1 00:00:10,400 --> 00:00:13,000 Speaker 1: Hello, and welcome to Stefanowie's the podcast that brings you 2 00:00:13,080 --> 00:00:16,200 Speaker 1: the global economy, and we're dedicating this week's episode to 3 00:00:16,280 --> 00:00:19,119 Speaker 1: a powerful book on a subject most of us have 4 00:00:19,160 --> 00:00:22,920 Speaker 1: been thinking a lot about recently, how new technology can 5 00:00:23,040 --> 00:00:26,360 Speaker 1: change the world. Chat GPT came out the end of 6 00:00:26,440 --> 00:00:29,440 Speaker 1: last year, had a million users in the space of 7 00:00:29,520 --> 00:00:33,080 Speaker 1: five days, and that was pretty cool. Then in March, 8 00:00:33,440 --> 00:00:36,840 Speaker 1: chat GPT four came out. Now that was a whole 9 00:00:36,840 --> 00:00:42,680 Speaker 1: lot better, but also alarming. Journalists, lawyers, accountants, teachers could 10 00:00:42,720 --> 00:00:45,120 Speaker 1: all see how it could not only help them do 11 00:00:45,240 --> 00:00:49,200 Speaker 1: their jobs but make them redundant. Not sometime in the future, 12 00:00:49,240 --> 00:00:53,199 Speaker 1: but next month. Should we worry then about where this 13 00:00:53,240 --> 00:00:57,320 Speaker 1: will lead? Well, the standard version of economic history says 14 00:00:57,720 --> 00:01:00,520 Speaker 1: not really. It tells a story where I and again 15 00:01:00,600 --> 00:01:04,160 Speaker 1: people fear technology will make the world worse, think of 16 00:01:04,200 --> 00:01:09,000 Speaker 1: those Luddites smashing up machines. But in the end it's better. Yeah, 17 00:01:09,120 --> 00:01:12,600 Speaker 1: there are adjustment costs, a bunch of people lose their jobs, maybe, 18 00:01:13,040 --> 00:01:17,080 Speaker 1: but overall, the majority of people get new opportunities, more 19 00:01:17,120 --> 00:01:20,840 Speaker 1: rewarding jobs, better lives. As the version of history I 20 00:01:20,959 --> 00:01:23,160 Speaker 1: was taught in graduate school and have heard from fellow 21 00:01:23,200 --> 00:01:26,440 Speaker 1: economists many many times since, and we've heard it again 22 00:01:26,640 --> 00:01:30,560 Speaker 1: often in response to AI. To be scared of this 23 00:01:30,720 --> 00:01:34,600 Speaker 1: new technology, to believe it will hurt workers, they say, 24 00:01:35,040 --> 00:01:38,240 Speaker 1: you have to believe this technological revolution will be different 25 00:01:38,319 --> 00:01:41,039 Speaker 1: from all those that have gone before. Now you might 26 00:01:41,040 --> 00:01:45,600 Speaker 1: think that's true. This time is different. Chat GPT feels different, 27 00:01:46,480 --> 00:01:49,320 Speaker 1: but it sets a high bar for being scared. After all, 28 00:01:49,360 --> 00:01:52,720 Speaker 1: the horseless carriage also felt pretty different at the time. 29 00:01:53,640 --> 00:01:56,920 Speaker 1: But two very distinguished economists have taken a fresh look 30 00:01:56,960 --> 00:02:01,800 Speaker 1: at that history and decided the basic reassuring story about 31 00:02:01,800 --> 00:02:04,560 Speaker 1: the past impact of technology on jobs and the quality 32 00:02:04,560 --> 00:02:10,559 Speaker 1: of life of working people is wrong or seriously incomplete. 33 00:02:10,760 --> 00:02:13,919 Speaker 1: So if their argument is right, we should be worried 34 00:02:13,919 --> 00:02:17,160 Speaker 1: about the way AI will transform our economy and society 35 00:02:17,280 --> 00:02:19,560 Speaker 1: because we are not now in a position to get 36 00:02:19,560 --> 00:02:22,720 Speaker 1: the best out of it. Quite the opposite. That book 37 00:02:22,800 --> 00:02:26,840 Speaker 1: is Power and Progress, Our thousand years Struggle over Technology 38 00:02:26,840 --> 00:02:31,440 Speaker 1: and Prosperity, and the authors are darn Smoglu and Simon Johnson, 39 00:02:31,560 --> 00:02:35,680 Speaker 1: both professors at the Massachusetts Institute of Technology. Simon's a 40 00:02:35,720 --> 00:02:38,960 Speaker 1: former chief economist at the International Monetary Fund, and Darren 41 00:02:39,040 --> 00:02:41,959 Speaker 1: won the John Bates Prize for the best US economist 42 00:02:42,080 --> 00:02:45,360 Speaker 1: under forty a few years ago. He also wrote possibly 43 00:02:45,360 --> 00:02:48,640 Speaker 1: the most widely read book of economic history in recent times, 44 00:02:49,040 --> 00:02:52,240 Speaker 1: Why Nations Fail. And he's here with me now, Darren, 45 00:02:52,480 --> 00:02:54,400 Speaker 1: Thank you very much. I'm really pleased we can have 46 00:02:54,480 --> 00:02:56,160 Speaker 1: this conversation on Stephanomics. 47 00:02:56,280 --> 00:02:58,040 Speaker 2: I am so happy to be here, and you give 48 00:02:58,120 --> 00:03:00,440 Speaker 2: such a wonderful introduction. I don't think I have much 49 00:03:00,480 --> 00:03:01,400 Speaker 2: to add, No, no. 50 00:03:01,320 --> 00:03:03,560 Speaker 1: I think we have plenty to discuss, and I'm sorry 51 00:03:03,600 --> 00:03:05,640 Speaker 1: about the long introduction, but I wanted to make clear 52 00:03:05,680 --> 00:03:08,480 Speaker 1: why I thought the book was so important, and we 53 00:03:08,560 --> 00:03:11,280 Speaker 1: do have I think a lot to unpack about the 54 00:03:11,320 --> 00:03:14,400 Speaker 1: past and the present and what you hope might be 55 00:03:14,639 --> 00:03:16,880 Speaker 1: a better future. But maybe we can start at that 56 00:03:17,040 --> 00:03:20,840 Speaker 1: endpoint with what worries you right now about the impact 57 00:03:20,880 --> 00:03:24,919 Speaker 1: that AI and similar technologies might have on the world. 58 00:03:25,080 --> 00:03:27,520 Speaker 2: Well, I think you put it so well. We need 59 00:03:27,560 --> 00:03:31,720 Speaker 2: to be concerned but not scared. I think this is 60 00:03:31,720 --> 00:03:37,000 Speaker 2: a turning point. There are many transformative choices we have 61 00:03:37,080 --> 00:03:39,520 Speaker 2: to make about the future of work, future of inequality, 62 00:03:39,600 --> 00:03:42,560 Speaker 2: future of democracy. And the two worst positions we can 63 00:03:42,600 --> 00:03:47,320 Speaker 2: take are to say everything's going to be fine, just 64 00:03:47,480 --> 00:03:52,640 Speaker 2: let experts worry about it or be scared about killer robots. 65 00:03:52,720 --> 00:03:56,920 Speaker 2: Both of them pacify us and push us not to 66 00:03:57,000 --> 00:04:01,160 Speaker 2: take up our responsibility of trying to shape the technology, 67 00:04:01,200 --> 00:04:04,440 Speaker 2: trying to get involved about decisions about the future of 68 00:04:04,440 --> 00:04:05,240 Speaker 2: this technology. 