Transcription
I hope you've been enjoying these, by the way. Oh, it's it's been fun. I don't think I've ever met anyone who, certainly not in his 70s, who sustained the work uh output that you have in the last few months. It's true that there were a few periods uh when I kind of now regret spending quite so much time on the on the newsletter and less time uh drinking Aperol spritzes on the piazza when we were in Italy.
So, let's go and talk about artificial intelligence, which is a nice change. This is the fourth in our series, the Wolf Krugman Exchange. I'm Martin Wolf, chief economics commentator at the Financial Times. And I'm Paul Krugman, professor at the City University of New York, author of an independent Substack newsletter. Uh, today's episode is being recorded on Friday, June 20th, at 10:30 in Massachusetts, because I'm actually not in New York right now. Uh, which is 3:30 in the afternoon over in London. And we're recording it on Friday. Uh, because next week I'm going to be in India. And the only rational explanation for this, I'm going to be in in Delhi, is that the 30° centigrade plus temperatures that we're experiencing here in London will there instead. I'll get a properly hot day, I imagine, around about 45.
Yeah. Well, I think I'll I'll keep busy and I am someplace which is marginally cooler. So, we decided that this week um we would look at artificial intelligence, partly because it allows us not to spend our whole time talking about what's going on in the US right now. And so we will look at artificial intelligence itself, but also how its impact is beginning to spread through our economies and our lives, and what its longer-term implications might possibly be. It's certainly the most interesting technological change we can see right now.
So Paul, when you think of what is now being called artificial intelligence, or uh, as I read today in the FT, one expert refers to these technologies not as artificial intelligence but "stochastic parrots," which I think is a lovely description. Anyway, whichever description you want, what excites you? What disturbs you about this phenomenon?
What we're calling artificial intelligence, you know, really isn't at this point uh intelligence. It's there's an endless dispute about whether it may be about to become something that you might really call that. But really, this is an evolution of large language models, of basically taking in tons and tons of data, uh, applying very clever algorithms. So clever, we don't quite understand how they work, uh, to be able to answer in natural language questions posed in natural language. Um, and it's not a minor thing. There's a a bunch of of areas where we used to joke about how bad attempts to automate thinking, or something that looked like thinking, really were. Uh, translation was a joke. I don't know if the the old anecdote about the supposed Russian English translation program that took uh uh "the spirit was strong but the flesh was weak" and it came back as "the vodka was good but the meat was spoiled." So, you know, used to be translation was a joke. Now it's actually quite good. You can actually, I can read foreign language news articles, and they may be slightly stilted, but they're really very, very good. Recognition of speech is quite good. So we certainly have have achieved something major, but whether it is truly revolutionary, what it's going to do, that's up in the air.
So I a couple of reactions to that. I I remember one very much supporting you that there's this famous notion of the Turing test. Um, obviously um from Alan Turing, the great um theoretician of computing from the the 1930s, uh, with John von Neumann, a sort of father of computing. And he argued, we think that computing was intelligent if we could have an exchange with that computer and it sounds like a human being. It feels like a human being, so that people are fooled into thinking it's a human being. And as far as I can see, they've passed that test. So, uh, that's something um quite significant. But I would also say it's quite interesting this question of how we define intelligence. Uh, to me, the most uh exciting use, and I get this very much um from somebody I've got to know quite well in this area, um Demis Hassabis, who's at Google DeepMind, what he is excited by is the capacity of computer programs now to do really deep uh scientific work. And they got the Nobel prize for the ability, which is a computational problem, but obviously fantastically complex one, to work out all the different ways proteins could be folded. I don't know whether that's intelligent, but at least it's very, very easy to see it's very, very powerful and useful, and human beings just couldn't do it on their own.
