WEBVTT - How AI Could Reduce Inequality 0:00:15.356 --> 0:00:32.556 Pushkin. I'm Jacob Goldstein. This is what's your problem? And 0:00:32.596 --> 0:00:36.476 my guest today is David Otter. David is a labor 0:00:36.516 --> 0:00:40.596 economist at MIT, and among other things, he's done really 0:00:40.596 --> 0:00:44.916 influential work on how new technology affected American workers over 0:00:44.956 --> 0:00:48.436 the past several decades. In particular, his work makes a 0:00:48.476 --> 0:00:53.196 compelling case that computers contributed to rising inequality in the 0:00:53.276 --> 0:00:58.196 late twentieth and early twenty first centuries. So I was interested, 0:00:58.436 --> 0:01:01.396 and I was pleasantly surprised when I recently read some 0:01:01.556 --> 0:01:05.796 new work of his that suggested that AI might actually 0:01:05.916 --> 0:01:11.356 drive inequality down. It's a nuanced story and it's a 0:01:11.356 --> 0:01:14.476 really interesting case that he makes, And of course we 0:01:14.516 --> 0:01:17.996 don't actually know what's going to happen, but whatever happens, 0:01:18.476 --> 0:01:21.996 David Otter's framework is a really useful way for thinking 0:01:21.996 --> 0:01:25.996 about the way technology affects work, affects who does what, 0:01:26.276 --> 0:01:29.636 who makes how much money? So to start, I asked 0:01:29.676 --> 0:01:33.036 him to talk about what happened when computers started to 0:01:33.076 --> 0:01:37.836 become common in offices back in the nineteen eighties, so you. 0:01:37.756 --> 0:01:42.196 Know, information technology, The computer was different from many other 0:01:42.236 --> 0:01:46.876 technologies that preceded, and what made computers different traditional computers 0:01:46.916 --> 0:01:51.716 like PREAI computers, is that they followed rules and they 0:01:51.796 --> 0:01:56.516 could you could write a program to accomplish a task 0:01:56.516 --> 0:02:00.236 that you would previously have thought of as a cognitive procedure. 0:02:00.636 --> 0:02:00.836 Right. 0:02:00.876 --> 0:02:05.276 It required all these ifens, all these steps gathering information, 0:02:06.636 --> 0:02:09.756 doing calculation and making things that we thought of were 0:02:09.996 --> 0:02:12.956 you know, exclusively in the minds dominion, right, And so 0:02:13.116 --> 0:02:17.276 that that codification might took out a lot of the 0:02:17.356 --> 0:02:20.516 routine tasks in the work that people did. How that 0:02:20.556 --> 0:02:25.316 affected work was really quite nuanced, right, So people who 0:02:25.316 --> 0:02:28.876 did bookkeeping and accounting, right, they spent much much less 0:02:28.876 --> 0:02:33.756 of their time on data you know, tabulation, data calculation, 0:02:33.796 --> 0:02:37.476 and much more on scenario planning right on forecasting, on 0:02:37.716 --> 0:02:40.556 you know, forensics, trying to you know, find errors and 0:02:40.636 --> 0:02:44.156 so among technical, professional managerial work, it made work more 0:02:44.196 --> 0:02:46.836 productive and it made more specialized. It eliminated a lot 0:02:46.836 --> 0:02:49.756 of the grunt work and enabled people to focus on 0:02:50.156 --> 0:02:53.476 the abstract problems. But for the other group of people 0:02:53.516 --> 0:02:56.276 whose work was simplified, whether it was in the kind 0:02:56.316 --> 0:02:59.476 of you know, uh, stock and inventory clerks, whether it's 0:02:59.476 --> 0:03:01.596 people who were doing who were working as cashiers who 0:03:01.676 --> 0:03:04.116 used to have to know, you know, how to be 0:03:04.116 --> 0:03:06.836 able to make change and you know, keep traffing numbers on. 0:03:07.236 --> 0:03:10.116 It made the work simpler and less specialized. 0:03:10.636 --> 0:03:13.316 And in the case of production work, and you know, 0:03:13.396 --> 0:03:16.156 it eliminated certain types of production jobs, certain types office 0:03:16.196 --> 0:03:19.436 jobs because they could be more or less fully automated. 0:03:19.076 --> 0:03:23.516 And that had the effect of taking people with you know, 0:03:23.596 --> 0:03:26.596 high school degrees and some college and making their skills 0:03:26.676 --> 0:03:29.716 less valuable in offices, less valuable in you know, in 0:03:29.756 --> 0:03:33.556 factories and assembly lines. And so it shifted the weight 0:03:33.636 --> 0:03:37.196 of employment for people without college degrees away from offices, 0:03:37.236 --> 0:03:44.556 away from factories, and towards a lot of services food service, cleaning, security, entertainment, recreation, 0:03:44.636 --> 0:03:48.236 home health aids. And that's socially valuable work, but it's 0:03:48.236 --> 0:03:50.836 poorly paid. And there's a number of reasons why it's 0:03:50.836 --> 0:03:53.636 poorly paid on oversimplified, but one of them is it's 0:03:53.676 --> 0:03:58.276 not specialized. Right, people can do it without training your certification. 0:03:59.156 --> 0:04:02.356 So one example that I find helpful in thinking about 0:04:03.036 --> 0:04:05.716 the kind of dynamics you're talking about is looking at 0:04:05.836 --> 0:04:09.116 accountants versus bookkeepers. Accountants focus more on the big pick 0:04:09.156 --> 0:04:12.036 sure typically have a college degree, often a special license. 0:04:12.276 --> 0:04:14.876 Bookkeepers focus more on the day to day look at 0:04:14.876 --> 0:04:16.756 you know, money coming in and out of a business. 0:04:16.796 --> 0:04:20.036 They don't typically have as much training as accountants, and 0:04:20.116 --> 0:04:23.676 so I mean, can you just talk about how computers 0:04:23.716 --> 0:04:26.156 affected accountants versus bookkeepers. 0:04:26.956 --> 0:04:33.156 So for accountants, it was the story of essentially eliminating 0:04:33.276 --> 0:04:37.156 the supporting work, the inexpert work, the drudge work, and 0:04:37.316 --> 0:04:39.956 allowing them to focus on the cognitively demanding stuff right, 0:04:40.436 --> 0:04:45.196 to do the problem solving, to do the forecasting, to 0:04:45.276 --> 0:04:48.036 do the scenario planning, to do the forensics of finding 0:04:48.156 --> 0:04:50.876 you know, errors and issues and so on. 0:04:51.156 --> 0:04:54.436 Accountants can be better accountants because of computers. They can 0:04:54.476 --> 0:04:57.636 do figure out more complicated ways to save people money 0:04:57.676 --> 0:05:00.796 on their taxes or to find errors or whatever. And 0:05:00.876 --> 0:05:05.716 so it's good for accountants yep, and other similar you 0:05:05.756 --> 0:05:08.596 know sort of professionals with a high amount of expertise. Right, 0:05:08.636 --> 0:05:10.596 and then the as a tool that's making them better 0:05:10.596 --> 0:05:13.156 at their jobs. What is it doing to the bookkeepers? 0:05:13.636 --> 0:05:16.236 So it's making your work like specialized less specialized, right, 0:05:16.676 --> 0:05:20.596 and so many more people can do it, and therefore 0:05:21.196 --> 0:05:23.556 it doesn't have to pay as well. It may grow 0:05:24.196 --> 0:05:26.356 right because firms like well, look it's cheaper than it 0:05:26.396 --> 0:05:27.796 used to be. It's more productive than it used to 0:05:27.836 --> 0:05:29.956 be and lots of people can do it h and 0:05:29.996 --> 0:05:32.676 we see this often, right, So like another nice another 0:05:32.716 --> 0:05:36.636 example this is ride hailing hailing software the you know 0:05:36.636 --> 0:05:38.356 the phone apps right, uber. 0:05:38.236 --> 0:05:39.876 Uber, uberlyft Right. 0:05:40.196 --> 0:05:42.556 They changed the work of taxi and chauffeur service in 0:05:42.596 --> 0:05:44.516 number of ways. They made it much easier to enterance on. 0:05:44.636 --> 0:05:47.116 But one thing they did is they entirely eliminated the 0:05:47.116 --> 0:05:50.436 expertise that was required to drive, because you used to 0:05:50.476 --> 0:05:51.556 have to know your way around. 0:05:51.356 --> 0:05:54.596 A place to drive, famously in London, Right, there's a 0:05:54.676 --> 0:05:57.676 test that you had to know every street in London 0:05:57.756 --> 0:05:59.516 or something to be a cap driver in to get it. 0:05:59.716 --> 0:06:02.556 Yeah, take about three years of memorization. It was an 0:06:02.596 --> 0:06:04.996 incredibly rigorous feed Yeah. 0:06:04.716 --> 0:06:06.236 And then you can now you can just get a 0:06:06.276 --> 0:06:09.596 map on your phone. Like now, technoledge is just profoundly obsolete. 0:06:09.716 --> 0:06:11.156 It's such a wild show, exactly. 0:06:11.196 --> 0:06:14.116 It's it's totally economically stranded, right. It's it's still out there, 0:06:14.116 --> 0:06:16.996 but it isn't useful. It has no scarcity value, right 0:06:17.276 --> 0:06:18.996 because everybody else gets it on their phone. 0:06:19.076 --> 0:06:23.436 So what did this do? It eliminated the. 0:06:23.556 --> 0:06:25.796 Expertise requirement to do this work, but it made the 0:06:25.796 --> 0:06:29.276 work very productive. Right, So we have you know, several 0:06:29.436 --> 0:06:32.796 hundred percent more people in this walk of life in 0:06:32.916 --> 0:06:35.756 driving show for service than we did twenty years ago, 0:06:36.996 --> 0:06:40.076 but their pay, the pay of a taxi driver's showpers 0:06:40.116 --> 0:06:41.676 has fallen relative to the average. 