June 18, 2026 • 48 mins
Betsey Stevenson is a labor economist at the University of Michigan, and she was an economic adviser to President Obama. Betsey’s problem is this: How can we create a world where the benefits of AI are broadly shared?
Betsey draws on history – including how the invention of household appliances created a crisis of meaning for American women – to understand how we should respond to the challenge of AI. And she suggests policies to help spread the wealth AI could bring.
In this episode, Betsey explains:
- How Engels’ Pause serves as a warning for workers
- How 20th century women adapted to automation
- How AI has changed life for college students
- The argument for taxing AI firms and distributing the proceeds to the public
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Episode Transcript
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Speaker 1 (00:15):
Pushkin. I'm Jacob Goldstein, and this is what's your problem?
My yesterday is Betsy Stevenson. Betsy is a labor economist
(00:36):
at the University of Michigan. She's worked in Washington. She
was the chief economist at the Department of Labor. She
was on President Obama's Council of Economic Advisors, and I
wanted to talk with her because she's recently written a
couple of really interesting papers about AI. The problem Betsy
is thinking about ultimately boils down to how can we
(00:58):
create a world where the benefits of AI are broadly shared,
where lots of people benefit, almost everybody benefits. In our conversation,
Betsy and I talked about a few day different things.
We talked about what history economic history has to teach
us about what's happening now with AI. We talked about
the effects of AI that she is seeing as a
(01:19):
college teacher, and we also discussed the public policies that
Betsy thinks might help us live better in an AI world.
I appreciate the way Betsy focuses on meaning as well
as on money when she thinks about AI and its implications.
Speaker 2 (01:37):
And I also.
Speaker 1 (01:37):
Appreciate the way that she balances her worries about AI
with hopes about how AI might make life better.
Speaker 2 (01:44):
I'm not anti AI at all. I'm very pro AI.
The pro is that AI will make us so productive
that we can all have much higher living standards than
we have right now. That AI will give us enormous bounty,
that it will cure diseases, that it will solve problems.
So the fear is not the productivity gains. The fear
(02:07):
is always around does one person get the productivity gains
and all the income and other people just lose work.
Speaker 1 (02:14):
Did you have a moment yourself when you realized that
AI generative AI is a big deal.
Speaker 2 (02:23):
I think when chat TPT came out. That's what then
led me to think about it as Okay, we have
had an utterly transformative technological change before, and that was
when human strength got made completely redundant by machine strength.
Speaker 1 (02:42):
So let's talk about the industrial revolution. You have walked
us up to the Industrial revolution, and in particular, let's
talk about Engles pause name for Friedrich Engels Robin to
Karl Marx as batman. What was Engles pause?
Speaker 2 (03:00):
So engles pause is a period of time, and I
should say that phrase was coined by the economic historian
Robert Allen. I love that phrase that he coined, so
I want to make sure he gets his credit. And
then I want to explain to you what happened is
that we got this amazing technology that allowed us to
(03:21):
produce a lot more, and the workers who were living
in that era, who were born at the dawn of
this revolution, got nothing. Yeah, right, so real what do
I mean by got nothing? Real wages in England, in
the UK where this technology was being developed stagnated, so
(03:43):
they did not grow for the majority of workers, and
actually life expectancy fell.
Speaker 1 (03:51):
They got less than nothing. They got nothing would have
been great compared to what they got.
Speaker 2 (03:55):
Well, that created a lot of agitation and frustration. And
so what did we get? What we get out of
that is descriptions of this era by my great authors
like Charles Dickens. And then we get writers like and
Engels who start talking about how this just doesn't work.
It doesn't work if real people, the workers are being
(04:20):
you know, treated as an appendage of a machine dragged
under the juggernaut of capitalism.
Speaker 1 (04:25):
And so this this period is like basically what eighteen
hundred to eighteen fifty or something right.
Speaker 2 (04:31):
Seventeen sixty to eighteen forty. And the reason the Industrial
Revolution came to the UK was because it was the
most advanced economy around and the workers were most expensive
and the returns to capital were highest. So that's actually
really an important part of the story. It didn't go
to France. Those peasants weren't as expensive.
Speaker 1 (04:52):
As there wasn't as much of an incentive to create
machines that could do the work people could do because
the people were cheaper.
