow as long as there's a human spending a lot of time focused on this step. And if the human is focused on that step, the entire workflow will be more efficient.

Starting point is 00:56:47 and in the process we get this bonus that we train that person better than we could before. Now, the easier process might be to just sort of mandate, okay, just find somewhere to put the human in. And then I agree with you that's worrisome. If instead it's let's think through strategically within each of our divisions, each job within those divisions, each job within those divisions and each task within those jobs, when, if ever, the human is needed, and then can we get people to really understand that well? And that's fantastic.

Starting point is 00:57:31 And that would be great for, I imagine, using the word imagine on purpose again, great for the workers there and great for the firm. One thing that we're seeing AI can do is it helps you draft emails. Okay. And another thing we're seeing AI can do is it helps you summarize emails. Okay. And I'm often in executive classes and I ask the class, okay, what are you, what are using AI for? And someone says, I'm drafting emails and someone else in the same organization says I'm using it to summarize. And then the question is, well, why are you even bothering with the AI at all? Or why are you even

Starting point is 00:58:05 bothering with the email itself? It should just be, you know, somehow, the bullet point to bullet point should make things much more efficient. But then once you're doing that, maybe you can question whether you even need that communication at all. And when I push them, they say, well, I still want to read the bullet points before I send them. Because there's a verification of, is that what I want? That still matters. And so four tasks where it does matter that the message is what the human wants, then that ends up being, I think in many cases, this verification step, something that humans are like, are going to be central to, even in a world of AGI. There's a paper by a couple of former students of mine called the simple economics of AGI.

Starting point is 00:59:01 So Christian Catalini, Jane Wu, and in their paper, they say like as the AI gets better and better, even, even if we're in a world of artificial general intelligence, that we still need the humans to verify that it's what they wanted. If ultimately the role of humans as judgment to figure out what we want, then that verification tool layer becomes in like in their model, almost all of what we do. And you can see that with, in the story you just told within a workflow,

Starting point is 00:59:34 you can see that play out. And so what do you say? Because there are some, even some economists that say, but to prevent that the threat of the job apocalypse, that may happen even in the short run. So I think we're both aligned that in the long run, things may actually be okay.

Starting point is 00:59:49 The next 15 years, that's what I'm really concerned about. So we can avoid all that if we just only augment roles instead of automate them. Okay. So honestly, I don't think that makes any sense. But I know very smart people disagree with me. And the reason I don't think it makes sense is going back to this idea that augmenting some people automates others.

Starting point is 01:00:08 And we saw this with the rise of the personal computer and the word processor. What did that do? It augmented what the knowledge worker could do because they could type directly and delete their mistakes. So they didn't have to be great at typing. But there was a profession called typist. And that profession ended up becoming irrelevant effectively as the word processing. became part of what knowledge workers did. And so, like, word processing was very clearly

Starting point is 01:00:43 in augmenting technology. What it did is it made knowledge workers able to think as they write and erase, and they became much more productive. But that doesn't mean it didn't hurt people or create job loss because the typists were no longer relevant. So I don't really, the narrative

Starting point is 01:01:07 that we should focus on augmentation and not automation. Honestly, unless that's a statement about augmenting everybody without automating anybody, which I haven't seen a story where that's possible, it seems to me that a constructive alternative is to say if you're choosing what to augment and who to augment and what to automate and who to automate, then think carefully about whether you,

Starting point is 01:01:37 you're automating the roles of the vulnerable or instead potentially augmenting the roles of vulnerable relative to less vulnerable people. And I want to ask you about your recent paper, O-ring automation and the logic of task concentration. Because every few months, a paper drops about jobs or sectors that are exposed to artificial intelligence. And then we hear all of these viral headlines about if your job or your sector is exposed to this technology, it's the beginning of the end for you. your paper shows that actually something else could be happening. If a job is exposed to artificial intelligence or if a sector is exposed to artificial intelligence, it's not only that it might not be automated at all,

Starting point is 01:02:18 that could actually mean your job is going to increase in value and you could be making more money. So can you set the record straight on what these AI automation indices are getting wrong? A paper is called O-ring Automation. And the reason it's called O-Ring Automation, is because it's about how all the components potentially of a workflow, of a job, can be essential to the final product. And the metaphor comes from Michael Kramer paper on O-ring production, where the space shuttle challenger, the disaster in 1986,