0:06:42.076 --> 0:06:42.356 Right. 0:06:42.436 --> 0:06:46.636 So this so this dynamic of simplifying work enabling more 0:06:46.636 --> 0:06:48.316 people to do it but at lower rates of pay. 0:06:48.356 --> 0:06:52.356 That's one of the kind of directions that automation can push, 0:06:52.396 --> 0:06:54.476 but it can push in the other just like those accountants. 0:06:54.916 --> 0:06:58.036 So you have this phrase, at least I associate the 0:06:58.076 --> 0:07:03.396 phrase with you that describes this phenomenon, the hollowing out 0:07:03.396 --> 0:07:07.396 of the middle. And it seems like this is roughly 0:07:07.636 --> 0:07:11.596 as a sort of crude sure, college degree not college degree, 0:07:11.636 --> 0:07:13.996 seems like a way to draw the line for who 0:07:14.396 --> 0:07:17.156 on average benefited from the arrival of computers and who 0:07:17.196 --> 0:07:19.036 on average was hurt by It is that about, right? 0:07:19.116 --> 0:07:19.676 Absolutely? 0:07:19.756 --> 0:07:21.796 I mean that has been over the last forty years 0:07:21.796 --> 0:07:23.636 has been the huge economic dividing line. 0:07:23.716 --> 0:07:23.836 Right. 0:07:23.876 --> 0:07:27.396 The earnings differential between college and on college you know, 0:07:28.036 --> 0:07:31.556 was always you know, substantial, and then around nineteen eighty 0:07:31.716 --> 0:07:34.236 started rising and rising, and it rose for thirty years 0:07:34.516 --> 0:07:36.596 and then has plateaued at that point. So now the 0:07:36.596 --> 0:07:39.396 college high school differential on average and is about seventy 0:07:39.396 --> 0:07:40.396 five percent. 0:07:40.676 --> 0:07:43.156 So people with college degrees make seventy five percent more 0:07:43.196 --> 0:07:44.276 for our people. 0:07:44.196 --> 0:07:47.476 On average, Yeah, on average, so huge, huge difference. 0:07:48.076 --> 0:07:51.196 So okay, now we have set the table for the 0:07:51.316 --> 0:07:54.236 arrival of AI. Right, this is this is a story, 0:07:54.276 --> 0:07:57.556 a true story of what's happened over the last several 0:07:57.596 --> 0:07:59.876 decades because of computers. Now we have this new kind 0:07:59.876 --> 0:08:05.916 of technology showing up at our doorstep. You've written a 0:08:05.916 --> 0:08:08.476 story about how AI might arrive quite differently in the 0:08:08.516 --> 0:08:13.996 labor force than computers did, right, with different distributional effects. 0:08:14.436 --> 0:08:15.196 Tell me about that. 0:08:15.796 --> 0:08:19.276 So over the last you know, really over the last 0:08:19.276 --> 0:08:25.716 four decades, we've become a society dominated by services. You know, 0:08:25.796 --> 0:08:28.916 we spend twenty percent of GDP on health care and education. 0:08:29.396 --> 0:08:31.796 It's like, you know, so back in the you know, 0:08:32.356 --> 0:08:34.516 nineteen forty, right, we spent a lot more money on 0:08:35.356 --> 0:08:38.236 you know, cars and appliances and food and clothing. 0:08:38.316 --> 0:08:40.716 Food. Food was a huge at the household budget in 0:08:40.796 --> 0:08:43.756 nineteen forty and now it's ten percent instead of thirty 0:08:43.796 --> 0:08:44.516 or forty or something. 0:08:44.556 --> 0:08:46.556 Right, But why so the mixed news in that is 0:08:47.116 --> 0:08:49.876 why do we spend less on those things? Partly because 0:08:49.916 --> 0:08:53.236 we've gotten really productive with them. They've gotten really cheap, right, 0:08:53.836 --> 0:08:58.676 all these services like healthcare, like education, like law, they've 0:08:58.756 --> 0:09:01.516 not We've not gotten more productive with them, right, we 0:09:01.676 --> 0:09:04.316 just have expensive people doing them. And those people have 0:09:04.356 --> 0:09:06.876 gotten more expensive as the services become more necessary. 0:09:07.036 --> 0:09:08.316 They've not gotten faster with them. 0:09:08.316 --> 0:09:11.076 And so this created it's a distribution income that very 0:09:11.156 --> 0:09:14.596 much favors the professional class and the guilds of people, 0:09:14.996 --> 0:09:18.276 be they lawyers, be they doctors, they be they professors, right, 0:09:18.916 --> 0:09:24.916 you know, engineers, architects, you know, contractors, And so that's 0:09:24.956 --> 0:09:27.436 not a great scenario for most people. You know, if 0:09:27.436 --> 0:09:29.516 you're a professional, you're on both sides that transaction. Like 0:09:29.996 --> 0:09:31.316 you know, yes, you have to pay a ton to 0:09:31.676 --> 0:09:33.156 a lawyer and to send your kid to college. But 0:09:33.196 --> 0:09:35.236 on the other hand, you're paid a lot to you know, 0:09:35.356 --> 0:09:37.436 do marketing, whatever it is that you do. But for 0:09:37.476 --> 0:09:39.836 most people, most people are not in that line of work, right. 0:09:39.996 --> 0:09:42.236 Only you know, forty percent of us workers have a 0:09:42.276 --> 0:09:44.956 college degree. Most of them are not highly paid professionals. 0:09:45.196 --> 0:09:47.476 So most people are only on the side of the 0:09:47.516 --> 0:09:48.996 transaction where they pay a lot. 0:09:48.796 --> 0:09:50.556 Of money for those services. 0:09:51.236 --> 0:09:56.316 So the potential for AI is to enable more people 0:09:56.396 --> 0:10:00.716 to do that expert work and bring down its price 0:10:01.436 --> 0:10:05.076 while increasing the set of jobs available to people. 0:10:05.396 --> 0:10:08.716 Right, So there's two sides of the coin you're sort 0:10:08.756 --> 0:10:10.676 of packing into that last half of the sentence. Right, 0:10:10.676 --> 0:10:14.076 So you're saying more people could do jobs that are 0:10:14.116 --> 0:10:16.636 now done by a small guild of expects. So that's 0:10:16.636 --> 0:10:19.476 good for the people doing the jobs. And also if 0:10:19.476 --> 0:10:22.156 that happened, it would bring down the price of those services, 0:10:22.196 --> 0:10:23.036 So that's good forever. 0:10:23.156 --> 0:10:24.036 That's good for customers. 0:10:24.196 --> 0:10:26.796 Like that sounds like a lot to ask for, Like 0:10:27.556 --> 0:10:29.516 what what is an example? 0:10:29.556 --> 0:10:29.716 Sure? 0:10:29.796 --> 0:10:30.596 Like what do you think? Sure? 0:10:30.716 --> 0:10:33.476 But let me also say, like, not everybody loves that story, right, 0:10:33.476 --> 0:10:34.676 Who doesn't love that story? 0:10:35.236 --> 0:10:37.036 The people at the top of that pyramid. 0:10:37.196 --> 0:10:38.996 Right, right, you know, the people in the guilds. That's 0:10:39.036 --> 0:10:39.876 why they're in a gilt. 0:10:39.956 --> 0:10:40.156 Right. 0:10:40.196 --> 0:10:42.876 But so let's do an example because it'll become much clearer. 0:10:43.036 --> 0:10:45.716 Like you you've written about doctors and nurse practitioners right 0:10:45.796 --> 0:10:47.036 in this context, So you. 0:10:46.956 --> 0:10:49.836 Know nurse practitioners, you know, they're registered nurses who have 0:10:49.836 --> 0:10:55.036 an additional master's degree, and that enables them to diagnose. 0:10:55.556 --> 0:10:56.756 Treat and prescribe. 0:10:57.436 --> 0:10:59.196 In many states they can debscribe, not all of them, 0:10:59.236 --> 0:11:02.116 it depends, you know, there's different rules, and they now 0:11:02.236 --> 0:11:05.396 that really encroaches on the territory that used to be 0:11:05.556 --> 0:11:11.156 wholly owned by physicians, right, and you know this, they 0:11:11.156 --> 0:11:15.796 have not been welcomed by the American Medical Association, not all. 0:11:15.796 --> 0:11:18.796 They fought very hard to limit their scope of practice. 0:11:19.116 --> 0:11:21.876 And in fact, you know, the neurors practitioners basically it's 0:11:21.916 --> 0:11:25.396 been a you know, they started sixty years ago, like 0:11:25.476 --> 0:11:28.436 trying to create a credential, a curriculum of scope of practice. 0:11:28.476 --> 0:11:30.476 They you know, fought their way and that it's only 0:11:30.516 --> 0:11:31.956 in the last twenty years they've grown, and they expected 0:11:31.956 --> 0:11:36.036 to grow a lot, but always facing great pushback from 0:11:36.036 --> 0:11:38.956 the medical establishment. Why because they're encroaching on their expert 0:11:39.076 --> 0:11:43.556 territory that's very threatened. So I think like the you know, 0:11:43.596 --> 0:11:45.996 the growth of mirrors practitioners has in many ways been 0:11:46.156 --> 0:11:48.236 good for the labor market and good for patients. 0:11:48.316 --> 0:11:49.836 Right, It's brought down costs. 0:11:49.516 --> 0:11:51.356 And is the evidence I haven't looked at the evidence 0:11:51.356 --> 0:11:52.236 on this in particular. 0:11:52.396 --> 0:11:56.076 The evidence is not there's there isn't enough evidence on this, Okay, 0:11:56.356 --> 0:11:58.076 So I mean I. 0:11:57.516 --> 0:11:59.716 If there isn't a clear evidence one way or the other, 0:11:59.756 --> 0:12:00.076 that's right. 