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Finding Meaning (and Money) in the AI Age · What's Your Problem?

• 0:15
  Speaker 1: Pushkin. I'm Jacob Goldstein, and this is what's your problem?
• 0:32
  Speaker 1: My yesterday is Betsy Stevenson. Betsy is a labor economist
• 0:36
  Speaker 1: at the University of Michigan. She's worked in Washington. She
• 0:40
  Speaker 1: was the chief economist at the Department of Labor. She
• 0:43
  Speaker 1: was on President Obama's Council of Economic Advisors, and I
• 0:47
  Speaker 1: wanted to talk with her because she's recently written a
• 0:50
  Speaker 1: couple of really interesting papers about AI. The problem Betsy
• 0:55
  Speaker 1: is thinking about ultimately boils down to how can we
• 0:58
  Speaker 1: create a world where the benefits of AI are broadly shared,
• 1:03
  Speaker 1: where lots of people benefit, almost everybody benefits. In our conversation,
• 1:07
  Speaker 1: Betsy and I talked about a few day different things.
• 1:10
  Speaker 1: We talked about what history economic history has to teach
• 1:14
  Speaker 1: us about what's happening now with AI. We talked about
• 1:17
  Speaker 1: the effects of AI that she is seeing as a
• 1:19
  Speaker 1: college teacher, and we also discussed the public policies that
• 1:24
  Speaker 1: Betsy thinks might help us live better in an AI world.
• 1:28
  Speaker 1: I appreciate the way Betsy focuses on meaning as well
• 1:32
  Speaker 1: as on money when she thinks about AI and its implications.
• 1:37
  Speaker 2: And I also.
• 1:37
  Speaker 1: Appreciate the way that she balances her worries about AI
• 1:41
  Speaker 1: with hopes about how AI might make life better.
• 1:44
  Speaker 2: I'm not anti AI at all. I'm very pro AI.
• 1:47
  Speaker 2: The pro is that AI will make us so productive
• 1:52
  Speaker 2: that we can all have much higher living standards than
• 1:55
  Speaker 2: we have right now. That AI will give us enormous bounty,
• 2:00
  Speaker 2: that it will cure diseases, that it will solve problems.
• 2:03
  Speaker 2: So the fear is not the productivity gains. The fear
• 2:07
  Speaker 2: is always around does one person get the productivity gains
• 2:11
  Speaker 2: and all the income and other people just lose work.
• 2:14
  Speaker 1: Did you have a moment yourself when you realized that
• 2:19
  Speaker 1: AI generative AI is a big deal.
• 2:23
  Speaker 2: I think when chat TPT came out. That's what then
• 2:26
  Speaker 2: led me to think about it as Okay, we have
• 2:31
  Speaker 2: had an utterly transformative technological change before, and that was
• 2:36
  Speaker 2: when human strength got made completely redundant by machine strength.
• 2:42
  Speaker 1: So let's talk about the industrial revolution. You have walked
• 2:45
  Speaker 1: us up to the Industrial revolution, and in particular, let's
• 2:51
  Speaker 1: talk about Engles pause name for Friedrich Engels Robin to
• 2:57
  Speaker 1: Karl Marx as batman. What was Engles pause?
• 3:00
  Speaker 2: So engles pause is a period of time, and I
• 3:03
  Speaker 2: should say that phrase was coined by the economic historian
• 3:06
  Speaker 2: Robert Allen. I love that phrase that he coined, so
• 3:10
  Speaker 2: I want to make sure he gets his credit. And
• 3:13
  Speaker 2: then I want to explain to you what happened is
• 3:16
  Speaker 2: that we got this amazing technology that allowed us to
• 3:21
  Speaker 2: produce a lot more, and the workers who were living
• 3:27
  Speaker 2: in that era, who were born at the dawn of
• 3:29
  Speaker 2: this revolution, got nothing. Yeah, right, so real what do
• 3:34
  Speaker 2: I mean by got nothing? Real wages in England, in
• 3:39
  Speaker 2: the UK where this technology was being developed stagnated, so
• 3:43
  Speaker 2: they did not grow for the majority of workers, and
• 3:48
  Speaker 2: actually life expectancy fell.
• 3:51
  Speaker 1: They got less than nothing. They got nothing would have
• 3:53
  Speaker 1: been great compared to what they got.
• 3:55
  Speaker 2: Well, that created a lot of agitation and frustration. And
• 4:00
  Speaker 2: so what did we get? What we get out of
• 4:02
  Speaker 2: that is descriptions of this era by my great authors
• 4:06
  Speaker 2: like Charles Dickens. And then we get writers like and
• 4:09
  Speaker 2: Engels who start talking about how this just doesn't work.
• 4:13
  Speaker 2: It doesn't work if real people, the workers are being
• 4:20
  Speaker 2: you know, treated as an appendage of a machine dragged
• 4:23
  Speaker 2: under the juggernaut of capitalism.
• 4:25
  Speaker 1: And so this this period is like basically what eighteen
• 4:28
  Speaker 1: hundred to eighteen fifty or something right.
• 4:31
  Speaker 2: Seventeen sixty to eighteen forty. And the reason the Industrial
• 4:36
  Speaker 2: Revolution came to the UK was because it was the
• 4:38
  Speaker 2: most advanced economy around and the workers were most expensive
• 4:42
  Speaker 2: and the returns to capital were highest. So that's actually
• 4:47
  Speaker 2: really an important part of the story. It didn't go
• 4:49
  Speaker 2: to France. Those peasants weren't as expensive.
• 4:52
  Speaker 1: As there wasn't as much of an incentive to create
• 4:56
  Speaker 1: machines that could do the work people could do because
• 4:58
  Speaker 1: the people were cheaper.
• 4:59
  Speaker 2: Exactly, yeah, exactly. So this is now you're starting to
• 5:03
  Speaker 2: like see the dots I'm going to connect here, like
• 5:07
  Speaker 2: where are workers right now expensive? Yeah, in the United States?
• 5:13
  Speaker 1: And even which workers are expensive?
• 5:15
  Speaker 2: Which workers are expensive? Where are the incentives to replace
• 5:21
  Speaker 2: workers with machines? And it's American white collar workers.
• 5:27
  Speaker 1: Yes, so right, so the incentives are aligned. The cautionary
• 5:32
  Speaker 1: tail in angles pause is you can have huge productivity
• 5:37
  Speaker 1: gains without wages going up. And I should add that
• 5:41
  Speaker 1: in the long run, wages have gone up a lot, right,
• 5:44
  Speaker 1: Like Marx and Engels came along at a moment where
• 5:47
  Speaker 1: it was like, look, there's the Industrial Revolution and wages
• 5:49
  Speaker 1: don't go up, and the rich guys won the capitol,
• 5:50
  Speaker 1: get all the money, but in the long run, that
• 5:53
  Speaker 1: in fact has not happened. And like wages today are
• 5:57
  Speaker 1: higher than they have ever been. Nobody will believe me
• 5:59
  Speaker 1: if I say it, and people will get mad if
• 6:01
  Speaker 1: I say it, But it's true.
• 6:02
  Speaker 2: Of course it's true. I mean the at the end
• 6:05
  Speaker 2: of the day, wages can't go up unless we get
• 6:08
  Speaker 2: technological change.
• 6:09
  Speaker 1: Well, right, But they don't have to, is the point
• 6:12
  Speaker 1: of the story. And yet they have in the long.
• 6:14
  Speaker 2: Run exactly, so they don't have to. So that's why
• 6:18
  Speaker 2: Angeles Pause is a cautionary tale. Yeah, And so then
• 6:22
  Speaker 2: the question is what are the institutional and societal frameworks
• 6:26
  Speaker 2: we need to make sure that they do.