69 00:04:05,720 --> 00:04:07,600 Speaker 1: And I guess there is a lot there, but one 70 00:04:07,640 --> 00:04:09,200 Speaker 1: piece of it that I was struck by because I 71 00:04:09,200 --> 00:04:12,440 Speaker 1: feel like that was definitely many many of my economic 72 00:04:12,560 --> 00:04:15,240 Speaker 1: history lessons had this sort of underlying premise. And you 73 00:04:15,280 --> 00:04:18,480 Speaker 1: talk about the productivity bandwagon, so maybe you should explain that. 74 00:04:18,839 --> 00:04:22,520 Speaker 2: Let me actually take a step back, Simon, and I 75 00:04:22,640 --> 00:04:26,599 Speaker 2: are definitely not arguing that we haven't tremendously benefited from 76 00:04:26,640 --> 00:04:30,000 Speaker 2: industrial technology and the scientific advances. Today we are so 77 00:04:30,080 --> 00:04:32,960 Speaker 2: much more comfortable, so much more prosperous, so much healthier 78 00:04:33,000 --> 00:04:35,000 Speaker 2: than people who lived three hundred years ago, and that's 79 00:04:35,040 --> 00:04:38,760 Speaker 2: thanks to industrial technology and use of scientific knowledge in 80 00:04:39,000 --> 00:04:43,359 Speaker 2: further improving technology in every aspect of our lives. What 81 00:04:43,440 --> 00:04:46,480 Speaker 2: we are questioning that that was an automatic process. So 82 00:04:46,560 --> 00:04:49,640 Speaker 2: the techno optimism that you so eloquently described at the 83 00:04:49,680 --> 00:04:53,120 Speaker 2: beginning is that we don't need to take any drastic 84 00:04:53,200 --> 00:04:58,120 Speaker 2: actions or become involved in shaping the future of technology 85 00:04:58,160 --> 00:05:01,440 Speaker 2: because there is a very powerful in axorable, automatic process 86 00:05:01,480 --> 00:05:03,839 Speaker 2: that's going to bring all sorts of good things to people. 87 00:05:03,880 --> 00:05:05,400 Speaker 2: And at the center of it is what we call 88 00:05:05,440 --> 00:05:08,600 Speaker 2: the productivity bandwagon. Because most of us earn our livings 89 00:05:09,000 --> 00:05:12,680 Speaker 2: by supplying our labor. So the process via which technology 90 00:05:12,720 --> 00:05:14,880 Speaker 2: is going to improve brings shared prosperity has to go 91 00:05:14,920 --> 00:05:17,560 Speaker 2: through the labor market, which means wages have to improve. 92 00:05:17,800 --> 00:05:23,279 Speaker 2: And the productivity bandwagon says that if productivity grows, if 93 00:05:23,360 --> 00:05:27,840 Speaker 2: technological capabilities improve, that's going to create a very powerful 94 00:05:27,839 --> 00:05:32,279 Speaker 2: force towards employers wanting more labor, and that raises wages 95 00:05:32,320 --> 00:05:37,040 Speaker 2: and employment. If the productivity bandwagon breaks down or doesn't 96 00:05:37,040 --> 00:05:40,440 Speaker 2: have many people on it, then shared prosperity would become 97 00:05:40,440 --> 00:05:43,560 Speaker 2: a dream. And the real fear is that that's exactly 98 00:05:43,600 --> 00:05:45,880 Speaker 2: what could happen with AI, and we've seen some of 99 00:05:45,920 --> 00:05:48,880 Speaker 2: that happen with digital technologies over the last forty years anyway, 100 00:05:49,400 --> 00:05:54,200 Speaker 2: and history says productivity bandwagon can work, but only if 101 00:05:54,240 --> 00:05:57,160 Speaker 2: we create the right institutions and the right direction of technology. 102 00:05:57,320 --> 00:06:00,679 Speaker 1: So what matters most for making an outcome better worse 103 00:06:00,760 --> 00:06:01,360 Speaker 1: for workers? 104 00:06:01,520 --> 00:06:04,800 Speaker 2: Perfect That's exactly the right question. And what Simon and 105 00:06:04,839 --> 00:06:08,159 Speaker 2: I argue is that there are two pillars to it, 106 00:06:08,200 --> 00:06:12,720 Speaker 2: and you can see them very clearly in most historical episodes. First, 107 00:06:12,760 --> 00:06:15,880 Speaker 2: you need a direction of technology that doesn't just automate work. 108 00:06:15,880 --> 00:06:18,080 Speaker 2: Automation is always going to be with us, but it 109 00:06:18,120 --> 00:06:20,480 Speaker 2: doesn't just automate work, but at the same time creates 110 00:06:20,520 --> 00:06:24,200 Speaker 2: new tasks, new capabilities, new things in which human labor 111 00:06:24,279 --> 00:06:27,400 Speaker 2: can be productively used. And second, you need an institutional 112 00:06:27,400 --> 00:06:31,839 Speaker 2: framework in which there are forces such as worker voice 113 00:06:31,880 --> 00:06:35,520 Speaker 2: and worker power that induces employers to share some of 114 00:06:35,560 --> 00:06:38,560 Speaker 2: the gains with workers. If either of those two things 115 00:06:38,560 --> 00:06:42,680 Speaker 2: are broken down, then we're in trouble. If both of 116 00:06:42,720 --> 00:06:45,640 Speaker 2: them break down, that's really damaging. And that's the age 117 00:06:45,680 --> 00:06:48,159 Speaker 2: we are living in. There is no worker voice. AI 118 00:06:48,320 --> 00:06:50,800 Speaker 2: is being used to sideline workers even more in the 119 00:06:50,839 --> 00:06:54,480 Speaker 2: production process, and there isn't a democratic process that's actually 120 00:06:54,720 --> 00:06:58,479 Speaker 2: contributing to it, to a sharing of prosperity, or to 121 00:06:59,040 --> 00:07:01,440 Speaker 2: reshaping the direction of technology. 122 00:07:01,000 --> 00:07:02,720 Speaker 1: You're talking about where we are now, which I think 123 00:07:02,720 --> 00:07:05,560 Speaker 1: we should definitely get to. But for those who sort 124 00:07:05,600 --> 00:07:08,640 Speaker 1: of feel like, oh, this sounds like people who are 125 00:07:08,720 --> 00:07:14,960 Speaker 1: just seeing this new technology and fearing the worst, you know, 126 00:07:15,000 --> 00:07:17,360 Speaker 1: I think at least we should show how it's rooted 127 00:07:17,400 --> 00:07:20,679 Speaker 1: in that understanding of history that you mentioned, and maybe 128 00:07:21,080 --> 00:07:23,840 Speaker 1: draw some contrasts when you're looking back, for example, at 129 00:07:23,840 --> 00:07:25,400 Speaker 1: the nineteenth century. I mean, there's a lot of a 130 00:07:25,400 --> 00:07:28,560 Speaker 1: book which is looking at what happened in the UK 131 00:07:28,720 --> 00:07:31,120 Speaker 1: and the Industrial Revolution, So maybe say a little bit 132 00:07:31,120 --> 00:07:35,080 Speaker 1: about the sort of contrasting impact of the different technologies 133 00:07:35,120 --> 00:07:35,720 Speaker 1: that came in there. 134 00:07:35,720 --> 00:07:38,720 Speaker 2: Thank you them for bringing that up absolutely. I mentioned 135 00:07:38,760 --> 00:07:43,200 Speaker 2: already that we are so fortunate to have had the 136 00:07:43,240 --> 00:07:48,840 Speaker 2: industrial technological improvement that started somewhere in the UK in 137 00:07:48,880 --> 00:07:52,160 Speaker 2: the middle of the eighteenth century. We are so fortunate, 138 00:07:52,160 --> 00:07:54,760 Speaker 2: but the people who live through it weren't. The first 139 00:07:54,800 --> 00:07:58,560 Speaker 2: eighty ninety years of the British Industrial Revolution was dreadful 140 00:07:58,600 --> 00:08:05,080 Speaker 2: for people. Incomes stagnated, working hours, expanded working conditions, Work 141 00:08:05,280 --> 00:08:09,000 Speaker 2: worsened in factories which much greater discipline, much less autonomy. 