1 00:00:15,356 --> 00:00:32,596 Speaker 1: Pushkin. I'm Jacob Goldstein, and this is what's your problem? 2 00:00:32,756 --> 00:00:36,676 Speaker 1: My yesterday is Betsy Stevenson. Betsy is a labor economist 3 00:00:36,796 --> 00:00:40,316 Speaker 1: at the University of Michigan. She's worked in Washington. She 4 00:00:40,596 --> 00:00:43,076 Speaker 1: was the chief economist at the Department of Labor. She 5 00:00:43,196 --> 00:00:47,156 Speaker 1: was on President Obama's Council of Economic Advisors, and I 6 00:00:47,236 --> 00:00:50,436 Speaker 1: wanted to talk with her because she's recently written a 7 00:00:50,476 --> 00:00:55,236 Speaker 1: couple of really interesting papers about AI. The problem Betsy 8 00:00:55,316 --> 00:00:58,876 Speaker 1: is thinking about ultimately boils down to how can we 9 00:00:58,916 --> 00:01:02,676 Speaker 1: create a world where the benefits of AI are broadly shared, 10 00:01:03,116 --> 00:01:07,836 Speaker 1: where lots of people benefit, almost everybody benefits. In our conversation, 11 00:01:07,916 --> 00:01:09,636 Speaker 1: Betsy and I talked about a few day different things. 12 00:01:10,156 --> 00:01:14,036 Speaker 1: We talked about what history economic history has to teach 13 00:01:14,116 --> 00:01:17,476 Speaker 1: us about what's happening now with AI. We talked about 14 00:01:17,476 --> 00:01:19,956 Speaker 1: the effects of AI that she is seeing as a 15 00:01:19,996 --> 00:01:24,276 Speaker 1: college teacher, and we also discussed the public policies that 16 00:01:24,316 --> 00:01:27,916 Speaker 1: Betsy thinks might help us live better in an AI world. 17 00:01:28,516 --> 00:01:32,196 Speaker 1: I appreciate the way Betsy focuses on meaning as well 18 00:01:32,236 --> 00:01:36,596 Speaker 1: as on money when she thinks about AI and its implications. 19 00:01:37,076 --> 00:01:37,716 Speaker 2: And I also. 20 00:01:37,556 --> 00:01:40,756 Speaker 1: Appreciate the way that she balances her worries about AI 21 00:01:41,316 --> 00:01:44,196 Speaker 1: with hopes about how AI might make life better. 22 00:01:44,876 --> 00:01:47,276 Speaker 2: I'm not anti AI at all. I'm very pro AI. 23 00:01:47,876 --> 00:01:52,596 Speaker 2: The pro is that AI will make us so productive 24 00:01:52,716 --> 00:01:55,956 Speaker 2: that we can all have much higher living standards than 25 00:01:55,956 --> 00:02:00,036 Speaker 2: we have right now. That AI will give us enormous bounty, 26 00:02:00,076 --> 00:02:03,156 Speaker 2: that it will cure diseases, that it will solve problems. 27 00:02:03,636 --> 00:02:07,716 Speaker 2: So the fear is not the productivity gains. The fear 28 00:02:07,796 --> 00:02:11,356 Speaker 2: is always around does one person get the productivity gains 29 00:02:11,396 --> 00:02:13,916 Speaker 2: and all the income and other people just lose work. 30 00:02:14,476 --> 00:02:18,276 Speaker 1: Did you have a moment yourself when you realized that 31 00:02:19,476 --> 00:02:21,676 Speaker 1: AI generative AI is a big deal. 32 00:02:23,276 --> 00:02:26,796 Speaker 2: I think when chat TPT came out. That's what then 33 00:02:26,916 --> 00:02:31,076 Speaker 2: led me to think about it as Okay, we have 34 00:02:31,436 --> 00:02:36,156 Speaker 2: had an utterly transformative technological change before, and that was 35 00:02:36,196 --> 00:02:41,356 Speaker 2: when human strength got made completely redundant by machine strength. 36 00:02:42,596 --> 00:02:45,476 Speaker 1: So let's talk about the industrial revolution. You have walked 37 00:02:45,556 --> 00:02:51,436 Speaker 1: us up to the Industrial revolution, and in particular, let's 38 00:02:51,436 --> 00:02:57,196 Speaker 1: talk about Engles pause name for Friedrich Engels Robin to 39 00:02:57,316 --> 00:02:59,756 Speaker 1: Karl Marx as batman. What was Engles pause? 40 00:03:00,596 --> 00:03:03,716 Speaker 2: So engles pause is a period of time, and I 41 00:03:03,716 --> 00:03:06,436 Speaker 2: should say that phrase was coined by the economic historian 42 00:03:06,516 --> 00:03:10,676 Speaker 2: Robert Allen. I love that phrase that he coined, so 43 00:03:10,956 --> 00:03:13,396 Speaker 2: I want to make sure he gets his credit. And 44 00:03:13,436 --> 00:03:16,156 Speaker 2: then I want to explain to you what happened is 45 00:03:16,196 --> 00:03:21,636 Speaker 2: that we got this amazing technology that allowed us to 46 00:03:21,676 --> 00:03:27,676 Speaker 2: produce a lot more, and the workers who were living 47 00:03:27,876 --> 00:03:29,836 Speaker 2: in that era, who were born at the dawn of 48 00:03:29,836 --> 00:03:34,396 Speaker 2: this revolution, got nothing. Yeah, right, so real what do 49 00:03:34,436 --> 00:03:39,676 Speaker 2: I mean by got nothing? Real wages in England, in 50 00:03:39,716 --> 00:03:43,636 Speaker 2: the UK where this technology was being developed stagnated, so 51 00:03:43,676 --> 00:03:47,996 Speaker 2: they did not grow for the majority of workers, and 52 00:03:48,356 --> 00:03:50,036 Speaker 2: actually life expectancy fell. 53 00:03:51,116 --> 00:03:53,916 Speaker 1: They got less than nothing. They got nothing would have 54 00:03:53,916 --> 00:03:55,476 Speaker 1: been great compared to what they got. 55 00:03:55,636 --> 00:04:00,556 Speaker 2: Well, that created a lot of agitation and frustration. And 56 00:04:00,676 --> 00:04:02,596 Speaker 2: so what did we get? What we get out of 57 00:04:02,636 --> 00:04:06,236 Speaker 2: that is descriptions of this era by my great authors 58 00:04:06,276 --> 00:04:09,516 Speaker 2: like Charles Dickens. And then we get writers like and 59 00:04:09,556 --> 00:04:12,956 Speaker 2: Engels who start talking about how this just doesn't work. 60 00:04:13,076 --> 00:04:19,476 Speaker 2: It doesn't work if real people, the workers are being 61 00:04:20,116 --> 00:04:23,196 Speaker 2: you know, treated as an appendage of a machine dragged 62 00:04:23,236 --> 00:04:24,956 Speaker 2: under the juggernaut of capitalism. 63 00:04:25,276 --> 00:04:28,716 Speaker 1: And so this this period is like basically what eighteen 64 00:04:28,796 --> 00:04:31,636 Speaker 1: hundred to eighteen fifty or something right. 65 00:04:31,516 --> 00:04:35,996 Speaker 2: Seventeen sixty to eighteen forty. And the reason the Industrial 66 00:04:36,036 --> 00:04:38,596 Speaker 2: Revolution came to the UK was because it was the 67 00:04:38,636 --> 00:04:41,996 Speaker 2: most advanced economy around and the workers were most expensive 68 00:04:42,676 --> 00:04:47,556 Speaker 2: and the returns to capital were highest. So that's actually 69 00:04:47,636 --> 00:04:49,476 Speaker 2: really an important part of the story. It didn't go 70 00:04:49,516 --> 00:04:53,036 Speaker 2: to France. Those peasants weren't as expensive. 71 00:04:52,916 --> 00:04:55,996 Speaker 1: As there wasn't as much of an incentive to create 72 00:04:56,116 --> 00:04:58,356 Speaker 1: machines that could do the work people could do because 73 00:04:58,356 --> 00:04:59,196 Speaker 1: the people were cheaper. 74 00:04:59,436 --> 00:05:03,556 Speaker 2: Exactly, yeah, exactly. So this is now you're starting to 75 00:05:03,636 --> 00:05:06,116 Speaker 2: like see the dots I'm going to connect here, like 76 00:05:07,036 --> 00:05:12,956 Speaker 2: where are workers right now expensive? Yeah, in the United States? 77 00:05:13,276 --> 00:05:15,676 Speaker 1: And even which workers are expensive? 78 00:05:15,956 --> 00:05:20,836 Speaker 2: Which workers are expensive? Where are the incentives to replace 79 00:05:21,596 --> 00:05:26,836 Speaker 2: workers with machines? And it's American white collar workers. 80 00:05:27,436 --> 00:05:32,156 Speaker 1: Yes, so right, so the incentives are aligned. The cautionary 81 00:05:32,996 --> 00:05:37,716 Speaker 1: tail in angles pause is you can have huge productivity 82 00:05:37,756 --> 00:05:41,636 Speaker 1: gains without wages going up. And I should add that 83 00:05:41,716 --> 00:05:44,596 Speaker 1: in the long run, wages have gone up a lot, right, 84 00:05:44,716 --> 00:05:46,996 Speaker 1: Like Marx and Engels came along at a moment where 85 00:05:47,036 --> 00:05:49,236 Speaker 1: it was like, look, there's the Industrial Revolution and wages 86 00:05:49,236 --> 00:05:50,916 Speaker 1: don't go up, and the rich guys won the capitol, 87 00:05:50,916 --> 00:05:53,036 Speaker 1: get all the money, but in the long run, that 88 00:05:53,076 --> 00:05:56,996 Speaker 1: in fact has not happened. And like wages today are 89 00:05:57,076 --> 00:05:59,356 Speaker 1: higher than they have ever been. Nobody will believe me 90 00:05:59,396 --> 00:06:01,116 Speaker 1: if I say it, and people will get mad if 91 00:06:01,116 --> 00:06:02,556 Speaker 1: I say it, But it's true. 92 00:06:02,836 --> 00:06:05,796 Speaker 2: Of course it's true. I mean the at the end 93 00:06:05,796 --> 00:06:08,356 Speaker 2: of the day, wages can't go up unless we get 94 00:06:08,396 --> 00:06:09,676 Speaker 2: technological change. 95 00:06:09,876 --> 00:06:12,076 Speaker 1: Well, right, But they don't have to, is the point 96 00:06:12,116 --> 00:06:14,356 Speaker 1: of the story. And yet they have in the long. 97 00:06:14,196 --> 00:06:18,876 Speaker 2: Run exactly, so they don't have to. So that's why 98 00:06:18,956 --> 00:06:22,516 Speaker 2: Angeles Pause is a cautionary tale. Yeah, And so then 99 00:06:22,556 --> 00:06:26,636 Speaker 2: the question is what are the institutional and societal frameworks 100 00:06:26,676 --> 00:06:29,076 Speaker 2: we need to make sure that they do. 101 00:06:29,876 --> 00:06:33,716 Speaker 1: Right to make sure that productivity gains are shared by 102 00:06:33,796 --> 00:06:36,236 Speaker 1: workers as well as by the owners of capital, which 103 00:06:36,236 --> 00:06:39,636 Speaker 1: in this case is basically the people who own the 104 00:06:39,676 --> 00:06:41,236 Speaker 1: four Frontier AI companies. 