0:12:00.596 --> 0:12:02.396 Okay, there's one paper I know, but it's in a 0:12:02.476 --> 0:12:05.876 very very limited scope, so I would know it's in 0:12:05.956 --> 0:12:08.996 the Veterans Administration. But I think like from a from 0:12:08.996 --> 0:12:11.996 a like, like I brought my kids to the pediatrician. 0:12:12.556 --> 0:12:14.356 And my began bringing my kids to pediatrician. 0:12:14.356 --> 0:12:16.116 There were no open nurse practitioners and I would wait 0:12:16.156 --> 0:12:18.636 hours to see the pediatrician. He was terrific, but then 0:12:18.676 --> 0:12:20.916 it was like a half day lost by the time 0:12:20.996 --> 0:12:23.436 my kids were ten, Right, I would just go into 0:12:23.436 --> 0:12:25.316 the p diatrican's office and boom, I would see a 0:12:25.356 --> 0:12:28.636 nurse practitioner and it was so great. And they were attentive, right, 0:12:28.836 --> 0:12:30.556 and they were knowledgeable, they're up to date, and they 0:12:30.556 --> 0:12:31.196 were accessible. 0:12:31.636 --> 0:12:31.876 Uh. 0:12:31.916 --> 0:12:33.996 And of course and that's a that's a high pay 0:12:34.076 --> 0:12:37.716 job and it's growing rapidly. So now that's not a 0:12:37.756 --> 0:12:39.916 function of AI clearly, right. 0:12:39.996 --> 0:12:41.796 So so okay, so now we have a we have 0:12:41.956 --> 0:12:45.236 this structure, bring AI into the story. Now, how does 0:12:45.276 --> 0:12:46.916 how does AI bear our story? 0:12:46.996 --> 0:12:53.076 So? AI gives people tools to accomplish complicated things that 0:12:53.196 --> 0:12:55.436 might be out of reach without those tools. But let 0:12:55.516 --> 0:12:57.156 me be clear, it doesn't mean anyone can do anything. 0:12:57.196 --> 0:12:58.996 Doesn't mean I can just go into met doctors often 0:12:59.036 --> 0:12:59.836 be a nurse practitioner. 0:12:59.956 --> 0:13:01.876 Right, you need to have domain knowledge. 0:13:01.956 --> 0:13:03.916 Right, I have to understand, like what do I do 0:13:03.916 --> 0:13:05.836 with someone starts bleeding or has a heart attack. 0:13:05.876 --> 0:13:06.196 I need to. 0:13:06.236 --> 0:13:08.036 Understand, you know, a ton of things. But given that, 0:13:08.916 --> 0:13:10.596 with better tools, I can do more. 0:13:11.116 --> 0:13:11.316 Right. 0:13:11.396 --> 0:13:16.196 So AI can help with diagnosis, right, it can help 0:13:16.476 --> 0:13:20.236 figure out how a medical practitioner should allocate their time 0:13:20.276 --> 0:13:23.916 and attention. Appointment with a nurse practitioner, even in a clinic, 0:13:23.916 --> 0:13:27.236 maybe twelve minutes, maybe twenty minutes, there's a ton that 0:13:27.276 --> 0:13:27.836 could happen. 0:13:28.156 --> 0:13:28.316 Right. 0:13:28.356 --> 0:13:31.556 Having a tool that says, hey, here's the medical record, right, 0:13:31.556 --> 0:13:33.756 this is what we saw last time. You know, here's 0:13:33.796 --> 0:13:36.556 your high priority item, and here's it you know, and 0:13:36.596 --> 0:13:37.836 here's the right resources. 0:13:37.956 --> 0:13:39.876 And then you, between the AI. 0:13:39.756 --> 0:13:41.676 And yourself, you can say, okay, now I can triage 0:13:41.676 --> 0:13:43.676 this and decide should I send this person home? 0:13:43.716 --> 0:13:46.036 Should I refer them upward? Can I care for them now? 0:13:46.236 --> 0:13:49.716 And so you are it's a force multiplier for your 0:13:49.756 --> 0:13:52.756 expertise right enables you to go further, and that would 0:13:52.796 --> 0:13:56.356 be true. You know, whether you're if you're doing legal work, 0:13:56.476 --> 0:13:59.516 if you're doing accounting work, if you're developing software. Software 0:13:59.596 --> 0:14:02.076 is an area where AI is going to allow lots. 0:14:01.796 --> 0:14:02.636 And lots of entry. 0:14:03.076 --> 0:14:06.916 A lot more people can do software using AI because 0:14:06.916 --> 0:14:10.556 they don't have to understand a much of the kind 0:14:10.556 --> 0:14:14.596 of engineering of coding. Now as you hear software is 0:14:14.596 --> 0:14:16.636 going to you knows exist at many levels. Right the 0:14:16.636 --> 0:14:20.916 people who are training AI models, running data centers, doing 0:14:21.076 --> 0:14:24.916 enterprise software, they will be very specialized, very highly paid. 0:14:25.556 --> 0:14:27.716 But there will be a lot more software that's like 0:14:28.396 --> 0:14:29.116 Uber drivers. 0:14:29.436 --> 0:14:29.596 Right. 0:14:29.956 --> 0:14:32.516 Oh, I want some to vibe code, you know, an 0:14:32.516 --> 0:14:34.276 app for my home to do X, Y and Z. 0:14:35.076 --> 0:14:37.556 I can easily find someone who can write that software 0:14:37.596 --> 0:14:39.636 for me because they'll just use an AI. I probably 0:14:39.636 --> 0:14:41.916 probably you won't do it yourself. Probably you'll still hire someone. 0:14:42.276 --> 0:14:45.316 I mean, that's not obvious to me. Why do you 0:14:45.356 --> 0:14:46.796 say probably you won't do it yourself. 0:14:47.996 --> 0:14:50.836 There's you know, for example, you can easily build a 0:14:50.836 --> 0:14:54.236 website right using wicx or squarespace things, But most people 0:14:54.236 --> 0:14:56.476 who have a business, they hire someone to build a 0:14:56.476 --> 0:14:59.476 website for them. Why, Well, One, there's a lot of 0:14:59.516 --> 0:15:01.876 design that goes into it. It's not just a matter 0:15:01.956 --> 0:15:05.396 of getting it running right. And two there's a lot 0:15:05.396 --> 0:15:07.876 of back end behind it in terms of oh it's 0:15:07.876 --> 0:15:10.116 going to take payments, Oh it's gonna you know. 0:15:10.556 --> 0:15:12.996 But these things seem aliable so that. 0:15:13.516 --> 0:15:15.716 You know it could change. But there are many many 0:15:15.756 --> 0:15:17.876 things that you could in theory do for yourself. 0:15:17.956 --> 0:15:20.156 I would not want to be in the website building 0:15:20.196 --> 0:15:23.996 business right now. I don't know, right, does that not 0:15:24.156 --> 0:15:26.436 like I'm not sure you can definitely do design and 0:15:26.476 --> 0:15:30.676 back end work with whatever claude We'll see. 0:15:31.316 --> 0:15:32.196 Yeah, we'll say. 0:15:32.036 --> 0:15:34.556 The likelihood is there will be more entry. It will 0:15:34.596 --> 0:15:36.276 come in at lower cost, but more people will be 0:15:36.316 --> 0:15:39.076 able to do it, and you know, just like the 0:15:39.156 --> 0:15:42.316 Uber example, right, So I think they will be more 0:15:42.356 --> 0:15:45.396 demand for software because it's cheaper. There'll be more people 0:15:45.396 --> 0:15:48.516 who can produce good software at lower cost. It will 0:15:48.556 --> 0:15:52.796 be less specialized, and therefore I suspect it it'll be 0:15:52.876 --> 0:15:55.236 numerically large, but it won't be as well paid as 0:15:55.276 --> 0:15:56.196 the typical software job. 0:15:57.996 --> 0:16:00.636 So you'll have more software engineers and the media software 0:16:00.636 --> 0:16:02.516 engineer will make less than they make today. 0:16:02.596 --> 0:16:05.396 Yeah, and they won't. They won't have a degree in engineering, right. 0:16:05.436 --> 0:16:07.436 Right, they'll just be somebody who's good a viating. 0:16:07.636 --> 0:16:09.916 I mean, like you know, website design now is primarily 0:16:09.916 --> 0:16:12.716 done by people who are designers, not programmers. 0:16:13.756 --> 0:16:17.276 Yes, but design is is this is something that AI 0:16:17.396 --> 0:16:18.356 is getting getting better. 0:16:18.396 --> 0:16:19.396 Absolutely, that's right. 0:16:20.156 --> 0:16:22.516 So let what are some other domains. I mean you 0:16:22.556 --> 0:16:23.116 mentioned the law. 0:16:23.236 --> 0:16:25.316 Law is a good example, but again laws guilt. 0:16:25.436 --> 0:16:27.716 I could see law being more hollowing out of the 0:16:27.716 --> 0:16:31.236 middle though, right, like more, you know, you don't need 0:16:31.276 --> 0:16:33.356 to hire the junior lawyer. You don't need to hire 0:16:33.836 --> 0:16:35.596 or at least as many junior lawyers. You don't need 0:16:35.636 --> 0:16:38.716 to hire as many paralegals. You can have one lawyer 0:16:38.796 --> 0:16:40.436 with an army of agents. 0:16:41.756 --> 0:16:46.556 Uh, it's possible. But I think in many cases you 0:16:47.756 --> 0:16:54.396 want a a knowledgeable, reliable, trustworthy person working with you. 0:16:55.036 --> 0:16:57.276 Yeah, you the you, the CLO, the person who knows 0:16:57.316 --> 0:16:58.756 a lawyer. But you would like them to. 0:16:58.716 --> 0:16:59.916 Be efficient and cheap. 0:17:00.036 --> 0:17:01.716 Yeah, right, so you'd like them to be able to 0:17:01.756 --> 0:17:03.756 write a contract for you in an hour rather than 0:17:03.796 --> 0:17:04.156 a day. 0:17:04.556 --> 0:17:07.276 Yeah. There are the sort of software first versions of 0:17:07.316 --> 0:17:12.596 this already. Right, there's legal Zoom, right has existed since before. 0:17:12.596 --> 0:17:15.676 It's right actually for kind of routine things like a 0:17:15.716 --> 0:17:16.596 will or whatever. 0:17:16.796 --> 0:17:20.396 I mean, So in many cases something is not legal 0:17:20.516 --> 0:17:22.276 unless it's done by a lawyer, Like if a lawyer 0:17:22.316 --> 0:17:23.676 doesn't sign it, it doesn't count. 