142 00:08:09,400 --> 00:08:15,480 Speaker 2: People were filled into unhealthy cities in which their life 143 00:08:15,520 --> 00:08:20,520 Speaker 2: expectancy dropped, and there was no worker voice, no democratic process. 144 00:08:21,200 --> 00:08:25,480 Speaker 2: The whole thing was just a very difficult time for 145 00:08:25,640 --> 00:08:30,880 Speaker 2: most working people. But it didn't remain that way in 146 00:08:30,920 --> 00:08:33,520 Speaker 2: the second half of the nineteenth century. You already see 147 00:08:33,679 --> 00:08:38,439 Speaker 2: higher wages, much greater use of technology for improving conditions, 148 00:08:38,480 --> 00:08:44,000 Speaker 2: both as public infrastructure, health improves and factories improved. And 149 00:08:44,040 --> 00:08:46,960 Speaker 2: why why did that happen? Was that automatic? Again, our 150 00:08:47,000 --> 00:08:49,160 Speaker 2: reading of history with a lot of evidence says no, 151 00:08:49,280 --> 00:08:53,320 Speaker 2: that wasn't automatic. There was a complete transformation of British 152 00:08:53,320 --> 00:09:00,800 Speaker 2: institutions with democracy, public sector involvement in cleaning up the cities, 153 00:09:01,080 --> 00:09:05,320 Speaker 2: education and other public infrastructure, and very transformatively trade unions. 154 00:09:05,360 --> 00:09:08,439 Speaker 2: You know, being a unionist was illegal in the ant 155 00:09:08,720 --> 00:09:11,480 Speaker 2: Kingdom and that started changing in the second half of 156 00:09:11,520 --> 00:09:14,920 Speaker 2: the nineteenth century, and that worker voice, worker negotiation were critical. 157 00:09:15,200 --> 00:09:18,559 Speaker 2: As part of that process, the direction of technology changed significantly. 158 00:09:19,800 --> 00:09:23,600 Speaker 2: What brought part of that misery was the automation focus 159 00:09:23,800 --> 00:09:28,680 Speaker 2: and the very high discipline modern factory system. All of 160 00:09:28,720 --> 00:09:32,440 Speaker 2: that started improving. No longer you could allow child labor 161 00:09:32,679 --> 00:09:36,360 Speaker 2: or you know, twelve hour days and mind shaft for people, 162 00:09:36,520 --> 00:09:39,320 Speaker 2: for children as young as five. All of these things 163 00:09:39,320 --> 00:09:41,800 Speaker 2: were institutional in nature as well as technological. 164 00:09:42,000 --> 00:09:44,280 Speaker 1: And actually I was struck by the example. I mean, 165 00:09:44,320 --> 00:09:46,840 Speaker 1: we going up in Britain. You feel like you hear 166 00:09:46,880 --> 00:09:49,040 Speaker 1: about the industrial revolution all the time, but I think 167 00:09:49,360 --> 00:09:52,319 Speaker 1: and indeed about child labor and some of the worst aspects. 168 00:09:52,320 --> 00:09:54,719 Speaker 1: But I think what was in your book you sort 169 00:09:54,720 --> 00:09:59,080 Speaker 1: of bring home what a deterioration in circumstances it was 170 00:09:59,120 --> 00:10:02,520 Speaker 1: that actually six seven year olds hadn't been doing twelve 171 00:10:02,600 --> 00:10:06,240 Speaker 1: hours work, certainly not in the dark underground before, and 172 00:10:06,280 --> 00:10:08,880 Speaker 1: so there was this a period where people's lives were 173 00:10:08,960 --> 00:10:12,040 Speaker 1: actively worse. I think is worth reminding. 174 00:10:11,640 --> 00:10:15,000 Speaker 2: People absolutely absolutely, and people were very exercised about it. 175 00:10:15,040 --> 00:10:17,840 Speaker 2: I mean, at some point it reached such alarming proportion 176 00:10:18,000 --> 00:10:22,600 Speaker 2: that middle class Brits, you know, said disc cannot go on. 177 00:10:23,840 --> 00:10:27,280 Speaker 2: But all wishful thinking would not have done anything unless 178 00:10:27,280 --> 00:10:29,520 Speaker 2: we changed the institutions and the direction of technology, and 179 00:10:29,559 --> 00:10:31,520 Speaker 2: that's what Britain managed in the second half of the 180 00:10:31,559 --> 00:10:32,400 Speaker 2: nineteenth century. 181 00:10:34,600 --> 00:10:37,480 Speaker 1: So there's quite a few things there, because there's the 182 00:10:37,559 --> 00:10:40,440 Speaker 1: nature of the technology and whether it tends to just 183 00:10:40,520 --> 00:10:44,400 Speaker 1: replace workers or actually also produce more demand more other 184 00:10:44,520 --> 00:10:51,719 Speaker 1: kinds of jobs for workers. There's also the institutions surrounding it. 185 00:10:52,679 --> 00:10:56,640 Speaker 1: But part of that is about the companies that are 186 00:10:56,679 --> 00:10:59,560 Speaker 1: producing the technology, that are driving technology, and how powerful 187 00:10:59,600 --> 00:11:02,599 Speaker 1: they are relative to other parts of society. And I 188 00:11:02,600 --> 00:11:05,199 Speaker 1: guess what the example of that is in the Gilded Age, 189 00:11:04,840 --> 00:11:08,040 Speaker 1: in the US. So how does that feed into it 190 00:11:08,080 --> 00:11:10,199 Speaker 1: the sort of market power of companies. 191 00:11:10,320 --> 00:11:12,040 Speaker 2: I think the market power is one of the very 192 00:11:12,040 --> 00:11:17,040 Speaker 2: important elements as well, because new technologies, especially those that 193 00:11:17,080 --> 00:11:19,800 Speaker 2: make better use of labor, come out of the competitive process. 194 00:11:19,960 --> 00:11:23,320 Speaker 2: A more diverse approach to innovation is an important part 195 00:11:23,360 --> 00:11:26,200 Speaker 2: of it. Now, large companies have always been with us. 196 00:11:26,280 --> 00:11:28,760 Speaker 2: I don't think we're going to be able to reverse that, 197 00:11:28,800 --> 00:11:32,520 Speaker 2: and I don't think we should. Ford Motor Company started 198 00:11:32,559 --> 00:11:35,400 Speaker 2: small but became one of the most important employers in 199 00:11:35,400 --> 00:11:37,600 Speaker 2: the United States, and it was at the forefront of 200 00:11:38,080 --> 00:11:40,120 Speaker 2: automating work. But it was also at the forefront of 201 00:11:40,160 --> 00:11:45,760 Speaker 2: creating new tasks, much better working conditions for workers with 202 00:11:46,080 --> 00:11:49,960 Speaker 2: higher wages, and accommodating workers into the production process so 203 00:11:50,000 --> 00:11:54,800 Speaker 2: that they could actually reduce a turnover. But all of 204 00:11:54,840 --> 00:11:58,040 Speaker 2: that becomes much more likely when we have countervailing powers, 205 00:11:58,200 --> 00:12:02,520 Speaker 2: and countervailing powers have to have several sources. For large companies, 206 00:12:02,559 --> 00:12:06,200 Speaker 2: you need competition. If they become so secure that nobody 207 00:12:06,200 --> 00:12:08,840 Speaker 2: can replace them, that's not going to be good. You 208 00:12:08,880 --> 00:12:12,320 Speaker 2: need countervailing powers in the form of worker voice, worker involvement. 