105 00:06:41,236 --> 00:06:43,156 Speaker 2: If we're being reductive at it, exactly. 106 00:06:44,396 --> 00:06:46,716 Speaker 1: Okay, So that's one. I want to do. One more 107 00:06:48,476 --> 00:06:51,076 Speaker 1: historical story that you tell that frankly was more surprising 108 00:06:51,116 --> 00:06:54,316 Speaker 1: to me that I hadn't heard before in this context 109 00:06:54,396 --> 00:06:56,476 Speaker 1: before we get to the present and then to the future, 110 00:06:56,916 --> 00:07:01,676 Speaker 1: and that one is about labor saving devices in the 111 00:07:01,756 --> 00:07:04,956 Speaker 1: home in the mid twentieth century, the story of sort 112 00:07:04,996 --> 00:07:07,556 Speaker 1: of you know, the a washing machine and the dishwasher 113 00:07:07,556 --> 00:07:10,316 Speaker 1: and what that meant for economics and for meaning in 114 00:07:10,356 --> 00:07:13,356 Speaker 1: people's life, which you talk about in a recent paper. 115 00:07:13,396 --> 00:07:14,156 Speaker 1: Tell me about that. 116 00:07:14,436 --> 00:07:19,316 Speaker 2: You know, women have already gone through a transition where 117 00:07:19,356 --> 00:07:22,036 Speaker 2: their work was completely replaced by machines, and they had 118 00:07:22,116 --> 00:07:27,116 Speaker 2: to adapt. And that's because women played big roles inside 119 00:07:27,116 --> 00:07:32,476 Speaker 2: the home as homemakers who baked food from scratch, who 120 00:07:33,196 --> 00:07:38,236 Speaker 2: kept houses clean, who did all the things somebody needed 121 00:07:38,276 --> 00:07:42,876 Speaker 2: to do at home. And then they got vacuum cleaners 122 00:07:43,196 --> 00:07:47,516 Speaker 2: and dishwashers and prepackaged foods. And the first thing that 123 00:07:47,596 --> 00:07:52,076 Speaker 2: happens is the standards and expectations rise. So the first 124 00:07:52,076 --> 00:07:55,196 Speaker 2: thing that happens is the houses have to be cleaner, 125 00:07:56,076 --> 00:08:00,516 Speaker 2: the food has to be nicer, everything has to be 126 00:08:00,596 --> 00:08:05,676 Speaker 2: done better. So they use all of this technological improvement, 127 00:08:05,676 --> 00:08:11,996 Speaker 2: these productivity gains to just produce more home stuff. And 128 00:08:12,036 --> 00:08:15,876 Speaker 2: that lasts for a little while, but it starts to 129 00:08:16,796 --> 00:08:19,716 Speaker 2: really gnaw at some women. And that's when we get 130 00:08:20,116 --> 00:08:23,996 Speaker 2: a problem that has no name. And Betty Fergan basically 131 00:08:24,036 --> 00:08:27,356 Speaker 2: says the problem is women really have nothing to do 132 00:08:27,436 --> 00:08:27,836 Speaker 2: at home. 133 00:08:28,476 --> 00:08:31,476 Speaker 1: Huh. This is the feminine mystique who published in what 134 00:08:31,636 --> 00:08:33,036 Speaker 1: nineteen sixty or so? 135 00:08:33,796 --> 00:08:36,996 Speaker 2: Right, why are the housewives so bored? They're bored because 136 00:08:37,596 --> 00:08:41,596 Speaker 2: robots can do all their jobs and they need something more. 137 00:08:42,916 --> 00:08:46,756 Speaker 2: And you know, if you go back to your you know, 138 00:08:46,836 --> 00:08:52,156 Speaker 2: nineteen seventies eighties feminist literature, you see that some of 139 00:08:52,196 --> 00:08:55,196 Speaker 2: them are advocating for women getting into the workplace, creating 140 00:08:55,236 --> 00:09:00,636 Speaker 2: new identities as you know, breadwinners as career women. 141 00:09:00,636 --> 00:09:03,396 Speaker 1: Which happened at a profound level, right, I think that 142 00:09:03,556 --> 00:09:07,316 Speaker 1: shift is underappreciated, like the I mean, people know at 143 00:09:07,316 --> 00:09:10,236 Speaker 1: a superficial level, oh yes, women to the workforce, But 144 00:09:10,276 --> 00:09:12,076 Speaker 1: when you look at a macro level, it is a 145 00:09:12,236 --> 00:09:15,836 Speaker 1: profound transformation that happened in a relatively short period of time. 146 00:09:16,076 --> 00:09:19,236 Speaker 2: A profound transformation not only in what women do, but 147 00:09:19,356 --> 00:09:24,036 Speaker 2: how they see themselves that happened very quickly. Over like 148 00:09:24,556 --> 00:09:29,636 Speaker 2: you know, one to two generations, women went from you know, 149 00:09:29,876 --> 00:09:36,796 Speaker 2: seeing themselves as you know, potentially happy housewives to wanting more. 150 00:09:37,236 --> 00:09:40,156 Speaker 2: They expect more from their marriages, they expect more from 151 00:09:40,196 --> 00:09:45,956 Speaker 2: their life, they expect more from themselves as independent intellectual creatures. 152 00:09:46,556 --> 00:09:46,756 Speaker 1: Yeah. 153 00:09:46,796 --> 00:09:50,396 Speaker 2: And one of the things I found interesting to tie 154 00:09:50,396 --> 00:09:55,156 Speaker 2: it to this current moment is not every feminist thought 155 00:09:55,396 --> 00:09:57,996 Speaker 2: or things that women need to enter the labor force 156 00:09:58,076 --> 00:09:59,716 Speaker 2: to have this richer identity. 157 00:10:00,276 --> 00:10:04,316 Speaker 1: Yeah, there's this amazing quote in your paper. You know what, 158 00:10:04,356 --> 00:10:05,676 Speaker 1: it's going to be your smind you say it. 159 00:10:06,196 --> 00:10:08,436 Speaker 2: I know it is like my favorite quote. I feel 160 00:10:08,436 --> 00:10:11,916 Speaker 2: like I need to my office bulletin board, which is 161 00:10:12,716 --> 00:10:15,996 Speaker 2: I didn't fight to get women out from behind the 162 00:10:16,076 --> 00:10:18,516 Speaker 2: hoover just to get them on the board of Hoover. 163 00:10:19,236 --> 00:10:21,116 Speaker 1: Yes, Hoover, the vacuum cleaner. 164 00:10:21,236 --> 00:10:24,236 Speaker 2: Yes, right, and Hoover was a big brand of vacuum 165 00:10:24,276 --> 00:10:26,996 Speaker 2: cleaner back in the day. And that's your main greer. 166 00:10:27,036 --> 00:10:29,356 Speaker 2: And she's pointing out, no, that's not. The point is 167 00:10:29,396 --> 00:10:32,276 Speaker 2: not to swap one form of drudgery for another. 168 00:10:32,356 --> 00:10:36,156 Speaker 1: Yeah, which is profound and different. Right, Like I guess 169 00:10:36,196 --> 00:10:40,276 Speaker 1: because of my age or you know whatever, the universe 170 00:10:40,316 --> 00:10:43,436 Speaker 1: I live in is the is the universe of like, oh, yes, 171 00:10:43,476 --> 00:10:45,556 Speaker 1: women have jobs like men, and men help out around 172 00:10:45,596 --> 00:10:48,396 Speaker 1: the house, and like this is the happy new equilibrium. 173 00:10:48,436 --> 00:10:52,476 Speaker 1: But this quote is something entirely different and interesting in 174 00:10:52,596 --> 00:10:56,236 Speaker 1: the context of AI. Right, what we're walking toward I 175 00:10:56,276 --> 00:10:58,356 Speaker 1: mean the sort of these two. I like these two 176 00:10:58,436 --> 00:11:02,996 Speaker 1: stories in parallel because the the Engles pause story is 177 00:11:03,036 --> 00:11:05,436 Speaker 1: the one we're always thinking about and talking about, which 178 00:11:05,476 --> 00:11:09,396 Speaker 1: is money and essentially labor versus capital. Right, So that's 179 00:11:09,596 --> 00:11:12,876 Speaker 1: every day everybody's talking about. The other one is meaning, right, 180 00:11:12,996 --> 00:11:16,956 Speaker 1: like the problem that has no name, the housewife whose 181 00:11:16,956 --> 00:11:20,716 Speaker 1: skills are are devalued by automation. It's not a money problem, 182 00:11:20,836 --> 00:11:24,676 Speaker 1: it's a meaning problem, right, And this idea that like, no, no, 183 00:11:24,916 --> 00:11:27,636 Speaker 1: the point is not just to go to work. The 184 00:11:27,676 --> 00:11:30,236 Speaker 1: point is find some new source of meaning, like, oh, 185 00:11:30,236 --> 00:11:32,476 Speaker 1: this is interesting when one thinks about ai. 186 00:11:32,996 --> 00:11:38,316 Speaker 2: Yeah, it's a meaning and identity to you know. The 187 00:11:38,316 --> 00:11:42,236 Speaker 2: the Japanese have this concept of meaning called eke guy Yeah, 188 00:11:42,276 --> 00:11:44,996 Speaker 2: that I pay a lot of attention to. And I 189 00:11:45,196 --> 00:11:47,476 Speaker 2: just was in Japan about a year ago discussing a 190 00:11:47,516 --> 00:11:50,876 Speaker 2: paper where they were using in their national survey. They're 191 00:11:50,996 --> 00:11:56,716 Speaker 2: surveying people on their their life satisfaction, their subjective well being, happiness, 192 00:11:57,436 --> 00:11:59,036 Speaker 2: and on their eke guy. 193 00:11:59,596 --> 00:12:02,356 Speaker 1: Yeah. And just to be clear, just define ekey guy. 194 00:12:02,716 --> 00:12:07,476 Speaker 2: Eke guy is your purpose for living, Okay, your your 195 00:12:07,516 --> 00:12:10,276 Speaker 2: purpose for living. And so it's kind of what is 196 00:12:10,316 --> 00:12:12,076 Speaker 2: the thing that drives you. What is it that you 197 00:12:12,116 --> 00:12:14,476 Speaker 2: care about every day? What do you up and do? 198 00:12:15,276 --> 00:12:18,796 Speaker 2: And so for people, their purpose could be really small, 199 00:12:18,956 --> 00:12:21,716 Speaker 2: like tending their garden. They get it up every day. 200 00:12:21,716 --> 00:12:24,036 Speaker 2: And I tend my garden because my garden's important to me, 201 00:12:24,076 --> 00:12:27,356 Speaker 2: and my purpose of maintaining a beautiful garden is contributing 202 00:12:27,356 --> 00:12:28,556 Speaker 2: to this world in some way. 203 00:12:28,996 --> 00:12:33,516 Speaker 1: Yeah, And and we have these sort of set ones. Well, 204 00:12:33,556 --> 00:12:35,676 Speaker 1: the big one for lots of us is work, right 205 00:12:35,796 --> 00:12:38,396 Speaker 1: or a big one and a relevant one in the 206 00:12:38,436 --> 00:12:42,116 Speaker 1: context of what we're talking about is work. So we've 207 00:12:42,116 --> 00:12:44,636 Speaker 1: set the table now in a nice way. And so 208 00:12:45,636 --> 00:12:47,676 Speaker 1: I want to talk. I want to talk a little 209 00:12:47,676 --> 00:12:49,396 Speaker 1: bit about the present, and then I want to talk 210 00:12:49,436 --> 00:12:51,596 Speaker 1: about the future. I want to return to money and meaning. 