Yeah, there's a lot of tasks that we uh have regarded as being tasks that required very smart people, uh, that were very highly paid, uh, you know, and they were really difficult, that can now be done by um, by whatever it is we're calling this thing, generative AI, or whatever, "stochastic parrots," but they're "stochastic parrots" that kind of produce uh really useful stuff. And this is significant. Now, whether it's the same thing as what we you, it's clear that the Turing test, Turing screwed up. Uh, hard to say that, but Alan Turing was kind of wrong about what would be involved, because we clearly now have programs that pass the Turing test quite easily, and yet we don't think that they're people. Nobody really thinks that they're people yet. On the other hand, well, I'm not sure how many hands I've already used here, but radical improvements in productivity in something that's an old story that's happened repeatedly in many parts of the economy. And is this one really different, or is this just hitting an area that has not previously been much touched by technology?
Well, this is obviously the big question. I I I think it's an interesting question of uh whether Turing got it wrong, or whether he actually had a perfectly plausible view, but we we don't actually feel that the machines that can do this are in fact people. So we're more even more suspicious of machines than he thought we would be. But anyway, that's let's leave that to one side. Let's go back to this history you talked about, because as you said, going back to the Luddites in the early 19th century, the Luddites were um a movement of workers in against the introduction of machines in the early 19th century. And their skill was weaving, using power looms, and they were seeing that replaced by new machines. Pretty well every major technological revolution. In that case, machinery really was dramatic, what it could do, and how many jobs it got rid of. Um, and if you think of the history of machinery and other innovations. Um, every time people have said, well, all the jobs will be destroyed, we'll have mass unemployment. And after a while, we've had an adjustment process. We've found new ways to spend our incomes in different areas, and it ends up with um just a whole new set of jobs which nobody imagined. So if you told anyone in 1800 that nobody would work on farms, essentially, when that was overwhelmingly the biggest industry of the world, they would have said, "What? So what do you all do?" Well, if we listed all the jobs we now do, they would have no idea what they were.
That's right. Or or there are things that people did, but that were marginal. And but as you get richer, and as you can do the old stuff very efficiently, you discover that, well, okay, let's do more of those other things. Let's uh, we have an awful lot of people employed in healthcare now. I don't actually know the numbers, but we may very well have more yoga instructors than um than coal miners at this point in America. Uh, so we do different things. And the the history of predicting mass unemployment from technology is very, very long. Um, I I've been even personally watched repeated episodes. There was a whole stretch in the 90s when everyone was sure that mass unemployment was just around the corner because we were de-industrializing. Uh, there was a lot of predictions of mass unemployment in in the early 2010s, and constant disbelief that periods of high unemployment could actually be just because we have insufficient aggregate demand. You know, macroeconomics just sort of doesn't appeal to people intuitively, and technological unemployment does. And yet, it never really seems to happen, except on a sort of very localized basis.
I think this is clearly right. I mean, it was in the 50s, roughly 40% of the British labor force was employed in in industry, uh, overwhelmingly manufacturing. Now it's about 10%. So, and and if you told them that, and this is not so long ago, that that could happen, and they would actually be employing a higher proportion of the overall population because all the women are working too, they wouldn't have believed it.
So let me, however, play devil's advocate, just to see how this works out. Uh, if these uh new programs are able, that's a very big question, to do a huge proportion of the analytical um thinking work that we um we now do, which one would also think is sort of the core activity of human beings, at least for human beings like ourselves, that you know, thinking, creating, launching, so much of our activities. If machines do all this basic analysis, um, maybe we'll decide that actually it would really be much better to have a computer as a judge in a court, because computers are completely reliable. They're not going to be emotional. There's not going to be the famous effect which is explored in social science, that judges in the morning behave quite differently from judges in the afternoon. So one could imagine a world in which we decide, well, really wouldn't we rather our president were a computer? I mean, so many mistakes will be avoided. We are in addition having clearly a very significant robotic revolution underway. Isn't it possible that what we're going to lose here, first of all, there's really going to be a vast amount of employment that's going to be affected. So even if we do find jobs, they're sort of unimaginably uh uh different. And isn't it all possible also that we're going to find the marginal product of a very large part of the labor force isn't much above subsistence, because we don't really want them for anything?
