925: AI, Automation and the Future of Work, with Oxford’s Prof. Carl Benedikt Frey

31:47And I think another example of this is related to the game of Go, right? We all know 2016, AlphaGo beats the world champion. Back in 2023, Stuart Russell and his lab puts out this paper showing that well, we already achieved superhuman performance in that domain, right? But still human amateurs using standard laptops are capable of beating the best Go programs convincingly by exposing them to new concepts. And so I think that tells us something. There are probably people out there that have ideas how to proceed on that front, but we're not there yet. And so I do think that that is a remaining challenge.

32:35Jon Krohn:That was a fascinating answer, as all of your answers have been so far. It's nice to feel that there's a little bit of evidence that at least for the coming few years, maybe, people like me, people like our listeners who are writing code, developing AI systems, that we might still have some ingenuity to provide that the machines we create won't be able to replicate. We'll see what happens. And so on this note of the AI labor market transformation, something that I haven't mentioned yet is that your title at the University of Oxford is the Dieter Schwartz Associate Professor of AI and Work. And you're also the Director for Future of Work Research at the Oxford Martin School, which is one of the world's foremost futurist research departments.

33:25Jon Krohn:And so there's a lot to be said about AI and work and through your articles, interviews, talks, books, you've contributed enormously to the public discourse on this subject. So starting with a, to kind of get this conversation going, a fairly recent development is this wide reporting that these kind of deep fake AI job applicants are successfully applying, interviewing and securing remote jobs, sometimes even as coordinated scams out of North Korea. And so if LLMs can be generating plausible essays and AI agents can participate in meetings, what does authentic intellectual and social mastery look like?

34:13Jon Krohn:I guess, in other words, how should we be redesigning assessments and collaborative, you know, the norms for how we collaborate in order to separate deep, critical understanding from this kind of sophisticated, increasingly sophisticated mimicry that we get from LLMs? It's a great question. So to be clear, I might sort of come off as an AI skeptic, but I'm thoroughly impressed by what I've been seeing over the past couple of years. The latest models are probably better teachers and tutors than I am across the board, right? And they're probably better than almost anyone apart from perhaps the world's leading experts in a given field.

35:02And I think a key challenge for developers of these tools is that beyond a certain point, and we've probably reached that point, people are not going to notice improvements in those dimensions because they're already providing better answers than we do, right? And so, you know, I think where, you know, we have bodies of fairly developed, sophisticated knowledge out there, AI performs tremendously well. And I think the real challenge is then adapting that on the fly. It's a little bit from economists, right? You know, AI knows all, you know, variety of models, but then the challenge is to apply the right model to the right problem at the right time.

35:52And so, you know, I think humans hold a comparative advantage there. I do worry about what you mentioned. And, you know, we do have some evidence that, for example, General TVI has reduced trust in news. and that's a key concern. At the same time, it seems that it boosts the relevance of brands. And so I think brands that build trust are becoming increasingly important as artificial intelligence improves, right? And so the same applies to podcasts. You know, if podcast hosts that develop a certain degree of trust with their listeners are more likely to continue to be popular, regardless of how good, you know, much better AI gets.

36:54And, you know, it's also a key challenge when it comes to technology adoption, right? So, you know, many medicines, for example, I don't know whether I should take them or not. I trust the institution behind my doctor that provides good evidence. But those institutions do not exist all around the world. And that means that sometimes people don't take their medicine or they take the wrong medicine. And I think that trust is just going to get more important.

37:24Jon Krohn:Interesting that trust sounds like a key component there. And hopefully, maybe I should start running sponsor messages about how much my audience should trust me. What a reliable podcast host I am. Continuing on this note of how AI is disrupting labor markets, in an interview recently, you noted that the distinction between jobs changing, occupational change, job elimination, occupational elimination, that these lines are very blurry, making it hard to gauge the real scope of automation. And then in another presentation, you showed how startups are creating fewer jobs than they once did, suggesting that new technologies may be relying less on human labor.

38:13Jon Krohn:And separate from you, Sam Altman, the OpenAI CEO, predicted that AI may make it possible for one person to build a billion dollar company very soon, he says. And so this seems to be quite a potentially transformative moment in the labor market. Even in ancient civilizations, manual labor was treated as lower value work and societies were stratified accordingly. But it seems like we're heading into a world where potentially plumbers could be earning much more than lawyers. And yeah, so what are your thoughts on how labor is being transformed so rapidly by AI and how income, how social stratification could change in the coming years?