Speaker 2 (04:59):
Exactly, yeah, exactly. So this is now you're starting to
like see the dots I'm going to connect here, like
where are workers right now expensive? Yeah, in the United States?
Speaker 1 (05:13):
And even which workers are expensive?
Speaker 2 (05:15):
Which workers are expensive? Where are the incentives to replace
workers with machines? And it's American white collar workers.
Speaker 1 (05:27):
Yes, so right, so the incentives are aligned. The cautionary
tail in angles pause is you can have huge productivity
gains without wages going up. And I should add that
in the long run, wages have gone up a lot, right,
Like Marx and Engels came along at a moment where
it was like, look, there's the Industrial Revolution and wages
(05:49):
don't go up, and the rich guys won the capitol,
get all the money, but in the long run, that
in fact has not happened. And like wages today are
higher than they have ever been. Nobody will believe me
if I say it, and people will get mad if
I say it, But it's true.
Speaker 2 (06:02):
Of course it's true. I mean the at the end
of the day, wages can't go up unless we get
technological change.
Speaker 1 (06:09):
Well, right, But they don't have to, is the point
of the story. And yet they have in the long.
Speaker 2 (06:14):
Run exactly, so they don't have to. So that's why
Angeles Pause is a cautionary tale. Yeah, And so then
the question is what are the institutional and societal frameworks
we need to make sure that they do.
Speaker 1 (06:29):
Right to make sure that productivity gains are shared by
workers as well as by the owners of capital, which
in this case is basically the people who own the
four Frontier AI companies.
Speaker 2 (06:41):
If we're being reductive at it, exactly.
Speaker 1 (06:44):
Okay, So that's one. I want to do. One more
historical story that you tell that frankly was more surprising
to me that I hadn't heard before in this context
before we get to the present and then to the future,
and that one is about labor saving devices in the
home in the mid twentieth century, the story of sort
(07:04):
of you know, the a washing machine and the dishwasher
and what that meant for economics and for meaning in
people's life, which you talk about in a recent paper.
Tell me about that.
Speaker 2 (07:14):
You know, women have already gone through a transition where
their work was completely replaced by machines, and they had
to adapt. And that's because women played big roles inside
the home as homemakers who baked food from scratch, who
kept houses clean, who did all the things somebody needed
(07:38):
to do at home. And then they got vacuum cleaners
and dishwashers and prepackaged foods. And the first thing that
happens is the standards and expectations rise. So the first
thing that happens is the houses have to be cleaner,
the food has to be nicer, everything has to be
(08:00):
done better. So they use all of this technological improvement,
these productivity gains to just produce more home stuff. And
that lasts for a little while, but it starts to
really gnaw at some women. And that's when we get
a problem that has no name. And Betty Fergan basically
(08:24):
says the problem is women really have nothing to do
at home.
Speaker 1 (08:28):
Huh. This is the feminine mystique who published in what
nineteen sixty or so?
Speaker 2 (08:33):
Right, why are the housewives so bored? They're bored because
robots can do all their jobs and they need something more.
And you know, if you go back to your you know,
nineteen seventies eighties feminist literature, you see that some of
them are advocating for women getting into the workplace, creating
(08:55):
new identities as you know, breadwinners as career women.
Speaker 1 (09:00):
Which happened at a profound level, right, I think that
shift is underappreciated, like the I mean, people know at
a superficial level, oh yes, women to the workforce, But
when you look at a macro level, it is a
profound transformation that happened in a relatively short period of time.
Speaker 2 (09:16):
A profound transformation not only in what women do, but
how they see themselves that happened very quickly. Over like
you know, one to two generations, women went from you know,
seeing themselves as you know, potentially happy housewives to wanting more.
(09:37):
They expect more from their marriages, they expect more from
their life, they expect more from themselves as independent intellectual creatures.
Speaker 1 (09:46):
Yeah.
Speaker 2 (09:46):
And one of the things I found interesting to tie
it to this current moment is not every feminist thought
or things that women need to enter the labor force
to have this richer identity.
Speaker 1 (10:00):
Yeah, there's this amazing quote in your paper. You know what,
it's going to be your smind you say it.