0:17:24.236 --> 0:17:25.876 This is the guild part, right. 0:17:25.756 --> 0:17:26.116 That's right. 0:17:26.156 --> 0:17:28.356 But even so, you could say, well, look, you know 0:17:28.556 --> 0:17:31.516 more lawyers can offer some of these services at lower 0:17:31.516 --> 0:17:33.076 prices if they can do them effectively. 0:17:33.516 --> 0:17:37.076 Yeah, or you could have one lawyer with one hundred 0:17:38.436 --> 0:17:41.796 call them paralegals plus ais, and the lawyer is signing 0:17:41.836 --> 0:17:44.356 it and the paralegal is talking to the customer and marketing. Okay, 0:17:44.356 --> 0:17:44.756 But that. 0:17:44.716 --> 0:17:46.636 Even so, that would in many ways would be a 0:17:46.636 --> 0:17:49.996 better world. Right, legal services be cheaper, and there'd be 0:17:49.996 --> 0:17:53.996 this whole layer of people who are doing that intermediate role, right, 0:17:54.076 --> 0:17:56.636 as opposed to going to one very expensive person. And 0:17:56.916 --> 0:18:00.196 hopefully in the good scenario, there's a layer of people 0:18:00.236 --> 0:18:02.036 who are not just doing grunt work. They have some 0:18:02.036 --> 0:18:03.236 specialized knowledge, they're. 0:18:03.116 --> 0:18:05.316 Using the tools. Right often to use these tools, well, 0:18:05.396 --> 0:18:06.276 you have to know something. 0:18:07.116 --> 0:18:11.356 So this is an important point, right, because it's easy 0:18:11.436 --> 0:18:13.756 for me to imagine in the world where you actually 0:18:13.796 --> 0:18:16.356 don't have to know much to use the tool well, right, 0:18:16.396 --> 0:18:18.356 and that this sort of current moment we're in is 0:18:18.476 --> 0:18:22.556 just the you know, the foothills of real AI, and 0:18:22.636 --> 0:18:27.516 it'll look like some terrible scratchy TV broadcasts from ninety 0:18:27.556 --> 0:18:29.516 years ago in ten years, do you know what I mean? 0:18:30.436 --> 0:18:34.756 And so it seems totally likely that the AI could 0:18:34.756 --> 0:18:38.316 get good enough that you wouldn't need a paralegal like 0:18:38.636 --> 0:18:41.236 that the paralegal wouldn't add value because the AI would 0:18:41.316 --> 0:18:44.476 just figure out what you needed better than a paralegal would. 0:18:44.956 --> 0:18:47.836 Uh, I don't you know. 0:18:47.836 --> 0:18:50.956 I don't want to dismiss that possibility, but I do 0:18:51.036 --> 0:18:54.276 think in many cases, you're ultimately going to want another 0:18:54.356 --> 0:18:58.396 person involved in what you're doing, whether for its medical care, 0:18:58.796 --> 0:19:02.916 whether it's for law, whether design, and so you will 0:19:02.956 --> 0:19:07.116 want a person who has better tools and some specialized knowledge, 0:19:08.156 --> 0:19:10.316 but they will not be as expensive, they will be 0:19:10.356 --> 0:19:13.836 more productive. And let me say that, you know, and 0:19:13.876 --> 0:19:15.796 there's something else to bear in mind, right, many of 0:19:15.796 --> 0:19:20.636 these services that we're talking about about law, design, contracting, 0:19:20.996 --> 0:19:24.356 like you know, even like restaurants, right, they don't lend 0:19:24.396 --> 0:19:27.476 themselves to a superstar type model where one person does 0:19:27.516 --> 0:19:29.356 all the work, right, like in entertainment. Right, we have 0:19:29.396 --> 0:19:31.516 you know, Taylor swim Right, she can entertain two billion 0:19:31.516 --> 0:19:34.556 people at a time, and so it actually crowds out 0:19:34.556 --> 0:19:38.516 like there, it's hard you know, in movies, in music, right, 0:19:38.756 --> 0:19:42.276 the superstars crowd out everybody else. Right, And that didn't 0:19:42.316 --> 0:19:43.596 used to be true in a time when we didn't 0:19:43.596 --> 0:19:45.476 have broadcasts, we didn't have television. 0:19:45.596 --> 0:19:47.556 Right, there was an opera house in every face in 0:19:47.556 --> 0:19:47.956 your town. 0:19:48.076 --> 0:19:51.076 Right. Yeah, but this can't be true in medicine. It 0:19:51.076 --> 0:19:53.156 can't be true in law, It can't be true in restaurants. 0:19:53.156 --> 0:19:55.956 It can't be true in contracting, you know, in many 0:19:55.956 --> 0:20:00.516 of these services because they're congestible people, people have finite capacity. Right, 0:20:00.636 --> 0:20:03.516 there's no doctor who can serve two billion people simultaneously. 0:20:03.916 --> 0:20:08.476 Did you see the movie Her? You're describing the part 0:20:08.596 --> 0:20:12.636 right before big reveal in that movie. 0:20:11.796 --> 0:20:12.876 The I'm Meanna. 0:20:13.116 --> 0:20:14.796 There's a lot of hands on work, and there's a 0:20:14.836 --> 0:20:17.396 lot of a lot of communication. And I think, so 0:20:17.436 --> 0:20:19.916 someone's going to see the best doctor, someone's gonna see 0:20:19.916 --> 0:20:23.516 the median doctor, someone's gonna see the twenty fifth percentile doctor, right, 0:20:23.636 --> 0:20:27.636 and if those doctors have better tools and better you know, 0:20:27.876 --> 0:20:31.236 supports and by and decision supports they will be better doctors. 0:20:31.436 --> 0:20:33.196 Well should That part is the easy part, But I 0:20:33.196 --> 0:20:35.236 feel like that's neither here nor there with respect to 0:20:35.276 --> 0:20:36.756 what we're talking. No, no, no, that's. 0:20:36.796 --> 0:20:39.836 That's very critical because it means that you know, it 0:20:39.956 --> 0:20:43.476 means it just because some doctor has AI, right, that 0:20:43.596 --> 0:20:45.916 doctor does not crowd out all the other workers. 0:20:46.036 --> 0:20:48.436 Yeah, I mean doctor is like a weird case that 0:20:48.556 --> 0:20:51.876 is not like most things, right, Like doctor, like you 0:20:51.996 --> 0:20:53.916 care about having a person in the room at least 0:20:53.916 --> 0:20:55.716 for now when it's a doctor, more than you care 0:20:55.716 --> 0:20:59.676 about it with almost anything else, right Like like with 0:21:00.396 --> 0:21:02.996 most legal stuff. I mean, unless it was some huge thing, 0:21:03.796 --> 0:21:05.996 as long as you, like everybody use the AI and 0:21:06.036 --> 0:21:07.516 you know it was fine, you would just use the AI, 0:21:07.796 --> 0:21:10.596 do you know what I mean? Like for sure at 0:21:10.716 --> 0:21:13.116 least in a lot of cases. Yes, some people want 0:21:13.156 --> 0:21:13.916 a person, but. 0:21:13.876 --> 0:21:15.596 Do you know what I mean, I'm following what you're saying. 0:21:15.636 --> 0:21:18.556 Somebody who's a teenager now, like they don't want to 0:21:18.596 --> 0:21:21.196 talk to a person, Like they would rather not talk 0:21:21.196 --> 0:21:24.836 to a person if they could. I know teenagers, Yeah, 0:21:24.876 --> 0:21:25.996 I mean live in my house. 0:21:28.156 --> 0:21:31.916 Yeah, I think I think medicine is maybe is the 0:21:31.996 --> 0:21:32.996 strongest example. 0:21:33.116 --> 0:21:35.436 So, yeah, medicine is the strongest example. Yes, you want 0:21:35.476 --> 0:21:37.476 you want a person, you want a doctor or a nurse, 0:21:37.556 --> 0:21:39.436 and it's you're scared. 0:21:39.276 --> 0:21:41.636 And well, I mean, let's first let's remember that, you know, 0:21:41.676 --> 0:21:44.076 medicine is a huge part of employment. It's also about 0:21:44.116 --> 0:21:47.036 twenty percent of US employment healthcare, and it's growing. 0:21:47.716 --> 0:21:47.916 Right. 0:21:48.116 --> 0:21:51.316 Health is sort of the ultimate luxury good. Right, It's 0:21:51.356 --> 0:21:54.196 the one thing that as you get more and more affluent, 0:21:54.756 --> 0:21:57.236 you want to spend more and more on, right, because 0:21:57.276 --> 0:21:59.436 you know, you don't want more and more food you 0:21:59.436 --> 0:22:01.196 can have. You you know, some people can have one 0:22:01.236 --> 0:22:03.836 hundred cars, but most people are good with two. But 0:22:03.956 --> 0:22:07.436 your longevity and your well being, right, those are kind 0:22:07.436 --> 0:22:10.356 of fundamental to everything else that you do. And so 0:22:10.676 --> 0:22:12.476 and you look at all these billionaire bros right whore 0:22:12.516 --> 0:22:15.116 you know, they have their blood boys and there you know, 0:22:15.276 --> 0:22:17.436 and they're spending infinite you know why are they doing that? 0:22:17.556 --> 0:22:17.716 Right? 0:22:17.756 --> 0:22:20.556 Why is why is healthcare becoming a larger and larger 0:22:20.596 --> 0:22:22.356 part of expenditure over time? 0:22:22.636 --> 0:22:22.796 Right? 0:22:22.836 --> 0:22:25.956 Because we're getting more affluent h and so it matters 0:22:25.996 --> 0:22:28.316 more h And they're getting more effective. 0:22:29.036 --> 0:22:29.316 Right. 0:22:29.436 --> 0:22:31.036 You know, there was a one hundred years ago, it 0:22:31.076 --> 0:22:32.636 wasn't clear it was good for your health. Nego see 0:22:32.636 --> 0:22:33.756 a doctor, right, No. 0:22:33.756 --> 0:22:35.956 It quite possibly bad for you, exactly. 