209 00:12:12,720 --> 00:12:15,800 Speaker 2: Trade unions provided that, labor movement provided that in the past, 210 00:12:15,840 --> 00:12:17,920 Speaker 2: What will provide it in the future That remains to 211 00:12:17,920 --> 00:12:20,479 Speaker 2: be seen, and you need the government regulation in there. 212 00:12:20,679 --> 00:12:24,000 Speaker 2: You know, if companies can do whatever they want to 213 00:12:24,200 --> 00:12:29,080 Speaker 2: their customers, to the environment, two workers, that's not going 214 00:12:29,080 --> 00:12:32,800 Speaker 2: to lead to good outcomes. So a regulatory framework is 215 00:12:32,840 --> 00:12:36,280 Speaker 2: also quite critical. Overall. I think a good way of 216 00:12:36,320 --> 00:12:40,120 Speaker 2: thinking about this is democratic control of technology. Technology is 217 00:12:40,120 --> 00:12:43,560 Speaker 2: something that affects us all. To say that one or 218 00:12:43,559 --> 00:12:46,440 Speaker 2: two genius in Silicon Valley have to be responsible for 219 00:12:46,480 --> 00:12:48,320 Speaker 2: the future of technology, I mean we all have to 220 00:12:48,600 --> 00:12:50,560 Speaker 2: take whatever it's dished out to us, that's not the 221 00:12:50,640 --> 00:12:54,120 Speaker 2: right perspective. And the democratic control comes from companies being 222 00:12:54,160 --> 00:12:57,200 Speaker 2: at the forefront of technological progress. But those companies are 223 00:12:57,920 --> 00:13:01,800 Speaker 2: threatened by rivals, countable to their workers, and they're accountable 224 00:13:01,840 --> 00:13:03,560 Speaker 2: to society through democratic means. 225 00:13:03,679 --> 00:13:07,559 Speaker 1: Because we do tend to think of invention and technology 226 00:13:07,880 --> 00:13:11,760 Speaker 1: as a sort of as a being out being outside 227 00:13:11,800 --> 00:13:14,160 Speaker 1: the system, that there's you know, people are sitting around 228 00:13:14,240 --> 00:13:16,880 Speaker 1: in their labs or where you know, wherever you can, 229 00:13:17,080 --> 00:13:20,640 Speaker 1: wherever you picture them, or in their garages, coming up 230 00:13:20,640 --> 00:13:23,440 Speaker 1: with their ideas, and there's only it's only after a 231 00:13:23,480 --> 00:13:27,240 Speaker 1: certain point that they're they're interacting with the broader world, 232 00:13:27,280 --> 00:13:29,840 Speaker 1: that there is this sort of natural process of invention 233 00:13:29,960 --> 00:13:33,360 Speaker 1: that happens. It doesn't feel like that process has ever 234 00:13:33,440 --> 00:13:39,480 Speaker 1: been really organized or run by government or with a 235 00:13:39,559 --> 00:13:41,880 Speaker 1: kind of democratic infact. So can you democratic? 236 00:13:41,960 --> 00:13:45,760 Speaker 2: Interesting? You're right at some degree, But there is a 237 00:13:45,840 --> 00:13:50,880 Speaker 2: broader ecosystem. First of all, a lot of innovation is 238 00:13:50,920 --> 00:13:53,920 Speaker 2: coordinated by large companies. Today. If you look at the 239 00:13:55,040 --> 00:13:58,920 Speaker 2: in the United States, most RND is by large and 240 00:13:58,960 --> 00:14:04,560 Speaker 2: publicly traded companies. But second, even the innovation that takes 241 00:14:04,559 --> 00:14:10,000 Speaker 2: place in universities, in people's garages, in small companies, it's 242 00:14:10,040 --> 00:14:13,360 Speaker 2: influenced by the market system where people think profits are, 243 00:14:13,360 --> 00:14:16,200 Speaker 2: and it's influenced by what we call a vision what 244 00:14:16,520 --> 00:14:20,360 Speaker 2: is the best use of our scientific knowledge. And I 245 00:14:20,400 --> 00:14:25,160 Speaker 2: think we have created an incorrect direction of technology because 246 00:14:25,200 --> 00:14:28,920 Speaker 2: of both reasons. We have provided the wrong market incentives 247 00:14:29,640 --> 00:14:32,280 Speaker 2: to digital technologies and we've provided the wrong vision. And 248 00:14:32,280 --> 00:14:36,600 Speaker 2: they've both met in saying digital technologies should be designed 249 00:14:36,600 --> 00:14:39,720 Speaker 2: by geniuses to be imposed on people, and they should 250 00:14:39,760 --> 00:14:43,000 Speaker 2: be used for automation, for surveillance, for data collection, for 251 00:14:43,240 --> 00:14:47,000 Speaker 2: reducing labour's involvement in the production process, for creating some 252 00:14:47,040 --> 00:14:50,480 Speaker 2: sort of amorphous autonomous machine intelligence, and all of those 253 00:14:50,520 --> 00:14:53,240 Speaker 2: are related, and they're the wrong direction. What we call 254 00:14:53,320 --> 00:14:55,520 Speaker 2: for in the book is that we should strive for 255 00:14:55,640 --> 00:14:58,960 Speaker 2: machine usefulness, not machine intelligence. Machines are valuable to us 256 00:14:59,040 --> 00:15:03,280 Speaker 2: because they enable us to do useful things. The calculator Wikipedia, 257 00:15:03,320 --> 00:15:07,040 Speaker 2: those are amazing inventions because they expand what we can do. 258 00:15:07,600 --> 00:15:10,680 Speaker 2: The amorphous notion of AI that is so good that 259 00:15:10,720 --> 00:15:13,440 Speaker 2: it can do everything that humans do, actually in practice 260 00:15:13,480 --> 00:15:17,320 Speaker 2: not so well. But that vision, which guides a lot 261 00:15:17,360 --> 00:15:18,640 Speaker 2: of research, is the wrong one. 262 00:15:19,480 --> 00:15:22,400 Speaker 1: And actually, when you talk about vision, you had a 263 00:15:22,440 --> 00:15:25,200 Speaker 1: fascinating phrase which for me was quite resonant in a 264 00:15:25,320 --> 00:15:29,280 Speaker 1: kind of broader way, which is vision oligarchy. Tell us 265 00:15:29,280 --> 00:15:30,240 Speaker 1: a bit more about that. 266 00:15:30,400 --> 00:15:32,000 Speaker 2: You know, at the end of the day, I described 267 00:15:32,000 --> 00:15:40,000 Speaker 2: a vision which is this machine intelligence created by a 268 00:15:40,080 --> 00:15:44,640 Speaker 2: few very smart engineers and scientists that's going to transform 269 00:15:44,680 --> 00:15:49,800 Speaker 2: everybody's lives. That is a very powerful vision. But where 270 00:15:49,800 --> 00:15:54,080 Speaker 2: did it come from? Well, it came from Turing to 271 00:15:54,120 --> 00:15:58,640 Speaker 2: some degree, but in a very different context. But it 272 00:15:58,760 --> 00:16:02,760 Speaker 2: got operationalized by a number of very like minded people 273 00:16:02,840 --> 00:16:06,360 Speaker 2: in Silicon Valley who've pushed this vision and have achieved 274 00:16:06,360 --> 00:16:11,040 Speaker 2: some degree of commercial success early on and now are 275 00:16:11,240 --> 00:16:15,880 Speaker 2: influencing the rest of society through their oversized role in 276 00:16:15,960 --> 00:16:20,160 Speaker 2: the media, in all public debates, in policy, and of 277 00:16:20,200 --> 00:16:23,880 Speaker 2: course their amazing wealth. And that's what we mean by 278 00:16:23,960 --> 00:16:27,160 Speaker 2: division oligarchy. That's a small group of people who have 279 00:16:27,680 --> 00:16:30,560 Speaker 2: captured the vision of what we can do with technology 280 00:16:30,600 --> 00:16:32,080 Speaker 2: and what we should do with technology. 