Speaker 2 (10:06):
I know it is like my favorite quote. I feel
like I need to my office bulletin board, which is
I didn't fight to get women out from behind the
hoover just to get them on the board of Hoover.
Speaker 1 (10:19):
Yes, Hoover, the vacuum cleaner.
Speaker 2 (10:21):
Yes, right, and Hoover was a big brand of vacuum
cleaner back in the day. And that's your main greer.
And she's pointing out, no, that's not. The point is
not to swap one form of drudgery for another.
Speaker 1 (10:32):
Yeah, which is profound and different. Right, Like I guess
because of my age or you know whatever, the universe
I live in is the is the universe of like, oh, yes,
women have jobs like men, and men help out around
the house, and like this is the happy new equilibrium.
But this quote is something entirely different and interesting in
(10:52):
the context of AI. Right, what we're walking toward I
mean the sort of these two. I like these two
stories in parallel because the the Engles pause story is
the one we're always thinking about and talking about, which
is money and essentially labor versus capital. Right, So that's
every day everybody's talking about. The other one is meaning, right,
(11:12):
like the problem that has no name, the housewife whose
skills are are devalued by automation. It's not a money problem,
it's a meaning problem, right, And this idea that like, no, no,
the point is not just to go to work. The
point is find some new source of meaning, like, oh,
this is interesting when one thinks about ai.
Speaker 2 (11:32):
Yeah, it's a meaning and identity to you know. The
the Japanese have this concept of meaning called eke guy Yeah,
that I pay a lot of attention to. And I
just was in Japan about a year ago discussing a
paper where they were using in their national survey. They're
surveying people on their their life satisfaction, their subjective well being, happiness,
(11:57):
and on their eke guy.
Speaker 1 (11:59):
Yeah. And just to be clear, just define ekey guy.
Speaker 2 (12:02):
Eke guy is your purpose for living, Okay, your your
purpose for living. And so it's kind of what is
the thing that drives you. What is it that you
care about every day? What do you up and do?
And so for people, their purpose could be really small,
like tending their garden. They get it up every day.
And I tend my garden because my garden's important to me,
(12:24):
and my purpose of maintaining a beautiful garden is contributing
to this world in some way.
Speaker 1 (12:28):
Yeah, And and we have these sort of set ones. Well,
the big one for lots of us is work, right
or a big one and a relevant one in the
context of what we're talking about is work. So we've
set the table now in a nice way. And so
I want to talk. I want to talk a little
bit about the present, and then I want to talk
(12:49):
about the future. I want to return to money and meaning.
But the present is really interesting to me, and I
feel like kind of under discussed in the context of AI.
Like everybody mostly talks about what's going to be like
if whatever the eye takes our jobs. But a lot
is happening right now. And so I'm curious about your
experience of AI the present, both as a as a
(13:11):
teacher and as a as an economist. Right, how has
AI changed being a college teacher.
Speaker 2 (13:19):
Oh, so there's so many things in there. So one
thing that's obvious is that the students are struggling with
the idea of productive struggle. And I think the reason
for that is cognitive offloading is too easy.
Speaker 1 (13:43):
So there's a lot of You've said a lot of
semi jargon things in there, like productive struggle is interesting.
I get it. I think what's productive struggle?
Speaker 2 (13:55):
So productive struggle? I mean I talk about this with
my thirteen year old. Productive struggle is sitting in the
math class and not knowing how to do the problem
and just sitting with that and trying to figure out
how you would solve it. How are you going to
figure out how to do the math problem? And you don't.
(14:17):
It feels frustrating, but you can stay in that moment
of frustration and you can work through it, and you
know what, it's super satisfying when you get to the
other end. The productive struggle is that it's awful.
Speaker 1 (14:34):
I don't know.
Speaker 2 (14:34):
I bet you've had this in your career. Have you
ever sat there writing something and been like, this is
the worst thing happen?
Speaker 1 (14:39):
Oh? Yeah, I hated right. I wrote a book and
that was my experience of writing a book. It was
I was. I was unhappy for a year. Basically, you're
I'm happy that I wrote the book. I'm happy that
I wrote the book, and I would do it again exactly.
Speaker 2 (14:53):
But you would do it again. You would do it
again because actually it's probably meeting that ekey guy.