211 00:12:51,956 --> 00:12:53,956 Speaker 1: But the present is really interesting to me, and I 212 00:12:53,956 --> 00:12:57,156 Speaker 1: feel like kind of under discussed in the context of AI. 213 00:12:57,196 --> 00:12:59,756 Speaker 1: Like everybody mostly talks about what's going to be like 214 00:12:59,796 --> 00:13:02,076 Speaker 1: if whatever the eye takes our jobs. But a lot 215 00:13:02,156 --> 00:13:06,996 Speaker 1: is happening right now. And so I'm curious about your 216 00:13:07,436 --> 00:13:11,396 Speaker 1: experience of AI the present, both as a as a 217 00:13:11,436 --> 00:13:15,356 Speaker 1: teacher and as a as an economist. Right, how has 218 00:13:15,436 --> 00:13:17,796 Speaker 1: AI changed being a college teacher. 219 00:13:19,956 --> 00:13:25,716 Speaker 2: Oh, so there's so many things in there. So one 220 00:13:25,996 --> 00:13:32,116 Speaker 2: thing that's obvious is that the students are struggling with 221 00:13:32,236 --> 00:13:37,636 Speaker 2: the idea of productive struggle. And I think the reason 222 00:13:37,756 --> 00:13:42,836 Speaker 2: for that is cognitive offloading is too easy. 223 00:13:43,796 --> 00:13:46,156 Speaker 1: So there's a lot of You've said a lot of 224 00:13:47,116 --> 00:13:52,036 Speaker 1: semi jargon things in there, like productive struggle is interesting. 225 00:13:52,916 --> 00:13:55,076 Speaker 1: I get it. I think what's productive struggle? 226 00:13:55,236 --> 00:13:58,876 Speaker 2: So productive struggle? I mean I talk about this with 227 00:13:58,956 --> 00:14:02,436 Speaker 2: my thirteen year old. Productive struggle is sitting in the 228 00:14:02,436 --> 00:14:05,596 Speaker 2: math class and not knowing how to do the problem 229 00:14:06,436 --> 00:14:09,756 Speaker 2: and just sitting with that and trying to figure out 230 00:14:09,836 --> 00:14:12,596 Speaker 2: how you would solve it. How are you going to 231 00:14:12,676 --> 00:14:17,356 Speaker 2: figure out how to do the math problem? And you don't. 232 00:14:17,436 --> 00:14:21,956 Speaker 2: It feels frustrating, but you can stay in that moment 233 00:14:22,236 --> 00:14:26,556 Speaker 2: of frustration and you can work through it, and you 234 00:14:26,556 --> 00:14:29,036 Speaker 2: know what, it's super satisfying when you get to the 235 00:14:29,036 --> 00:14:33,036 Speaker 2: other end. The productive struggle is that it's awful. 236 00:14:34,196 --> 00:14:34,596 Speaker 1: I don't know. 237 00:14:34,836 --> 00:14:36,516 Speaker 2: I bet you've had this in your career. Have you 238 00:14:36,556 --> 00:14:38,636 Speaker 2: ever sat there writing something and been like, this is 239 00:14:38,676 --> 00:14:39,356 Speaker 2: the worst thing happen? 240 00:14:39,396 --> 00:14:41,436 Speaker 1: Oh? Yeah, I hated right. I wrote a book and 241 00:14:41,476 --> 00:14:44,396 Speaker 1: that was my experience of writing a book. It was 242 00:14:44,636 --> 00:14:49,196 Speaker 1: I was. I was unhappy for a year. Basically, you're 243 00:14:49,756 --> 00:14:51,556 Speaker 1: I'm happy that I wrote the book. I'm happy that 244 00:14:51,636 --> 00:14:53,956 Speaker 1: I wrote the book, and I would do it again exactly. 245 00:14:53,996 --> 00:14:55,756 Speaker 2: But you would do it again. You would do it 246 00:14:55,796 --> 00:14:59,636 Speaker 2: again because actually it's probably meeting that ekey guy. 247 00:15:00,316 --> 00:15:00,876 Speaker 1: Yeah it was. 248 00:15:01,436 --> 00:15:05,916 Speaker 2: It wasn't fun, yeah, but it was purposeful. You did 249 00:15:05,956 --> 00:15:09,036 Speaker 2: it with purpose, and it made you feel like you 250 00:15:09,156 --> 00:15:13,636 Speaker 2: we're on a journey to somewhere good in your brain 251 00:15:13,956 --> 00:15:17,756 Speaker 2: and your heart and your soul. But you gotta have 252 00:15:17,836 --> 00:15:20,956 Speaker 2: enough incentives to want to be on that path because 253 00:15:20,996 --> 00:15:25,076 Speaker 2: it's not actually fun. Yeah, And that's like I see that. 254 00:15:25,956 --> 00:15:27,636 Speaker 2: As I said, I got a thirteen year old in 255 00:15:27,676 --> 00:15:30,436 Speaker 2: seventh grade. We talk about productive struggle a lot. 256 00:15:31,596 --> 00:15:33,476 Speaker 1: I'm bringing that one home. I'm bringing that I got 257 00:15:33,476 --> 00:15:37,036 Speaker 1: two teenagers. It's putting it in my dad tool belt. 258 00:15:38,676 --> 00:15:43,636 Speaker 2: It productive struggle is like, this isn't fun. I'm not 259 00:15:43,716 --> 00:15:45,876 Speaker 2: gonna tell you it's fun. I'm not gonna tell you 260 00:15:45,916 --> 00:15:49,516 Speaker 2: it's not that hard, buddy. Just you know, I'm gonna say, oh, yeah, 261 00:15:50,036 --> 00:15:53,916 Speaker 2: this sucks, but we're gonna go through it and then 262 00:15:53,956 --> 00:15:55,756 Speaker 2: we're gonna get to the other side. We're gonna be 263 00:15:55,756 --> 00:15:59,396 Speaker 2: glad because now you're gonna know how to do you know, 264 00:15:59,596 --> 00:16:05,236 Speaker 2: a multi equation algebra problem. Yeah, and a lot of 265 00:16:05,276 --> 00:16:07,916 Speaker 2: like how we process the world as a place we 266 00:16:07,956 --> 00:16:12,396 Speaker 2: want to be, it is through something's hard and then 267 00:16:12,436 --> 00:16:16,156 Speaker 2: we achieve it and then we feel pride, and that 268 00:16:16,436 --> 00:16:21,036 Speaker 2: is an important set of emotions and I think that 269 00:16:21,236 --> 00:16:23,996 Speaker 2: AI is robbing students of that. 270 00:16:24,556 --> 00:16:28,116 Speaker 1: So okay, So that's an interesting sort of macro ish 271 00:16:28,476 --> 00:16:30,916 Speaker 1: college teacher problem. So basically they don't have any incentives 272 00:16:30,956 --> 00:16:32,316 Speaker 1: to do the work because they can just get chet 273 00:16:32,316 --> 00:16:33,956 Speaker 1: Gpt to do it for them, Claud to do it 274 00:16:33,996 --> 00:16:34,276 Speaker 1: for them. 275 00:16:34,436 --> 00:16:36,956 Speaker 2: Yes, And so then when you're asking them to do things, 276 00:16:37,636 --> 00:16:41,956 Speaker 2: you know they're struggling, but they also are not having 277 00:16:42,116 --> 00:16:45,676 Speaker 2: fun learning. I had a student who presented I taught 278 00:16:45,716 --> 00:16:49,356 Speaker 2: an AI policy class, and she actually presented on this 279 00:16:49,916 --> 00:16:53,476 Speaker 2: topic and she said, school's not as fun as it 280 00:16:53,556 --> 00:16:53,996 Speaker 2: used to be. 281 00:16:54,476 --> 00:16:54,916 Speaker 1: Wow. 282 00:16:55,796 --> 00:16:58,436 Speaker 2: And she was a graduate student who was doing a 283 00:16:58,516 --> 00:17:01,036 Speaker 2: master's and she was one of those students who'd been 284 00:17:01,076 --> 00:17:03,236 Speaker 2: a great student in high school, grade student in college 285 00:17:03,236 --> 00:17:06,116 Speaker 2: and she was doing great in her master's degree. But 286 00:17:08,116 --> 00:17:11,516 Speaker 2: she really described it as I no longer have productive 287 00:17:11,516 --> 00:17:13,156 Speaker 2: struggle with the aha moment. 288 00:17:14,356 --> 00:17:16,036 Speaker 1: It's the problem that has no name. 289 00:17:16,436 --> 00:17:19,996 Speaker 2: Yes, we're already getting to the problem that has a name. 290 00:17:20,276 --> 00:17:22,836 Speaker 1: Yeah, do you feel it in your own work? 291 00:17:23,276 --> 00:17:25,516 Speaker 2: So I feel like I have to be very careful, 292 00:17:25,716 --> 00:17:27,996 Speaker 2: and I definitely see myself where I get lazy. So 293 00:17:28,036 --> 00:17:31,076 Speaker 2: I use the word cognitive offloading. Yeah, there's a problem 294 00:17:31,076 --> 00:17:35,716 Speaker 2: I could wrestle with, or chatchipt could wrestle with it, 295 00:17:35,756 --> 00:17:38,356 Speaker 2: and then I could just much more easily absorb it. 296 00:17:39,476 --> 00:17:43,556 Speaker 2: Research does show when you stop the struggle, you stop 297 00:17:43,636 --> 00:17:44,156 Speaker 2: the learning. 