All of that is possible. Um, history would say probably not, because it just hasn't happened before. And again, this stuff goes back, you know, forever and ever. Um, uh, Ricardo, uh, in the third edition of his um, Principles of Political Economy, worried about um unemployment due to machinery. And this is, you know, 1819 or thereabouts. So um, the, and the, you know, right now we think of the important stuff, the stuff that the the really good jobs is analytical thinking, judgment. Um, maybe I I could be wrong, but I think we're quite a ways from robot plumbers. Uh, that we're quite a ways from having a lot of what we think of right now as being relatively mundane things that require no more than common sense. But uh, you know, common sense is actually one of the things that AI appears to be quite bad at. And uh, and it's something that people are quite good at. Um, so I can argue this either way. I mean, one version of what we're calling AI is it's just a souped-up version of autocorrect. You know, it's sort of filling things in based upon what other people have done. Um, and then you can say, "Yeah, but aren't an awful lot of jobs that real people do and and earn fairly high salaries doing basically souped-up autocorrect?" Which is also true. Um, I mean, history always says that we we find other stuff to do. And so far, the successful applications are of AI are fairly limited. So far, we're certainly not seeing a productivity surge commensurate with what people are saying.
So just sort of focus on what we know from past experience about the adjustment process. So there's very, very famous work, which I think you've cited frequently, and others have, going back to the in introduction of electricity, which was obviously one of the great general purpose technologies of the the second industrial revolution, changed everything, really everything. And the case turned out to be that it was transformative. It did change everything, but it took about 40 years to do so, uh, before it got into the factories. They redesigned factories. They started developing all the clever motors you could put in everything that would refrigerate the houses and do the washing and all the rest of it. It's just a long, slow process in which the adjustment in the labor force and the new jobs come along. And the fact that we're seeing this implemented quite slowly, but it's still very early stages, might suggest we're going through a similar process, and the effects will be very large, but they will be bigger than we think, as it were, than many think, but they will also take longer than we think. Do you think that's a plausible way of thinking about the future?
In principle, that should be my view. Um, there's this, it's a wonderful paper, um, old paper by Paul David. Yeah, indeed. Um, uh, about, you know, why was information technology not showing up in the productivity numbers? I think it was called "The Computer and the Dynamo." And his point was that that um, it actually did take around 40 years for businesses to figure out what to do with electricity, because it requires, it's not just, not just you understanding, but you actually have to redesign the way you do work. Yes. You you uh, you know, an old-style factory is a is a six-story tall uh mill with a a steam engine in the basement and very cramped uh corridors, because you're trying to minimize power loss. Um, and it's very actually very awkward to work in. And you replace that with with electric motors, with a big sprawling one-story building with wide aisles. And, you know, but you have to change everything. You have to change where you locate, how you organize work, everything gets affected. So this is the story, and it many of us have invoked that story to explain why technologies don't transform things as fast as you think they're going to.
I have to say that my personal impression is that we're actually seeing on AI is not that story. What we're actually seeing is a rush to implement AI, um, before it has actually it's been proved that it's useful. That there's this enormous fashionability of of putting AI. I mean, I'm finding that stuff that I use routinely, you know, search engines, have actually been degraded because the companies involved are so eager to be there on the AI, and that I have to put in extra work to turn the damn stuff off, indeed, so I can just get a plain ordinary search result. So, um, I I wonder whether this time around we're not seeing instead something like a kind of rush to to be part of the the wave of the future before we're actually even sure that it really is the wave of the future.