Speaker 1 (15:00):
Yeah it was.
Speaker 2 (15:01):
It wasn't fun, yeah, but it was purposeful. You did
it with purpose, and it made you feel like you
we're on a journey to somewhere good in your brain
and your heart and your soul. But you gotta have
enough incentives to want to be on that path because
it's not actually fun. Yeah, And that's like I see that.
(15:25):
As I said, I got a thirteen year old in
seventh grade. We talk about productive struggle a lot.
Speaker 1 (15:31):
I'm bringing that one home. I'm bringing that I got
two teenagers. It's putting it in my dad tool belt.
Speaker 2 (15:38):
It productive struggle is like, this isn't fun. I'm not
gonna tell you it's fun. I'm not gonna tell you
it's not that hard, buddy. Just you know, I'm gonna say, oh, yeah,
this sucks, but we're gonna go through it and then
we're gonna get to the other side. We're gonna be
glad because now you're gonna know how to do you know,
(15:59):
a multi equation algebra problem. Yeah, and a lot of
like how we process the world as a place we
want to be, it is through something's hard and then
we achieve it and then we feel pride, and that
is an important set of emotions and I think that
(16:21):
AI is robbing students of that.
Speaker 1 (16:24):
So okay, So that's an interesting sort of macro ish
college teacher problem. So basically they don't have any incentives
to do the work because they can just get chet
Gpt to do it for them, Claud to do it
for them.
Speaker 2 (16:34):
Yes, And so then when you're asking them to do things,
you know they're struggling, but they also are not having
fun learning. I had a student who presented I taught
an AI policy class, and she actually presented on this
topic and she said, school's not as fun as it
used to be.
Speaker 1 (16:54):
Wow.
Speaker 2 (16:55):
And she was a graduate student who was doing a
master's and she was one of those students who'd been
a great student in high school, grade student in college
and she was doing great in her master's degree. But
she really described it as I no longer have productive
struggle with the aha moment.
Speaker 1 (17:14):
It's the problem that has no name.
Speaker 2 (17:16):
Yes, we're already getting to the problem that has a name.
Speaker 1 (17:20):
Yeah, do you feel it in your own work?
Speaker 2 (17:23):
So I feel like I have to be very careful,
and I definitely see myself where I get lazy. So
I use the word cognitive offloading. Yeah, there's a problem
I could wrestle with, or chatchipt could wrestle with it,
and then I could just much more easily absorb it.
Research does show when you stop the struggle, you stop
(17:43):
the learning.
Speaker 1 (17:44):
I mean the dream, right, the sort of Pollyanna ish
version is the model. The AI allows you to struggle
at a higher level, right, Like, it gets somewhere, It
gets through some work that you could have done but
that wouldn't really push any frontiers, and it gets you
to this new place. And then you struggle, but you
struggle better, or you struggle farther, or something like. Yep,
(18:07):
I think I plus AI can write better still than
just a I. I think I can write a better podcast
script for me than Claude can. And so maybe not forever,
but at least for now. And so then how can
I use it optimally? How can I use it to whatever,
be more efficient or be smarter.
Speaker 2 (18:27):
So I'll give you a couple of examples of how
I've used it recently. So I was writing, I was
participating in a little debate, and I had to have
my five minute opening statement, and I just went back
and forth and asked a ton of questions to chat GPT,
and then I followed the links to the sources it found.
And then that made me come back with more questions
(18:49):
and ask about more sources. And it was it was like,
basically I would have given that to research assistant. Yeah,
I would have come back and said, here, look at
these five you know sources, and that would really helpful.
But I'll tell you the other funny thing was I
drafted my five minute opening and then I pasted it
in and I said fact check this and te h,
(19:10):
and it immediately gave me back just like a rewrite,
and I wrote, that was sloppy. I want you to
go line by line and fact check it. I'll do
my own tightened things. Yes, And it was like, whoops, Okay.
Speaker 1 (19:24):
You're right to push back. It gives you your right
to push back.
Speaker 2 (19:29):
Yeah. It then complimented me on my Yes, my great job.
But the line by line fact checking was amazing, Like
it gave me an external source for everything I said,
and then it said, oof, this Clayton seems a little
maybe over the top. Do you really want to make it? Yeah?