281 00:16:32,720 --> 00:16:36,320 Speaker 1: And I guess moving on to how we think about 282 00:16:36,360 --> 00:16:42,600 Speaker 1: the more recent waves of technology, you're quite damning about 283 00:16:42,640 --> 00:16:46,600 Speaker 1: the impact that recent automation that AI has had. I 284 00:16:46,600 --> 00:16:49,720 Speaker 1: guess some would say it's just too soon to tell 285 00:16:50,120 --> 00:16:52,600 Speaker 1: how some of these technologies are really going to affect 286 00:16:52,600 --> 00:16:54,760 Speaker 1: the nature of jobs and the workplace. 287 00:16:54,880 --> 00:16:58,000 Speaker 2: That's right, You're right, it's too soon to tell. But 288 00:16:58,080 --> 00:17:00,880 Speaker 2: there is a problem in there. It's too soon to tell. 289 00:17:00,920 --> 00:17:04,400 Speaker 2: Why are we rushing to automate work so quickly? What's 290 00:17:04,440 --> 00:17:08,600 Speaker 2: the rush? So? I have no doubt that automation will 291 00:17:08,640 --> 00:17:10,760 Speaker 2: be part of our future. It has to be part 292 00:17:10,800 --> 00:17:13,840 Speaker 2: of our future. There will be things that machines can 293 00:17:13,880 --> 00:17:17,000 Speaker 2: do better than us, and nothing wrong with that, but 294 00:17:19,920 --> 00:17:22,440 Speaker 2: a we should do that only when they are truly 295 00:17:22,480 --> 00:17:28,359 Speaker 2: better than humans, and in humane way, rearranging work in 296 00:17:28,400 --> 00:17:32,480 Speaker 2: a manner that's consistent with human priorities. And second, we 297 00:17:32,520 --> 00:17:35,520 Speaker 2: should at the same time create better jobs, better tasks 298 00:17:35,560 --> 00:17:39,560 Speaker 2: for humans unique skills. That's the problem. We are rushing 299 00:17:39,560 --> 00:17:44,320 Speaker 2: to automate work, even when it's not so productive customer service. 300 00:17:45,560 --> 00:17:48,359 Speaker 2: It's done by AI in many places, and nobody's happy 301 00:17:48,400 --> 00:17:52,000 Speaker 2: with it. We've displaced workers and we are faced with 302 00:17:52,040 --> 00:17:54,560 Speaker 2: these menus that are supposed to be smart. They never work. 303 00:17:57,119 --> 00:18:01,359 Speaker 2: Productivity gains from that are minimal, perhaps negative. But we're 304 00:18:01,440 --> 00:18:03,400 Speaker 2: rushing to do it. And at the same time we're 305 00:18:03,400 --> 00:18:05,840 Speaker 2: not creating any new tasks in new jobs and new capabilities. 306 00:18:05,840 --> 00:18:08,600 Speaker 2: And we can do that. AI enables us or large 307 00:18:08,680 --> 00:18:13,080 Speaker 2: language models, they have the capacity to help us. As 308 00:18:13,119 --> 00:18:15,919 Speaker 2: you said in your introduction, we're not doing that. 309 00:18:19,960 --> 00:18:22,840 Speaker 1: I think that what you've just said describes a lot 310 00:18:22,880 --> 00:18:24,960 Speaker 1: of the technologies that we've lived with over the last 311 00:18:25,040 --> 00:18:29,720 Speaker 1: few years. But it does feel like CHATGPT and that 312 00:18:30,040 --> 00:18:36,359 Speaker 1: much more interactive technology is different to interact with and 313 00:18:36,480 --> 00:18:39,800 Speaker 1: certainly seems to be learning faster than many of these 314 00:18:39,840 --> 00:18:43,880 Speaker 1: other technologies. People who are making an effort to make 315 00:18:43,920 --> 00:18:48,240 Speaker 1: it part of their lives, whether it's professors or lawyers 316 00:18:48,359 --> 00:18:54,080 Speaker 1: or people working in human resources are finding very quickly 317 00:18:54,200 --> 00:18:56,639 Speaker 1: that it can change the way they do their work. 318 00:18:56,920 --> 00:18:59,800 Speaker 1: So is there something a bit different about the generative AI. 319 00:19:00,040 --> 00:19:05,320 Speaker 2: Well, I would say first, it is impressive, but part 320 00:19:05,359 --> 00:19:09,360 Speaker 2: of the reason why it's impressive is because that's how 321 00:19:09,359 --> 00:19:14,560 Speaker 2: it's been marketed. So what people are impressed by chat 322 00:19:14,600 --> 00:19:18,440 Speaker 2: GIPT is it gives authoritative answers. It can write sonnets 323 00:19:18,480 --> 00:19:24,600 Speaker 2: and poetry. It feels like these are things machines shouldn't 324 00:19:24,640 --> 00:19:27,200 Speaker 2: be able to do, and that's what we're impressed by. 325 00:19:28,400 --> 00:19:32,480 Speaker 2: Don't get me wrong. I do completely believe that large 326 00:19:32,560 --> 00:19:35,720 Speaker 2: language models and generative AI can be used in ways 327 00:19:35,760 --> 00:19:39,280 Speaker 2: that are very positive for humans, and some people have 328 00:19:39,400 --> 00:19:42,640 Speaker 2: found ways of doing that with chat GPT, But chat 329 00:19:42,680 --> 00:19:47,399 Speaker 2: GPT's architecture is not optimal for that. What we want, 330 00:19:47,920 --> 00:19:53,520 Speaker 2: if we believe my pitch for machine usefulness, is that 331 00:19:53,640 --> 00:19:57,800 Speaker 2: these programs should make us better in our jobs, in 332 00:19:57,840 --> 00:20:03,040 Speaker 2: our lives, in our cognition. It doesn't work. If chachipet 333 00:20:03,160 --> 00:20:06,640 Speaker 2: gives you an authoritative answer without explaining to you why 334 00:20:06,640 --> 00:20:08,680 Speaker 2: you should believe it, you either believe it, which is 335 00:20:08,800 --> 00:20:12,680 Speaker 2: not good, or you completely dismiss it. If I were 336 00:20:12,760 --> 00:20:14,399 Speaker 2: to give you an argument, you would ask me, why 337 00:20:14,440 --> 00:20:16,960 Speaker 2: are you saying that, what's your evidence? Where does that 338 00:20:17,000 --> 00:20:20,080 Speaker 2: come from? Give me the provenance of that. You can't 339 00:20:20,080 --> 00:20:24,120 Speaker 2: do that with chatchipt. It's not designed that way. If 340 00:20:24,160 --> 00:20:27,720 Speaker 2: you ask it to provide references, it will make up some. 341 00:20:28,600 --> 00:20:33,400 Speaker 2: It never really processes the reliability of information. It is 342 00:20:33,480 --> 00:20:36,320 Speaker 2: not designed so that it can interact with you in 343 00:20:36,359 --> 00:20:39,320 Speaker 2: a way that filters the vast amount of information that 344 00:20:39,359 --> 00:20:42,160 Speaker 2: you have available. But you don't know which one is reliable. 345 00:20:42,280 --> 00:20:44,760 Speaker 2: So there are many things that we could design these 346 00:20:44,840 --> 00:20:48,359 Speaker 2: machines or these models differently that could be more useful 347 00:20:48,359 --> 00:20:51,160 Speaker 2: to us, but as not the direction the industry is going. 348 00:20:51,240 --> 00:20:53,360 Speaker 2: Many employers are excited by it not to make their 349 00:20:53,400 --> 00:20:55,679 Speaker 2: labor more productive, but they want to eliminate labor. 