• 6:29
  Speaker 1: Right to make sure that productivity gains are shared by
• 6:33
  Speaker 1: workers as well as by the owners of capital, which
• 6:36
  Speaker 1: in this case is basically the people who own the
• 6:39
  Speaker 1: four Frontier AI companies.
• 6:41
  Speaker 2: If we're being reductive at it, exactly.
• 6:44
  Speaker 1: Okay, So that's one. I want to do. One more
• 6:48
  Speaker 1: historical story that you tell that frankly was more surprising
• 6:51
  Speaker 1: to me that I hadn't heard before in this context
• 6:54
  Speaker 1: before we get to the present and then to the future,
• 6:56
  Speaker 1: and that one is about labor saving devices in the
• 7:01
  Speaker 1: home in the mid twentieth century, the story of sort
• 7:04
  Speaker 1: of you know, the a washing machine and the dishwasher
• 7:07
  Speaker 1: and what that meant for economics and for meaning in
• 7:10
  Speaker 1: people's life, which you talk about in a recent paper.
• 7:13
  Speaker 1: Tell me about that.
• 7:14
  Speaker 2: You know, women have already gone through a transition where
• 7:19
  Speaker 2: their work was completely replaced by machines, and they had
• 7:22
  Speaker 2: to adapt. And that's because women played big roles inside
• 7:27
  Speaker 2: the home as homemakers who baked food from scratch, who
• 7:33
  Speaker 2: kept houses clean, who did all the things somebody needed
• 7:38
  Speaker 2: to do at home. And then they got vacuum cleaners
• 7:43
  Speaker 2: and dishwashers and prepackaged foods. And the first thing that
• 7:47
  Speaker 2: happens is the standards and expectations rise. So the first
• 7:52
  Speaker 2: thing that happens is the houses have to be cleaner,
• 7:56
  Speaker 2: the food has to be nicer, everything has to be
• 8:00
  Speaker 2: done better. So they use all of this technological improvement,
• 8:05
  Speaker 2: these productivity gains to just produce more home stuff. And
• 8:12
  Speaker 2: that lasts for a little while, but it starts to
• 8:16
  Speaker 2: really gnaw at some women. And that's when we get
• 8:20
  Speaker 2: a problem that has no name. And Betty Fergan basically
• 8:24
  Speaker 2: says the problem is women really have nothing to do
• 8:27
  Speaker 2: at home.
• 8:28
  Speaker 1: Huh. This is the feminine mystique who published in what
• 8:31
  Speaker 1: nineteen sixty or so?
• 8:33
  Speaker 2: Right, why are the housewives so bored? They're bored because
• 8:37
  Speaker 2: robots can do all their jobs and they need something more.
• 8:42
  Speaker 2: And you know, if you go back to your you know,
• 8:46
  Speaker 2: nineteen seventies eighties feminist literature, you see that some of
• 8:52
  Speaker 2: them are advocating for women getting into the workplace, creating
• 8:55
  Speaker 2: new identities as you know, breadwinners as career women.
• 9:00
  Speaker 1: Which happened at a profound level, right, I think that
• 9:03
  Speaker 1: shift is underappreciated, like the I mean, people know at
• 9:07
  Speaker 1: a superficial level, oh yes, women to the workforce, But
• 9:10
  Speaker 1: when you look at a macro level, it is a
• 9:12
  Speaker 1: profound transformation that happened in a relatively short period of time.
• 9:16
  Speaker 2: A profound transformation not only in what women do, but
• 9:19
  Speaker 2: how they see themselves that happened very quickly. Over like
• 9:24
  Speaker 2: you know, one to two generations, women went from you know,
• 9:29
  Speaker 2: seeing themselves as you know, potentially happy housewives to wanting more.
• 9:37
  Speaker 2: They expect more from their marriages, they expect more from
• 9:40
  Speaker 2: their life, they expect more from themselves as independent intellectual creatures.
• 9:46
  Speaker 1: Yeah.
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  Speaker 2: And one of the things I found interesting to tie
• 9:50
  Speaker 2: it to this current moment is not every feminist thought
• 9:55
  Speaker 2: or things that women need to enter the labor force
• 9:58
  Speaker 2: to have this richer identity.
• 10:00
  Speaker 1: Yeah, there's this amazing quote in your paper. You know what,
• 10:04
  Speaker 1: it's going to be your smind you say it.
• 10:06
  Speaker 2: I know it is like my favorite quote. I feel
• 10:08
  Speaker 2: like I need to my office bulletin board, which is
• 10:12
  Speaker 2: I didn't fight to get women out from behind the
• 10:16
  Speaker 2: hoover just to get them on the board of Hoover.
• 10:19
  Speaker 1: Yes, Hoover, the vacuum cleaner.
• 10:21
  Speaker 2: Yes, right, and Hoover was a big brand of vacuum
• 10:24
  Speaker 2: cleaner back in the day. And that's your main greer.
• 10:27
  Speaker 2: And she's pointing out, no, that's not. The point is
• 10:29
  Speaker 2: not to swap one form of drudgery for another.
• 10:32
  Speaker 1: Yeah, which is profound and different. Right, Like I guess
• 10:36
  Speaker 1: because of my age or you know whatever, the universe
• 10:40
  Speaker 1: I live in is the is the universe of like, oh, yes,
• 10:43
  Speaker 1: women have jobs like men, and men help out around
• 10:45
  Speaker 1: the house, and like this is the happy new equilibrium.
• 10:48
  Speaker 1: But this quote is something entirely different and interesting in
• 10:52
  Speaker 1: the context of AI. Right, what we're walking toward I
• 10:56
  Speaker 1: mean the sort of these two. I like these two
• 10:58
  Speaker 1: stories in parallel because the the Engles pause story is
• 11:03
  Speaker 1: the one we're always thinking about and talking about, which
• 11:05
  Speaker 1: is money and essentially labor versus capital. Right, So that's
• 11:09
  Speaker 1: every day everybody's talking about. The other one is meaning, right,
• 11:12
  Speaker 1: like the problem that has no name, the housewife whose
• 11:16
  Speaker 1: skills are are devalued by automation. It's not a money problem,
• 11:20
  Speaker 1: it's a meaning problem, right, And this idea that like, no, no,
• 11:24
  Speaker 1: the point is not just to go to work. The
• 11:27
  Speaker 1: point is find some new source of meaning, like, oh,
• 11:30
  Speaker 1: this is interesting when one thinks about ai.
• 11:32
  Speaker 2: Yeah, it's a meaning and identity to you know. The
• 11:38
  Speaker 2: the Japanese have this concept of meaning called eke guy Yeah,
• 11:42
  Speaker 2: that I pay a lot of attention to. And I
• 11:45
  Speaker 2: just was in Japan about a year ago discussing a
• 11:47
  Speaker 2: paper where they were using in their national survey. They're
• 11:50
  Speaker 2: surveying people on their their life satisfaction, their subjective well being, happiness,
• 11:57
  Speaker 2: and on their eke guy.
• 11:59
  Speaker 1: Yeah. And just to be clear, just define ekey guy.
• 12:02
  Speaker 2: Eke guy is your purpose for living, Okay, your your
• 12:07
  Speaker 2: purpose for living. And so it's kind of what is
• 12:10
  Speaker 2: the thing that drives you. What is it that you
• 12:12
  Speaker 2: care about every day? What do you up and do?
• 12:15
  Speaker 2: And so for people, their purpose could be really small,
• 12:18
  Speaker 2: like tending their garden. They get it up every day.