So then it finally gave me some like negative feedback
(19:50):
because I made it go line by line and tell
me where's the external source that justifies it? So it
is on how we use it. But I don't think
we're training young people right now properly on how to
use it.
Speaker 1 (20:04):
Are you getting better at helping your students use it?
Getting better? Like it seems like what you want is
to figure out a way that your students can use
AI and still have the productive struggle.
Speaker 2 (20:16):
I think like we're really in the Like I would
say that at the end of this semester, I found
that there are changes I'll make for next year.
Speaker 1 (20:27):
So it's tough times. So you're saying it's tough times.
Speaker 2 (20:30):
I'm saying, the students are getting worse quite quickly on
this than and so the speed at which I need
to address it is getting faster.
Speaker 1 (20:39):
Like do you feel like you're going to have to
start making the students do work in class?
Speaker 2 (20:45):
Boy? Like, I definitely think I have to change the
assignments and the way that I assess them because having
them write an essay at home is useless. Now, Yeah,
I like in one of my classes, one of the
best sets of essays I got was from a student
who couldn't answer a single question I asked class about
his essays because he clearly didn't write them, but he
(21:06):
obviously knew how to prompt well. GBT wrote me some
beautiful essays I was telling another instructor. Yeah, I loved
reading those essays says I prepared for class because I
knew it was a really good summary of what I
needed to cover that day, because it was they were
reading summaries, and he was the best at the class
(21:30):
at prompting. But the students who I could see the authenticity,
and students who might have used AI to help them
a little bit, but the actual genuine things that they
were noticing from the reading that they thought were most relevant.
It was clearly authentic to them and their approach. And
then when I have a dozen unique perspectives, now we
(21:51):
have a discussion, we have a dozen beautifully crafted summaries
from AI. We don't have a discussion.
Speaker 1 (22:02):
After the break, Betsy and I talk about the future.
We talk about money and meaning and purpose in an
A I world. Let's go to the long term, right,
you have written this paper where you have you sort
(22:24):
of lay out kind of what your worries are and
what you think we should do in response to your worries.
So one, it's kind of a classic but interesting in
this context is how much we tax capital gains basically
profits from investments, versus how much we tax labor work, right,
(22:45):
income tax? Talk about your view on that.
Speaker 2 (22:48):
So the historic view on taxing labor versus taxing capital
is that people are going to work, and they're going
to work sort of no matter whether you tax them
or you don't. So we tax labor income because we
need revenue. So in contrast, the idea was that people
(23:09):
really like to consume today, and so getting them to
invest their money in something that they're gonna not get
any money for a long time. You know, they invest
in a new factory and that's going to give them
purchasing power in ten years but nothing today. So they're
gonna have to give up purchasing power today in order
(23:30):
to build a factory. Well, we figured that they needed
a lot of incentives to do that because people like
spending today. So how do I get you to give
up spending today in order to save and invest in
that factory. Well, I don't want to tax that behavior.
And so we have traditionally put more of our taxes
on labor than on capital investments. So if I go
(23:55):
out and I work and I make one hundred thousand dollars,
I pay more in taxes than if I, you know,
if I invest and I'm starting to make one hundred
thousand dollars a year off of my investment, and I
think with a it's going to start to change how
we should think about that because it makes capital cheaper
(24:16):
because it's a smaller tax wedge.
Speaker 1 (24:19):
So if you have a choice between employing somebody and
well paying for AI, buying a bunch of AI tokens
that costs the same amount of as the person, it's
the tax system makes you prefer the AI investment to
the human being exactly under our current rules.
Speaker 2 (24:37):
Okay, yeah, so the tax system prefers capital investment over
human labor. And so right now we have a tax
system that is rewarding what is currently happening, which is
the labor share of income is going down, and the
share of income that's generated by investments by capital is
(25:00):
going up, and that also creates a revenue problem. You
might have heard the US is running some debt, but
it's also creating a disincentive to hire people over capital
that we probably want to move the other direction. We
actually probably want to subsidize hiring people and maybe just
(25:22):
slow down a little bit the rate at which companies
are moving towards capital substitution.