You might argue that in the cases you mention, electricity, but even with the computer originally, initially it was a work tool. It businesses reorganized themselves. It's a bit closer to AI, but it involved a lot of reorganization, quite deep reorganization, to make the mainframe replace all your clerks, for example. You had to think about work processes in a profound way, and that may happen here. And electricity, as you point out, it meant changing every factory. But here, people I think are thinking it's cheap, from our point of view, it's there. It seems to be able to answer the sorts of questions we used to ask our law clerks or consultants of a medium grade. So why not ask them uh these questions? They do quite well. So we're we are rushing into it, but it doesn't seem yet, maybe it's just very early days, that we are seeing mass unemployment. I don't know whether this is different in the US. I haven't looked so closely of the sort of people who are working in these sorts of activities. Though I do hear and I have read that there's a very significant reduction in quite a number of economies in graduate recruitment, which might be affected by uh this. I don't know.
I've actually just written about it uh just before we had this conversation. There has been a dramatic fall-off in job opportunities for new college graduates. Seems to be happening in China too, but I don't know whether it has anything to do with this. Yeah. And the trouble is, it is so sudden. This is really, I mean, there's been a downward trend in the sort of employment advantage of having a college degree. That's been going on for some years. But this abrupt surge in unemployment among recent college graduates, and this apparent virtual collapse of jobs uh this year, makes you wonder, can is that really the technology, or is it something else? And unfortunately, there are a few other things going on in the world, like the US going wild on tariff policy, that are also probably affecting this. So, we don't know. But for there to be significant um dislocations, and even quite quick dislocations, is certainly possible. That doesn't tell you very much about what the long-term effects are, but um, it the idea that we could be seeing a really rapid um change in elimination of whole categories of jobs on a fairly short time frame, maybe. Although again, I read the news stories and I never know quite how much is hype and how much is is reality.
I think that's right. And the the processes of this kind, I mean, AI is after all, really quite new. Uh, the businesses are very excited about it, as you rightly said, but I think most of them don't really know what to do with it and how far they should trust it. So there seem to be two sorts of views out there. One is that it's going to end up as a complement to skilled people. It may remove some of the sort of middle-grade analysts and so forth that we've had, but AI plus highly skilled humans will still be the best way of operating. It will change the structure of employment, but human beings will be very actively involved in most of the tasks they still do. Or actually, we will find over time that, uh, if you're going to be treated by a doctor, well, the the principal uh analyst of what's going on and diagnostician and all the rest of it is actually going to be a one of these AI programs, and that would reduce or at least profoundly change the relationship of um us to uh the people in charge, as as it were. Um, but I my impression at the moment is there quite a lot of very different views among experts on how it will play out.
Yeah. And there I I this has been one of those subjects where um I I, you know, I've tried been talked to people, people who who really do pay attention in a way that I can't, and have come to the conclusion that um anything that I want to believe about the prospects of AI and its economic effects, uh all I need to do is do a little searching and I can find some expert who will tell me whatever it is I want to believe. It's it's one of those situations where where there's just such a range of possible interpretations. And not saying that these people are dishonest or anything, it's just that it it's really, really unknown at this point, and there's so little actual experience.
One of the problems that I do have some connection with, um, partly because uh I have grandchildren and partly because of my my wife does, she's an expert on skills policy and universities, academic expert on these things, is sort of the question, okay, we don't know what's going to happen, but should we be already be thinking about how we should teach people, what children should learn? Because I suppose you might feel, well, they should learn something different. Or is it the case, I think we obviously don't know, but is it the case that actually the sorts of things people learned how to do, because it just a way of developing the mind, writing essays, doing analytical work, doing equations, all the rest of it, is still the best way of training human beings, and then then we'll see what happens when it comes along? Yeah.