• 12:21
  Speaker 2: And I tend my garden because my garden's important to me,
• 12:24
  Speaker 2: and my purpose of maintaining a beautiful garden is contributing
• 12:27
  Speaker 2: to this world in some way.
• 12:28
  Speaker 1: Yeah, And and we have these sort of set ones. Well,
• 12:33
  Speaker 1: the big one for lots of us is work, right
• 12:35
  Speaker 1: or a big one and a relevant one in the
• 12:38
  Speaker 1: context of what we're talking about is work. So we've
• 12:42
  Speaker 1: set the table now in a nice way. And so
• 12:45
  Speaker 1: I want to talk. I want to talk a little
• 12:47
  Speaker 1: bit about the present, and then I want to talk
• 12:49
  Speaker 1: about the future. I want to return to money and meaning.
• 12:51
  Speaker 1: But the present is really interesting to me, and I
• 12:53
  Speaker 1: feel like kind of under discussed in the context of AI.
• 12:57
  Speaker 1: Like everybody mostly talks about what's going to be like
• 12:59
  Speaker 1: if whatever the eye takes our jobs. But a lot
• 13:02
  Speaker 1: is happening right now. And so I'm curious about your
• 13:07
  Speaker 1: experience of AI the present, both as a as a
• 13:11
  Speaker 1: teacher and as a as an economist. Right, how has
• 13:15
  Speaker 1: AI changed being a college teacher.
• 13:19
  Speaker 2: Oh, so there's so many things in there. So one
• 13:25
  Speaker 2: thing that's obvious is that the students are struggling with
• 13:32
  Speaker 2: the idea of productive struggle. And I think the reason
• 13:37
  Speaker 2: for that is cognitive offloading is too easy.
• 13:43
  Speaker 1: So there's a lot of You've said a lot of
• 13:47
  Speaker 1: semi jargon things in there, like productive struggle is interesting.
• 13:52
  Speaker 1: I get it. I think what's productive struggle?
• 13:55
  Speaker 2: So productive struggle? I mean I talk about this with
• 13:58
  Speaker 2: my thirteen year old. Productive struggle is sitting in the
• 14:02
  Speaker 2: math class and not knowing how to do the problem
• 14:06
  Speaker 2: and just sitting with that and trying to figure out
• 14:09
  Speaker 2: how you would solve it. How are you going to
• 14:12
  Speaker 2: figure out how to do the math problem? And you don't.
• 14:17
  Speaker 2: It feels frustrating, but you can stay in that moment
• 14:22
  Speaker 2: of frustration and you can work through it, and you
• 14:26
  Speaker 2: know what, it's super satisfying when you get to the
• 14:29
  Speaker 2: other end. The productive struggle is that it's awful.
• 14:34
  Speaker 1: I don't know.
• 14:34
  Speaker 2: I bet you've had this in your career. Have you
• 14:36
  Speaker 2: ever sat there writing something and been like, this is
• 14:38
  Speaker 2: the worst thing happen?
• 14:39
  Speaker 1: Oh? Yeah, I hated right. I wrote a book and
• 14:41
  Speaker 1: that was my experience of writing a book. It was
• 14:44
  Speaker 1: I was. I was unhappy for a year. Basically, you're
• 14:49
  Speaker 1: I'm happy that I wrote the book. I'm happy that
• 14:51
  Speaker 1: I wrote the book, and I would do it again exactly.
• 14:53
  Speaker 2: But you would do it again. You would do it
• 14:55
  Speaker 2: again because actually it's probably meeting that ekey guy.
• 15:00
  Speaker 1: Yeah it was.
• 15:01
  Speaker 2: It wasn't fun, yeah, but it was purposeful. You did
• 15:05
  Speaker 2: it with purpose, and it made you feel like you
• 15:09
  Speaker 2: we're on a journey to somewhere good in your brain
• 15:13
  Speaker 2: and your heart and your soul. But you gotta have
• 15:17
  Speaker 2: enough incentives to want to be on that path because
• 15:20
  Speaker 2: it's not actually fun. Yeah, And that's like I see that.
• 15:25
  Speaker 2: As I said, I got a thirteen year old in
• 15:27
  Speaker 2: seventh grade. We talk about productive struggle a lot.
• 15:31
  Speaker 1: I'm bringing that one home. I'm bringing that I got
• 15:33
  Speaker 1: two teenagers. It's putting it in my dad tool belt.
• 15:38
  Speaker 2: It productive struggle is like, this isn't fun. I'm not
• 15:43
  Speaker 2: gonna tell you it's fun. I'm not gonna tell you
• 15:45
  Speaker 2: it's not that hard, buddy. Just you know, I'm gonna say, oh, yeah,
• 15:50
  Speaker 2: this sucks, but we're gonna go through it and then
• 15:53
  Speaker 2: we're gonna get to the other side. We're gonna be
• 15:55
  Speaker 2: glad because now you're gonna know how to do you know,
• 15:59
  Speaker 2: a multi equation algebra problem. Yeah, and a lot of
• 16:05
  Speaker 2: like how we process the world as a place we
• 16:07
  Speaker 2: want to be, it is through something's hard and then
• 16:12
  Speaker 2: we achieve it and then we feel pride, and that
• 16:16
  Speaker 2: is an important set of emotions and I think that
• 16:21
  Speaker 2: AI is robbing students of that.
• 16:24
  Speaker 1: So okay, So that's an interesting sort of macro ish
• 16:28
  Speaker 1: college teacher problem. So basically they don't have any incentives
• 16:30
  Speaker 1: to do the work because they can just get chet
• 16:32
  Speaker 1: Gpt to do it for them, Claud to do it
• 16:33
  Speaker 1: for them.
• 16:34
  Speaker 2: Yes, And so then when you're asking them to do things,
• 16:37
  Speaker 2: you know they're struggling, but they also are not having
• 16:42
  Speaker 2: fun learning. I had a student who presented I taught
• 16:45
  Speaker 2: an AI policy class, and she actually presented on this
• 16:49
  Speaker 2: topic and she said, school's not as fun as it
• 16:53
  Speaker 2: used to be.
• 16:54
  Speaker 1: Wow.
• 16:55
  Speaker 2: And she was a graduate student who was doing a
• 16:58
  Speaker 2: master's and she was one of those students who'd been
• 17:01
  Speaker 2: a great student in high school, grade student in college
• 17:03
  Speaker 2: and she was doing great in her master's degree. But
• 17:08
  Speaker 2: she really described it as I no longer have productive
• 17:11
  Speaker 2: struggle with the aha moment.
• 17:14
  Speaker 1: It's the problem that has no name.
• 17:16
  Speaker 2: Yes, we're already getting to the problem that has a name.
• 17:20
  Speaker 1: Yeah, do you feel it in your own work?
• 17:23
  Speaker 2: So I feel like I have to be very careful,
• 17:25
  Speaker 2: and I definitely see myself where I get lazy. So
• 17:28
  Speaker 2: I use the word cognitive offloading. Yeah, there's a problem
• 17:31
  Speaker 2: I could wrestle with, or chatchipt could wrestle with it,
• 17:35
  Speaker 2: and then I could just much more easily absorb it.
• 17:39
  Speaker 2: Research does show when you stop the struggle, you stop
• 17:43
  Speaker 2: the learning.