Speaker 1 (25:29):
So concretely, does this boil down to raise the capital
gains tax.
Speaker 2 (25:36):
Lower taxes on wages, and make it up by raising
the capital gains tax.
Speaker 1 (25:41):
The revenue neutral version. Yeah, I mean, to be clear,
from the point of view of the employer, lowering the
income tax, not the payroll text, but the income tax.
You don't care about that as the employer. I mean,
it might make workers a little bit more willing to
work at the market, Yes.
Speaker 2 (25:58):
It should make workers a little bit more willing to work. Yeah,
And that means, frankly, you can pay them a little
bit less. And that is in fact what the data
says happens. Okay, so this would show up as savings
and compensation costs to employers.
Speaker 1 (26:13):
So higher income.
Speaker 2 (26:15):
That's not going to be a lot of people are
properly disappointed right now. Wait, wait, you mean the employers
capture a cut in labor income taxes. Yeah, yeah, that's
what happens.
Speaker 1 (26:27):
Okay, So that's one. So you also have this idea,
uh you call the data dividend, which is very good.
Reminds me of your time in Washington. What's the data dividend?
Speaker 2 (26:38):
So, I mean, the thing that's most remarkable about AI
is we've all built it open AI didn't build AI.
Anthropic didn't build AI. They're training on every word I've
ever written, every word, my grandparents wrote, every word, my
grandparents grandparents wrote, and you know all it's not just
(27:00):
you know, I yes, have speeches in the public domain,
but I really mean it more more broadly than that.
All of the knowledge of humanity is is what has
trained these AIS. So when Sam Altman says, I envision
a world where cognition is sold to you by the token, Yeah,
like wait, he's envisioning the world in which he's sucked
(27:21):
up all of human cognition and now he's going to
sell it back to us. Well, I'd like him to
pay us for it first.
Speaker 1 (27:28):
Uh huh.
Speaker 2 (27:29):
And that shared resource, that public good that all of
human cognition that has trained these models, that should give
each and every one of us a steak and the benefits.
And that's what the data dividends about. So think about
it like the Alaska Permanent Fund. Alaska's sitting on a
(27:51):
bunch of oil. That oil belongs to the citizens of Alaska,
and every year they get a check from the government
that says, this is your share of what we made
off the oil.
Speaker 1 (28:02):
Yeah, it's such an interesting If I was an economist,
I'd move to Alaska and just study that for my
whole life.
Speaker 2 (28:08):
There are economists who have studied it, and it turns
out it doesn't people still go to work, but they now,
I'm going to get beyond that. I mean, it's more
than economics. So from an economics perspective, that puts a
certain amount of consumption in every Alaskan's hands, a data
dividend could put a certain amount of consumption in everybody's hands.
(28:31):
But it does more than that. It also says you belong,
you're part of this. And so when we developed this,
everybody's probably heard a lot about like universal basic incomes,
but it's not about giving losers a handout. That language
(28:51):
infuriates me. You belong, you're part of this. We built
this together, and we all get to benefit. That's what
we have to recognize. And those are the models that
institutional models and frameworks we're going to have to build
for this to be something that's successful, that levels up everybody.
Speaker 1 (29:15):
Not to bring crass details to the slevated discourse, but like,
in practice, is the thing you're talking about, the sort
of frontier AI companies paying some kind of tax that
has then distributed to everybody in America? Is that fundamentally
the thing you're imagining?
Speaker 2 (29:34):
Yeah, what's wrong with that? I mean, they could have
paid for all the data they've used, But we had
this model where we put a bunch of stuff in
the commons. That's not how we envisioned the commons. We
didn't create the commons so one company could capitalize on
all of it, and that I think it means that
we have to think about how we want to treat
(29:56):
the commons in this new era, because it can't just
be one company sucks the whole thing up and then
we all lose. I think it's an institutional failure to
let people get rich off of what's in the commons
and then try to sell it back to us. I
don't want my own cognition sold back to me.
Speaker 1 (30:16):
So we've been talking mostly about the money piece, although
you started talking about meaning in there. But let's talk
more about meaning. I mean, we sort of framed this
in those two ways, right. The meaning piace in some
ways is harder to figure out for me. I mean,
the money base is like, well, if productivity goes up,
there's gonna be more money, and like that's a good
(30:38):
starting place and then hopefully we could figure it out.