0:22:36.036 --> 0:22:38.476 Now there's just an amazing number of things and that 0:22:38.556 --> 0:22:42.356 number is expanded, so we will spend more on health services, 0:22:42.436 --> 0:22:44.196 it will be a larger part of employment. And of 0:22:44.236 --> 0:22:46.756 course we're aging, right, I mean, you know our population 0:22:46.996 --> 0:22:49.996 is getting older, so it's not at all crazy to 0:22:49.996 --> 0:22:52.916 think this will be remain And I don't care how 0:22:52.996 --> 0:22:55.476 much AI you throw in there. It's not going to 0:22:55.516 --> 0:22:58.516 shrink in terms of employment. It's going to be ill, 0:22:58.636 --> 0:23:01.796 provide more services, ideally at lower cost, and make it 0:23:01.836 --> 0:23:05.676 more accessible. It's not going to become small. So although 0:23:05.756 --> 0:23:10.276 it is a special case, it's not like a ledge cases. 0:23:10.356 --> 0:23:13.036 So it's a big special, big special special exactly. 0:23:17.156 --> 0:23:29.196 Will be back in just a minute. I want to 0:23:29.236 --> 0:23:32.036 go back to the macro idea. Right, So you have 0:23:32.116 --> 0:23:37.716 this argument, this story about how computers before AI essentially 0:23:37.756 --> 0:23:39.876 contributed to rising at a quality. Right, Like, if you 0:23:39.876 --> 0:23:42.036 were in the top whatever, twenty percent of the distribution, 0:23:42.436 --> 0:23:44.436 computers are good for you. If you were in the middle, 0:23:44.436 --> 0:23:45.076 they were bad for you. 0:23:45.116 --> 0:23:45.516 That's correct. 0:23:45.556 --> 0:23:51.476 In short, how what's the macro story for AI. 0:23:52.036 --> 0:23:56.516 So I don't want to oversimplify. I think it's quite nuanced. 0:23:56.676 --> 0:24:02.716 It's possible that it will enable war competition in these 0:24:02.836 --> 0:24:05.276 high end services that we can spend so much money on, 0:24:05.556 --> 0:24:09.196 and that will could contribute to lowering inequality somewhat and 0:24:09.276 --> 0:24:13.116 sort of growing back a different middle, right, middle set 0:24:13.196 --> 0:24:16.716 of software engineers, a middle set of kitchen designers and contractors, 0:24:17.116 --> 0:24:19.436 you know, middle set of very skilled repair people who 0:24:19.516 --> 0:24:22.796 you know, there's a ton of knowledge and expertise involved. 0:24:22.996 --> 0:24:25.716 It's not all you know comes from college degrees, right, 0:24:25.836 --> 0:24:27.676 people who are in the trades, who do electrical work, 0:24:27.676 --> 0:24:29.796 who do plumbing, who do you know construction? 0:24:30.076 --> 0:24:32.556 I mean people the trades have been doing pretty well, right, exactly. 0:24:32.596 --> 0:24:34.396 I avoided this hollowing out of the middle because a 0:24:34.436 --> 0:24:38.916 computer can't whatever, fix your fix the wiring in your house. 0:24:38.996 --> 0:24:41.156 Yeah, absolutely not. That's right, And I think AI can 0:24:41.156 --> 0:24:43.756 actually be helpful, and we already see examples of this, right, 0:24:43.796 --> 0:24:46.316 you know, you can solve a harder problem. So that 0:24:46.356 --> 0:24:49.116 would create a better a better middle, a different middle. 0:24:49.276 --> 0:24:52.396 It wouldn't be the same middle, and so it's somewhat 0:24:52.436 --> 0:24:53.316 expertise leveling. 0:24:53.396 --> 0:24:53.516 Right. 0:24:53.556 --> 0:24:57.716 It creates competition where we desperately need it and creates 0:24:57.716 --> 0:25:02.796 opportunity for people who have appropriate skills foundational expertise. Right, 0:25:02.796 --> 0:25:04.316 it's got to be got to know something about medicine. 0:25:04.316 --> 0:25:05.676 You gotta know something about the trades, You got to 0:25:05.676 --> 0:25:06.956 know something about the law, and you need to know 0:25:06.996 --> 0:25:07.836 something about software. 0:25:07.916 --> 0:25:11.116 But maybe not as much. Right. You could imagine we 0:25:11.196 --> 0:25:11.556 have a. 0:25:11.476 --> 0:25:14.276 Four year you know, healthcare degree, right where you learn 0:25:14.356 --> 0:25:15.996 a bunch of stuff and then you're ready to do 0:25:16.036 --> 0:25:17.796 a lot of services, a lot of health services. 0:25:18.836 --> 0:25:20.036 So that would be that would be. 0:25:20.036 --> 0:25:23.436 A world where there's more opportunity for middle skilled workers 0:25:23.436 --> 0:25:26.716 and lower prices, and so that would have less inequality. 0:25:27.596 --> 0:25:29.836 But I want to say that even if that's true 0:25:29.836 --> 0:25:32.156 my scenario and there's less, there's gonna be a ton 0:25:32.196 --> 0:25:35.436 of disruption, right, because we're talking about expertise and sometimes 0:25:35.476 --> 0:25:36.116 being vacated. 0:25:36.156 --> 0:25:36.276 Right. 0:25:36.316 --> 0:25:39.316 If you're a medical transcriptionist, that job is almost gone. 0:25:39.476 --> 0:25:39.676 Right. 0:25:39.716 --> 0:25:42.476 If you're a language translator, some will remain and they'll 0:25:42.476 --> 0:25:45.316 be highly highly skilled, right, but there'll be few of them. 0:25:45.476 --> 0:25:47.956 I'm very worried about people who work in call centers, right, 0:25:48.716 --> 0:25:52.996 you know, there's several million of them. It's very disruptive 0:25:54.196 --> 0:26:00.036 psychically damaging, economically scarring for people to have their expertise 0:26:00.676 --> 0:26:02.676 suddenly devalued. The thing that you know how to do. 0:26:02.916 --> 0:26:04.676 For most most people are doing the best paid thing 0:26:04.716 --> 0:26:07.556 they can do. Not everybody, right, some people are podcasting, 0:26:08.396 --> 0:26:13.236 but out our professors, right, many professors, yes, could you know. 0:26:13.236 --> 0:26:15.036 There are higher paying things they could do. They love 0:26:15.076 --> 0:26:15.396 what they do. 0:26:15.676 --> 0:26:18.636 So most people are doing you know, they don't have 0:26:18.636 --> 0:26:21.756 that luxury, right, They're doing the job that pays them 0:26:21.756 --> 0:26:23.836 the best income they can get. And if that work 0:26:23.876 --> 0:26:26.676 becomes less valuable, there's not an equally good job, right, 0:26:26.716 --> 0:26:28.476 They're gonna have to conness like WHI is loss of 0:26:28.516 --> 0:26:32.796 manufacturing work so devastating makings. It's often specialized work that 0:26:32.916 --> 0:26:35.916 you know is relatively highly paid, and the alternative work 0:26:36.316 --> 0:26:38.516 for someone with that education, you know, it's not going 0:26:38.596 --> 0:26:41.556 to be as good. So there's gonna be a lot 0:26:41.556 --> 0:26:44.876 of change, and I expect AI to devalue some forms 0:26:44.836 --> 0:26:48.276 of expertise really rapidly. Others will become more valuable, and 0:26:48.596 --> 0:26:50.756 it's not the same people who are on the upside 0:26:50.796 --> 0:26:52.836 and downsid You could say the average is great, but 0:26:52.916 --> 0:26:56.076 like no one experiences the average, And so even if 0:26:56.076 --> 0:26:57.716 you say, oh, yeah, that could be a better world. 0:26:57.916 --> 0:27:00.156 It's like, yeah, but we don't get to that world overnight, 0:27:00.836 --> 0:27:02.996 and there's a lot of change that happens along the way, 0:27:03.076 --> 0:27:05.196 and we should be investing and preparing for that. 0:27:05.756 --> 0:27:08.116 So let's talk about that, I mean, and that we 0:27:08.236 --> 0:27:11.076 can be like there's the policy WII and then there's 0:27:11.196 --> 0:27:14.356 like I'm some guy, what should I do? So, like 0:27:14.676 --> 0:27:16.436 what do we do on both of those levels? 0:27:16.596 --> 0:27:19.356 Let's start with the policy week? Yeah, okay, So there 0:27:19.356 --> 0:27:22.436 are three buckets of things that I think are very important. 0:27:22.676 --> 0:27:25.676 One of them is to help people transition between jobs. 0:27:25.676 --> 0:27:28.476 So the first of those is wage insurance. Wage insurance 0:27:28.596 --> 0:27:31.916 is like you're working a manufacturing job. You're making fifty 0:27:31.956 --> 0:27:34.476 thousand dollars a year, twenty five dollars an hour. You 0:27:34.516 --> 0:27:37.796 lose that job and you know manufacturing is going down. 0:27:37.996 --> 0:27:40.276 There's non an equivalent job, and the next job you 0:27:40.276 --> 0:27:42.236 can get is fifteen bucks an hour, thirty thousand dollars 0:27:42.236 --> 0:27:44.996 a year, and you say, I'm not gonna take that job. 0:27:45.076 --> 0:27:47.316 That's like beneath my digity, and so you. 0:27:47.276 --> 0:27:49.156 Don't, and then you spend a long time looking and 0:27:49.196 --> 0:27:51.036 that you know it has its own costs, and people 0:27:51.276 --> 0:27:53.356 the longer they're at a laborports, they're harder, they find Again, 0:27:53.436 --> 0:27:56.316 so wage Insurance says, hey, you're like, we understand that 0:27:56.716 --> 0:27:59.396 we're gonna make up half the difference between your twenty 0:27:59.396 --> 0:28:01.076 five dollars hour job and your fifteen dollar an hour 0:28:01.156 --> 0:28:02.996 job up to a finite amount of time, up to 0:28:03.036 --> 0:28:05.796 two years, up to eight thousand dollars total. And we're 0:28:05.836 --> 0:28:07.716 not telling you stay in a low wage job forever. 