• 17:44
  Speaker 1: I mean the dream, right, the sort of Pollyanna ish
• 17:49
  Speaker 1: version is the model. The AI allows you to struggle
• 17:54
  Speaker 1: at a higher level, right, Like, it gets somewhere, It
• 17:57
  Speaker 1: gets through some work that you could have done but
• 17:59
  Speaker 1: that wouldn't really push any frontiers, and it gets you
• 18:02
  Speaker 1: to this new place. And then you struggle, but you
• 18:04
  Speaker 1: struggle better, or you struggle farther, or something like. Yep,
• 18:07
  Speaker 1: I think I plus AI can write better still than
• 18:11
  Speaker 1: just a I. I think I can write a better podcast
• 18:13
  Speaker 1: script for me than Claude can. And so maybe not forever,
• 18:19
  Speaker 1: but at least for now. And so then how can
• 18:22
  Speaker 1: I use it optimally? How can I use it to whatever,
• 18:25
  Speaker 1: be more efficient or be smarter.
• 18:27
  Speaker 2: So I'll give you a couple of examples of how
• 18:29
  Speaker 2: I've used it recently. So I was writing, I was
• 18:32
  Speaker 2: participating in a little debate, and I had to have
• 18:34
  Speaker 2: my five minute opening statement, and I just went back
• 18:37
  Speaker 2: and forth and asked a ton of questions to chat GPT,
• 18:41
  Speaker 2: and then I followed the links to the sources it found.
• 18:45
  Speaker 2: And then that made me come back with more questions
• 18:49
  Speaker 2: and ask about more sources. And it was it was like,
• 18:53
  Speaker 2: basically I would have given that to research assistant. Yeah,
• 18:56
  Speaker 2: I would have come back and said, here, look at
• 18:57
  Speaker 2: these five you know sources, and that would really helpful.
• 19:01
  Speaker 2: But I'll tell you the other funny thing was I
• 19:03
  Speaker 2: drafted my five minute opening and then I pasted it
• 19:06
  Speaker 2: in and I said fact check this and te h,
• 19:10
  Speaker 2: and it immediately gave me back just like a rewrite,
• 19:13
  Speaker 2: and I wrote, that was sloppy. I want you to
• 19:16
  Speaker 2: go line by line and fact check it. I'll do
• 19:18
  Speaker 2: my own tightened things. Yes, And it was like, whoops, Okay.
• 19:24
  Speaker 1: You're right to push back. It gives you your right
• 19:27
  Speaker 1: to push back.
• 19:29
  Speaker 2: Yeah. It then complimented me on my Yes, my great job.
• 19:35
  Speaker 2: But the line by line fact checking was amazing, Like
• 19:38
  Speaker 2: it gave me an external source for everything I said,
• 19:41
  Speaker 2: and then it said, oof, this Clayton seems a little
• 19:44
  Speaker 2: maybe over the top. Do you really want to make it? Yeah?
• 19:46
  Speaker 2: So then it finally gave me some like negative feedback
• 19:50
  Speaker 2: because I made it go line by line and tell
• 19:52
  Speaker 2: me where's the external source that justifies it? So it
• 19:55
  Speaker 2: is on how we use it. But I don't think
• 20:00
  Speaker 2: we're training young people right now properly on how to
• 20:03
  Speaker 2: use it.
• 20:04
  Speaker 1: Are you getting better at helping your students use it?
• 20:06
  Speaker 1: Getting better? Like it seems like what you want is
• 20:09
  Speaker 1: to figure out a way that your students can use
• 20:11
  Speaker 1: AI and still have the productive struggle.
• 20:16
  Speaker 2: I think like we're really in the Like I would
• 20:19
  Speaker 2: say that at the end of this semester, I found
• 20:24
  Speaker 2: that there are changes I'll make for next year.
• 20:27
  Speaker 1: So it's tough times. So you're saying it's tough times.
• 20:30
  Speaker 2: I'm saying, the students are getting worse quite quickly on
• 20:33
  Speaker 2: this than and so the speed at which I need
• 20:36
  Speaker 2: to address it is getting faster.
• 20:39
  Speaker 1: Like do you feel like you're going to have to
• 20:40
  Speaker 1: start making the students do work in class?
• 20:45
  Speaker 2: Boy? Like, I definitely think I have to change the
• 20:48
  Speaker 2: assignments and the way that I assess them because having
• 20:52
  Speaker 2: them write an essay at home is useless. Now, Yeah,
• 20:55
  Speaker 2: I like in one of my classes, one of the
• 20:58
  Speaker 2: best sets of essays I got was from a student
• 21:00
  Speaker 2: who couldn't answer a single question I asked class about
• 21:03
  Speaker 2: his essays because he clearly didn't write them, but he
• 21:06
  Speaker 2: obviously knew how to prompt well. GBT wrote me some
• 21:10
  Speaker 2: beautiful essays I was telling another instructor. Yeah, I loved
• 21:16
  Speaker 2: reading those essays says I prepared for class because I
• 21:21
  Speaker 2: knew it was a really good summary of what I
• 21:23
  Speaker 2: needed to cover that day, because it was they were
• 21:26
  Speaker 2: reading summaries, and he was the best at the class
• 21:30
  Speaker 2: at prompting. But the students who I could see the authenticity,
• 21:35
  Speaker 2: and students who might have used AI to help them
• 21:38
  Speaker 2: a little bit, but the actual genuine things that they
• 21:42
  Speaker 2: were noticing from the reading that they thought were most relevant.
• 21:45
  Speaker 2: It was clearly authentic to them and their approach. And
• 21:49
  Speaker 2: then when I have a dozen unique perspectives, now we
• 21:51
  Speaker 2: have a discussion, we have a dozen beautifully crafted summaries
• 21:57
  Speaker 2: from AI. We don't have a discussion.
• 22:02
  Speaker 1: After the break, Betsy and I talk about the future.
• 22:05
  Speaker 1: We talk about money and meaning and purpose in an
• 22:08
  Speaker 1: A I world. Let's go to the long term, right,
• 22:21
  Speaker 1: you have written this paper where you have you sort
• 22:24
  Speaker 1: of lay out kind of what your worries are and
• 22:28
  Speaker 1: what you think we should do in response to your worries.
• 22:30
  Speaker 1: So one, it's kind of a classic but interesting in
• 22:34
  Speaker 1: this context is how much we tax capital gains basically
• 22:40
  Speaker 1: profits from investments, versus how much we tax labor work, right,
• 22:45
  Speaker 1: income tax? Talk about your view on that.
• 22:48
  Speaker 2: So the historic view on taxing labor versus taxing capital
• 22:54
  Speaker 2: is that people are going to work, and they're going
• 22:56
  Speaker 2: to work sort of no matter whether you tax them
• 22:59
  Speaker 2: or you don't. So we tax labor income because we
• 23:04
  Speaker 2: need revenue. So in contrast, the idea was that people
• 23:09
  Speaker 2: really like to consume today, and so getting them to
• 23:14
  Speaker 2: invest their money in something that they're gonna not get
• 23:18
  Speaker 2: any money for a long time. You know, they invest
• 23:21
  Speaker 2: in a new factory and that's going to give them
• 23:24
  Speaker 2: purchasing power in ten years but nothing today. So they're
• 23:28
  Speaker 2: gonna have to give up purchasing power today in order
• 23:30
  Speaker 2: to build a factory. Well, we figured that they needed
• 23:34
  Speaker 2: a lot of incentives to do that because people like
• 23:36
  Speaker 2: spending today. So how do I get you to give
• 23:38
  Speaker 2: up spending today in order to save and invest in
• 23:42
  Speaker 2: that factory. Well, I don't want to tax that behavior.