Starting point is 01:03:01 it's a very complicated machine in space shuttle. And the failure turned out to be because of a little tiny washer-sized piece, a seal that was broken that allowed fuel to leak. And so a one tiny piece was essential. And so the whole thing ended up failing. It's catastrophic failure. Now, to the extent that in a particular workflow, a particular job, there is one piece that requires, a human, then, or actually, let's be more precise, to the extent that there's, that each piece is essential to the overall workflow. So you can't just get rid of one. As we start automating

Starting point is 01:03:49 some of those tasks, what's going to happen to the human workers? They're going to start spending more and more time in the other tasks. What that means is they're going to get really good at them, which is then going to make it even harder to automate those remaining tasks. And so as long as there is one task within that workflow that the human is more productive than the machine, then those humans are super productive. They get high wages and they remain in the organization. Now, so that's the essence of Obring automation, which is that as long as there is one thing that the humans can do if they spend all of their time on it, more productive than more productively than the machine. then they're truly, then there's a real opportunity. And there'll be higher wages and they'll keep their jobs.

Starting point is 01:04:45 So they'll actually make more. So if you see your job is 75% exposed to AI based on this latest study, that might not mean anything. You could actually be making more. Absolutely. If your job is 100% exposed, that's a different world. But if your job is 75% exposed, then you're going to be spending your time on the remaining 25%. Okay. And that could mean you're going to get better at that. It means you're going to be more productive. And that could mean more people like you or less people like you. It could mean higher wages and lower wages depending on another thing, which is back to elasticity of demand, which is as your firm gets more productive, are the people who actually want that many more of what your company builds? And if so, then that combined with an O-ring model means that the task exposure,

Starting point is 01:05:35 models just miss something fundamentally important. I want to be a little cautious, which is that a lot of the published research papers on task exposure are very careful. Like there's this Elan Do It All paper that Open AI put out along with Dan Rock in science two years ago that was at the time and probably still the best of the task exposure papers. and the newspaper headlines around that paper are these jobs are exposed, the headlines, the TikToks, whatever else are disaster. But if you read that paper at least once on every page, they say exposure does not mean substitution.

Starting point is 01:06:25 It could mean compliments. So the measurement, often the people doing the measurement are being careful. Right. Interpreting the measurement. Interpreting. It's just so tempting. say, ah, it's automation and humans are out of luck. And I've been dying to ask you about this AI paradox, because there's a growing argument

Starting point is 01:06:43 that the way we are rolling out AI is economically self-destructive. So the more firms race to try to replace workers, as long as that's the thesis, they're also actually automating their own consumer base theoretically. So the more people AI replaces, there's going to be fewer people who can actually afford anything in the economy, the economy self-destructs, and this becomes a tragedy of the commons. As an economist, do you agree with this thesis? No. Or I should be cautious, as I always am as an economist, which is there are a very, very narrow set of assumptions that on a knife edge that could possibly happen. But for that to happen, And essentially, as AI gets better and better and makes us more productive, it creates stuff that nobody wants.

Starting point is 01:07:34 And that there's nothing left for us humans to do. So there's no O-ring. There's no Bommels Cost disease as in there's nothing left for humans. And not only is there nothing left for humans, but the AI is producing nothing more useful than we already have. So another version of that is like human wants are satiated, perhaps. under that scenario, then it's possible. I don't think that's likely. I don't think it's likely because, first,

Starting point is 01:08:03 I don't think all jobs are going to be automated. Second, I think the productivity benefits of AI will mean we get more of what we want. And so there will be demand. And as consumers are demanding stuff, then the machines are going to produce for that. And if people don't want stuff, then why would the machines produce?

Starting point is 01:08:26 And so the circularity of that argument, why would we be automating if there's no demand for the thing downstream? And so sort of a necessary step for that automation to happen where, again, I don't think it could happen in the way those people describe. But even if we accept that,

Starting point is 01:08:48 there has to be a reason why the owners of the machines would want that to happen. And then there's another layer which is that presumably the automation of certain workers in companies creates profits. And those profits are distributed throughout society, not just to the billionaire owners, but a large fraction of the population has exposure to the stock market. for example, everybody who has a pension plan. And so, or not just the stock market, but two investments. And so again, this extraordinary wealth is going to be distributed to people who can then spend it.