I mean, a few years ago, the sort of slogan for young people was "learn to code," because that was clearly not very good advice anymore, is it? No. It turns out that one of the things that that AI is pretty good at is is writing code. Uh, not presumably the the highest end, most sophisticated, but sort of basic code that sort of gets stuff done is one of those things that you can kind of turn over to the software. And so that was a really bad advice. Um, other things we don't know. I mean, it's kind of wild. I actually, let me give you an idea of the kinds of things that make me skeptical. So there was a big announcement by Amazon that it expects to get rid of a lot of workers thanks to AI, which sounds fine, except that I have actually done a little bit of work on, you know, Amazon as a business. And, you know, Amazon is one of those things. It's there's an illusion that it's untouched by human hands. You just click on something and stuff magically appears at your door. And if what it really has is it has a million, 1.1 million workers, mostly in distribution centers and warehouses moving stuff around. And how is AI going to replace? I mean, eventually maybe, if we have robots who can do that, maybe. But at the moment, it's not at all clear how ChatGPT or something like that is going to replace those. So, is this just hype? Is this like there was a period a few years ago when every everybody out there was putting "blockchain" in their name as a way of of making them seem cutting edge, and is this is this comparable, sort of just hype rather than reality?
I think that's a really, really interesting question. I was always intensely suspicious of blockchain and cryptocurrencies and all those things. I wrote about it. So I'm very cheered up that it doesn't seem to have amounted to much, though it does seem to have played a big part in buying the US presidency. But anyway, we should leave that uh for the moment. Let's think of some of the more concrete aspects. Let's suppose there is a sizable labor market adjustment. Um, yeah, one of the points that David Autor made in the podcast I had with him, which I thought was a very good one, is that unlike what he called the "China shock," which basically just the the rapid um uh collapse of quite a number of manufacturing businesses and therefore factories located in very specific locations across industrial countries, which obviously created a big adjustment problem because it tended to create a big shock to very specific locations and they lost the tradable output, and we've discussed that already. The good thing about AI is it looks as though it's sort of the sort of technology like the use of computers, which mostly will have a broad effect but not a very concentrated effect. So it should be, in principle, we can adjust to relatively easily. I would, you know, it is difficult to come up with I with examples of highly concentrated, geographically concentrated uh industries where AI will take away the jobs. It's uh you might worry a little bit about actually, if all things, New York and London, um, you know, how many of the jobs being done by people in the finance industry can be automated. So that the localities at at risk might actually not be some small town producing furniture, but some uh major financial center that really doesn't need all of these guys u pouring over spreadsheets anymore. But um, it's probably this is much more kind of a general purpose technology. Although even there, you know, going back, we were talking about electricity. One of the effects of electrification was that factories, you know, when you shifted to sprawling one-story factories, they moved out of city centers, and that uh um was actually quite disruptive. It did eliminate a lot of the blue-collar jobs that used to serve people in the inner cities. But yeah, it's this is probably, we're all speculating, everything's speculation, but it's probably not something where you say, "Oh, uh, everybody who works in Bradford, Yorkshire is, uh, is going to lose their jobs to AI."
I think you've got there something quite important. Uh, because if you do think about it, and if you think who might be most affected, plausibly, and I leave aside the robotic side, so I mean, I've sort of convinced myself that the safest job in the world is probably gardener. But the um, assume that we really do displace a lot of white-collar jobs, a lot of the sorts of jobs that young graduates do, the sorts of jobs done by legal assistants, even junior lawyers. This is the broadly uh the group of people uh whose jobs have expanded enormously in the last three or four decades, and that's partly why we've had this enormous expansion in universities, less so in the US because you already had such a huge university system, but in Britain, I often mention this, I mean, when I went to university, 5% of the generation went to university, now it's 40%. And it's because these jobs have expanded so much. So that's if you have a lot of very, very unhappy educated people expecting a better life than they're going to have, and many of them are already pretty unhappy. It does seem to me this could have quite, if it happens, really quite difficult social and political effects in societies that are already suffering from those effects.
Yeah. Yeah, I mean, it's, you know, the Luddites always worth remembering. The Luddites were not unskilled. They were elite, they were the skilled elite, absolutely. Although, can I say, there, give a slightly maybe a slightly optimistic take? While yes, we're going to have a lot of unhappy people, on the other hand, um, in some ways, AI may be an equalizing force. You know, I I grew up in the US of the of the 60s, um, where skilled blue-collar workers uh earned incomes. It seemed like they earned incomes not very different from say, middle managers. In fact, I grew up literally on a street where some of the people on the street were plumbers, and my father was a middle manager. And that completely changed. Uh, maybe we go back to that. Maybe we go back to a situation where people who can actually deal with the material world um become appropriately valued again, and people who push symbols around um find that, well, you know, computers can also push symbols around.