• 23:46
  Speaker 2: And so we have traditionally put more of our taxes
• 23:48
  Speaker 2: on labor than on capital investments. So if I go
• 23:55
  Speaker 2: out and I work and I make one hundred thousand dollars,
• 23:58
  Speaker 2: I pay more in taxes than if I, you know,
• 24:01
  Speaker 2: if I invest and I'm starting to make one hundred
• 24:04
  Speaker 2: thousand dollars a year off of my investment, and I
• 24:08
  Speaker 2: think with a it's going to start to change how
• 24:11
  Speaker 2: we should think about that because it makes capital cheaper
• 24:16
  Speaker 2: because it's a smaller tax wedge.
• 24:19
  Speaker 1: So if you have a choice between employing somebody and
• 24:24
  Speaker 1: well paying for AI, buying a bunch of AI tokens
• 24:27
  Speaker 1: that costs the same amount of as the person, it's
• 24:31
  Speaker 1: the tax system makes you prefer the AI investment to
• 24:35
  Speaker 1: the human being exactly under our current rules.
• 24:37
  Speaker 2: Okay, yeah, so the tax system prefers capital investment over
• 24:43
  Speaker 2: human labor. And so right now we have a tax
• 24:47
  Speaker 2: system that is rewarding what is currently happening, which is
• 24:53
  Speaker 2: the labor share of income is going down, and the
• 24:57
  Speaker 2: share of income that's generated by investments by capital is
• 25:00
  Speaker 2: going up, and that also creates a revenue problem. You
• 25:05
  Speaker 2: might have heard the US is running some debt, but
• 25:10
  Speaker 2: it's also creating a disincentive to hire people over capital
• 25:15
  Speaker 2: that we probably want to move the other direction. We
• 25:17
  Speaker 2: actually probably want to subsidize hiring people and maybe just
• 25:22
  Speaker 2: slow down a little bit the rate at which companies
• 25:26
  Speaker 2: are moving towards capital substitution.
• 25:29
  Speaker 1: So concretely, does this boil down to raise the capital
• 25:34
  Speaker 1: gains tax.
• 25:36
  Speaker 2: Lower taxes on wages, and make it up by raising
• 25:39
  Speaker 2: the capital gains tax.
• 25:41
  Speaker 1: The revenue neutral version. Yeah, I mean, to be clear,
• 25:44
  Speaker 1: from the point of view of the employer, lowering the
• 25:49
  Speaker 1: income tax, not the payroll text, but the income tax.
• 25:52
  Speaker 1: You don't care about that as the employer. I mean,
• 25:54
  Speaker 1: it might make workers a little bit more willing to
• 25:57
  Speaker 1: work at the market, Yes.
• 25:58
  Speaker 2: It should make workers a little bit more willing to work. Yeah,
• 26:01
  Speaker 2: And that means, frankly, you can pay them a little
• 26:05
  Speaker 2: bit less. And that is in fact what the data
• 26:07
  Speaker 2: says happens. Okay, so this would show up as savings
• 26:10
  Speaker 2: and compensation costs to employers.
• 26:13
  Speaker 1: So higher income.
• 26:15
  Speaker 2: That's not going to be a lot of people are
• 26:17
  Speaker 2: properly disappointed right now. Wait, wait, you mean the employers
• 26:21
  Speaker 2: capture a cut in labor income taxes. Yeah, yeah, that's
• 26:25
  Speaker 2: what happens.
• 26:27
  Speaker 1: Okay, So that's one. So you also have this idea,
• 26:31
  Speaker 1: uh you call the data dividend, which is very good.
• 26:34
  Speaker 1: Reminds me of your time in Washington. What's the data dividend?
• 26:38
  Speaker 2: So, I mean, the thing that's most remarkable about AI
• 26:41
  Speaker 2: is we've all built it open AI didn't build AI.
• 26:45
  Speaker 2: Anthropic didn't build AI. They're training on every word I've
• 26:52
  Speaker 2: ever written, every word, my grandparents wrote, every word, my
• 26:54
  Speaker 2: grandparents grandparents wrote, and you know all it's not just
• 27:00
  Speaker 2: you know, I yes, have speeches in the public domain,
• 27:03
  Speaker 2: but I really mean it more more broadly than that.
• 27:05
  Speaker 2: All of the knowledge of humanity is is what has
• 27:10
  Speaker 2: trained these AIS. So when Sam Altman says, I envision
• 27:14
  Speaker 2: a world where cognition is sold to you by the token, Yeah,
• 27:18
  Speaker 2: like wait, he's envisioning the world in which he's sucked
• 27:21
  Speaker 2: up all of human cognition and now he's going to
• 27:24
  Speaker 2: sell it back to us. Well, I'd like him to
• 27:26
  Speaker 2: pay us for it first.
• 27:28
  Speaker 1: Uh huh.
• 27:29
  Speaker 2: And that shared resource, that public good that all of
• 27:35
  Speaker 2: human cognition that has trained these models, that should give
• 27:41
  Speaker 2: each and every one of us a steak and the benefits.
• 27:45
  Speaker 2: And that's what the data dividends about. So think about
• 27:48
  Speaker 2: it like the Alaska Permanent Fund. Alaska's sitting on a
• 27:51
  Speaker 2: bunch of oil. That oil belongs to the citizens of Alaska,
• 27:56
  Speaker 2: and every year they get a check from the government
• 27:58
  Speaker 2: that says, this is your share of what we made
• 28:01
  Speaker 2: off the oil.
• 28:02
  Speaker 1: Yeah, it's such an interesting If I was an economist,
• 28:04
  Speaker 1: I'd move to Alaska and just study that for my
• 28:07
  Speaker 1: whole life.
• 28:08
  Speaker 2: There are economists who have studied it, and it turns
• 28:12
  Speaker 2: out it doesn't people still go to work, but they now,
• 28:16
  Speaker 2: I'm going to get beyond that. I mean, it's more
• 28:19
  Speaker 2: than economics. So from an economics perspective, that puts a
• 28:24
  Speaker 2: certain amount of consumption in every Alaskan's hands, a data
• 28:28
  Speaker 2: dividend could put a certain amount of consumption in everybody's hands.
• 28:31
  Speaker 2: But it does more than that. It also says you belong,
• 28:36
  Speaker 2: you're part of this. And so when we developed this,
• 28:41
  Speaker 2: everybody's probably heard a lot about like universal basic incomes,
• 28:45
  Speaker 2: but it's not about giving losers a handout. That language
• 28:51
  Speaker 2: infuriates me. You belong, you're part of this. We built
• 28:57
  Speaker 2: this together, and we all get to benefit. That's what
• 29:01
  Speaker 2: we have to recognize. And those are the models that
• 29:05
  Speaker 2: institutional models and frameworks we're going to have to build
• 29:09
  Speaker 2: for this to be something that's successful, that levels up everybody.
• 29:15
  Speaker 1: Not to bring crass details to the slevated discourse, but like,
• 29:23
  Speaker 1: in practice, is the thing you're talking about, the sort
• 29:25
  Speaker 1: of frontier AI companies paying some kind of tax that
• 29:29
  Speaker 1: has then distributed to everybody in America? Is that fundamentally
• 29:33
  Speaker 1: the thing you're imagining?
• 29:34
  Speaker 2: Yeah, what's wrong with that? I mean, they could have
• 29:37
  Speaker 2: paid for all the data they've used, But we had
• 29:41
  Speaker 2: this model where we put a bunch of stuff in
• 29:42
  Speaker 2: the commons. That's not how we envisioned the commons. We
• 29:45
  Speaker 2: didn't create the commons so one company could capitalize on
• 29:49
  Speaker 2: all of it, and that I think it means that
• 29:54
  Speaker 2: we have to think about how we want to treat
• 29:56
  Speaker 2: the commons in this new era, because it can't just
• 30:00
  Speaker 2: be one company sucks the whole thing up and then
• 30:03
  Speaker 2: we all lose. I think it's an institutional failure to
• 30:07
  Speaker 2: let people get rich off of what's in the commons
• 30:10
  Speaker 2: and then try to sell it back to us. I
• 30:12
  Speaker 2: don't want my own cognition sold back to me.