Starting point is 01:09:36 And so there have to be a whole set of assumptions on like where that wealth goes to and what people continue to want that goes away. And the final thing is this transition that I wanted to come back to. I think that we both share, I mean, you share it from the perspective of an economist, I share it from more of the perspective of the belief in the papers that I read, that we, it is unlikely that all jobs are suddenly going to be automated by this technology were more likely to see people become more productive or augmented. It's much harder to automate a full workflow than many of the AI leaders would like us to believe, and it will probably

Starting point is 01:10:12 take longer than anybody is forecasting, especially people who are quickly jumping into the stock market and hoping for the best next week. But it is probably true that we're going to transition to a different type of economy. And maybe we're in economy A right now and we'll get to economy B and maybe that's 10, 15, 20 years. But it could be very shaky over that time period. How do you think this transition could go and what do you think could happen? And how do we think about preparing? Yeah. Tough set of questions. So what I, one scenario that I think is most likely, but I don't have any empirical reason to say that, is that a lot of the, that the transition happens much more slowly than people anticipate. So in a long run, it may even be bigger than people

Starting point is 01:11:06 anticipate, but the getting industry by industry to figure out what their business model looks like as AI takes off, what that new system looks like could be slow. The electricity, it took 40 years, with computers, it took something like 30 to maybe even 40. Figuring out what the new business model is in the AI world will take time. They had to redesign the factory before they could take advantage of what electricity could offer. And even in things like digital payments, there were many retailers as of 2019 who were not taking credit cards. and so to say now suddenly something's happening and those people who took a pandemic

Starting point is 01:11:55 for them to move to digital payments are going to transition everything they do instantly because of AI. I find that hard to get my head around. So I think it's going to be, it's going to happen more slowly than many in Silicon Valley and elsewhere intespate. So, but there's other scenarios.

Starting point is 01:12:18 that are more pessimistic or optimistic, depending how you think about it. One is that because this technology is an intelligence technology, we figure out stuff faster. And so the change does happen fast. And that could mean a bumpy couple years as people lose jobs. It could mean the opposite as we suddenly have cures. to diseases we never imagined and we, as long as we can figure out what to do with ourselves when we're, when we have all this free time. There's another more pessimistic version of this,

Starting point is 01:13:03 which was, I once gave a talk and took a group of executives and government people and said, the optimistic scenario I just described or described earlier where AI, so far is helping people in the middle and the bottom and hurting people in the 80th percentile of the income distribution. And I framed that as an optimistic because it's equalizing. And they said, I don't see why you think that's optimistic. Historically, revolutions come from people toward the top of the income distribution when their opportunities are lost.

Starting point is 01:13:41 I don't know if that's historically true, but that was the argument. And that seems pretty compelling. So that's the, well, if the disruption happens and it happens to people who, want to hold on to power, then things could go bad quickly. And so that's, that's, and I could keep telling you these scenarios, I can tell you optimistic, pessimistic, empirically, what we saw with the internet, what we saw with computing, what we saw with electricity is, in the long run, the impact was extraordinary. And had it meaningfully helped society get more of what we want,

Starting point is 01:14:15 while at the same time having a real negative impact on segments of that society, that it will be important for us to think carefully about how to mitigate that impact. But the scenarios go from, we can tell stories that are super, super pessimistic or super, super, super optimistic. And so if you're, if you are a student in this moment, let's say you're in college right now, How should you be thinking? What are the concrete things that we could be doing? Are there skills that become quite non-negotiable that it's okay, wake up tomorrow and think about how you're going to build this scale? Yes. I think there's four things. And I love a list. And I do this. This is part of my standard teaching now. So the first thing is whatever industry you're going to go into, there's still things you need to know about the world. You need to understand some facts. If you go into marketing and you don't know what CPM is, it doesn't mean. matter that you can look it up on your AI tool, you're going to look like an idiot to the other people if you don't know the simple language of the industry. So you need to know facts. And