298 00:17:44,636 --> 00:17:49,156 Speaker 1: I mean the dream, right, the sort of Pollyanna ish 299 00:17:49,236 --> 00:17:54,356 Speaker 1: version is the model. The AI allows you to struggle 300 00:17:54,396 --> 00:17:57,916 Speaker 1: at a higher level, right, Like, it gets somewhere, It 301 00:17:57,956 --> 00:17:59,556 Speaker 1: gets through some work that you could have done but 302 00:17:59,596 --> 00:18:01,996 Speaker 1: that wouldn't really push any frontiers, and it gets you 303 00:18:02,036 --> 00:18:03,996 Speaker 1: to this new place. And then you struggle, but you 304 00:18:04,036 --> 00:18:07,276 Speaker 1: struggle better, or you struggle farther, or something like. Yep, 305 00:18:07,436 --> 00:18:11,516 Speaker 1: I think I plus AI can write better still than 306 00:18:11,556 --> 00:18:13,476 Speaker 1: just a I. I think I can write a better podcast 307 00:18:13,476 --> 00:18:19,436 Speaker 1: script for me than Claude can. And so maybe not forever, 308 00:18:19,516 --> 00:18:22,436 Speaker 1: but at least for now. And so then how can 309 00:18:22,476 --> 00:18:25,076 Speaker 1: I use it optimally? How can I use it to whatever, 310 00:18:25,316 --> 00:18:26,636 Speaker 1: be more efficient or be smarter. 311 00:18:27,076 --> 00:18:29,436 Speaker 2: So I'll give you a couple of examples of how 312 00:18:29,436 --> 00:18:32,276 Speaker 2: I've used it recently. So I was writing, I was 313 00:18:32,316 --> 00:18:34,836 Speaker 2: participating in a little debate, and I had to have 314 00:18:34,836 --> 00:18:37,836 Speaker 2: my five minute opening statement, and I just went back 315 00:18:37,836 --> 00:18:41,356 Speaker 2: and forth and asked a ton of questions to chat GPT, 316 00:18:41,476 --> 00:18:45,116 Speaker 2: and then I followed the links to the sources it found. 317 00:18:45,836 --> 00:18:49,076 Speaker 2: And then that made me come back with more questions 318 00:18:49,076 --> 00:18:52,196 Speaker 2: and ask about more sources. And it was it was like, 319 00:18:53,316 --> 00:18:56,036 Speaker 2: basically I would have given that to research assistant. Yeah, 320 00:18:56,076 --> 00:18:57,836 Speaker 2: I would have come back and said, here, look at 321 00:18:57,876 --> 00:19:01,356 Speaker 2: these five you know sources, and that would really helpful. 322 00:19:01,996 --> 00:19:03,876 Speaker 2: But I'll tell you the other funny thing was I 323 00:19:03,956 --> 00:19:06,556 Speaker 2: drafted my five minute opening and then I pasted it 324 00:19:06,596 --> 00:19:10,076 Speaker 2: in and I said fact check this and te h, 325 00:19:10,076 --> 00:19:12,676 Speaker 2: and it immediately gave me back just like a rewrite, 326 00:19:13,076 --> 00:19:16,116 Speaker 2: and I wrote, that was sloppy. I want you to 327 00:19:16,156 --> 00:19:18,876 Speaker 2: go line by line and fact check it. I'll do 328 00:19:18,956 --> 00:19:25,196 Speaker 2: my own tightened things. Yes, And it was like, whoops, Okay. 329 00:19:24,876 --> 00:19:27,636 Speaker 1: You're right to push back. It gives you your right 330 00:19:27,676 --> 00:19:28,316 Speaker 1: to push back. 331 00:19:29,036 --> 00:19:34,316 Speaker 2: Yeah. It then complimented me on my Yes, my great job. 332 00:19:35,396 --> 00:19:38,636 Speaker 2: But the line by line fact checking was amazing, Like 333 00:19:38,676 --> 00:19:41,036 Speaker 2: it gave me an external source for everything I said, 334 00:19:41,276 --> 00:19:43,556 Speaker 2: and then it said, oof, this Clayton seems a little 335 00:19:44,036 --> 00:19:46,596 Speaker 2: maybe over the top. Do you really want to make it? Yeah? 336 00:19:46,716 --> 00:19:49,876 Speaker 2: So then it finally gave me some like negative feedback 337 00:19:50,196 --> 00:19:52,276 Speaker 2: because I made it go line by line and tell 338 00:19:52,276 --> 00:19:55,916 Speaker 2: me where's the external source that justifies it? So it 339 00:19:55,996 --> 00:20:00,516 Speaker 2: is on how we use it. But I don't think 340 00:20:00,516 --> 00:20:03,436 Speaker 2: we're training young people right now properly on how to 341 00:20:03,556 --> 00:20:03,916 Speaker 2: use it. 342 00:20:04,436 --> 00:20:06,836 Speaker 1: Are you getting better at helping your students use it? 343 00:20:06,876 --> 00:20:09,236 Speaker 1: Getting better? Like it seems like what you want is 344 00:20:09,316 --> 00:20:11,316 Speaker 1: to figure out a way that your students can use 345 00:20:11,356 --> 00:20:14,596 Speaker 1: AI and still have the productive struggle. 346 00:20:16,596 --> 00:20:19,996 Speaker 2: I think like we're really in the Like I would 347 00:20:19,996 --> 00:20:23,716 Speaker 2: say that at the end of this semester, I found 348 00:20:24,716 --> 00:20:27,516 Speaker 2: that there are changes I'll make for next year. 349 00:20:27,996 --> 00:20:30,396 Speaker 1: So it's tough times. So you're saying it's tough times. 350 00:20:30,356 --> 00:20:33,316 Speaker 2: I'm saying, the students are getting worse quite quickly on 351 00:20:33,356 --> 00:20:36,356 Speaker 2: this than and so the speed at which I need 352 00:20:36,356 --> 00:20:38,916 Speaker 2: to address it is getting faster. 353 00:20:39,596 --> 00:20:40,876 Speaker 1: Like do you feel like you're going to have to 354 00:20:40,916 --> 00:20:43,596 Speaker 1: start making the students do work in class? 355 00:20:45,036 --> 00:20:48,316 Speaker 2: Boy? Like, I definitely think I have to change the 356 00:20:48,356 --> 00:20:52,596 Speaker 2: assignments and the way that I assess them because having 357 00:20:52,596 --> 00:20:55,876 Speaker 2: them write an essay at home is useless. Now, Yeah, 358 00:20:55,916 --> 00:20:58,516 Speaker 2: I like in one of my classes, one of the 359 00:20:58,516 --> 00:21:00,876 Speaker 2: best sets of essays I got was from a student 360 00:21:00,916 --> 00:21:03,676 Speaker 2: who couldn't answer a single question I asked class about 361 00:21:03,676 --> 00:21:06,836 Speaker 2: his essays because he clearly didn't write them, but he 362 00:21:06,956 --> 00:21:10,316 Speaker 2: obviously knew how to prompt well. GBT wrote me some 363 00:21:10,356 --> 00:21:16,116 Speaker 2: beautiful essays I was telling another instructor. Yeah, I loved 364 00:21:16,116 --> 00:21:21,516 Speaker 2: reading those essays says I prepared for class because I 365 00:21:21,596 --> 00:21:23,276 Speaker 2: knew it was a really good summary of what I 366 00:21:23,316 --> 00:21:26,796 Speaker 2: needed to cover that day, because it was they were 367 00:21:26,796 --> 00:21:30,276 Speaker 2: reading summaries, and he was the best at the class 368 00:21:30,276 --> 00:21:35,596 Speaker 2: at prompting. But the students who I could see the authenticity, 369 00:21:35,676 --> 00:21:38,516 Speaker 2: and students who might have used AI to help them 370 00:21:38,556 --> 00:21:42,516 Speaker 2: a little bit, but the actual genuine things that they 371 00:21:42,516 --> 00:21:45,316 Speaker 2: were noticing from the reading that they thought were most relevant. 372 00:21:45,356 --> 00:21:49,276 Speaker 2: It was clearly authentic to them and their approach. And 373 00:21:49,316 --> 00:21:51,836 Speaker 2: then when I have a dozen unique perspectives, now we 374 00:21:51,876 --> 00:21:57,156 Speaker 2: have a discussion, we have a dozen beautifully crafted summaries 375 00:21:57,196 --> 00:21:59,276 Speaker 2: from AI. We don't have a discussion. 376 00:22:02,676 --> 00:22:05,076 Speaker 1: After the break, Betsy and I talk about the future. 377 00:22:05,276 --> 00:22:08,916 Speaker 1: We talk about money and meaning and purpose in an 378 00:22:08,916 --> 00:22:21,876 Speaker 1: A I world. Let's go to the long term, right, 379 00:22:21,916 --> 00:22:24,596 Speaker 1: you have written this paper where you have you sort 380 00:22:24,636 --> 00:22:28,276 Speaker 1: of lay out kind of what your worries are and 381 00:22:28,316 --> 00:22:30,556 Speaker 1: what you think we should do in response to your worries. 382 00:22:30,636 --> 00:22:34,836 Speaker 1: So one, it's kind of a classic but interesting in 383 00:22:34,876 --> 00:22:40,876 Speaker 1: this context is how much we tax capital gains basically 384 00:22:40,916 --> 00:22:45,116 Speaker 1: profits from investments, versus how much we tax labor work, right, 385 00:22:45,236 --> 00:22:47,836 Speaker 1: income tax? Talk about your view on that. 386 00:22:48,036 --> 00:22:53,316 Speaker 2: So the historic view on taxing labor versus taxing capital 387 00:22:54,036 --> 00:22:56,756 Speaker 2: is that people are going to work, and they're going 388 00:22:56,796 --> 00:22:59,596 Speaker 2: to work sort of no matter whether you tax them 389 00:22:59,836 --> 00:23:04,596 Speaker 2: or you don't. So we tax labor income because we 390 00:23:04,676 --> 00:23:09,836 Speaker 2: need revenue. So in contrast, the idea was that people 391 00:23:09,876 --> 00:23:13,996 Speaker 2: really like to consume today, and so getting them to 392 00:23:14,116 --> 00:23:18,876 Speaker 2: invest their money in something that they're gonna not get 393 00:23:18,916 --> 00:23:21,556 Speaker 2: any money for a long time. You know, they invest 394 00:23:21,636 --> 00:23:24,076 Speaker 2: in a new factory and that's going to give them 395 00:23:24,676 --> 00:23:28,516 Speaker 2: purchasing power in ten years but nothing today. So they're 396 00:23:28,516 --> 00:23:30,716 Speaker 2: gonna have to give up purchasing power today in order 397 00:23:30,756 --> 00:23:34,196 Speaker 2: to build a factory. Well, we figured that they needed 398 00:23:34,236 --> 00:23:36,716 Speaker 2: a lot of incentives to do that because people like 399 00:23:36,836 --> 00:23:38,916 Speaker 2: spending today. So how do I get you to give 400 00:23:38,996 --> 00:23:42,476 Speaker 2: up spending today in order to save and invest in 401 00:23:42,476 --> 00:23:46,116 Speaker 2: that factory. Well, I don't want to tax that behavior. 