39:06So lots are in there to unpack. To start with your first question around jobs and tasks and jobs changing might have the same effect on workers as jobs being displaced. So if you take a job like a laundress or a lamplight, we didn't automate the way the jobs of laundresses by building a robot that would walk down too well, perform the motions of hand washing, and then walk up to the house and hang the clothes to dry. We did that through the electric washing machine, which does a sort of very different set of motions and procedures. And so if you would just have looked at what laundresses do, robots today even that would be able to navigate the forest, walk down to a well, perform the motions of hand washing and then walk up to the house and hang the clothes to dry.

40:17And so the same is true with what artists and craftsmen did. The way we automated the way those work was by simplifying it in a factory setting and then applying specialist machinery to better defined tasks. And so often if you try to look at whether your job is automatable or not just by the tasks it entails, it doesn't necessarily tell you that much about whether that job is going to be automatable or not. And I think more importantly, from the perspective of the individual, it might not even matter that much if the job just changes or is replaced. So sometimes I'm here in school buses. Even if the bus drives itself, you will still need somebody in the bus to look after the children.

41:13And that might well be true, but that person is not going to need a driver's license and is going to have an entirely different skill set than the bus driver. So the bus driver will probably be replaced with somebody else. And from the viewpoint of that person, that doesn't make necessarily much of a difference. So I think the distinction there is quite blurry indeed. When it comes to new job creation, as you alluded to, I think it's important to remember that a key reason that we're not having mass unemployment today is that we have created new types of work. So most work that is done today did not exist in the US in 1940.

42:06So most people work in new types of work. And so going forward, it's absolutely critical that we invent new lines of technologies that also create new types of work for people to make a living through their labor. And a key concern is that new firms are not expanding and growing as rapidly and not as job creating as they once were. And I think that is not just a concern in the sense that it created fewer jobs. It also means that we are having less productivity growth, right? So think about it this way. If all we had done since 1800 was automation, we would have cheap textiles and we would have productive agriculture, but not much else.

43:06We wouldn't have vaccines, antibiotics, airplanes, rockets, computers, etc. And so most prosperity comes from actually doing new and previously inconceivable things. So if we overwhelmingly use AI for automation, we're actually not creating that much value. We can get a sort of short-term productivity boost. But if AI is just a productivity tool, then we shouldn't expect to get that much productivity growth out of it and also not that much job creation. And so the hope from a standpoint of economic prosperity and job creation is that we can use AI to create new types of industries. Now, if we create a billion dollar firm with one employee, that's good for that employee, right?

44:03And that might be good for some of the people that use that services, presuming it's creating something that is of use and value and that's behind the valuation. And it might be good because we can potentially tax that and provide social services, education, health care, et cetera. But if we just have a few unicorns, Unicorns, that's not going to be shared prosperity. And unless their services are much incorporated into all different sectors of the economy, it's not going to create much productivity growth either. So I think for AI to truly be transformative, it needs to create new sectors. And that's what we saw during the first industrial revolution as well.

44:54So the first seven decades, most of the technological changes that we see during the first industrial revolution is focused on mechanization of textiles. It's only really with the railroads that growth in Britain takes off. Similarly, the second industrial revolution, we see a lot of new industries. Automobile industry, the largest manufacturing enterprise the world had ever seen. On top of that, the range of electrical industries, every gadget you have in your home is basically from that era and there's an industry behind it. And then there's all the components that go into the car and then the machine tools to produce those components, like huge industries as well.

45:32And then road commerce and tourism. There's a lot of new sectors being created and that drives a huge upsurge in productivity growth over the post-war period. We see that to some degree with the computer revolution, but not to the same extent. And it peaches off quite rapidly. And I think we were likely to see something similar with AI, unless we are able to create those large sectors that we saw in the mid-20th century.

45:58Jon Krohn:Yeah, I suppose it's very hard to predict what industries will emerge, what kinds of jobs will emerge when it's so many steps down the line, just as it would be difficult in 1900 to imagine. You talked about in 1900, if we trained in LLM back then, how it might not have predicted that humans would be able to fly. Likewise, it would be difficult to envision that a century later, we would have so much around the airplane industry. You know, all these kinds of jobs that are supported, you know, people cleaning airports and, you know, selling snacks at airports, you know, baggage handlers, all these kinds of jobs would be difficult to foresee, you know, with that new innovation.