350 00:20:56,160 --> 00:20:57,639 Speaker 1: And it's interesting because I guess a lot of the 351 00:20:57,680 --> 00:21:00,520 Speaker 1: commentators I was reading something by Ethan Molly other day, 352 00:21:00,640 --> 00:21:04,639 Speaker 1: you know, who are excited about it, have tended to 353 00:21:04,640 --> 00:21:07,399 Speaker 1: be the ones who are finding ways to make it 354 00:21:07,440 --> 00:21:10,679 Speaker 1: more valuable for them. And it doesn't feel like a 355 00:21:10,680 --> 00:21:12,920 Speaker 1: big leap, you know. It comes down to the way 356 00:21:13,000 --> 00:21:15,640 Speaker 1: we interact with it, whether we trust its answers, whether 357 00:21:15,680 --> 00:21:18,480 Speaker 1: we come back. So that doesn't seem like a big change. 358 00:21:19,520 --> 00:21:23,240 Speaker 1: What you've described about changing the whole environment in which 359 00:21:23,320 --> 00:21:27,760 Speaker 1: these technologies are implemented, the whole public attitude towards them. 360 00:21:28,080 --> 00:21:29,320 Speaker 1: That's a pretty big change. 361 00:21:29,320 --> 00:21:33,359 Speaker 2: Absolutely, absolutely, the power attitude regulation. I think these are 362 00:21:33,440 --> 00:21:37,360 Speaker 2: big changes, and you're absolutely right there are people who 363 00:21:37,400 --> 00:21:40,040 Speaker 2: are using chatchipitin a productive where there are some companies 364 00:21:40,080 --> 00:21:42,919 Speaker 2: that have already used in productive ways. But I think 365 00:21:43,359 --> 00:21:47,439 Speaker 2: the model attitude of the corporate world is not the 366 00:21:47,440 --> 00:21:50,200 Speaker 2: healthy one. And that's partly because of the corporate world, 367 00:21:50,280 --> 00:21:52,320 Speaker 2: but a lot also because of the way that the 368 00:21:52,359 --> 00:21:55,840 Speaker 2: technology is structured right now and is marketed right now. 369 00:21:56,280 --> 00:21:58,960 Speaker 1: And we should get into that because you describe certain 370 00:21:58,960 --> 00:22:01,639 Speaker 1: things about it, the way it's been driven towards automation, 371 00:22:02,359 --> 00:22:07,560 Speaker 1: the way that it's led down a path of being 372 00:22:07,680 --> 00:22:10,600 Speaker 1: used for surveillance of individuals, and the impact that that's 373 00:22:10,600 --> 00:22:14,480 Speaker 1: had on democracy. So tell us about that. How you 374 00:22:14,600 --> 00:22:18,959 Speaker 1: think the technology itself has been pushed in a certain 375 00:22:19,000 --> 00:22:20,920 Speaker 1: direction by its origins. 376 00:22:21,000 --> 00:22:25,000 Speaker 2: Well, I think let's start with surveillance. The current field 377 00:22:25,080 --> 00:22:32,000 Speaker 2: of AI is contintely intermingled with data collection, and it 378 00:22:32,080 --> 00:22:37,760 Speaker 2: is hungry for data and employers are hungry for getting 379 00:22:37,760 --> 00:22:41,439 Speaker 2: more information about their workers'. Governments, especially authoritarian governments, are 380 00:22:41,520 --> 00:22:45,880 Speaker 2: hungry for getting more information about dissident activities. So there 381 00:22:45,960 --> 00:22:52,199 Speaker 2: is a confluence of factors that is intensifying monitoring of 382 00:22:52,320 --> 00:22:54,800 Speaker 2: surveillance of both citizens and workers. I think that's one 383 00:22:54,800 --> 00:22:57,040 Speaker 2: of the things that we have to worry about, and 384 00:22:59,080 --> 00:23:02,080 Speaker 2: generative AI is going to push more in that direction. 385 00:23:02,920 --> 00:23:10,600 Speaker 2: Automation is related, but quite a distinct phenomenon. US corporations 386 00:23:10,640 --> 00:23:13,760 Speaker 2: are under pressure competitively because of their shareholders, because of 387 00:23:13,760 --> 00:23:17,960 Speaker 2: the vision of their managers to reduce labor costs. Nobody 388 00:23:17,960 --> 00:23:20,640 Speaker 2: in the schooling system is, for example, talking about, let's 389 00:23:20,720 --> 00:23:24,040 Speaker 2: hire more teachers, give them better tools, make them more skill, 390 00:23:24,080 --> 00:23:26,040 Speaker 2: pay them higher wages so that they can do a 391 00:23:26,040 --> 00:23:29,400 Speaker 2: better job of creating the human capital of the next generation. 392 00:23:29,440 --> 00:23:31,760 Speaker 2: But that's what we need. We need much more individualized 393 00:23:31,760 --> 00:23:34,040 Speaker 2: teaching in the education system. In the United States and 394 00:23:34,040 --> 00:23:37,760 Speaker 2: the United Kingdom, a lot of low socioeconomic background children 395 00:23:37,840 --> 00:23:41,840 Speaker 2: are having trouble getting the right type of education, the 396 00:23:41,920 --> 00:23:46,200 Speaker 2: right type of skills from the schooling system. More individualized 397 00:23:46,280 --> 00:23:49,480 Speaker 2: education targeted to their strengths and weaknesses could be a 398 00:23:49,560 --> 00:23:52,480 Speaker 2: great boot. We can use AI for doing that, but 399 00:23:52,520 --> 00:23:55,160 Speaker 2: nobody is doing that, because that means actually hiring more teachers. 400 00:23:55,520 --> 00:23:58,760 Speaker 2: What schools are interested in hiring less teachers. What companies 401 00:23:58,800 --> 00:24:01,359 Speaker 2: are interested in is, let's eliminate some of the more 402 00:24:01,400 --> 00:24:03,080 Speaker 2: of the blue color task, let's get rid of some 403 00:24:03,160 --> 00:24:06,520 Speaker 2: of the clerical tasks. So that mindset needs to change. 404 00:24:06,720 --> 00:24:09,960 Speaker 1: And of course the response to that has often been, well, 405 00:24:10,000 --> 00:24:13,320 Speaker 1: those individual companies, particularly in the US, those individual companies 406 00:24:13,320 --> 00:24:15,720 Speaker 1: will make their decisions about how many workers they want, 407 00:24:16,680 --> 00:24:20,200 Speaker 1: but the increased productivity will create more jobs in other 408 00:24:20,280 --> 00:24:23,359 Speaker 1: parts of the economy. You think it's just it won't 409 00:24:23,400 --> 00:24:25,679 Speaker 1: operate this time or has not always It. 410 00:24:25,720 --> 00:24:28,679 Speaker 2: Will it will if it really increased productivity by a 411 00:24:28,760 --> 00:24:34,120 Speaker 2: tremendous amount, it would. The question is, can we get 412 00:24:34,160 --> 00:24:38,240 Speaker 2: for example, let's say three percent productivity growth in real 413 00:24:38,320 --> 00:24:41,040 Speaker 2: terms every year by automating. I think that's very difficult. 