Let's talk about just the social and political dimensions. Um, how plausible is it? I've just been writing about copyrights and AI, but this is a a broader question. You've written quite a few pieces recently about inequality. Um, we are seeing enormous concentrations of wealth and income in our societies, in the US particularly. And some people have referred to this as "techno-feudalism." Do you think that if this AI revolution continues, and that's clearly what the companies that dominate hope, and you mentioned some of them, um, we're going to find this sort of extreme concentration of power and influence and money in, uh, the tech elite that are driving this, relatively small number of companies, um, proceed even further? And how worried should we be about that? Because it doesn't look very healthy to me.
Yeah, I'm actually not quite sure how AI plays into this, and I'm actually, as we speak, working on this. And it seems to me that the the defining feature of a lot of the technology, the reason that we have uh these immense fortunes, and it really is true at this point that uh the top ranks of of wealth are very much dominated by tech bros, really, is basically there there's Warren Buffett, who actually seems to have a a genuine unreproducible skill, and everybody else. We try to understand, you know, why do we have these giant fortunes in tech? Why why are there handful of tech bros with this enormous amount of money? It really is very much about network externalities, which is economics jargon, but basically means that you do something or you use something uh because everybody else does. Everybody uses Amazon because everybody else uses Amazon, and it's very much easier to get regular stuff, or for that matter, uh um I'm still I'm still doing a lot of work in Excel, um, you know, which is crazy, but it's it's um Excel is universal, and everybody knows how to use it. And um, and these things are these kind of self-reinforcing, self-locking in advantages are the basis really of a different kind of monopoly power, very different from the kind of monopoly power you had in the Gilded Age, but it's it's monopoly power all the same, and it gives rise to a handful of incredibly large fortunes. It's just very difficult will to break into that. And does AI reinforce that tendency? Possibly. Does it on the other hand, make it easier? I don't know. May maybe an AI model will make it easier to get stuff uh quickly on demand from some smaller. I actually have no idea. It really, it it I don't think people have actually uh tried to work out that consequence. I think people have tended to say, well, it it displaces workers, therefore it must enhance the power of the corporate bosses, which it might, but we don't know that. Um, I think the corporations like the idea of not having to actually deal with, you know, workers. But that may be again part of the hype.
Yes, it's a it's a very interesting question of how that plays out. First, within the AI sector itself. Uh, I was very interested because it happened very recently, in this sudden emergence of this Chinese company DeepS, and that seemed to suggest that the idea that there were infinite economies of scale and scope in the AI industry itself might not be right. Obviously, we'll see whether that's true. And then, of course, there's the question of what effect it has on the users, the industries that use it. It's pretty clear at this stage. We have no idea. But right now, the firms that have the resources to do the colossal investments that at least the Americans are pursuing are relatively limited, because the investments are so stupendous. Yeah. And these things do, I mean, these, you know, these are not AI, what we're calling AI, certainly doesn't function anything like uh human intelligence. What it does is it scoops up vast quantities of data and does very, very complex calculations on that data, which is a big, big upfront investment in a peculiar ways, information, but actually seems to require a lot of physical capital, you giant server farms, huge amounts of uh of power consumption. So this may actually be something that favors not so much technological dominance as it favors, you know, basically people with lots of of money to invest in largely physical capital. But at the moment, for the reasons of history, the people who have the know-how to invest it and the money are already established players in the tech industry. Relatively limited, very limited number of uh major firms, which interestingly doesn't include some of them. The most valuable of the tech companies for quite a while was Apple, but it doesn't seem to be a significant player in this at all. Uh, and uh, some of them, OpenAI is obviously a new player, but it's got Microsoft linked to it. But it does look at this is one of those things in which incumbents seem, or some incumbents seem to be incredibly well-positioned to expand their reach further, and that's why people are concerned about this notion that there will be a sort of feudal lords over us all. They did certainly play some part, or some of them, in this election, uh, in the recent election. So it sort of links up with this idea that at least in the US, politics is becoming um a plutocratic sport, and so it links up with also the future of democracy. Although we should say that the the really big money, apparently accounting for something like 40% of corporate spending on the election, was crypto. Yes. And I'm highly uncertain about what the economic payoff to AI is, but I'm quite certain about what the payoff to crypto is, which is nothing. But unfortunately, it it turns out to be able to to buy a government. Yes. Um, extraordinary bubbles do can them all of themselves have remarkably distorting effects for a while. Yeah. And it's it's going to be an interesting question, actually. How much though, coming back to this techno-feudalism, how much is the power of incumbency versus just being able to deploy very large amounts of capital? And I think we're going to find that out.