402 00:23:46,436 --> 00:23:48,716 Speaker 2: And so we have traditionally put more of our taxes 403 00:23:48,716 --> 00:23:55,716 Speaker 2: on labor than on capital investments. So if I go 404 00:23:55,756 --> 00:23:58,636 Speaker 2: out and I work and I make one hundred thousand dollars, 405 00:23:58,636 --> 00:24:01,436 Speaker 2: I pay more in taxes than if I, you know, 406 00:24:01,836 --> 00:24:04,556 Speaker 2: if I invest and I'm starting to make one hundred 407 00:24:04,596 --> 00:24:07,996 Speaker 2: thousand dollars a year off of my investment, and I 408 00:24:08,036 --> 00:24:11,516 Speaker 2: think with a it's going to start to change how 409 00:24:11,556 --> 00:24:15,996 Speaker 2: we should think about that because it makes capital cheaper 410 00:24:16,676 --> 00:24:18,956 Speaker 2: because it's a smaller tax wedge. 411 00:24:19,196 --> 00:24:23,076 Speaker 1: So if you have a choice between employing somebody and 412 00:24:24,476 --> 00:24:27,436 Speaker 1: well paying for AI, buying a bunch of AI tokens 413 00:24:27,476 --> 00:24:30,516 Speaker 1: that costs the same amount of as the person, it's 414 00:24:31,076 --> 00:24:35,516 Speaker 1: the tax system makes you prefer the AI investment to 415 00:24:35,596 --> 00:24:37,676 Speaker 1: the human being exactly under our current rules. 416 00:24:37,716 --> 00:24:43,316 Speaker 2: Okay, yeah, so the tax system prefers capital investment over 417 00:24:43,636 --> 00:24:47,076 Speaker 2: human labor. And so right now we have a tax 418 00:24:47,116 --> 00:24:53,116 Speaker 2: system that is rewarding what is currently happening, which is 419 00:24:53,156 --> 00:24:56,956 Speaker 2: the labor share of income is going down, and the 420 00:24:57,076 --> 00:25:00,716 Speaker 2: share of income that's generated by investments by capital is 421 00:25:00,756 --> 00:25:05,196 Speaker 2: going up, and that also creates a revenue problem. You 422 00:25:05,276 --> 00:25:10,396 Speaker 2: might have heard the US is running some debt, but 423 00:25:10,516 --> 00:25:15,156 Speaker 2: it's also creating a disincentive to hire people over capital 424 00:25:15,236 --> 00:25:17,756 Speaker 2: that we probably want to move the other direction. We 425 00:25:17,756 --> 00:25:22,276 Speaker 2: actually probably want to subsidize hiring people and maybe just 426 00:25:22,356 --> 00:25:26,076 Speaker 2: slow down a little bit the rate at which companies 427 00:25:26,116 --> 00:25:29,436 Speaker 2: are moving towards capital substitution. 428 00:25:29,756 --> 00:25:34,636 Speaker 1: So concretely, does this boil down to raise the capital 429 00:25:34,636 --> 00:25:35,556 Speaker 1: gains tax. 430 00:25:36,716 --> 00:25:39,876 Speaker 2: Lower taxes on wages, and make it up by raising 431 00:25:39,876 --> 00:25:40,996 Speaker 2: the capital gains tax. 432 00:25:41,316 --> 00:25:44,796 Speaker 1: The revenue neutral version. Yeah, I mean, to be clear, 433 00:25:44,836 --> 00:25:49,556 Speaker 1: from the point of view of the employer, lowering the 434 00:25:49,596 --> 00:25:51,916 Speaker 1: income tax, not the payroll text, but the income tax. 435 00:25:52,596 --> 00:25:54,556 Speaker 1: You don't care about that as the employer. I mean, 436 00:25:54,596 --> 00:25:57,236 Speaker 1: it might make workers a little bit more willing to 437 00:25:57,316 --> 00:25:58,596 Speaker 1: work at the market, Yes. 438 00:25:58,636 --> 00:26:01,876 Speaker 2: It should make workers a little bit more willing to work. Yeah, 439 00:26:01,556 --> 00:26:05,076 Speaker 2: And that means, frankly, you can pay them a little 440 00:26:05,076 --> 00:26:07,236 Speaker 2: bit less. And that is in fact what the data 441 00:26:07,236 --> 00:26:10,916 Speaker 2: says happens. Okay, so this would show up as savings 442 00:26:10,956 --> 00:26:13,436 Speaker 2: and compensation costs to employers. 443 00:26:13,756 --> 00:26:15,396 Speaker 1: So higher income. 444 00:26:15,716 --> 00:26:17,036 Speaker 2: That's not going to be a lot of people are 445 00:26:17,076 --> 00:26:20,956 Speaker 2: properly disappointed right now. Wait, wait, you mean the employers 446 00:26:21,076 --> 00:26:25,236 Speaker 2: capture a cut in labor income taxes. Yeah, yeah, that's 447 00:26:25,276 --> 00:26:25,796 Speaker 2: what happens. 448 00:26:27,316 --> 00:26:30,796 Speaker 1: Okay, So that's one. So you also have this idea, 449 00:26:31,876 --> 00:26:34,756 Speaker 1: uh you call the data dividend, which is very good. 450 00:26:34,836 --> 00:26:37,516 Speaker 1: Reminds me of your time in Washington. What's the data dividend? 451 00:26:38,156 --> 00:26:40,716 Speaker 2: So, I mean, the thing that's most remarkable about AI 452 00:26:41,236 --> 00:26:45,276 Speaker 2: is we've all built it open AI didn't build AI. 453 00:26:45,956 --> 00:26:51,996 Speaker 2: Anthropic didn't build AI. They're training on every word I've 454 00:26:52,036 --> 00:26:54,876 Speaker 2: ever written, every word, my grandparents wrote, every word, my 455 00:26:54,916 --> 00:26:59,356 Speaker 2: grandparents grandparents wrote, and you know all it's not just 456 00:27:00,196 --> 00:27:02,636 Speaker 2: you know, I yes, have speeches in the public domain, 457 00:27:03,116 --> 00:27:05,716 Speaker 2: but I really mean it more more broadly than that. 458 00:27:05,796 --> 00:27:09,956 Speaker 2: All of the knowledge of humanity is is what has 459 00:27:10,076 --> 00:27:14,436 Speaker 2: trained these AIS. So when Sam Altman says, I envision 460 00:27:14,476 --> 00:27:18,876 Speaker 2: a world where cognition is sold to you by the token, Yeah, 461 00:27:18,996 --> 00:27:21,556 Speaker 2: like wait, he's envisioning the world in which he's sucked 462 00:27:21,596 --> 00:27:24,436 Speaker 2: up all of human cognition and now he's going to 463 00:27:24,476 --> 00:27:26,756 Speaker 2: sell it back to us. Well, I'd like him to 464 00:27:26,796 --> 00:27:28,036 Speaker 2: pay us for it first. 465 00:27:28,436 --> 00:27:29,796 Speaker 1: Uh huh. 466 00:27:29,836 --> 00:27:35,716 Speaker 2: And that shared resource, that public good that all of 467 00:27:35,836 --> 00:27:41,276 Speaker 2: human cognition that has trained these models, that should give 468 00:27:41,396 --> 00:27:44,476 Speaker 2: each and every one of us a steak and the benefits. 469 00:27:45,076 --> 00:27:48,516 Speaker 2: And that's what the data dividends about. So think about 470 00:27:48,516 --> 00:27:51,876 Speaker 2: it like the Alaska Permanent Fund. Alaska's sitting on a 471 00:27:51,876 --> 00:27:56,036 Speaker 2: bunch of oil. That oil belongs to the citizens of Alaska, 472 00:27:56,396 --> 00:27:58,276 Speaker 2: and every year they get a check from the government 473 00:27:58,356 --> 00:28:00,996 Speaker 2: that says, this is your share of what we made 474 00:28:01,036 --> 00:28:01,636 Speaker 2: off the oil. 475 00:28:02,116 --> 00:28:04,556 Speaker 1: Yeah, it's such an interesting If I was an economist, 476 00:28:04,596 --> 00:28:07,156 Speaker 1: I'd move to Alaska and just study that for my 477 00:28:07,236 --> 00:28:07,996 Speaker 1: whole life. 478 00:28:08,716 --> 00:28:12,276 Speaker 2: There are economists who have studied it, and it turns 479 00:28:12,276 --> 00:28:16,276 Speaker 2: out it doesn't people still go to work, but they now, 480 00:28:16,316 --> 00:28:19,036 Speaker 2: I'm going to get beyond that. I mean, it's more 481 00:28:19,076 --> 00:28:24,116 Speaker 2: than economics. So from an economics perspective, that puts a 482 00:28:24,196 --> 00:28:28,196 Speaker 2: certain amount of consumption in every Alaskan's hands, a data 483 00:28:28,196 --> 00:28:31,276 Speaker 2: dividend could put a certain amount of consumption in everybody's hands. 484 00:28:31,556 --> 00:28:35,236 Speaker 2: But it does more than that. It also says you belong, 485 00:28:36,756 --> 00:28:41,076 Speaker 2: you're part of this. And so when we developed this, 486 00:28:41,516 --> 00:28:44,476 Speaker 2: everybody's probably heard a lot about like universal basic incomes, 487 00:28:45,396 --> 00:28:51,636 Speaker 2: but it's not about giving losers a handout. That language 488 00:28:51,716 --> 00:28:57,196 Speaker 2: infuriates me. You belong, you're part of this. We built 489 00:28:57,196 --> 00:29:01,196 Speaker 2: this together, and we all get to benefit. That's what 490 00:29:01,716 --> 00:29:04,276 Speaker 2: we have to recognize. And those are the models that 491 00:29:05,676 --> 00:29:08,916 Speaker 2: institutional models and frameworks we're going to have to build 492 00:29:09,996 --> 00:29:14,076 Speaker 2: for this to be something that's successful, that levels up everybody. 493 00:29:15,996 --> 00:29:22,236 Speaker 1: Not to bring crass details to the slevated discourse, but like, 494 00:29:23,076 --> 00:29:25,876 Speaker 1: in practice, is the thing you're talking about, the sort 495 00:29:25,916 --> 00:29:29,516 Speaker 1: of frontier AI companies paying some kind of tax that 496 00:29:29,556 --> 00:29:33,116 Speaker 1: has then distributed to everybody in America? Is that fundamentally 497 00:29:33,156 --> 00:29:34,196 Speaker 1: the thing you're imagining? 