46:43Jon Krohn:as a quick kind of anecdotal story. I recently flew from San Francisco to Australia. And in San Francisco, one of the things that I love to do is ride in fully self-driving Waymos. You know, they're often can arrive more rapidly and are more affordable and frankly, even seem safer to me than being in an Uber or Lyft or a taxi. And after flying from San Francisco to Australia, I couldn't, obviously, I couldn't hail a Waymo today in Sydney. And so used Uber. And the Uber driver, I had a particularly chatty Uber driver. And he says, Oh, what do you do for a living? And I explained, Oh, you know, I do this AI stuff.

47:28Jon Krohn:And he says, Oh, yeah, you know, it's, you know, white collar work is he was actually pretty well informed about AI innovation. And he said, you know, it's interesting how white collar work is being displaced more rapidly. And I said, yes, but there are some interesting categories where it isn't just white collar work. For example, I just flew from San Francisco where I was in a fully autonomous Waymo. And he, you know, to him, he was kind of like, well, but, you know, do you trust that? Would you actually, that's how it came up. He said, would you trust being in a fully self-driving car? And to which I responded, actually, I just came from San Francisco.

48:08Jon Krohn:And I said, I actually prefer that experience in a Waymo over an Uber. And so absolutely, I do feel safer. I'm not sure if I would necessarily feel safer in a Tesla, which is only relying on computer vision. But in the kind of LiDAR-based system that Waymo uses and a lot of the self-driving companies use, it feels extremely safe. And so, yeah, so I think this is kind of a, yeah, you know, it shows a lot of people aren't expecting what's coming, even if it's your job. And so it seems like there's a good chance that AI's job displacement in the coming years and wage changes will be substantial. Are there lessons we can draw from the previous industrial revolutions to enact sensible policies that will balance innovation with worker protection and ease the social shocks?

49:02So first of all, I think what you just described is very similar to the sentiment and the debates that people had around the automatic elevator, right? So a lot of people, or at least the unions argue that most people would not feel comfortable going into an elevator with no operator responsible for their safety. And then obviously there was all sorts of accidents with elevator operators doing all sorts of mistakes. And some of them ended up being even fatal. and so I think there is tendency often to think that humans trust humans more but I think there's not that much required for us to be convinced that that is not the case when we see the performance of these technologies and so as more and more people will come in contact with driverless cars as you just described I think that's going to change very quickly.

50:06And as a bit of a side note, back in our 2013 paper, most of the jobs that we estimated are fully automatable, right? They are in transportation and logistics. And I think we're still just very much at the beginning of that transformation. When it comes to white-collar work, I think it's true that for the most part right now, it's not the entire job that is going to be replaced to certain tasks within. But I think it's also true that AI is making a lot of white-collar work, a lot of creative work, much, much easier. And if it becomes easier, then it reduces barriers to entry. So it's a bit like GPS technology and taxi services.

51:00So, you know, GPS technology essentially meant that knowing the name of every single street in Oxford was no longer a particularly valuable skill. And so, you know, when Uber came along, anybody could essentially get into his or her car and then top up their incomes on the side. With professional service jobs, on top of that, those jobs are not trade, those jobs are tradable, right? And so it means that, you know, if you think about, let's say, the work of an accountant. An accountant in New York City earns a six-digit salary. An accountant in Manila earns a lower four-digit salary, right? And so if AI reduces the productivity differential between those workers, I think it's very likely that we're going to see a lot of white-collar work moving across borders to places where labor is cheaper.

51:58And then obviously for white-collar professionals in Europe and the United States, well, the effect on them is the same as if their job were automated, even though what AI is currently doing is just reducing barriers to entry in these professions. Now, what should governments do? I think, you know, what you don't want is governments. You want government to protect people, but not jobs. And so, you know, if you look at Europe, right, where you have strong employment protection, what that means is that when a technology like generative AI arrives, firms like S &P, they find it very important. very hard to adjust because they can't just hire and fire.

52:48Whereas firms like, you know, Meta and Google, they can pivot much more quickly. And that, I think, is a drag on the European economy. At the same time, you know, you want to have a social safety net that takes care of people. And that means that the broader population see the benefits also from the technological changes that we're seeing. So in my previous book, The Technology Trap, I make the case that the Luddites, they were essentially right to riot against the mechanized factory because they were not the ones to benefit from it. Wages in Britain were stagnant, even falling for two decades.

53:32So two generations lost out, and it took a long time to see the benefits of new technologies for most people to see those benefits in their pockets. And what you also see is that in places in Britain where the poor laws at the time were more generous, you saw less social unrest as a consequence. So Britain was the only country in the world at the time to tax itself 2 % of GDP to provide for the poor. And that actually had an impact on whether workers would riot against making a decision or not. And when autonomous cargo trucks arrived in LA, there was a huge strike. When the same technology was used in the harbors of Norway and in the Netherlands, no reports of such incidents.