So, let's do the cultural coda. Okay. Uh, so, I think I'm the lead off on the cultural coda, and it is some music. It's Loretta Lynn singing "Coal Miner's Daughter," which is also the basis of a wonderful old film, um, which I think is, you know, what what on earth does this have to do with it? But in fact, there are basically no coal miners' daughters anymore. Uh, uh, coal mining was more than half a million workers in the United States in the immediate aftermath of World War II. Um, by 2000, uh, coal production was actually higher in 2000 than it had been in the in the 1940s. But uh, 85% of the workers were gone. And what was that about? It was it was all about technology. First strip mining, and then blowing the tops off mountains to get out the coal, which meant that you didn't need a whole lot of workers. Which is showing that you can get massive displacements of particular kinds of workers by technology. We did not suffer mass unemployment because of the disappearance of the coal industry. We did suffer a lot of uh changes. Some places were hurt, but also ways of life disappeared. So I think in some ways, I I like coal as an example of just how, first of all, of how much technology really can change things, but also that, you know, the latest fanciest technology is not the first time we've seen this movie, or the second, or the third, or the fourth. This has been happening again and again over the past couple of centuries.
My memory is that in Britain, the the coal mining industry at its peak employed about a million, which is an extraordinary number for a much smaller country. And my view of the disappearance of coal mining was set by the very famous description of George Orwell of what it's actually like to be a miner. And I decided we should be very happy. Now, my cultural coda this week, it's a novel, and I think it, to me, it's the most important novel of the 20th century, at least the most revealing. It's Thomas Mann's "The Magic Mountain." It was published in 1924, and the 20s and 30s are a period I think more and more about. And this the core of the book was the intellectual ferment going on in the first half of the 20th century between old-fashioned, stayed, civilized liberal humanism, embodied in this case in the figure of a man called Settembrini, putting forward the sort of views I hold, and I'm beginning to feel are equally old-fashioned. And on the other hand, Naphta, who is a Marxist revolutionary, but actually, when you push him, turns out to be very similar to the um, the far-right revolutionaries. And really, the difference between Hitler and Stalin turned out to be pretty small when all things are done. And it gets you this sense of profound conflict. And Mann puts forward the idea that the First World War was the beginning of the destruction that followed from this um, it was written during the war and published shortly afterwards. So it's extraordinary how a book, a novel published 100 years ago, um, can be so brilliant at describing the sort of things we are seeing right now, and the sort of challenges we're now seeing between pretty feeble liberal humanist type people and passionate authoritarians.
So, thank you very much for joining us for the part four of the Wolf Krugman Exchange, something very different on AI. We'll be back with you again next week when we'll be discussing the ways in which the economic system has changed as a result of recent events, possibly forever. Is this time perhaps really different? And if you're looking for interesting ways to fill your time before then, I've recently put together a selection of what I think are the most interesting economics books for your summer reading, the books published in the first part of this year. And a link to that list will be in the show notes.