498 00:29:34,556 --> 00:29:37,796 Speaker 2: Yeah, what's wrong with that? I mean, they could have 499 00:29:37,916 --> 00:29:41,076 Speaker 2: paid for all the data they've used, But we had 500 00:29:41,076 --> 00:29:42,756 Speaker 2: this model where we put a bunch of stuff in 501 00:29:42,756 --> 00:29:45,916 Speaker 2: the commons. That's not how we envisioned the commons. We 502 00:29:45,956 --> 00:29:49,916 Speaker 2: didn't create the commons so one company could capitalize on 503 00:29:49,996 --> 00:29:54,516 Speaker 2: all of it, and that I think it means that 504 00:29:54,516 --> 00:29:56,436 Speaker 2: we have to think about how we want to treat 505 00:29:56,476 --> 00:30:00,196 Speaker 2: the commons in this new era, because it can't just 506 00:30:00,236 --> 00:30:03,276 Speaker 2: be one company sucks the whole thing up and then 507 00:30:03,356 --> 00:30:07,156 Speaker 2: we all lose. I think it's an institutional failure to 508 00:30:07,516 --> 00:30:09,956 Speaker 2: let people get rich off of what's in the commons 509 00:30:10,396 --> 00:30:12,916 Speaker 2: and then try to sell it back to us. I 510 00:30:12,956 --> 00:30:15,316 Speaker 2: don't want my own cognition sold back to me. 511 00:30:16,076 --> 00:30:18,716 Speaker 1: So we've been talking mostly about the money piece, although 512 00:30:18,716 --> 00:30:22,036 Speaker 1: you started talking about meaning in there. But let's talk 513 00:30:22,116 --> 00:30:24,236 Speaker 1: more about meaning. I mean, we sort of framed this 514 00:30:24,316 --> 00:30:29,116 Speaker 1: in those two ways, right. The meaning piace in some 515 00:30:29,276 --> 00:30:32,996 Speaker 1: ways is harder to figure out for me. I mean, 516 00:30:33,036 --> 00:30:34,996 Speaker 1: the money base is like, well, if productivity goes up, 517 00:30:35,116 --> 00:30:38,716 Speaker 1: there's gonna be more money, and like that's a good 518 00:30:38,756 --> 00:30:41,036 Speaker 1: starting place and then hopefully we could figure it out. 519 00:30:41,076 --> 00:30:44,316 Speaker 1: The meaning piece seems squishier and harder, Like, what do 520 00:30:44,356 --> 00:30:48,196 Speaker 1: we do about meaning if there is in fact a 521 00:30:48,236 --> 00:30:50,716 Speaker 1: lot of unemployment and we have solved money. 522 00:30:51,236 --> 00:30:54,836 Speaker 2: Well, you know, the person who really identified that we 523 00:30:54,836 --> 00:30:59,316 Speaker 2: were on a really negative trajectory on meaning was Bob Putnam, 524 00:30:59,716 --> 00:31:03,036 Speaker 2: who wrote a book called Bowling Alone in the nineties, 525 00:31:03,556 --> 00:31:08,036 Speaker 2: and there what he was saying is we're abandoning our communities. 526 00:31:08,076 --> 00:31:10,436 Speaker 2: We don't bowl and bowling leagues anymore, even though we 527 00:31:10,436 --> 00:31:12,356 Speaker 2: could pull just as often, we just go into it 528 00:31:12,356 --> 00:31:15,876 Speaker 2: by ourselves or in small groups. And what had what 529 00:31:15,916 --> 00:31:18,116 Speaker 2: that has meant is that we've loaded too much onto 530 00:31:18,156 --> 00:31:24,396 Speaker 2: work so work is becoming our community. Are people refer 531 00:31:24,436 --> 00:31:29,516 Speaker 2: to their work family? Right? So we have successful transitions 532 00:31:29,556 --> 00:31:33,596 Speaker 2: and unsuccessful. So we talked about women successfully transitioned outside 533 00:31:33,636 --> 00:31:36,916 Speaker 2: the home and it developed new identities, new meaning, new purpose. 534 00:31:37,996 --> 00:31:43,716 Speaker 2: People successfully transitioned out of agriculture. You know it. You 535 00:31:43,716 --> 00:31:46,996 Speaker 2: know even since two thousand, we went from forty percent 536 00:31:46,996 --> 00:31:50,116 Speaker 2: of the globe working and agriculture to about twenty five 537 00:31:50,156 --> 00:31:54,716 Speaker 2: percent today and there's no agricultural meaning crisis. And what 538 00:31:54,756 --> 00:31:58,436 Speaker 2: that meant was people were pulled out of agriculture by opportunity. 539 00:31:59,076 --> 00:32:03,836 Speaker 2: Now let's compare that with what happened with manufacturing, where 540 00:32:03,876 --> 00:32:07,236 Speaker 2: men have been pushed out of manufacturing, not pulled, not 541 00:32:07,356 --> 00:32:13,036 Speaker 2: pulled by opportunity, pushed by closures and factory closures that 542 00:32:13,076 --> 00:32:17,956 Speaker 2: have left nothing in its way, no alternative. And that's 543 00:32:17,996 --> 00:32:21,836 Speaker 2: what I think is a really important lesson for us 544 00:32:21,876 --> 00:32:24,876 Speaker 2: to think about from a meaning and identity perspective, because 545 00:32:24,916 --> 00:32:28,476 Speaker 2: you have a ton of men who still are thinking 546 00:32:28,836 --> 00:32:31,596 Speaker 2: there are no jobs for me because the only jobs 547 00:32:31,636 --> 00:32:34,156 Speaker 2: being created are in health care, and health care jobs 548 00:32:34,156 --> 00:32:40,436 Speaker 2: are so feminized, and so that real identity and meaning 549 00:32:40,476 --> 00:32:44,836 Speaker 2: crisis exists, and it's spilling into family formation. And having 550 00:32:44,836 --> 00:32:48,276 Speaker 2: a lot of negative effects. And I think it is 551 00:32:48,316 --> 00:32:51,476 Speaker 2: a warning to us that we have to figure out 552 00:32:51,516 --> 00:32:57,116 Speaker 2: how to pull people out of opportunities that are closing 553 00:32:57,236 --> 00:33:00,476 Speaker 2: into something that's better, rather than letting them get pushed 554 00:33:00,516 --> 00:33:05,956 Speaker 2: off a cliff. But how well, one thing to do 555 00:33:06,076 --> 00:33:09,836 Speaker 2: is to start to build more community works, to try 556 00:33:09,876 --> 00:33:14,516 Speaker 2: to find ways to reward volunteer work, to reward community involvement. 557 00:33:15,196 --> 00:33:19,236 Speaker 2: You know, there are lots of things people do outside 558 00:33:19,236 --> 00:33:21,996 Speaker 2: of work that build meaning. That could be being a 559 00:33:22,076 --> 00:33:27,476 Speaker 2: member of a club, that can be you know, volunteering 560 00:33:27,476 --> 00:33:28,916 Speaker 2: at a particular organization. 561 00:33:29,516 --> 00:33:32,076 Speaker 1: I mean, I know what the things are, but like 562 00:33:32,636 --> 00:33:33,396 Speaker 1: how do we do it? 563 00:33:33,596 --> 00:33:33,796 Speaker 2: Yeah? 564 00:33:33,796 --> 00:33:37,036 Speaker 1: I mean, as you pointed out, somebody wrote whatever forty 565 00:33:37,116 --> 00:33:40,756 Speaker 1: years ago, thirty years ago, that we're not doing it anymore. 566 00:33:40,756 --> 00:33:44,676 Speaker 1: And that was before we had phones and you know, 567 00:33:44,836 --> 00:33:49,396 Speaker 1: things to just lock up our brains all day. Like everything, 568 00:33:49,476 --> 00:33:52,236 Speaker 1: even with jobs, is going in the opposite direction. People 569 00:33:52,276 --> 00:33:54,116 Speaker 1: are becoming less and less and less social. 570 00:33:54,316 --> 00:33:57,316 Speaker 2: Well, this is why I think about putting something together 571 00:33:57,436 --> 00:34:01,996 Speaker 2: that creates rewards. Whether it's your data dividend is a 572 00:34:02,036 --> 00:34:05,676 Speaker 2: function of logging community service hours. 573 00:34:06,116 --> 00:34:08,636 Speaker 1: Uh huh. You got to leave the house to get 574 00:34:08,636 --> 00:34:10,916 Speaker 1: your data divid you gotta get talking to a human 575 00:34:11,036 --> 00:34:12,596 Speaker 1: being face to face. 576 00:34:12,716 --> 00:34:17,516 Speaker 2: Right, and you know, people want to interact with people. 577 00:34:17,596 --> 00:34:19,316 Speaker 2: I really really believe that. 578 00:34:19,596 --> 00:34:22,876 Speaker 1: I mean, are there, like, you know, are there successful 579 00:34:22,916 --> 00:34:26,276 Speaker 1: examples of this like that, you know, people leaving agriculture 580 00:34:26,276 --> 00:34:28,916 Speaker 1: to go into manufacturing. Like that is fundamentally the market 581 00:34:29,036 --> 00:34:35,356 Speaker 1: solving the problem, right, and a weird potential outcome we're 582 00:34:35,396 --> 00:34:37,876 Speaker 1: looking at is like there is not a market to 583 00:34:37,956 --> 00:34:41,356 Speaker 1: solve the problem, and like the notion that you can 584 00:34:41,436 --> 00:34:45,476 Speaker 1: have some kind of top down solution to this problem 585 00:34:45,556 --> 00:34:46,476 Speaker 1: of meaning. 586 00:34:46,876 --> 00:34:50,276 Speaker 2: Well, we could have one thing we could do is 587 00:34:50,636 --> 00:34:54,316 Speaker 2: government could start to subsidize third spaces and draw them in. 588 00:34:54,876 --> 00:34:59,636 Speaker 2: So we could choose to make third spaces all tax free, 589 00:35:00,596 --> 00:35:03,356 Speaker 2: no property tax on public space. 590 00:35:03,676 --> 00:35:07,596 Speaker 1: That's the Starbucks Act of twenty This is. 591 00:35:07,596 --> 00:35:11,236 Speaker 2: Going to let you in without buying anything. Then they 592 00:35:11,276 --> 00:35:17,276 Speaker 2: get they get a property tax reduction. You start to 593 00:35:17,436 --> 00:35:20,836 Speaker 2: think of creative ways to pull people into the community 594 00:35:20,916 --> 00:35:23,876 Speaker 2: so they do things, and honestly, and afternoon at Starbucks 595 00:35:23,996 --> 00:35:27,516 Speaker 2: is a refreshing way to engage with humans. Talking to 596 00:35:27,636 --> 00:35:30,196 Speaker 2: the burista, seeing the people who go around the same 597 00:35:30,236 --> 00:35:33,996 Speaker 2: time you do every day, Those small moments of connection 598 00:35:34,316 --> 00:35:37,276 Speaker 2: actually are really important to people, more important than they 599 00:35:37,316 --> 00:35:39,396 Speaker 2: realize or they would prioritize them more. 600 00:35:40,756 --> 00:35:47,436 Speaker 1: So. You teach teenagers and very young adults, and you 601 00:35:47,516 --> 00:35:53,996 Speaker 1: are the mother of one teenagers of two teenagers, Like, 602 00:35:54,316 --> 00:35:57,036 Speaker 1: what do you tell the kids? As a labor economist 603 00:35:57,076 --> 00:35:59,836 Speaker 1: who has studied AI, what is your professional advice for 604 00:35:59,916 --> 00:36:00,396 Speaker 1: the children? 