0:28:08.116 --> 0:28:10.316 By all means get a better job, and so. And 0:28:10.356 --> 0:28:12.396 we have evidence on it, not enough as much as 0:28:12.396 --> 0:28:15.396 we want. But during the twenty tens, right as part 0:28:15.396 --> 0:28:17.396 of the Trade Adjestment Act, there was an experiment done 0:28:17.756 --> 0:28:22.196 with wage insurance. The result of this policy experiment was 0:28:22.796 --> 0:28:24.476 it got people back to work faster, and it was 0:28:24.476 --> 0:28:26.436 so effective that it paid for itself, in other words, 0:28:26.436 --> 0:28:32.596 by the reductions in unemployment insurance payments and the increase 0:28:32.756 --> 0:28:36.596 in tax revenue from people working. It was self funding. Now, 0:28:36.836 --> 0:28:38.996 that's on a limited base of evidences. Were just placed 0:28:38.996 --> 0:28:42.596 manufacturing workers, they were older. But there are now major 0:28:44.196 --> 0:28:47.276 pilot studies underway working with states. I'm you know, more 0:28:47.276 --> 0:28:50.756 details will be revealed soon. They're quite expensive and they're 0:28:50.836 --> 0:28:54.476 you know, they're funded to try this at a larger scale, 0:28:55.076 --> 0:28:55.996 so wage insurance. 0:28:56.036 --> 0:28:57.756 This is one idea. 0:28:58.316 --> 0:29:00.036 And I'll call it's a no regrets idea in the 0:29:00.076 --> 0:29:02.996 sense that we won't be sorry we did it even 0:29:03.036 --> 0:29:04.636 if there isn't a jobs apocalypse. 0:29:04.836 --> 0:29:06.996 Right, It's a good idea. It was a good idea 0:29:07.076 --> 0:29:10.076 twenty years ago. It's a good idea now. It'll still 0:29:10.076 --> 0:29:10.916 be a good idea. 0:29:10.956 --> 0:29:14.316 And the idea is people are making transitions, and those 0:29:14.316 --> 0:29:17.756 transitions are frictional, they're costly, it's psychically damaging, and when 0:29:17.756 --> 0:29:19.356 people are out of work for a long time that 0:29:19.436 --> 0:29:21.996 has its own costs. You would like to facilitate them 0:29:21.996 --> 0:29:24.036 going back to work, even while they continue to search. 0:29:25.276 --> 0:29:31.036 And like, I feel like subsidizing work, essentially paying people 0:29:31.076 --> 0:29:33.796 to work it politically it feels good. It does, and 0:29:33.796 --> 0:29:36.516 frankly for the people like that. I mean, work is 0:29:36.596 --> 0:29:39.116 valuable in our society, and like, you can feel different 0:29:39.116 --> 0:29:41.836 ways about that, but I basically feel good about that, 0:29:41.916 --> 0:29:45.556 and like subsidizing people that work seems good to me, frankly. 0:29:45.236 --> 0:29:47.516 And I think that fits with the American psyche right. 0:29:47.636 --> 0:29:51.676 Our political system is very hostile towards people who are 0:29:51.676 --> 0:29:53.956 not working. Right, If you're not working and you're not 0:29:54.036 --> 0:29:56.236 a kid, and you're not elderly, what. 0:29:56.276 --> 0:29:58.756 Is wrong with you? Right, is what is the kind 0:29:58.796 --> 0:29:59.156 of message? 0:29:59.196 --> 0:30:01.836 That's why you're constantly we're always putting these work requirements. 0:30:02.076 --> 0:30:04.156 And I mean you're not making a normative statement. Just 0:30:04.196 --> 0:30:06.716 to be clear, you're describing the sort of political vibes. 0:30:06.796 --> 0:30:08.476 Yeah, no, no, clearly, No, no, I do not. I'm not 0:30:08.516 --> 0:30:09.756 I'm not endorsed that view at all. 0:30:09.796 --> 0:30:11.516 I'm just saying that that appears to be the perspective 0:30:11.556 --> 0:30:13.716 of the politicians, right, That's why they said, oh, you can't 0:30:13.716 --> 0:30:16.836 get you know, snap, unless you're working, you can't get medical. 0:30:16.956 --> 0:30:19.476 You know, they're constantly trying to condition things on work. 0:30:19.716 --> 0:30:24.076 So this says it says, okay, like this is about supporting. 0:30:23.636 --> 0:30:26.196 People to work. Okay, that's one thing one okay. 0:30:26.276 --> 0:30:26.516 Two. 0:30:26.636 --> 0:30:31.916 Two is we need to do retraining at scale much 0:30:31.916 --> 0:30:32.596 more effectively. 0:30:32.796 --> 0:30:35.076 Yeah, retraining seems like it never worked. 0:30:35.076 --> 0:30:38.156 Okay, See that's what everybody says. But in fact that's 0:30:38.236 --> 0:30:39.556 kind of based on old wisdom. 0:30:39.916 --> 0:30:41.236 We now know much. 0:30:41.076 --> 0:30:43.516 More about ai really good at retraining. 0:30:43.596 --> 0:30:45.916 Oh no, we don't know that, but we do know 0:30:45.956 --> 0:30:49.196 of really effective training programs. Now, they're small. 0:30:49.436 --> 0:30:51.036 So like Project. 0:30:50.716 --> 0:30:55.716 Quest you're a Jewish vocational services goodwill there what are 0:30:55.716 --> 0:30:58.996 called sexual training programs. They train people for specific occupations 0:30:58.996 --> 0:31:02.196 and activities. Right, so construction, it could be a medical technical, 0:31:02.236 --> 0:31:05.316 it could be diesel repair, et cetera. But we don't 0:31:05.316 --> 0:31:07.156 know how to scale those at a very large scale. 0:31:07.196 --> 0:31:09.476 So what folks are working on now is trying to 0:31:09.476 --> 0:31:13.676 evaluate those things in real time using big data without experiments, 0:31:13.676 --> 0:31:16.276 and so you can actually measure the incremental earnings that 0:31:16.276 --> 0:31:18.436 are coming out of these programs and then recycle some 0:31:18.476 --> 0:31:21.676 of that incremental revenue to feedback into these programs. 0:31:22.036 --> 0:31:22.836 And we because we. 0:31:22.876 --> 0:31:26.596 Need to have a phollanx of options available to you. 0:31:26.756 --> 0:31:32.036 Yeah, right, I mean that one. Maybe I just am 0:31:32.116 --> 0:31:33.876 out of date on it, but you know, like your 0:31:33.876 --> 0:31:37.476 own work on the effect of competition from China on 0:31:37.716 --> 0:31:39.756 you know, workers in the US in the first part 0:31:39.796 --> 0:31:41.956 of the century, Like, it's interesting to me that you 0:31:42.076 --> 0:31:44.876 found more recently that like the towns that were really 0:31:44.956 --> 0:31:47.196 impacted by that, the towns have come back, right to 0:31:47.236 --> 0:31:50.196 some extent, right, but the specific workers who were displaced 0:31:50.196 --> 0:31:52.356 have not, but that we did. 0:31:52.196 --> 0:31:54.476 Nothing to retrain them, right, I mean there was no 0:31:54.556 --> 0:31:56.436 policy in place, Right. 0:31:56.476 --> 0:31:58.676 So you think it's a policy. You think you think 0:31:58.676 --> 0:32:00.836 a policy could have helped the person who was somewhat 0:32:00.876 --> 0:32:03.756 fifty to my approximate age, someone got laid off. 0:32:03.836 --> 0:32:06.036 Yeah, okay, yeah, I mean it's not a panacea, but 0:32:06.076 --> 0:32:08.436 it's something. And you know, other countries do this much 0:32:08.436 --> 0:32:11.036 more effectively than we do, right, you know Denmark. 0:32:11.676 --> 0:32:13.476 Right, I was afraid you were going to say Denmark, 0:32:13.516 --> 0:32:14.436 it's always dead Mark. 0:32:14.596 --> 0:32:15.556 I know, I know everyone. 0:32:15.596 --> 0:32:17.516 It's like, you know, don't tell me about Scandinavia, like 0:32:17.556 --> 0:32:20.996 it's not a real places. 0:32:19.796 --> 0:32:20.996 Doesn't map to the US. 0:32:21.076 --> 0:32:23.836 Right, Well, I agree, so city, I think that's absolutely right. 0:32:23.836 --> 0:32:28.196 We can't do the Danish system, but it's an existence 0:32:28.196 --> 0:32:30.636 proof of you can do it. Well, right, they're affected, 0:32:30.676 --> 0:32:32.956 they can they can do it, Yell, it can be right. 0:32:32.996 --> 0:32:35.316 So that's why my main, second major policy is to 0:32:35.356 --> 0:32:38.676 send all unemployed workers to Denmark where they can be reattrained. 0:32:38.716 --> 0:32:40.516 I just think that's going to have an incredible safety 0:32:40.596 --> 0:32:42.796 net and swim in the harbor and co. Exactly, it's 0:32:42.796 --> 0:32:44.516 gonna what's number three? 0:32:44.716 --> 0:32:44.956 Okay? 0:32:45.036 --> 0:32:48.276 Number three is what I call universal basic capital. Other 0:32:48.316 --> 0:32:50.276 people call it that as well. So let me just 0:32:50.356 --> 0:32:53.196 same shate universal basic income. It's a notion that you know, 0:32:53.276 --> 0:32:54.876 whether you're working or not, we just write you check 0:32:54.876 --> 0:32:56.756 every month from the largest. 0:32:56.476 --> 0:32:57.796 Of the tech bros. Or whatever. 0:32:58.196 --> 0:33:00.396 Yeah, I don't think the political economy of that is 0:33:00.516 --> 0:33:02.956 viable for the same reason we just had the conversation 0:33:02.996 --> 0:33:05.356 about we don't like to as a country. We're just 0:33:05.396 --> 0:33:08.596 not supportive of people who are not working, or we 0:33:08.636 --> 0:33:12.196 don't believe even unconditional transfers. So universal basic capital is 0:33:12.196 --> 0:33:14.676 a little different idea. It says, look, at the time 0:33:14.716 --> 0:33:16.436 you were born, we're going to give you an endowment 0:33:16.476 --> 0:33:16.956 of capital. 