• 30:16
  Speaker 1: So we've been talking mostly about the money piece, although
• 30:18
  Speaker 1: you started talking about meaning in there. But let's talk
• 30:22
  Speaker 1: more about meaning. I mean, we sort of framed this
• 30:24
  Speaker 1: in those two ways, right. The meaning piace in some
• 30:29
  Speaker 1: ways is harder to figure out for me. I mean,
• 30:33
  Speaker 1: the money base is like, well, if productivity goes up,
• 30:35
  Speaker 1: there's gonna be more money, and like that's a good
• 30:38
  Speaker 1: starting place and then hopefully we could figure it out.
• 30:41
  Speaker 1: The meaning piece seems squishier and harder, Like, what do
• 30:44
  Speaker 1: we do about meaning if there is in fact a
• 30:48
  Speaker 1: lot of unemployment and we have solved money.
• 30:51
  Speaker 2: Well, you know, the person who really identified that we
• 30:54
  Speaker 2: were on a really negative trajectory on meaning was Bob Putnam,
• 30:59
  Speaker 2: who wrote a book called Bowling Alone in the nineties,
• 31:03
  Speaker 2: and there what he was saying is we're abandoning our communities.
• 31:08
  Speaker 2: We don't bowl and bowling leagues anymore, even though we
• 31:10
  Speaker 2: could pull just as often, we just go into it
• 31:12
  Speaker 2: by ourselves or in small groups. And what had what
• 31:15
  Speaker 2: that has meant is that we've loaded too much onto
• 31:18
  Speaker 2: work so work is becoming our community. Are people refer
• 31:24
  Speaker 2: to their work family? Right? So we have successful transitions
• 31:29
  Speaker 2: and unsuccessful. So we talked about women successfully transitioned outside
• 31:33
  Speaker 2: the home and it developed new identities, new meaning, new purpose.
• 31:37
  Speaker 2: People successfully transitioned out of agriculture. You know it. You
• 31:43
  Speaker 2: know even since two thousand, we went from forty percent
• 31:46
  Speaker 2: of the globe working and agriculture to about twenty five
• 31:50
  Speaker 2: percent today and there's no agricultural meaning crisis. And what
• 31:54
  Speaker 2: that meant was people were pulled out of agriculture by opportunity.
• 31:59
  Speaker 2: Now let's compare that with what happened with manufacturing, where
• 32:03
  Speaker 2: men have been pushed out of manufacturing, not pulled, not
• 32:07
  Speaker 2: pulled by opportunity, pushed by closures and factory closures that
• 32:13
  Speaker 2: have left nothing in its way, no alternative. And that's
• 32:17
  Speaker 2: what I think is a really important lesson for us
• 32:21
  Speaker 2: to think about from a meaning and identity perspective, because
• 32:24
  Speaker 2: you have a ton of men who still are thinking
• 32:28
  Speaker 2: there are no jobs for me because the only jobs
• 32:31
  Speaker 2: being created are in health care, and health care jobs
• 32:34
  Speaker 2: are so feminized, and so that real identity and meaning
• 32:40
  Speaker 2: crisis exists, and it's spilling into family formation. And having
• 32:44
  Speaker 2: a lot of negative effects. And I think it is
• 32:48
  Speaker 2: a warning to us that we have to figure out
• 32:51
  Speaker 2: how to pull people out of opportunities that are closing
• 32:57
  Speaker 2: into something that's better, rather than letting them get pushed
• 33:00
  Speaker 2: off a cliff. But how well, one thing to do
• 33:06
  Speaker 2: is to start to build more community works, to try
• 33:09
  Speaker 2: to find ways to reward volunteer work, to reward community involvement.
• 33:15
  Speaker 2: You know, there are lots of things people do outside
• 33:19
  Speaker 2: of work that build meaning. That could be being a
• 33:22
  Speaker 2: member of a club, that can be you know, volunteering
• 33:27
  Speaker 2: at a particular organization.
• 33:29
  Speaker 1: I mean, I know what the things are, but like
• 33:32
  Speaker 1: how do we do it?
• 33:33
  Speaker 2: Yeah?
• 33:33
  Speaker 1: I mean, as you pointed out, somebody wrote whatever forty
• 33:37
  Speaker 1: years ago, thirty years ago, that we're not doing it anymore.
• 33:40
  Speaker 1: And that was before we had phones and you know,
• 33:44
  Speaker 1: things to just lock up our brains all day. Like everything,
• 33:49
  Speaker 1: even with jobs, is going in the opposite direction. People
• 33:52
  Speaker 1: are becoming less and less and less social.
• 33:54
  Speaker 2: Well, this is why I think about putting something together
• 33:57
  Speaker 2: that creates rewards. Whether it's your data dividend is a
• 34:02
  Speaker 2: function of logging community service hours.
• 34:06
  Speaker 1: Uh huh. You got to leave the house to get
• 34:08
  Speaker 1: your data divid you gotta get talking to a human
• 34:11
  Speaker 1: being face to face.
• 34:12
  Speaker 2: Right, and you know, people want to interact with people.
• 34:17
  Speaker 2: I really really believe that.
• 34:19
  Speaker 1: I mean, are there, like, you know, are there successful
• 34:22
  Speaker 1: examples of this like that, you know, people leaving agriculture
• 34:26
  Speaker 1: to go into manufacturing. Like that is fundamentally the market
• 34:29
  Speaker 1: solving the problem, right, and a weird potential outcome we're
• 34:35
  Speaker 1: looking at is like there is not a market to
• 34:37
  Speaker 1: solve the problem, and like the notion that you can
• 34:41
  Speaker 1: have some kind of top down solution to this problem
• 34:45
  Speaker 1: of meaning.
• 34:46
  Speaker 2: Well, we could have one thing we could do is
• 34:50
  Speaker 2: government could start to subsidize third spaces and draw them in.
• 34:54
  Speaker 2: So we could choose to make third spaces all tax free,
• 35:00
  Speaker 2: no property tax on public space.
• 35:03
  Speaker 1: That's the Starbucks Act of twenty This is.
• 35:07
  Speaker 2: Going to let you in without buying anything. Then they
• 35:11
  Speaker 2: get they get a property tax reduction. You start to
• 35:17
  Speaker 2: think of creative ways to pull people into the community
• 35:20
  Speaker 2: so they do things, and honestly, and afternoon at Starbucks
• 35:23
  Speaker 2: is a refreshing way to engage with humans. Talking to
• 35:27
  Speaker 2: the burista, seeing the people who go around the same
• 35:30
  Speaker 2: time you do every day, Those small moments of connection
• 35:34
  Speaker 2: actually are really important to people, more important than they
• 35:37
  Speaker 2: realize or they would prioritize them more.
• 35:40
  Speaker 1: So. You teach teenagers and very young adults, and you
• 35:47
  Speaker 1: are the mother of one teenagers of two teenagers, Like,
• 35:54
  Speaker 1: what do you tell the kids? As a labor economist
• 35:57
  Speaker 1: who has studied AI, what is your professional advice for
• 35:59
  Speaker 1: the children?