The meaning piece seems squishier and harder, Like, what do
we do about meaning if there is in fact a
lot of unemployment and we have solved money.
Speaker 2 (30:51):
Well, you know, the person who really identified that we
were on a really negative trajectory on meaning was Bob Putnam,
who wrote a book called Bowling Alone in the nineties,
and there what he was saying is we're abandoning our communities.
We don't bowl and bowling leagues anymore, even though we
could pull just as often, we just go into it
(31:12):
by ourselves or in small groups. And what had what
that has meant is that we've loaded too much onto
work so work is becoming our community. Are people refer
to their work family? Right? So we have successful transitions
and unsuccessful. So we talked about women successfully transitioned outside
(31:33):
the home and it developed new identities, new meaning, new purpose.
People successfully transitioned out of agriculture. You know it. You
know even since two thousand, we went from forty percent
of the globe working and agriculture to about twenty five
percent today and there's no agricultural meaning crisis. And what
(31:54):
that meant was people were pulled out of agriculture by opportunity.
Now let's compare that with what happened with manufacturing, where
men have been pushed out of manufacturing, not pulled, not
pulled by opportunity, pushed by closures and factory closures that
have left nothing in its way, no alternative. And that's
(32:17):
what I think is a really important lesson for us
to think about from a meaning and identity perspective, because
you have a ton of men who still are thinking
there are no jobs for me because the only jobs
being created are in health care, and health care jobs
are so feminized, and so that real identity and meaning
(32:40):
crisis exists, and it's spilling into family formation. And having
a lot of negative effects. And I think it is
a warning to us that we have to figure out
how to pull people out of opportunities that are closing
into something that's better, rather than letting them get pushed
(33:00):
off a cliff. But how well, one thing to do
is to start to build more community works, to try
to find ways to reward volunteer work, to reward community involvement.
You know, there are lots of things people do outside
of work that build meaning. That could be being a
(33:22):
member of a club, that can be you know, volunteering
at a particular organization.
Speaker 1 (33:29):
I mean, I know what the things are, but like
how do we do it?
Speaker 2 (33:33):
Yeah?
Speaker 1 (33:33):
I mean, as you pointed out, somebody wrote whatever forty
years ago, thirty years ago, that we're not doing it anymore.
And that was before we had phones and you know,
things to just lock up our brains all day. Like everything,
even with jobs, is going in the opposite direction. People
are becoming less and less and less social.
Speaker 2 (33:54):
Well, this is why I think about putting something together
that creates rewards. Whether it's your data dividend is a
function of logging community service hours.
Speaker 1 (34:06):
Uh huh. You got to leave the house to get
your data divid you gotta get talking to a human
being face to face.
Speaker 2 (34:12):
Right, and you know, people want to interact with people.
I really really believe that.
Speaker 1 (34:19):
I mean, are there, like, you know, are there successful
examples of this like that, you know, people leaving agriculture
to go into manufacturing. Like that is fundamentally the market
solving the problem, right, and a weird potential outcome we're
looking at is like there is not a market to
solve the problem, and like the notion that you can
(34:41):
have some kind of top down solution to this problem
of meaning.
Speaker 2 (34:46):
Well, we could have one thing we could do is
government could start to subsidize third spaces and draw them in.
So we could choose to make third spaces all tax free,
no property tax on public space.
Speaker 1 (35:03):
That's the Starbucks Act of twenty This is.
Speaker 2 (35:07):
Going to let you in without buying anything. Then they
get they get a property tax reduction. You start to
think of creative ways to pull people into the community
so they do things, and honestly, and afternoon at Starbucks
is a refreshing way to engage with humans. Talking to
(35:27):
the burista, seeing the people who go around the same
time you do every day, Those small moments of connection
actually are really important to people, more important than they
realize or they would prioritize them more.
Speaker 1 (35:40):
So. You teach teenagers and very young adults, and you
are the mother of one teenagers of two teenagers, Like,
what do you tell the kids? As a labor economist
who has studied AI, what is your professional advice for
the children?
Speaker 2 (36:03):
Oh? I mean, I'm going to say, Actually, I find
it really hard. I was at a I gave a
talk at Syracuse and it was a big talk on AI.