605 00:36:03,876 --> 00:36:11,716 Speaker 2: Oh? I mean, I'm going to say, Actually, I find 606 00:36:11,716 --> 00:36:15,436 Speaker 2: it really hard. I was at a I gave a 607 00:36:15,556 --> 00:36:19,836 Speaker 2: talk at Syracuse and it was a big talk on AI. 608 00:36:20,676 --> 00:36:23,836 Speaker 2: And afterwards, a young man came up to me and 609 00:36:23,876 --> 00:36:26,796 Speaker 2: he said, I'm risking everything to be here. I borrowed 610 00:36:26,836 --> 00:36:29,916 Speaker 2: so much money, and how do I know my investment's 611 00:36:30,036 --> 00:36:32,716 Speaker 2: not going to go down the tube? And I almost 612 00:36:32,756 --> 00:36:35,756 Speaker 2: cried for him because I can't tell him his investment's 613 00:36:35,796 --> 00:36:37,356 Speaker 2: not going to go down the tubes. I don't know, 614 00:36:38,276 --> 00:36:44,036 Speaker 2: And I think I want just remember that when you're 615 00:36:44,036 --> 00:36:47,036 Speaker 2: talking to young people, remember the amount of risk and 616 00:36:47,116 --> 00:36:50,836 Speaker 2: uncertainty they're facing. It's like nothing you and I faced. 617 00:36:51,716 --> 00:36:58,116 Speaker 2: And I took all sorts of crazy gambles, but I 618 00:36:58,116 --> 00:37:01,356 Speaker 2: didn't have a spotlight on me. First of all, they 619 00:37:01,396 --> 00:37:03,916 Speaker 2: didn't have the risk of everything being on camera. Everything 620 00:37:03,956 --> 00:37:10,596 Speaker 2: being documented, and I could know with some certainty if 621 00:37:10,636 --> 00:37:13,716 Speaker 2: I study the field like economics, things are going to 622 00:37:13,716 --> 00:37:18,076 Speaker 2: be okay for me. I knew I was a math 623 00:37:18,156 --> 00:37:23,756 Speaker 2: econ major because I wanted to reduce to economic uncertainty 624 00:37:23,756 --> 00:37:25,516 Speaker 2: in my life. I'd grown up with a lot of it, 625 00:37:25,996 --> 00:37:28,396 Speaker 2: and I was like, these are two majors they got 626 00:37:28,396 --> 00:37:31,516 Speaker 2: to payoff. I can work in finance, I can work 627 00:37:31,556 --> 00:37:34,756 Speaker 2: at a corporation. I could you grow up to be 628 00:37:34,756 --> 00:37:37,916 Speaker 2: an econ teacher or a math teacher. I had a 629 00:37:37,916 --> 00:37:42,676 Speaker 2: lot of bases covered, and I don't There's almost nothing 630 00:37:42,716 --> 00:37:45,796 Speaker 2: I can tell kids now where I'm like, this will 631 00:37:45,796 --> 00:37:48,516 Speaker 2: work for you. But what I do tell them is 632 00:37:50,996 --> 00:37:56,196 Speaker 2: adaptability is going to really matter. Curiosity is your friend, 633 00:37:57,716 --> 00:38:05,396 Speaker 2: and creativity. Despite everything you hear about AI, your creativity 634 00:38:05,676 --> 00:38:10,316 Speaker 2: is unique to you. Everybody's creativity is like a fingerprint. 635 00:38:11,036 --> 00:38:15,636 Speaker 2: And I think that we will have a desire for 636 00:38:16,076 --> 00:38:21,156 Speaker 2: bespoke human creativity even as the AI starts to do things. 637 00:38:21,676 --> 00:38:24,316 Speaker 2: So I think at the end of the day, what 638 00:38:24,476 --> 00:38:28,636 Speaker 2: I use to say, ultimately there will be something to 639 00:38:28,716 --> 00:38:33,116 Speaker 2: do is to realize all exchange from the beginning of 640 00:38:33,156 --> 00:38:38,716 Speaker 2: time to now is two human beings, one of who 641 00:38:38,876 --> 00:38:42,556 Speaker 2: wants something that they cannot provide themselves, and the other 642 00:38:42,596 --> 00:38:45,556 Speaker 2: one can provide it. And I do not think AI 643 00:38:45,676 --> 00:38:48,436 Speaker 2: is going to eliminate all of our needs, and so 644 00:38:48,516 --> 00:38:50,596 Speaker 2: we will still be looking to each other to fulfill 645 00:38:50,676 --> 00:38:53,036 Speaker 2: those needs. And as long as you learn how to 646 00:38:53,076 --> 00:38:56,156 Speaker 2: be attentive to other people's needs, you will find your 647 00:38:56,196 --> 00:38:57,356 Speaker 2: space in this world. 648 00:39:01,596 --> 00:39:12,636 Speaker 1: We'll be back in a minute with the lightning round. Ah, 649 00:39:12,716 --> 00:39:16,396 Speaker 1: let's finish with the lightning round, all right. What is 650 00:39:16,436 --> 00:39:19,836 Speaker 1: something other than family and work that gives you meaning 651 00:39:19,876 --> 00:39:20,316 Speaker 1: in life? 652 00:39:21,916 --> 00:39:22,556 Speaker 2: Birds? 653 00:39:23,276 --> 00:39:23,676 Speaker 1: Birds? 654 00:39:23,916 --> 00:39:24,876 Speaker 2: I really like birds? 655 00:39:25,276 --> 00:39:28,316 Speaker 1: You got a list? You like that kind of burder No? 656 00:39:30,756 --> 00:39:34,756 Speaker 2: But I really I enjoy nature and wildlife. And when 657 00:39:34,756 --> 00:39:38,836 Speaker 2: we go to Australia, I am very into the Australian birds. 658 00:39:39,396 --> 00:39:43,276 Speaker 2: And I really like if you've ever spent time with 659 00:39:43,356 --> 00:39:49,236 Speaker 2: like cockatoos or other parents, they're so smart and they're 660 00:39:49,276 --> 00:39:51,396 Speaker 2: really like a joy to interact with. 661 00:39:52,396 --> 00:39:55,076 Speaker 1: I should say that you're what do you call justin 662 00:39:55,116 --> 00:39:57,996 Speaker 1: your life partner? Your common law husband is Australian? 663 00:39:58,156 --> 00:39:58,716 Speaker 2: Is Australian? 664 00:40:00,116 --> 00:40:02,716 Speaker 1: And like, do do people like hang out with cockatoos 665 00:40:02,716 --> 00:40:04,516 Speaker 1: and parrots in Australia? Is that just like a thing? 666 00:40:04,556 --> 00:40:06,716 Speaker 1: I don't know, I like, I go down. 667 00:40:06,636 --> 00:40:08,956 Speaker 2: To the park and I feed them all the time, 668 00:40:09,196 --> 00:40:10,956 Speaker 2: milk you right out of my hand, and he has 669 00:40:10,956 --> 00:40:13,996 Speaker 2: strains or like stop feeding the clocket two's I'm like. 670 00:40:13,996 --> 00:40:15,236 Speaker 1: No, it's too awesome. 671 00:40:18,276 --> 00:40:21,356 Speaker 2: Uh do it away from people's houses, though, because otherwise 672 00:40:21,436 --> 00:40:23,836 Speaker 2: they will they will throw a little tanty and knock 673 00:40:23,876 --> 00:40:26,756 Speaker 2: on the wood to get you to bring out the nuts. 674 00:40:27,516 --> 00:40:34,196 Speaker 1: Good to know. I'll keep that in mind. So, I 675 00:40:34,276 --> 00:40:35,716 Speaker 1: know you've done a fair bit of work on the 676 00:40:35,756 --> 00:40:40,796 Speaker 1: economics of family life, and I'm curious if there's any 677 00:40:40,876 --> 00:40:43,596 Speaker 1: insights from that that are like useful to you in 678 00:40:43,636 --> 00:40:46,396 Speaker 1: your own daily life. 679 00:40:46,716 --> 00:40:51,276 Speaker 2: Well, I guess thinking about the what you should make 680 00:40:51,396 --> 00:40:56,436 Speaker 2: versus buy and how to really think hard to opportunity 681 00:40:56,556 --> 00:41:00,076 Speaker 2: call us cost benefit analysis to make that decision, and 682 00:41:00,116 --> 00:41:03,236 Speaker 2: you might be surprised. Like, you know, I told you 683 00:41:03,276 --> 00:41:06,436 Speaker 2: I was on the board a lift and Justin's always like, 684 00:41:06,716 --> 00:41:08,996 Speaker 2: why do you act like a lift driver for our 685 00:41:09,156 --> 00:41:14,796 Speaker 2: teenager when you could actually pay for a lift. And 686 00:41:14,836 --> 00:41:19,356 Speaker 2: I'm like, because they talk more in the car. You know, 687 00:41:19,436 --> 00:41:22,516 Speaker 2: every parent heard this thing where your teenager tells you 688 00:41:22,596 --> 00:41:24,756 Speaker 2: something that they wouldn't tell you if they weren't in 689 00:41:24,836 --> 00:41:26,916 Speaker 2: the counter side by side with you and trapped there 690 00:41:26,996 --> 00:41:27,716 Speaker 2: for a little while. 