414 00:24:41,560 --> 00:24:44,359 Speaker 2: You're automating a few tasks in a given point in time, 415 00:24:44,960 --> 00:24:48,240 Speaker 2: say even if automation is on acceleration, you're going to 416 00:24:48,240 --> 00:24:50,600 Speaker 2: be automating perhaps three or four or five percent of 417 00:24:50,640 --> 00:24:54,760 Speaker 2: tasks that humans do. To get that kind of huge 418 00:24:54,760 --> 00:24:57,359 Speaker 2: productivity growth from automation is very difficult. That means that 419 00:24:57,400 --> 00:24:59,719 Speaker 2: machines need to be ten times as productive as humans 420 00:25:01,359 --> 00:25:03,160 Speaker 2: in the past. We haven't done that. In the past. 421 00:25:03,200 --> 00:25:05,760 Speaker 2: We've gotten very rapid productivity growth when we made humans 422 00:25:05,760 --> 00:25:09,320 Speaker 2: more productive, and I think therefore it's no surprise that 423 00:25:09,359 --> 00:25:12,240 Speaker 2: today we are in a productivity slump around the world. 424 00:25:12,960 --> 00:25:15,919 Speaker 2: We have six five to six times as many patterns 425 00:25:15,960 --> 00:25:18,200 Speaker 2: in the United States as we did forty years ago. 426 00:25:18,480 --> 00:25:23,000 Speaker 2: We have new widgets every day, amazing algorithm breakthroughs in AI, 427 00:25:23,480 --> 00:25:27,840 Speaker 2: and aggregate productivity is very very anemic in the United Kingdom, 428 00:25:27,840 --> 00:25:32,359 Speaker 2: it's stagnant. I think that's a course for alarm and 429 00:25:32,400 --> 00:25:35,080 Speaker 2: it says that we're not using these technologies the right way. 430 00:25:35,240 --> 00:25:37,320 Speaker 1: And it's so interesting because of course people look at 431 00:25:37,320 --> 00:25:39,760 Speaker 1: the there is there's been a productivity slump, particularly in 432 00:25:39,800 --> 00:25:43,080 Speaker 1: the UK, and often that's used as a reason why 433 00:25:43,640 --> 00:25:46,840 Speaker 1: we should be accelerating our introduction of these technologies. 434 00:25:46,880 --> 00:25:48,800 Speaker 2: Yes, so that's the question to me, are you going 435 00:25:48,880 --> 00:25:51,800 Speaker 2: to get out of that productivity slump by doing more 436 00:25:51,880 --> 00:25:56,280 Speaker 2: AI driven customer service self checkout kiosks? Is that the 437 00:25:56,320 --> 00:25:59,280 Speaker 2: way to double UK productivity? I mean, you know, sure 438 00:25:59,440 --> 00:26:02,359 Speaker 2: if we did self check out cuos together with better things, 439 00:26:02,920 --> 00:26:04,240 Speaker 2: perhaps it could contribute. 440 00:26:04,280 --> 00:26:06,880 Speaker 1: Look and as you point out in that example, that's 441 00:26:06,920 --> 00:26:10,199 Speaker 1: just labour shifting because we now do the work not 442 00:26:10,240 --> 00:26:11,440 Speaker 1: the casemet In banking. 443 00:26:11,560 --> 00:26:15,000 Speaker 2: ATMs were introduced, but at the same time, people who 444 00:26:15,040 --> 00:26:20,960 Speaker 2: used to be bank tellers became analysts, customer service reps 445 00:26:22,080 --> 00:26:26,280 Speaker 2: started doing other back office tasks. So actually banking productivity 446 00:26:26,359 --> 00:26:28,960 Speaker 2: increased during that period. We're not doing that latter part. 447 00:26:29,000 --> 00:26:30,879 Speaker 2: We're doing ATMs on overdrive. 448 00:26:31,280 --> 00:26:33,480 Speaker 1: Okay, so what do we do? How do we fix this? 449 00:26:34,440 --> 00:26:36,960 Speaker 2: Well, first, we need to change the narrative. This is 450 00:26:37,040 --> 00:26:39,480 Speaker 2: part of it. We need to stay away from blind 451 00:26:39,560 --> 00:26:41,919 Speaker 2: techno optimism. We need to stay away from a focus 452 00:26:41,920 --> 00:26:44,560 Speaker 2: on killer robots. Great in Hollywood movies, but that's not 453 00:26:44,600 --> 00:26:47,639 Speaker 2: what we should be worried about. But we should be 454 00:26:47,640 --> 00:26:50,960 Speaker 2: concerned about the direction of technology, and we need to 455 00:26:52,200 --> 00:26:56,280 Speaker 2: center the discussion on how we can use these technologies 456 00:26:56,280 --> 00:27:00,280 Speaker 2: better for democracy, better for workers, better for inequality. Then 457 00:27:00,320 --> 00:27:03,600 Speaker 2: we need to start building institutions. This is not going 458 00:27:03,640 --> 00:27:06,520 Speaker 2: to happen automatically, which means that we need countervailing powers. 459 00:27:06,640 --> 00:27:09,159 Speaker 2: How do we have a better regulatory system? We have 460 00:27:09,480 --> 00:27:12,840 Speaker 2: lost the regulatory muscle in the West. We used to 461 00:27:12,960 --> 00:27:15,320 Speaker 2: regulate public utility as well We used to be able 462 00:27:15,359 --> 00:27:18,840 Speaker 2: to regulate banking and financial services. Those have become harder, 463 00:27:18,880 --> 00:27:21,840 Speaker 2: and we have not even tried to regulate digital technology. 464 00:27:22,840 --> 00:27:26,000 Speaker 2: We need to build better democracy. Democracy has been in decline. 465 00:27:26,640 --> 00:27:29,199 Speaker 2: Labor movement we need some sort of labor voice. The 466 00:27:29,240 --> 00:27:31,560 Speaker 2: old model of trade unions is probably not the one 467 00:27:31,600 --> 00:27:34,399 Speaker 2: for future. How do we build an organic labor movement? 468 00:27:34,440 --> 00:27:37,440 Speaker 2: And then we need to talk about specific policies. Are 469 00:27:37,440 --> 00:27:41,679 Speaker 2: we using the right tax tools? Are we creating the 470 00:27:41,760 --> 00:27:46,560 Speaker 2: right support for private RND backed up by public RND 471 00:27:46,640 --> 00:27:50,600 Speaker 2: for the right direction of technology? Is the current business 472 00:27:50,640 --> 00:27:53,840 Speaker 2: model of the tech world, for example, centered on data 473 00:27:53,880 --> 00:27:56,760 Speaker 2: collection and individual as digital ads? Is that the right one? 474 00:27:56,840 --> 00:28:01,320 Speaker 2: Or should we actually tax digital ads? Are needs like Google, Microsoft, 475 00:28:01,320 --> 00:28:03,600 Speaker 2: Facebook too big? Should we think of breaking them up? 476 00:28:04,320 --> 00:28:06,960 Speaker 2: There are many policy leavers that we should be talking about. 477 00:28:07,359 --> 00:28:10,159 Speaker 2: Don't claim I have the answers on those, but we 478 00:28:10,240 --> 00:28:13,480 Speaker 2: make some suggestions in the book. But our purpose is 479 00:28:13,520 --> 00:28:15,479 Speaker 2: not to say we know these policies will work. We 480 00:28:15,520 --> 00:28:20,360 Speaker 2: need a ensemble of policies and it needs to be 481 00:28:20,600 --> 00:28:25,200 Speaker 2: the result of a democratic process and expertise that brings 482 00:28:25,280 --> 00:28:26,640 Speaker 2: us to the right solutions. 483 00:28:26,760 --> 00:28:30,520 Speaker 1: Are there any reasons to be hopeful looking around that 484 00:28:31,240 --> 00:28:35,520 Speaker 1: the government or broader society will be able to grapple 485 00:28:35,600 --> 00:28:37,160 Speaker 1: with the kind of things you just talked about. I mean, 486 00:28:37,160 --> 00:28:40,360 Speaker 1: we currently have in the US a Biden administration, which 487 00:28:40,400 --> 00:28:44,800 Speaker 1: is by recent standards pretty activist in these directions, talks 488 00:28:44,800 --> 00:28:47,960 Speaker 1: about reducing the monopoly control of the big tech companies. 