0:33:16.996 --> 0:33:18.116 It doesn't have to be a lot, a couple of 0:33:18.156 --> 0:33:18.916 that you can call. 0:33:18.836 --> 0:33:21.236 Them because capital means stock stock and we just say stock, 0:33:21.316 --> 0:33:21.676 you know, we. 0:33:21.636 --> 0:33:25.436 Call them Trump accounts, right, which is what exists already, right, Yeah, 0:33:25.676 --> 0:33:28.996 And it's a it's an asset you can you know, 0:33:29.156 --> 0:33:31.716 it's you can't spend it until you're of age. 0:33:31.916 --> 0:33:34.076 You so like, just just to be a little more specific, 0:33:34.076 --> 0:33:38.036 the notion is like the government will buy whatever a fund, 0:33:38.076 --> 0:33:39.916 an ETF, a mutual fund that tracks the S and 0:33:39.956 --> 0:33:41.876 P five hundred. The ob a great way to give 0:33:42.156 --> 0:33:44.836 one thousand dollars to every baby basically, and you now 0:33:44.876 --> 0:33:46.796 own a thousand dollars of S and P five hundred 0:33:46.796 --> 0:33:48.476 stock and you can't touch it till you're eighteen. Is 0:33:48.516 --> 0:33:49.596 that what you're thinking? Exactly? 0:33:50.356 --> 0:33:52.556 And you can invest in it further, right, you just 0:33:52.556 --> 0:33:55.276 like you can you have tax advantage for one K savings, right, 0:33:55.316 --> 0:33:55.556 you have. 0:33:55.516 --> 0:33:57.316 To like a five to nine like a college savings 0:33:57.356 --> 0:33:58.316 stock or something exactly. 0:33:58.596 --> 0:34:00.956 So, like, what what is the purpose of this? It does? 0:34:01.156 --> 0:34:02.036 It does three things. 0:34:02.316 --> 0:34:06.276 One is it it diversifies people, right, So most people, 0:34:06.316 --> 0:34:08.236 you know, most people's lifetime income is bound up in 0:34:08.236 --> 0:34:11.156 the value of their skill and expertise. And that's it's 0:34:11.196 --> 0:34:12.796 it's always vulnerable, it's always risky. 0:34:12.996 --> 0:34:15.196 It's labor. Most most people make most of their money 0:34:15.236 --> 0:34:15.956 from working. 0:34:15.756 --> 0:34:18.276 Exactly from the right, you're saying, it's always that's it's 0:34:18.316 --> 0:34:20.676 always risky to have all your eggs in one basket, right, 0:34:21.036 --> 0:34:22.876 especially if you know you do something specialized and all 0:34:22.876 --> 0:34:24.276 of a sudden that thing becomes obsolete. 0:34:24.316 --> 0:34:25.116 That's really risky. 0:34:25.316 --> 0:34:27.396 So one, is it gives you It's not it's by 0:34:27.396 --> 0:34:30.556 no means a complete diveristication start, but it's a start, right, 0:34:30.596 --> 0:34:33.916 And especially that's one thing. Two if we think we're 0:34:33.996 --> 0:34:35.756 entering a world where capital is going to rise in 0:34:35.836 --> 0:34:38.556 value relative to labor, right, that will be less than 0:34:38.676 --> 0:34:41.556 you especially want to have people have money in capital, right. 0:34:41.756 --> 0:34:44.796 Yeah, meaning if if more of the national income is 0:34:44.836 --> 0:34:47.876 going to go to companies are the owners of shares 0:34:47.916 --> 0:34:50.196 of stocks, and less is going to go to labor, right, 0:34:50.196 --> 0:34:52.076 the two places that can go, then you want more 0:34:52.156 --> 0:34:55.316 people to own stock, if you want everybody to prosperate. 0:34:54.956 --> 0:34:56.076 It, exactly, that's right. 0:34:56.156 --> 0:34:59.276 And you know labor is you know, everyone is born 0:34:59.556 --> 0:35:02.556 in you know the good scenario with one worker themselves, right, 0:35:02.556 --> 0:35:03.276 who work for them. 0:35:04.116 --> 0:35:06.516 But capital is not like that, right. 0:35:06.476 --> 0:35:08.396 Right, some people are born with a lot and most 0:35:08.436 --> 0:35:08.916 people are. 0:35:08.796 --> 0:35:11.196 Born people born and zero that's right. And the third 0:35:11.196 --> 0:35:15.076 thing it does is universal based capital. Again, is it 0:35:15.396 --> 0:35:17.956 gives them an ownership stake, almost like a voting stake 0:35:18.436 --> 0:35:19.156 in the economy. 0:35:19.356 --> 0:35:21.916 Yes, well, I mean most most companies, you have a 0:35:22.116 --> 0:35:24.916 voting voting rights if you're on stock, although not all. 0:35:24.916 --> 0:35:27.716 Exactly, And so if you know, if you're I'm very 0:35:27.756 --> 0:35:31.356 worried about a society where more people are seen as 0:35:31.476 --> 0:35:33.516 claimants without being seen as contributors. 0:35:33.676 --> 0:35:36.716 So claimant rather than contributor. That's the universal basic income. 0:35:36.836 --> 0:35:39.436 That's the world where's the machines take our jobs and 0:35:39.476 --> 0:35:42.156 people just get a check every month or whatever. 0:35:42.476 --> 0:35:44.796 I don't morally oppose that, let me be clearer. I 0:35:44.836 --> 0:35:47.476 just don't think it's our I don't think our body 0:35:47.516 --> 0:35:49.156 politic tolerates it. 0:35:49.676 --> 0:35:52.996 Yes well, and are like our structures of meaning and 0:35:53.116 --> 0:35:55.036 of value are not well set up. 0:35:55.356 --> 0:35:57.516 That I also agree with. That's even secondary point. 0:35:58.276 --> 0:36:01.276 So that's so University of the Capital says, you know again, 0:36:01.276 --> 0:36:02.476 it's a very small step in the way. 0:36:02.476 --> 0:36:02.756 It says. 0:36:02.836 --> 0:36:05.436 Look everyone, now, hey, I'm not just a claimant. I'm 0:36:05.436 --> 0:36:07.036 a contributor. I own part of the capital. 0:36:07.516 --> 0:36:10.316 And in this universe, is the government just buying shares 0:36:10.516 --> 0:36:13.596 in the in stock and everything and giving it to people. 0:36:13.756 --> 0:36:15.236 Sure, but it's not that hard to do. 0:36:15.396 --> 0:36:17.756 Like, there's only about four million babies born a year 0:36:17.796 --> 0:36:19.956 in the United States, right, so if you had to 0:36:19.996 --> 0:36:21.596 write them each a check for a thousand dollars, that's 0:36:21.596 --> 0:36:24.116 four billion bucks, right, That's less than we spend on 0:36:24.276 --> 0:36:28.836 You know, I don't know anything you can anything, any 0:36:28.916 --> 0:36:32.316 bomb you can drop, so it's not. 0:36:32.636 --> 0:36:35.316 But I means, so one thousand dollars, I suppose compounding 0:36:35.356 --> 0:36:36.036 it becomes non. 0:36:35.996 --> 0:36:37.636 True, right, and you can do more than that. 0:36:37.756 --> 0:36:37.836 Right. 0:36:37.916 --> 0:36:39.436 You could even say to billionaire and say, hey, you 0:36:39.476 --> 0:36:41.236 know what, here's a tax advantage way that you could 0:36:41.436 --> 0:36:44.436 donate to the public. Take your shares and just throw 0:36:44.436 --> 0:36:46.236 them into the baby bond fund and they will be destribed. 0:36:46.316 --> 0:36:48.916 That's nice, right, Yeah, that's interesting. I mean it's sort 0:36:48.956 --> 0:36:51.796 of a distributed sovereign wealth fund. Right, it's sort of 0:36:51.876 --> 0:36:53.236 rhymes with a sovereign wealth fund. 0:36:53.356 --> 0:36:56.796 That's that's exactly exactly what it is. Exactly what it is, 0:36:56.876 --> 0:36:58.916 but a sovereign wealth fund. Right, you don't get to 0:36:58.956 --> 0:37:00.836 choose your own investments. You can't say I need to 0:37:00.836 --> 0:37:01.676 take it out today. 0:37:01.876 --> 0:37:04.716 No, it's not yours in the way, that's what you're proposing. 0:37:05.356 --> 0:37:08.076 So you know, and again, none of these policies, the 0:37:08.076 --> 0:37:12.036 three I'm talking about, you know, uh, wage insurance, you know, modernized. 0:37:11.516 --> 0:37:15.876 Training, they're small ish, right, They're they're small issues, that's right. 0:37:15.916 --> 0:37:17.236 None of them is that, you know, and I think 0:37:17.276 --> 0:37:19.956 we need big you know, we face big challenges. These 0:37:19.996 --> 0:37:22.556 are around the edges. But they're all what I call 0:37:22.676 --> 0:37:25.436 no regrets policies. Right, we won't be sorry we did them. 0:37:25.476 --> 0:37:26.596 Even if the worst doesn't. 0:37:26.356 --> 0:37:26.996 Come to pass. 0:37:27.556 --> 0:37:29.796 These are things that are just you know, they are 0:37:30.236 --> 0:37:33.836 wise in terms of helping people make transitions and giving 0:37:33.836 --> 0:37:37.436 people diversification. You know, in the good scenario, right, if AI, 0:37:37.756 --> 0:37:41.316 if we use it, well, we're going to become more affluent. Right, 0:37:41.356 --> 0:37:43.796 but that doesn't mean we're going to have that will 0:37:43.796 --> 0:37:44.516 be well shared. 0:37:44.756 --> 0:37:44.916 Right. 0:37:44.996 --> 0:37:48.036 We need a society which says, look, if there's gonna 0:37:48.036 --> 0:37:50.996 be an AI dividend, meaning we become more productive, we 0:37:51.076 --> 0:37:53.396 want to make sure that people are brought along, right, 0:37:53.476 --> 0:37:56.636 that if you if you're if your expertise becomes devalued, 0:37:56.876 --> 0:37:58.796 that there's new ways to train that get you back 0:37:58.836 --> 0:38:01.396 in the labor market, that your portfolio, your income sources 0:38:01.396 --> 0:38:04.956 are more diversified. You know, people you know, the science 0:38:04.956 --> 0:38:08.236 fiction writer Ted Shang, you know, said look, when you 0:38:08.236 --> 0:38:10.676 know people are saying, oh, you know, people are scared 0:38:10.676 --> 0:38:13.396 of automation. They're scared of machines. They're not scared of automations, 0:38:13.396 --> 0:38:17.316 they're scared of capitalism. Right, They're scared of their work 0:38:17.356 --> 0:38:19.676 will be automated and there'll be nothing left for them 0:38:19.956 --> 0:38:21.236 to get they won't get paid. 0:38:21.556 --> 0:38:21.756 Right. 