• 36:03
  Speaker 2: Oh? I mean, I'm going to say, Actually, I find
• 36:11
  Speaker 2: it really hard. I was at a I gave a
• 36:15
  Speaker 2: talk at Syracuse and it was a big talk on AI.
• 36:20
  Speaker 2: And afterwards, a young man came up to me and
• 36:23
  Speaker 2: he said, I'm risking everything to be here. I borrowed
• 36:26
  Speaker 2: so much money, and how do I know my investment's
• 36:30
  Speaker 2: not going to go down the tube? And I almost
• 36:32
  Speaker 2: cried for him because I can't tell him his investment's
• 36:35
  Speaker 2: not going to go down the tubes. I don't know,
• 36:38
  Speaker 2: And I think I want just remember that when you're
• 36:44
  Speaker 2: talking to young people, remember the amount of risk and
• 36:47
  Speaker 2: uncertainty they're facing. It's like nothing you and I faced.
• 36:51
  Speaker 2: And I took all sorts of crazy gambles, but I
• 36:58
  Speaker 2: didn't have a spotlight on me. First of all, they
• 37:01
  Speaker 2: didn't have the risk of everything being on camera. Everything
• 37:03
  Speaker 2: being documented, and I could know with some certainty if
• 37:10
  Speaker 2: I study the field like economics, things are going to
• 37:13
  Speaker 2: be okay for me. I knew I was a math
• 37:18
  Speaker 2: econ major because I wanted to reduce to economic uncertainty
• 37:23
  Speaker 2: in my life. I'd grown up with a lot of it,
• 37:25
  Speaker 2: and I was like, these are two majors they got
• 37:28
  Speaker 2: to payoff. I can work in finance, I can work
• 37:31
  Speaker 2: at a corporation. I could you grow up to be
• 37:34
  Speaker 2: an econ teacher or a math teacher. I had a
• 37:37
  Speaker 2: lot of bases covered, and I don't There's almost nothing
• 37:42
  Speaker 2: I can tell kids now where I'm like, this will
• 37:45
  Speaker 2: work for you. But what I do tell them is
• 37:50
  Speaker 2: adaptability is going to really matter. Curiosity is your friend,
• 37:57
  Speaker 2: and creativity. Despite everything you hear about AI, your creativity
• 38:05
  Speaker 2: is unique to you. Everybody's creativity is like a fingerprint.
• 38:11
  Speaker 2: And I think that we will have a desire for
• 38:16
  Speaker 2: bespoke human creativity even as the AI starts to do things.
• 38:21
  Speaker 2: So I think at the end of the day, what
• 38:24
  Speaker 2: I use to say, ultimately there will be something to
• 38:28
  Speaker 2: do is to realize all exchange from the beginning of
• 38:33
  Speaker 2: time to now is two human beings, one of who
• 38:38
  Speaker 2: wants something that they cannot provide themselves, and the other
• 38:42
  Speaker 2: one can provide it. And I do not think AI
• 38:45
  Speaker 2: is going to eliminate all of our needs, and so
• 38:48
  Speaker 2: we will still be looking to each other to fulfill
• 38:50
  Speaker 2: those needs. And as long as you learn how to
• 38:53
  Speaker 2: be attentive to other people's needs, you will find your
• 38:56
  Speaker 2: space in this world.
• 39:01
  Speaker 1: We'll be back in a minute with the lightning round. Ah,
• 39:12
  Speaker 1: let's finish with the lightning round, all right. What is
• 39:16
  Speaker 1: something other than family and work that gives you meaning
• 39:19
  Speaker 1: in life?
• 39:21
  Speaker 2: Birds?
• 39:23
  Speaker 1: Birds?
• 39:23
  Speaker 2: I really like birds?
• 39:25
  Speaker 1: You got a list? You like that kind of burder No?
• 39:30
  Speaker 2: But I really I enjoy nature and wildlife. And when
• 39:34
  Speaker 2: we go to Australia, I am very into the Australian birds.
• 39:39
  Speaker 2: And I really like if you've ever spent time with
• 39:43
  Speaker 2: like cockatoos or other parents, they're so smart and they're
• 39:49
  Speaker 2: really like a joy to interact with.
• 39:52
  Speaker 1: I should say that you're what do you call justin
• 39:55
  Speaker 1: your life partner? Your common law husband is Australian?
• 39:58
  Speaker 2: Is Australian?
• 40:00
  Speaker 1: And like, do do people like hang out with cockatoos
• 40:02
  Speaker 1: and parrots in Australia? Is that just like a thing?
• 40:04
  Speaker 1: I don't know, I like, I go down.
• 40:06
  Speaker 2: To the park and I feed them all the time,
• 40:09
  Speaker 2: milk you right out of my hand, and he has
• 40:10
  Speaker 2: strains or like stop feeding the clocket two's I'm like.
• 40:13
  Speaker 1: No, it's too awesome.
• 40:18
  Speaker 2: Uh do it away from people's houses, though, because otherwise
• 40:21
  Speaker 2: they will they will throw a little tanty and knock
• 40:23
  Speaker 2: on the wood to get you to bring out the nuts.
• 40:27
  Speaker 1: Good to know. I'll keep that in mind. So, I
• 40:34
  Speaker 1: know you've done a fair bit of work on the
• 40:35
  Speaker 1: economics of family life, and I'm curious if there's any
• 40:40
  Speaker 1: insights from that that are like useful to you in
• 40:43
  Speaker 1: your own daily life.
• 40:46
  Speaker 2: Well, I guess thinking about the what you should make
• 40:51
  Speaker 2: versus buy and how to really think hard to opportunity
• 40:56
  Speaker 2: call us cost benefit analysis to make that decision, and
• 41:00
  Speaker 2: you might be surprised. Like, you know, I told you
• 41:03
  Speaker 2: I was on the board a lift and Justin's always like,
• 41:06
  Speaker 2: why do you act like a lift driver for our
• 41:09
  Speaker 2: teenager when you could actually pay for a lift. And
• 41:14
  Speaker 2: I'm like, because they talk more in the car. You know,
• 41:19
  Speaker 2: every parent heard this thing where your teenager tells you
• 41:22
  Speaker 2: something that they wouldn't tell you if they weren't in
• 41:24
  Speaker 2: the counter side by side with you and trapped there
• 41:26
  Speaker 2: for a little while.
• 41:28
  Speaker 1: Yeah.
• 41:28
  Speaker 2: So, I mean I spent it Saturday, like two weeks ago,
• 41:31
  Speaker 2: driving for four hours to get my daughter from like
• 41:35
  Speaker 2: attract me to a friend's house for prom too. Like
• 41:40
  Speaker 2: it was just ridiculous. But it's building a connection that
• 41:45
  Speaker 2: I can't get elsewhere. So if you think about what
• 41:48
  Speaker 2: cost benefit analysis says, it never says do or don't
• 41:51
  Speaker 2: do something. It says only do them when the benefits
• 41:54
  Speaker 2: succeed the costs. And so I'm very good at thinking
• 41:57
  Speaker 2: through non financial benefits and financial benefits and non financial
• 42:03
  Speaker 2: costs and financial costs and making decisions that I feel
• 42:07
  Speaker 2: really comfortable with.
• 42:09
  Speaker 1: That was a ten of ten answer, Most underrated Economists
• 42:16
  Speaker 1: of all Time.