691 00:41:28,036 --> 00:41:28,316 Speaker 1: Yeah. 692 00:41:28,396 --> 00:41:30,996 Speaker 2: So, I mean I spent it Saturday, like two weeks ago, 693 00:41:31,076 --> 00:41:35,556 Speaker 2: driving for four hours to get my daughter from like 694 00:41:35,756 --> 00:41:40,676 Speaker 2: attract me to a friend's house for prom too. Like 695 00:41:40,836 --> 00:41:45,596 Speaker 2: it was just ridiculous. But it's building a connection that 696 00:41:45,596 --> 00:41:48,156 Speaker 2: I can't get elsewhere. So if you think about what 697 00:41:48,236 --> 00:41:51,196 Speaker 2: cost benefit analysis says, it never says do or don't 698 00:41:51,236 --> 00:41:54,476 Speaker 2: do something. It says only do them when the benefits 699 00:41:54,516 --> 00:41:57,876 Speaker 2: succeed the costs. And so I'm very good at thinking 700 00:41:57,956 --> 00:42:03,996 Speaker 2: through non financial benefits and financial benefits and non financial 701 00:42:03,996 --> 00:42:07,516 Speaker 2: costs and financial costs and making decisions that I feel 702 00:42:07,596 --> 00:42:08,676 Speaker 2: really comfortable with. 703 00:42:09,916 --> 00:42:16,836 Speaker 1: That was a ten of ten answer, Most underrated Economists 704 00:42:16,836 --> 00:42:17,636 Speaker 1: of all Time. 705 00:42:19,636 --> 00:42:24,036 Speaker 2: Well, I mean there's a lot I should I Actually 706 00:42:24,036 --> 00:42:28,236 Speaker 2: I'm going to give you it. I just wrote the 707 00:42:28,676 --> 00:42:33,796 Speaker 2: forward to a book of Australian Women in Economics, Okay, 708 00:42:34,276 --> 00:42:39,236 Speaker 2: And actually there were some just absolutely incredible women in 709 00:42:39,316 --> 00:42:46,316 Speaker 2: that book because they were using the insights of family 710 00:42:46,436 --> 00:42:49,316 Speaker 2: life and bringing it into economics. And you know, you're 711 00:42:49,516 --> 00:42:53,796 Speaker 2: like mid eighteen hundred, huh, late eighteen hundreds Australia, so 712 00:42:54,316 --> 00:42:56,996 Speaker 2: you know what was really powerful about the book is 713 00:42:57,036 --> 00:42:58,956 Speaker 2: it was like, wait, let's just celebrate the people who 714 00:42:58,956 --> 00:43:02,076 Speaker 2: succeeded despite the fact that they did it against lots 715 00:43:02,076 --> 00:43:04,956 Speaker 2: of odds and maybe they didn't have traditional male success, 716 00:43:05,396 --> 00:43:08,596 Speaker 2: but they were there, they did get their degree, they 717 00:43:08,636 --> 00:43:13,596 Speaker 2: did their work, and let's learn about them. And so that, 718 00:43:13,876 --> 00:43:16,396 Speaker 2: I would say it was a big eye opener to me. 719 00:43:16,636 --> 00:43:22,276 Speaker 2: So there's a lot of underrated, particularly women or minority economists, 720 00:43:22,316 --> 00:43:25,676 Speaker 2: that we don't think about at all. 721 00:43:25,676 --> 00:43:27,916 Speaker 1: What's one thing your kids have taught you about AI? 722 00:43:29,116 --> 00:43:31,676 Speaker 2: They didn't ask for it and they don't really want it. 723 00:43:32,876 --> 00:43:36,756 Speaker 1: Yeah, my kids are also anti AI, like it's weird. 724 00:43:36,836 --> 00:43:38,396 Speaker 1: I'm way more into it than they are. 725 00:43:38,636 --> 00:43:41,196 Speaker 2: Yeah, that's that's where we're at in your household. But 726 00:43:41,276 --> 00:43:43,356 Speaker 2: I think what they taught me is, I didn't ask 727 00:43:43,396 --> 00:43:45,516 Speaker 2: for this. It's going to upend my life. I don't 728 00:43:45,556 --> 00:43:48,636 Speaker 2: know how to live with it. And why why do 729 00:43:48,756 --> 00:43:50,756 Speaker 2: I have to do this? You didn't have to Why 730 00:43:50,756 --> 00:43:51,916 Speaker 2: do I have to navigate this thing? 731 00:43:51,956 --> 00:43:53,756 Speaker 1: I mean, I also didn't ask for it, and I 732 00:43:53,796 --> 00:43:57,516 Speaker 1: also would prefer that it not exists. Uh, but it 733 00:43:57,556 --> 00:44:00,356 Speaker 1: does exist, right they did? 734 00:44:02,916 --> 00:44:08,716 Speaker 2: You know? They've also taught me how hard it is 735 00:44:08,716 --> 00:44:11,836 Speaker 2: living on spotlight. Yeah, I lived in the spotlight. I 736 00:44:11,876 --> 00:44:15,436 Speaker 2: mean when my son was eight, he said no for 737 00:44:15,556 --> 00:44:19,636 Speaker 2: social media of me please, mom Oh yeah, never, And 738 00:44:19,676 --> 00:44:21,836 Speaker 2: I have not posted a photo of him on social 739 00:44:21,876 --> 00:44:25,316 Speaker 2: media since he's maybe he was even seven. It was 740 00:44:25,396 --> 00:44:28,436 Speaker 2: just like, no, I don't want this big digital footprint 741 00:44:29,356 --> 00:44:30,796 Speaker 2: on a smart kid. 742 00:44:30,876 --> 00:44:33,196 Speaker 1: That's like a that's like a humble brag right there. 743 00:44:36,476 --> 00:44:38,676 Speaker 2: I thought it was really interesting at such a young 744 00:44:38,796 --> 00:44:41,396 Speaker 2: I mean, it's worrisome at such a young age to 745 00:44:41,396 --> 00:44:43,676 Speaker 2: be so cognizant of what that means. 746 00:44:43,876 --> 00:44:50,556 Speaker 1: It's tough to be a smart kid. Uh okay. Last one, 747 00:44:50,676 --> 00:44:52,716 Speaker 1: what's your favorite way to use AI that has nothing 748 00:44:52,716 --> 00:44:53,316 Speaker 1: to do with work? 749 00:44:54,476 --> 00:44:58,036 Speaker 2: Plan vacations? Spring break? I was like, I have four days. 750 00:44:58,756 --> 00:45:01,236 Speaker 2: I want a NonStop flight. I want to travel no 751 00:45:01,316 --> 00:45:04,516 Speaker 2: more than four hours three hours. Where should I go? 752 00:45:05,316 --> 00:45:07,516 Speaker 2: And it was like it kept pushing me to the 753 00:45:07,556 --> 00:45:08,956 Speaker 2: beach and I was like, no, I don't want to 754 00:45:08,956 --> 00:45:10,636 Speaker 2: be just like fine, go to Arizona. 755 00:45:11,156 --> 00:45:11,836 Speaker 1: So we did. 756 00:45:12,116 --> 00:45:16,516 Speaker 2: How was it was amazing. I had never hiked in 757 00:45:16,556 --> 00:45:20,276 Speaker 2: the Grand Canyon before, and now I am determined that 758 00:45:20,316 --> 00:45:22,636 Speaker 2: I'm going to do the Rim to hike from the 759 00:45:22,756 --> 00:45:24,276 Speaker 2: North Rim to the south Rim. 760 00:45:24,756 --> 00:45:26,676 Speaker 1: You do it in a day, you go down and up. 761 00:45:27,036 --> 00:45:29,036 Speaker 2: Well that some people do that, but I'm not insane. 762 00:45:29,196 --> 00:45:30,076 Speaker 2: I'm going to do it in two. 763 00:45:30,036 --> 00:45:32,636 Speaker 1: Days and you camp, you camp on the river. 764 00:45:33,036 --> 00:45:33,876 Speaker 2: You camp on the river. 765 00:45:33,996 --> 00:45:35,556 Speaker 1: Yeah that sounds incredible. 766 00:45:35,796 --> 00:45:39,796 Speaker 2: Yeah, I I that's where see, there's a lot of 767 00:45:39,796 --> 00:45:41,676 Speaker 2: things to do if we don't work so much. 768 00:45:42,836 --> 00:45:48,956 Speaker 1: Yes, although I'll be honest, I like working better than hiking. 769 00:45:49,596 --> 00:45:54,076 Speaker 1: I mean, I like hiking. I love exercise. I'm not 770 00:45:54,116 --> 00:45:56,916 Speaker 1: I like birds, But like, if I could do some work, 771 00:45:56,956 --> 00:45:59,156 Speaker 1: you know, I mean, I'm interested in some work, you 772 00:45:59,236 --> 00:46:00,916 Speaker 1: know what I mean? Like me too, Like if I 773 00:46:00,916 --> 00:46:03,156 Speaker 1: could work a few hours a day. The other thing 774 00:46:03,276 --> 00:46:06,116 Speaker 1: is like I mean, if there's if we're really dreaming big, 775 00:46:06,756 --> 00:46:09,076 Speaker 1: like if there's a universe where like I don't need 776 00:46:09,196 --> 00:46:11,236 Speaker 1: worry about money and I can make a podcast and 777 00:46:11,276 --> 00:46:14,356 Speaker 1: people like it. That's actually the part that I like, right, 778 00:46:14,636 --> 00:46:19,476 Speaker 1: And so uh, you know, I. 779 00:46:19,396 --> 00:46:21,836 Speaker 2: Think aren't we entering that era where we're all just 780 00:46:21,956 --> 00:46:25,636 Speaker 2: creating content for each other? So as long as we. 781 00:46:25,556 --> 00:46:33,116 Speaker 1: Go anything else you want to say or talk about, I. 782 00:46:33,476 --> 00:46:38,156 Speaker 2: Think like as a thing to remember is like we 783 00:46:38,196 --> 00:46:43,316 Speaker 2: can't actually improve our living standards without an increase productivity, 784 00:46:43,356 --> 00:46:46,756 Speaker 2: and so that's the reason why we can't be totally 785 00:46:46,796 --> 00:46:51,116 Speaker 2: afraid of AI. But we do need to realize that 786 00:46:51,596 --> 00:46:56,396 Speaker 2: we're responsible for how our society responds to it. We 787 00:46:56,956 --> 00:47:00,556 Speaker 2: don't need to let it just happen to us. It 788 00:47:00,596 --> 00:47:04,196 Speaker 2: is something that we have some control over and we 789 00:47:04,236 --> 00:47:09,196 Speaker 2: should take it. And this is where we need to 790 00:47:09,196 --> 00:47:12,196 Speaker 2: start to have how should we spend our new wealth? 791 00:47:12,636 --> 00:47:15,116 Speaker 2: We should think of it collectively. I'd like to spend 792 00:47:15,156 --> 00:47:17,556 Speaker 2: some of it on vacation. I'd like to spend some 793 00:47:17,596 --> 00:47:20,716 Speaker 2: of it on national healthcare. But I definitely want us 794 00:47:20,716 --> 00:47:24,436 Speaker 2: to get it. And that's what AI, that's its potential 795 00:47:24,636 --> 00:47:26,116 Speaker 2: is it's going to give us the wealth that could 796 00:47:26,116 --> 00:47:27,116 Speaker 2: get us those things. 797 00:47:27,556 --> 00:47:29,396 Speaker 1: Thank you for being so generous with your time. 798 00:47:29,716 --> 00:47:32,356 Speaker 2: Yeah, it was awesome to talk to you. This was 799 00:47:32,396 --> 00:47:32,956 Speaker 2: super fun. 800 00:47:40,116 --> 00:47:43,756 Speaker 1: Betsy Stephenson is a professor at the Gerald R. Ford 801 00:47:43,956 --> 00:47:47,556 Speaker 1: School of Public Policy at the University of Michigan. Today's 802 00:47:47,556 --> 00:47:50,356 Speaker 1: show was produced by Gabriel Hunter Chang and edited by 803 00:47:50,436 --> 00:47:54,436 Speaker 1: Lydia Jean Kott. Our engineer this week was Hansdale Sheet. 804 00:47:54,796 --> 00:47:57,356 Speaker 1: We're always looking for ideas for who to talk to 805 00:47:57,516 --> 00:48:00,396 Speaker 1: and what to cover on the show. You can email 806 00:48:00,476 --> 00:48:04,036 Speaker 1: us at problem at Pushkin dot fm. You can find 807 00:48:04,076 --> 00:48:06,396 Speaker 1: me on x at Jacob Goldstein. You can find me 808 00:48:06,476 --> 00:48:09,756 Speaker 1: on LinkedIn. I'm Jacob Goldstein. Thank you very much for 809 00:48:09,796 --> 00:48:11,956 Speaker 1: listening to the show, and we'll be back next week 810 00:48:12,036 --> 00:48:20,236 Speaker 1: with another episode.