489 00:28:48,360 --> 00:28:51,400 Speaker 1: It's quite pro worker in the way that it's designed 490 00:28:51,440 --> 00:28:55,239 Speaker 1: a lot of these big investments in it and in 491 00:28:56,120 --> 00:29:00,760 Speaker 1: green technology, and yet it's realistically going to achieve a 492 00:29:00,880 --> 00:29:02,440 Speaker 1: fraction of what. 493 00:29:02,320 --> 00:29:05,160 Speaker 2: You've just taught me. I think the Biden administration has 494 00:29:05,160 --> 00:29:06,840 Speaker 2: done very well, and indeed, as you said, it's the 495 00:29:06,840 --> 00:29:09,640 Speaker 2: more most pro worker government the United States has had 496 00:29:09,680 --> 00:29:14,560 Speaker 2: since FDR probably, and I upload them on passing two 497 00:29:17,040 --> 00:29:19,640 Speaker 2: major policies that many people would have thought would have 498 00:29:19,680 --> 00:29:24,080 Speaker 2: been impossible, the Chips Act and the IRA. But despite 499 00:29:24,120 --> 00:29:28,800 Speaker 2: those high ambitions, I think they're not sufficiently focused on 500 00:29:28,880 --> 00:29:33,040 Speaker 2: the direction of technology and creating the right technological environment 501 00:29:34,560 --> 00:29:37,880 Speaker 2: for generating jobs for all kinds of skills. So, yes, 502 00:29:37,920 --> 00:29:41,560 Speaker 2: there are many reasons to be concerned. There's only one 503 00:29:41,720 --> 00:29:47,120 Speaker 2: reason for a very cautious optimism. I believe in the 504 00:29:47,280 --> 00:29:51,800 Speaker 2: unique skills of humans and That's why I think automating 505 00:29:51,840 --> 00:29:53,920 Speaker 2: work and surveillance are not the right direction. I think 506 00:29:53,960 --> 00:29:58,160 Speaker 2: there is a diverse set of capabilities that humans have 507 00:29:58,400 --> 00:30:03,120 Speaker 2: that can be very well utiliz in a new work environment. 508 00:30:04,280 --> 00:30:08,760 Speaker 2: And I also believe that human ingenuity can be the 509 00:30:08,800 --> 00:30:14,160 Speaker 2: best way of furthering our productivity growth. So I hope 510 00:30:14,600 --> 00:30:16,640 Speaker 2: that those great opportunities are used. 511 00:30:18,200 --> 00:30:23,040 Speaker 1: Final question. You're an economic historian looking back in that 512 00:30:23,200 --> 00:30:25,520 Speaker 1: history that you describe with Simon Johnson in the book. 513 00:30:26,240 --> 00:30:30,880 Speaker 1: One thing that is clear is although the right kind 514 00:30:30,920 --> 00:30:34,920 Speaker 1: of institutions to make this technology work for people did appear, 515 00:30:35,680 --> 00:30:39,520 Speaker 1: they came much more slowly than the technology itself, and 516 00:30:39,560 --> 00:30:43,080 Speaker 1: that technology was not coming nearly as fast as what 517 00:30:43,120 --> 00:30:45,760 Speaker 1: we're seeing now. When you look realistically at the history, 518 00:30:47,000 --> 00:30:50,080 Speaker 1: does it not take an enormous amount of negative impact 519 00:30:50,960 --> 00:30:56,280 Speaker 1: for there to be a response from society to make 520 00:30:56,360 --> 00:30:58,160 Speaker 1: this work better? Aren't we going to have to live 521 00:30:58,240 --> 00:31:00,160 Speaker 1: through quite a lot of bad things? 522 00:31:00,280 --> 00:31:04,200 Speaker 2: Great question, and you know that's something that worries me. 523 00:31:04,400 --> 00:31:06,760 Speaker 2: We don't actually discuss in the book, because it has 524 00:31:07,240 --> 00:31:13,640 Speaker 2: gelled in my mind more recently. Early industrial revolution created 525 00:31:13,640 --> 00:31:16,400 Speaker 2: a lot of misery, as we talked about, and there 526 00:31:16,480 --> 00:31:23,440 Speaker 2: was nothing to be bilittled about that. But when reforms 527 00:31:23,480 --> 00:31:27,400 Speaker 2: and policy responses and the labor movement's reactions came, it 528 00:31:27,440 --> 00:31:30,880 Speaker 2: wasn't too late and things could be reorganized. Things are 529 00:31:30,880 --> 00:31:34,520 Speaker 2: happening much faster today. Are we going to be too late, 530 00:31:34,880 --> 00:31:38,560 Speaker 2: if not today, in the next month or next year. 531 00:31:38,600 --> 00:31:40,400 Speaker 2: I don't know the answer to that, but I was 532 00:31:40,440 --> 00:31:43,080 Speaker 2: worried enough that I was one of the early signatories 533 00:31:43,400 --> 00:31:45,800 Speaker 2: for the letter that asked for a six month pose 534 00:31:45,920 --> 00:31:48,920 Speaker 2: on the training of large language models. Not because I 535 00:31:48,960 --> 00:31:51,680 Speaker 2: agreed with the text. There was a lot of stuff 536 00:31:51,720 --> 00:31:55,840 Speaker 2: that are about super intelligent AI that I definitely don't 537 00:31:55,880 --> 00:31:58,920 Speaker 2: worry about. That's not the top of my agenda. But 538 00:31:59,040 --> 00:32:01,840 Speaker 2: I thought that building a broad coalition of academic and 539 00:32:02,120 --> 00:32:09,040 Speaker 2: entrepreneurial voices to say, let's just take some time. The 540 00:32:09,120 --> 00:32:12,280 Speaker 2: loss to humanity if we are six months late in 541 00:32:12,440 --> 00:32:16,960 Speaker 2: implementing some AI technology is trivial. The damage we can 542 00:32:17,040 --> 00:32:25,880 Speaker 2: do by irreversibly destroying democracy or cementing an approach that's 543 00:32:25,920 --> 00:32:28,400 Speaker 2: not the right one could be much much larger. So 544 00:32:28,560 --> 00:32:30,760 Speaker 2: take some time. There's no rush here. 545 00:32:31,480 --> 00:32:33,160 Speaker 1: But I guess the other lesson of history is there 546 00:32:33,200 --> 00:32:34,920 Speaker 1: are some things that are unstoppable. 547 00:32:35,760 --> 00:32:44,680 Speaker 2: I don't think technology's direction is preordained, and sure technology 548 00:32:44,720 --> 00:32:48,480 Speaker 2: should not be stopped, and in some sense advances are unstoppable. 549 00:32:48,680 --> 00:32:51,680 Speaker 2: But we can choose its direction, and doing so deliberatively 550 00:32:52,080 --> 00:32:54,160 Speaker 2: building the right institutions is feasible. 551 00:32:54,600 --> 00:32:56,080 Speaker 1: Darness Mark, thank you so much. 552 00:32:56,120 --> 00:32:57,840 Speaker 2: Thank you, it was a true pleasure to be here. 553 00:33:05,040 --> 00:33:07,680 Speaker 1: That's it for this special episode of Stephanomics. We'll be 554 00:33:07,720 --> 00:33:10,520 Speaker 1: back next week. In the meantime, you can, as always, 555 00:33:10,520 --> 00:33:13,000 Speaker 1: get a lot more economic insight and news from the 556 00:33:13,040 --> 00:33:18,000 Speaker 1: Bloomberg Terminal website or app. This episode was produced by Samasadi, 557 00:33:18,120 --> 00:33:21,240 Speaker 1: with special thanks to darn Asmo Blue and Ruth Kick. 558 00:33:21,920 --> 00:33:24,560 Speaker 1: The executive producer of Stephanomics is Molly Smith and the 559 00:33:24,560 --> 00:33:26,880 Speaker 1: head of Bloomberg Podcast is Sage Bowman.