54:22So I'm very much in favor of Danish-style flex security, flexible labor markets, but then actual security also when you lose your job.

54:33Jon Krohn:Yeah, there were so many facts in there and so much useful information. I don't even know where to begin. I think I'm just going to transition to my final technical question, which builds on what you were just talking about and kind of comes full circle with this episode. because at the beginning of the episode, I mentioned how the public awareness of you, of Carl Benedict Frey, exploded with your 2013 Frey and Osborne paper, The Future of Employment, How Susceptible Are Jobs to Computerization? And so I mentioned how that estimated that 47 % of jobs are at risk of automation. In light of generative AI, last year, you revisited your predictions in another article called generative AI and the future of work of reappraisal.

55:27Jon Krohn:But you actually, you stated for the BBC after that, that there's no way of knowing how many jobs will be replaced by generative AI, but you do highlight key areas where humans have an edge. So things like face-to-face interpersonal skills. And you've talked about how things like remote teams are consistently less likely to make breakthrough discoveries relative to in-person teams. And so I guess where I'm getting to with all of this context and this question that I'm asking is that for my listeners, are there some actionable takeaways that they can take as individuals? Most of this episode, we've talked about what governments can be doing, what enterprises can be doing, what can individual listeners to my show be doing to try to make the most of this generative AI era and to maybe protect their wages, protect their jobs?

56:26Jon Krohn:What are the kinds of skills that they should be developing or focusing on to try to limit gen AI eroding their earning potential? So back in 2013, we pointed to three key bottlenecks to automation. So one being complex social interactions, one being creative work, one being the capacity to navigate complex unstructured environments. I think in the first one, obviously, there's been tremendous progress with chatbots, and it's a different universe now in that space than it was in 2013. But I think general TVI is actually going to increase the value of in-person communication, And so we have quite a bit of evidence that AI is sort of having a homogenizing effect.

57:15Many of these outputs various models produce are quite similar. And so a question is, how do you distinguish yourself in such a world? How do you distinguish yourself in a world where AI writes your letters and everybody else's? And I think that differentiation is going to be in in-person communications. So I think that those skills are going to become more valuable. And then creativity, we touched a bit upon that earlier, right? So there's a lot of stuff that AI does that I would regard as creative. There's also some other stuff that's maybe not that creative, but seems creative. So writing prose in the style of William Shakespeare.

57:55Well, I can't do that. AI can, but how creative is that? Because Shakespeare did that. And so that's the reason that AI can do it. But then there is a lot of cutting edge creativity and maybe we should add resilience creativity to the same bottleneck because as we discussed earlier, responding to shocks, events in the real world, something that humans still have a comparative advantage at. So, you know, the combination of sort of resilience and social skills is still very important. And then, you know, navigating complex environments, et cetera. Well, we're in the domain of robotics. There's been noticed where the progress there too, not as rapid as in AI, but, you know, AI is aiding that to some degree.

58:44But, you know, as you said yourself earlier, you know, plumbers, electricians, gardeners, all of those jobs may well be higher status than being a lawyer or a consultant in the foreseeable future. And so acquiring some practical manual skills might be a good idea. And unfortunately, it doesn't put me in a very good place.

59:21Jon Krohn:Yeah, so earlier I was talking about how plumbers might have incomes that on average exceed those of lawyers in the near term. So it sounds like your advice is for the lawyer to get some skills as a plumber, potentially. I'm not sure if I want the lawyer to do my plumbing, but maybe that's another podcast. Yeah, I guess. Yeah. Anybody who's that litigious, I don't want in my household. So that's the end of my questions. I do have one from a listener. So I posted on social media, I posted on LinkedIn a week ahead of us recording that you would be on the show. We had a lot of engagement and one particularly good question from someone named now she's based in, in Munich.

1:00:09Jon Krohn:So I'm assuming she kind of pronounces this with German sound. So Eva Simone Lehotse. And she's a managing director of the AI lab at something called the service plan group, which is a big communications company. And she had a great question for you, which was, yeah, looking at past technological revolutions, what have we consistently misunderstood about the human side of innovation? And how can we design trust into today's AI transition so we don't repeat those mistakes? That's a very interesting question, actually. So I think pretty consistently, historically, we've overestimated how fast things go in the short run and then underestimated a change in the long run, as many people have observed.