• 42:19
  Speaker 2: Well, I mean there's a lot I should I Actually
• 42:24
  Speaker 2: I'm going to give you it. I just wrote the
• 42:28
  Speaker 2: forward to a book of Australian Women in Economics, Okay,
• 42:34
  Speaker 2: And actually there were some just absolutely incredible women in
• 42:39
  Speaker 2: that book because they were using the insights of family
• 42:46
  Speaker 2: life and bringing it into economics. And you know, you're
• 42:49
  Speaker 2: like mid eighteen hundred, huh, late eighteen hundreds Australia, so
• 42:54
  Speaker 2: you know what was really powerful about the book is
• 42:57
  Speaker 2: it was like, wait, let's just celebrate the people who
• 42:58
  Speaker 2: succeeded despite the fact that they did it against lots
• 43:02
  Speaker 2: of odds and maybe they didn't have traditional male success,
• 43:05
  Speaker 2: but they were there, they did get their degree, they
• 43:08
  Speaker 2: did their work, and let's learn about them. And so that,
• 43:13
  Speaker 2: I would say it was a big eye opener to me.
• 43:16
  Speaker 2: So there's a lot of underrated, particularly women or minority economists,
• 43:22
  Speaker 2: that we don't think about at all.
• 43:25
  Speaker 1: What's one thing your kids have taught you about AI?
• 43:29
  Speaker 2: They didn't ask for it and they don't really want it.
• 43:32
  Speaker 1: Yeah, my kids are also anti AI, like it's weird.
• 43:36
  Speaker 1: I'm way more into it than they are.
• 43:38
  Speaker 2: Yeah, that's that's where we're at in your household. But
• 43:41
  Speaker 2: I think what they taught me is, I didn't ask
• 43:43
  Speaker 2: for this. It's going to upend my life. I don't
• 43:45
  Speaker 2: know how to live with it. And why why do
• 43:48
  Speaker 2: I have to do this? You didn't have to Why
• 43:50
  Speaker 2: do I have to navigate this thing?
• 43:51
  Speaker 1: I mean, I also didn't ask for it, and I
• 43:53
  Speaker 1: also would prefer that it not exists. Uh, but it
• 43:57
  Speaker 1: does exist, right they did?
• 44:02
  Speaker 2: You know? They've also taught me how hard it is
• 44:08
  Speaker 2: living on spotlight. Yeah, I lived in the spotlight. I
• 44:11
  Speaker 2: mean when my son was eight, he said no for
• 44:15
  Speaker 2: social media of me please, mom Oh yeah, never, And
• 44:19
  Speaker 2: I have not posted a photo of him on social
• 44:21
  Speaker 2: media since he's maybe he was even seven. It was
• 44:25
  Speaker 2: just like, no, I don't want this big digital footprint
• 44:29
  Speaker 2: on a smart kid.
• 44:30
  Speaker 1: That's like a that's like a humble brag right there.
• 44:36
  Speaker 2: I thought it was really interesting at such a young
• 44:38
  Speaker 2: I mean, it's worrisome at such a young age to
• 44:41
  Speaker 2: be so cognizant of what that means.
• 44:43
  Speaker 1: It's tough to be a smart kid. Uh okay. Last one,
• 44:50
  Speaker 1: what's your favorite way to use AI that has nothing
• 44:52
  Speaker 1: to do with work?
• 44:54
  Speaker 2: Plan vacations? Spring break? I was like, I have four days.
• 44:58
  Speaker 2: I want a NonStop flight. I want to travel no
• 45:01
  Speaker 2: more than four hours three hours. Where should I go?
• 45:05
  Speaker 2: And it was like it kept pushing me to the
• 45:07
  Speaker 2: beach and I was like, no, I don't want to
• 45:08
  Speaker 2: be just like fine, go to Arizona.
• 45:11
  Speaker 1: So we did.
• 45:12
  Speaker 2: How was it was amazing. I had never hiked in
• 45:16
  Speaker 2: the Grand Canyon before, and now I am determined that
• 45:20
  Speaker 2: I'm going to do the Rim to hike from the
• 45:22
  Speaker 2: North Rim to the south Rim.
• 45:24
  Speaker 1: You do it in a day, you go down and up.
• 45:27
  Speaker 2: Well that some people do that, but I'm not insane.
• 45:29
  Speaker 2: I'm going to do it in two.
• 45:30
  Speaker 1: Days and you camp, you camp on the river.
• 45:33
  Speaker 2: You camp on the river.
• 45:33
  Speaker 1: Yeah that sounds incredible.
• 45:35
  Speaker 2: Yeah, I I that's where see, there's a lot of
• 45:39
  Speaker 2: things to do if we don't work so much.
• 45:42
  Speaker 1: Yes, although I'll be honest, I like working better than hiking.
• 45:49
  Speaker 1: I mean, I like hiking. I love exercise. I'm not
• 45:54
  Speaker 1: I like birds, But like, if I could do some work,
• 45:56
  Speaker 1: you know, I mean, I'm interested in some work, you
• 45:59
  Speaker 1: know what I mean? Like me too, Like if I
• 46:00
  Speaker 1: could work a few hours a day. The other thing
• 46:03
  Speaker 1: is like I mean, if there's if we're really dreaming big,
• 46:06
  Speaker 1: like if there's a universe where like I don't need
• 46:09
  Speaker 1: worry about money and I can make a podcast and
• 46:11
  Speaker 1: people like it. That's actually the part that I like, right,
• 46:14
  Speaker 1: And so uh, you know, I.
• 46:19
  Speaker 2: Think aren't we entering that era where we're all just
• 46:21
  Speaker 2: creating content for each other? So as long as we.
• 46:25
  Speaker 1: Go anything else you want to say or talk about, I.
• 46:33
  Speaker 2: Think like as a thing to remember is like we
• 46:38
  Speaker 2: can't actually improve our living standards without an increase productivity,
• 46:43
  Speaker 2: and so that's the reason why we can't be totally
• 46:46
  Speaker 2: afraid of AI. But we do need to realize that
• 46:51
  Speaker 2: we're responsible for how our society responds to it. We
• 46:56
  Speaker 2: don't need to let it just happen to us. It
• 47:00
  Speaker 2: is something that we have some control over and we
• 47:04
  Speaker 2: should take it. And this is where we need to
• 47:09
  Speaker 2: start to have how should we spend our new wealth?
• 47:12
  Speaker 2: We should think of it collectively. I'd like to spend
• 47:15
  Speaker 2: some of it on vacation. I'd like to spend some
• 47:17
  Speaker 2: of it on national healthcare. But I definitely want us
• 47:20
  Speaker 2: to get it. And that's what AI, that's its potential
• 47:24
  Speaker 2: is it's going to give us the wealth that could
• 47:26
  Speaker 2: get us those things.
• 47:27
  Speaker 1: Thank you for being so generous with your time.
• 47:29
  Speaker 2: Yeah, it was awesome to talk to you. This was
• 47:32
  Speaker 2: super fun.
• 47:40
  Speaker 1: Betsy Stephenson is a professor at the Gerald R. Ford
• 47:43
  Speaker 1: School of Public Policy at the University of Michigan. Today's
• 47:47
  Speaker 1: show was produced by Gabriel Hunter Chang and edited by
• 47:50
  Speaker 1: Lydia Jean Kott. Our engineer this week was Hansdale Sheet.
• 47:54
  Speaker 1: We're always looking for ideas for who to talk to
• 47:57
  Speaker 1: and what to cover on the show. You can email
• 48:00
  Speaker 1: us at problem at Pushkin dot fm. You can find
• 48:04
  Speaker 1: me on x at Jacob Goldstein. You can find me
• 48:06
  Speaker 1: on LinkedIn. I'm Jacob Goldstein. Thank you very much for
• 48:09
  Speaker 1: listening to the show, and we'll be back next week
• 48:12
  Speaker 1: with another episode.