Starting point is 01:15:23 universities, we test that with pen and paper exams. Second, you need to know how to use AI. So at graduation, you should have experience with lots and lots of projects where you embrace everything AI can do and try to superpower yourself. So for example, I now teach a, AI and marketing class, I used to have my students when it was called digital marketing, write a marketing plan. AI writes really good marketing plans. So I don't ask my students to do that anymore. But now I say, write the marketing plan, create the ad copy, design the website, do collect and analyze data, do so much more than any student project could have imagined over or you know a few weeks or months before and feel and understand what it means to work with an

Starting point is 01:16:14 AI so number one knows know the basic facts to be able to use AI well third be able to work through really hard problems so I got this idea I give a talk at Columbia University on an AI in education conference and one of the other panelists there was a math professor. And he said, in math, my undergrad classes, I'm not training future mathematicians. Like, it's wonderful when the hundred kids in a class, one of them becomes a mathematician, but most of them aren't going to become mathematicians. But he believed it was still useful for them to be math majors because in math, they teach people to solve really, really hard problems that require just a level of perseverance. that is perhaps not true of many other subjects.

Starting point is 01:17:11 But I think that skill is going to be even more important in a world where AI can do a lot of the easy stuff. And the perseverance? And perseverance and being able to figure out how to solve hard problems. So trying this, trying that, trying that, figuring out that this didn't work, that wasn't good enough over and over again

Starting point is 01:17:27 until you figure out something that you're excited about. And then the last piece is is harder to teach in university, but it's knowing what matters, having judgment. And having the ability to recognize an opportunity for your organization, having the ability to understand what you want and what you can accomplish, that's a key part of what anybody can do. And how are you going to learn that?

Starting point is 01:17:58 You're just going to try lots and lots of different experiences and different things and figure out which ones you enjoy and which ones turned out to be valuable for the people you're interacting with. The learn by doing. Learning by doing. And you just have to immerse yourself in it. The many different experiences you can. And there's an example that everything that you encompassed.

Starting point is 01:18:17 I was talking to a father who, his son just went into private equity and he's the junior in the firm. And there really isn't that much. The things they were having him do were kind of antiquated. And he's like, okay, my job is probably going to be done by an AI really shortly. But he knew the vernacular of the field. He had learned enough in university that he went up to the partners and said, can I build you a bunch of agents to do what I'm basically what you've hired me to do? And in building that agentic workflow that took his own job,

Starting point is 01:18:48 he is now doing things for the partners and helping them run different agentic simulations and all because he knew how to frame the problem, okay, this is what they're trying to achieve. I can technically automate my workflow, but I know the goal of what my team, is trying to build, I can actually do that with AI. And now he's, this is what he's doing for the summer. That's a great example of all four, right? So he understands, he knew the basic facts about the industry. He was able to use AI that's almost surely required him to think deeply and solve hard problems.

Starting point is 01:19:17 And he had the judgment to know that this mattered. So that's a fantastic example. I love it. And what would your advice be to policymakers for this transition? Um, to as we recognize all the legitimate things to worry, legitimate things to worry about as AI gets better and better. To also understand it's our best chance at productivity growth, which means it's our best chance for us to get more of what we want. Our economies grow through productivity, and a growing economy means we get more

Starting point is 01:19:55 of whatever we want, whether it's leisure or healthcare or education or other things. And that happens through productivity growth. And the, when you look at the suite of technologies available right now that you might think are going to lead to productivity growth, AI is by far the most likely to be a general purpose technology, which means it's by far the most likely to have a meaningful impact for the better on how we live and work. And so to protect people, so in the event that it might get bumpy, because there could be scenario A, scenario B, scenario C, a black swan for sure, how do they think about distribution, protecting people, training? What is the plan?

Starting point is 01:20:36 How should they think about building a plan in this? So different governments are going to have different values, but regardless of the values, they need to see some tradeoffs. When you ban something, it means that that thing will not be useful for what you want. When you price it, as in you put a tax, on it, it means people will use it less. When you subsidize it, it means people will use it more. And so the, to the extent you said to policymakers, I don't know the politics of these policymakers, and they're going to have different preferences. But regardless of their preferences, they need

Starting point is 01:21:17 to know that their decisions to slow down or speed up end up creating other tradeoffs throughout the economy. And as long as they go in with that eyes wide open, then I feel like I've done my job as an economist. Abby, thank you so much. It has been a pleasure. Look forward to having you back.

I've Got Questions with Sinead Bovell - A Rational Conversation on AI and Jobs With Economist Avi Goldfarb

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