0:38:21.996 --> 0:38:26.116 It's not that people are opposed to higher productivity or 0:38:26.156 --> 0:38:27.596 you know, things being more convenient. 0:38:27.756 --> 0:38:32.076 They're opposed to losing their livelihoods. Right, And AI could 0:38:32.436 --> 0:38:33.316 right produce. 0:38:32.996 --> 0:38:34.596 A lot of higher productivity and a lot of you know, 0:38:34.596 --> 0:38:36.596 convenience automation, but put a lot of people out of work, 0:38:36.596 --> 0:38:38.076 at least temporarily during the transition. 0:38:38.596 --> 0:38:41.676 Right. So, no wonder people are afraid of it. Right, 0:38:41.756 --> 0:38:43.916 No wonder people are opposed. No wonder people are angry 0:38:43.916 --> 0:38:46.596 about data centers. They don't trust our. 0:38:46.876 --> 0:38:49.956 Social and political systems to ensure them and make sure 0:38:49.956 --> 0:38:51.956 they're brought along. So it's very understandable. 0:38:52.596 --> 0:38:52.716 Right. 0:38:52.956 --> 0:38:54.796 So I would like to take the edge off of 0:38:54.796 --> 0:38:56.836 some of that, Right, I would like to have policies 0:38:57.156 --> 0:39:01.356 that help support the challenges you help people through those 0:39:01.436 --> 0:39:04.076 challenging things that are some of them are likely to 0:39:04.196 --> 0:39:06.236 ahead even in the good scenarios. 0:39:09.836 --> 0:39:12.116 We'll be back in a minute with the lightning ground. 0:39:23.796 --> 0:39:27.596 Let's finish with the lighting rounds. Okay, what's the most 0:39:27.636 --> 0:39:29.716 peril you've ever been in on a sailboat? 0:39:33.516 --> 0:39:33.876 Actually? 0:39:33.876 --> 0:39:36.996 I went, I went, I took my I went sailing 0:39:37.076 --> 0:39:37.916 during a hurricane. 0:39:39.836 --> 0:39:40.436 Bad idea. 0:39:40.516 --> 0:39:44.276 Yeah, and I got knocked over six times on purpose. Well, 0:39:44.316 --> 0:39:45.596 I know, I tried to stay upright. 0:39:46.516 --> 0:39:48.436 No, no, did you go sailing at a hurricane? 0:39:48.476 --> 0:39:51.916 I went sailing and Hurricane Lee a couple of years 0:39:51.916 --> 0:39:53.556 ago in New Hampshire. 0:39:53.036 --> 0:39:55.116 On a couple of years ago. I was gonna ask 0:39:55.156 --> 0:39:56.316 if you were fourteen. 0:39:55.956 --> 0:39:59.756 No, no, no, I was fifty nine. But I have 0:40:00.076 --> 0:40:03.596 an ultra light RSA. It's like a laser, and it's 0:40:03.876 --> 0:40:06.956 super fast and it's very competent even in high winds, 0:40:07.516 --> 0:40:08.916 and I really want to try it out. 0:40:08.916 --> 0:40:09.676 It was small lake. 0:40:10.276 --> 0:40:12.996 People were like my neighbors were all there with the cameras. 0:40:13.036 --> 0:40:16.196 I assume that if I started to drown, they would have, uh, 0:40:17.116 --> 0:40:20.396 I don't know, sent the footage to see exactly exactly. So, yeah, 0:40:20.436 --> 0:40:21.236 that was perilous. 0:40:21.436 --> 0:40:22.556 That was That was exciting. 0:40:23.596 --> 0:40:24.996 And did you capsize six times? 0:40:25.236 --> 0:40:25.676 Six times? 0:40:25.876 --> 0:40:26.356 Six times? 0:40:26.436 --> 0:40:28.076 Was exhausting. It was completely exhausting. 0:40:28.356 --> 0:40:29.956 It's like a little boat and you can kind of 0:40:30.036 --> 0:40:31.916 lean and ride it is now you have to climb. 0:40:31.716 --> 0:40:34.636 On the daggerboard and pull it up and then throw. 0:40:34.676 --> 0:40:36.276 You have to do what's called the walrusk, where you'd 0:40:36.316 --> 0:40:38.396 like sort of throw yourself out of the water into 0:40:38.436 --> 0:40:38.956 the center of the boat. 0:40:38.956 --> 0:40:41.916 Otherwise it capsize back on you. So that was fun. 0:40:43.156 --> 0:40:45.916 What's it like teaching college students in the age of 0:40:45.996 --> 0:40:47.356 AI at the dawn of AI? 0:40:49.076 --> 0:40:51.876 So the students I teach a MIT students and they're 0:40:52.316 --> 0:40:53.436 it's a great So. 0:40:53.476 --> 0:40:57.596 They're really good at prompt They're really good, they're very. 0:40:59.196 --> 0:41:01.996 There, they're super motivated. It's a it's a real luxury. 0:41:02.076 --> 0:41:03.716 So I don't worry that they great. 0:41:03.796 --> 0:41:06.116 It's perhaps never been a better moment to be an 0:41:06.236 --> 0:41:08.236 MIT student than right and you. 0:41:08.236 --> 0:41:10.636 Were being an MIT per I don't worry that they're 0:41:10.636 --> 0:41:12.516 going to take all the shortcuts and not learn anything. 0:41:12.836 --> 0:41:15.036 They're super hard working, so I feel like they're just 0:41:15.116 --> 0:41:15.436 power to. 0:41:15.436 --> 0:41:17.476 They're building the tools for everyone else to take the 0:41:17.516 --> 0:41:19.156 shortcuts and not learn anything, and. 0:41:19.236 --> 0:41:21.436 They know how to use the tools well, right, So 0:41:21.676 --> 0:41:24.196 I feel like I can push them harder and say, 0:41:24.276 --> 0:41:25.916 go figure this out, right. 0:41:26.076 --> 0:41:28.956 Well, are you building AI into your classes or just 0:41:29.036 --> 0:41:30.876 assume they're going to use AI to figure things out? 0:41:30.916 --> 0:41:31.436 And that's good. 0:41:32.036 --> 0:41:36.436 I my curriculum assumes they're using A like for example, 0:41:36.476 --> 0:41:38.636 I've downweighted out of class exams. Right, I still get 0:41:38.636 --> 0:41:41.236 problem sets and they're hard, but they don't count as much. 0:41:41.516 --> 0:41:43.596 You have to do it on exams or on your papers. Right, 0:41:44.276 --> 0:41:46.996 So I expect they're using it, but they know that 0:41:47.116 --> 0:41:49.036 eventually they will have to demonstrate the knowledge. Right. 0:41:49.316 --> 0:41:50.996 But I can throw the harder stuff with them, say 0:41:51.036 --> 0:41:51.556 you'll solve this. 0:41:52.156 --> 0:41:55.756 I can also say, hey, this can't be as badly 0:41:55.796 --> 0:41:57.916 written as it is right now because you have better tools, 0:41:57.916 --> 0:42:00.436 so go do it again. And of course when I 0:42:00.516 --> 0:42:03.516 building my lectures, I can incorporate new material more quickly 0:42:03.556 --> 0:42:06.156 because I can build slides and you know, master stuff 0:42:06.196 --> 0:42:06.956 faster myself. 0:42:07.756 --> 0:42:10.516 Most peril you've ever been in on a motorcycle. 0:42:10.556 --> 0:42:12.796 But you're always in parallel on a motorcycle. 0:42:13.036 --> 0:42:19.036 Yes, that is the correct answer. Anything else we should 0:42:19.076 --> 0:42:19.676 talk about. 0:42:20.996 --> 0:42:23.316 The uh, you know, I will say, I mean, just 0:42:23.596 --> 0:42:24.836 like you know, I think a lot of young people 0:42:24.836 --> 0:42:28.716 are extremely anxious about AI UH and understand I understand why. 0:42:28.756 --> 0:42:31.076 But I also think they are the group that's gonna 0:42:31.916 --> 0:42:33.836 use it well better than the rest of us and 0:42:33.876 --> 0:42:36.156 figure out what to do with it. And so there's 0:42:36.196 --> 0:42:38.956 never been a technology that's made old people great at 0:42:38.996 --> 0:42:41.636 the at the cost of young people that I'm aware of. Right, 0:42:41.636 --> 0:42:44.836 Almost all new technologies are developed and pioneered and ultimately 0:42:45.516 --> 0:42:49.636 kind of figured out by younger generation that figures out 0:42:49.676 --> 0:42:51.356 what to do with it. And so I think, you know, 0:42:51.516 --> 0:42:54.436 businesses are now looking very hard to find, like you know, 0:42:54.516 --> 0:42:58.316 kind of AI native kids, right, And so I think 0:42:58.356 --> 0:43:00.476 there's a lot of risk. I understand the uncertainty and 0:43:00.556 --> 0:43:02.196 the fear. I don't want to disminish that, but I 0:43:02.316 --> 0:43:03.316 also think there's opportunity. 0:43:10.956 --> 0:43:15.076 David Otter is a labor economist at MIT. Please let 0:43:15.196 --> 0:43:17.916 us know what you think of the show, which you 0:43:17.916 --> 0:43:19.156 want to hear more of, which you want to hear 0:43:19.276 --> 0:43:24.156 less of particular guest ideas. You can email us at 0:43:24.236 --> 0:43:26.916 problem at Pushkin dot fm. I read all the emails. 0:43:27.236 --> 0:43:30.836 You can also find me on x rom LinkedIn. 0:43:31.556 --> 0:43:33.956 Really do appreciate all the messages that we get. 0:43:34.676 --> 0:43:37.556 Today's show was produced by Gabriel Hunter Chang and Trina Menino. 0:43:37.716 --> 0:43:40.436 It was engineered by Hans Dale Sheet and edited by 0:43:40.516 --> 0:43:43.636 Lydia Jan Kott. I'm Jacob Goldstein, and we'll be back 0:43:43.716 --> 0:43:45.996 next week with another episode of What's Your Problem.