1:01:00But with regard to trust in particular, so when researching my first book, it struck me that the very first train ride between Manchester and Liverpool in Britain actually killed a member of parliament. And that was a huge tragedy, but it didn't stop the rollout of the railroad network. If you look at early tractors and automobiles, they were extremely unreliable, sometimes exploded, various accidents, still didn't stop adoption. I think we've become obviously much more cautious as a society when it comes to rolling out new technologies. And I think, you know, building trust today around these tools is more important than in the past.

1:01:59And specifically, as we were discussing earlier, that in a world where it's hard to distinguish between right and wrong, brands become more important. And then if you roll something out, which is not functioning properly, well, that can have severe consequences for trust and for your brand. So I think the environment in which firms operate today is very different from the environments that we saw in the early 20th century, for example. So there is this constant tension between trying to build trust and reliability, but also not let the perfect be the enemy of the good, because there's also a real risk that we end up over-regulating.

1:02:57There is a lot of things that are completely unforeseeable. There is no way that we can write rules of regulations around AI with a level of foresight that will capture every potential use case in five years from now. And so experimentation is also absolutely critical to the rollout of the technology. And I think it's fair to say that had ChatGPT not been released, we wouldn't even be having these conversations around AI regulation. And so we need to actually release some of these technologies into the world for them to be tested and for regulators to collect information about how to regulate in the first instance.

1:04:01Jon Krohn:Excellent. Thank you for that great answer. And thanks to Eva for the question to bring all of that out. to wrap up here again your brand new book is out now it's just come out in the past couple of days and it's called how progress ends technology innovation and the fate of nations obviously this is going to be a fantastic book for people to read based on the conversation that we had on the podcast today huge amounts of insight for people to glean from there something carl that i always ask my guests before i let them go however is whether they have a recommendation of anyone else's book that they should read as well.

1:04:37Jon Krohn:What do you have for us? That's an easy one. Joel McKeel's Liver of Riches. Everybody should read it. That's what got me interested in technology to begin with. There's no more comprehensive overview of what drives innovation, what's inhibited. And yeah, everyone should read it. Nice. And for people who want to get more of your brilliant insights after this episode, how or where should they follow you? I'm trying to post more on LinkedIn this day. So I think that's probably a good platform. Yeah, yeah, yeah. I agree. So yeah, so we'll be sure to have your LinkedIn URL in the show notes. Professor Frey, Carl, thank you so much for taking the time with us today.

1:05:18Jon Krohn:You know, especially at the time of this book release, you must have so much going on, a media flurry, and to grace us with your presence on the show. It's been an honor. Thank you so much for taking the time. Such a pleasure. Thank you very much for having me. well what an honor to have professor carl benedict frey on the show today in today's episode he covered how centralized systems like the soviet union excel at scaling existing technologies but struggle with breakthrough innovation the distinction between exploration and exploitation phases in innovation where big companies eventually stop innovating and start lobbying once easy gains are exhausted.

1:05:56Jon Krohn:Why LLMs function as engines of statistical consensus that excel at recombination but struggle with novel discoveries, the critical role of weak social ties and decentralized networks in driving innovation, predictions for AI's impact on the job market, and policy recommendations for balancing innovation with worker protection, emphasizing the need to protect people rather than jobs while maintaining strong social safety nets during tech transitions. As always, you can get all the show notes, including the transcript for this episode, the video recording, any materials mentioned on the show, the URLs for Carl's social media profiles, as well as my own at superdatascience.com slash 925.

1:06:37Jon Krohn:Thanks to everyone on the Super Data Science podcast team, our podcast manager, Sonja Breivich, media editor, Mario Pombo, partnerships manager, Natalie Jaisky, researcher, Serge Massis, writer, Dr. Zara Karche, and yes, our founder, Kirill Aromenko. Thanks to all of them for producing another excellent episode for us today. For enabling that super team to create this free podcast for you, please check out our sponsors links in the show notes. Our sponsors are what make this show happen other than of course you listening. And if you yourself are interested in sponsoring an episode, you can get the details on how to do that at johnkrone.com slash podcast.

1:07:18Jon Krohn:Otherwise, share this episode with folks that would enjoy it. Review the episode on your favorite podcasting app or on YouTube. Subscribe if you're not a subscriber. But most importantly, just keep on listening. I'm so grateful to have you listening. And I hope I can continue to make episodes you love for years and years to come. Till next time, keep on rocking it out there. And I'm looking forward to enjoying another round of the Super Data Science Podcast with you very soon.

1:07:48Thank you.