And afterwards, a young man came up to me and
he said, I'm risking everything to be here. I borrowed
(36:26):
so much money, and how do I know my investment's
not going to go down the tube? And I almost
cried for him because I can't tell him his investment's
not going to go down the tubes. I don't know,
And I think I want just remember that when you're
talking to young people, remember the amount of risk and
(36:47):
uncertainty they're facing. It's like nothing you and I faced.
And I took all sorts of crazy gambles, but I
didn't have a spotlight on me. First of all, they
didn't have the risk of everything being on camera. Everything
being documented, and I could know with some certainty if
(37:10):
I study the field like economics, things are going to
be okay for me. I knew I was a math
econ major because I wanted to reduce to economic uncertainty
in my life. I'd grown up with a lot of it,
and I was like, these are two majors they got
to payoff. I can work in finance, I can work
(37:31):
at a corporation. I could you grow up to be
an econ teacher or a math teacher. I had a
lot of bases covered, and I don't There's almost nothing
I can tell kids now where I'm like, this will
work for you. But what I do tell them is
adaptability is going to really matter. Curiosity is your friend,
(37:57):
and creativity. Despite everything you hear about AI, your creativity
is unique to you. Everybody's creativity is like a fingerprint.
And I think that we will have a desire for
bespoke human creativity even as the AI starts to do things.
(38:21):
So I think at the end of the day, what
I use to say, ultimately there will be something to
do is to realize all exchange from the beginning of
time to now is two human beings, one of who
wants something that they cannot provide themselves, and the other
(38:42):
one can provide it. And I do not think AI
is going to eliminate all of our needs, and so
we will still be looking to each other to fulfill
those needs. And as long as you learn how to
be attentive to other people's needs, you will find your
space in this world.
Speaker 1 (39:01):
We'll be back in a minute with the lightning round. Ah,
let's finish with the lightning round, all right. What is
something other than family and work that gives you meaning
in life?
Speaker 2 (39:21):
Birds?
Speaker 1 (39:23):
Birds?
Speaker 2 (39:23):
I really like birds?
Speaker 1 (39:25):
You got a list? You like that kind of burder No?
Speaker 2 (39:30):
But I really I enjoy nature and wildlife. And when
we go to Australia, I am very into the Australian birds.
And I really like if you've ever spent time with
like cockatoos or other parents, they're so smart and they're
really like a joy to interact with.
Speaker 1 (39:52):
I should say that you're what do you call justin
your life partner? Your common law husband is Australian?
Speaker 2 (39:58):
Is Australian?
Speaker 1 (40:00):
And like, do do people like hang out with cockatoos
and parrots in Australia? Is that just like a thing?
I don't know, I like, I go down.
Speaker 2 (40:06):
To the park and I feed them all the time,
milk you right out of my hand, and he has
strains or like stop feeding the clocket two's I'm like.
Speaker 1 (40:13):
No, it's too awesome.
Speaker 2 (40:18):
Uh do it away from people's houses, though, because otherwise
they will they will throw a little tanty and knock
on the wood to get you to bring out the nuts.
Speaker 1 (40:27):
Good to know. I'll keep that in mind. So, I
know you've done a fair bit of work on the
economics of family life, and I'm curious if there's any
insights from that that are like useful to you in
your own daily life.
Speaker 2 (40:46):
Well, I guess thinking about the what you should make
versus buy and how to really think hard to opportunity
call us cost benefit analysis to make that decision, and
you might be surprised. Like, you know, I told you
I was on the board a lift and Justin's always like,
why do you act like a lift driver for our
(41:09):
teenager when you could actually pay for a lift. And
I'm like, because they talk more in the car. You know,
every parent heard this thing where your teenager tells you
something that they wouldn't tell you if they weren't in
the counter side by side with you and trapped there
for a little while.
Speaker 1 (41:28):
Yeah.
Speaker 2 (41:28):
So, I mean I spent it Saturday, like two weeks ago,
driving for four hours to get my daughter from like
attract me to a friend's house for prom too. Like
it was just ridiculous. But it's building a connection that
I can't get elsewhere. So if you think about what
cost benefit analysis says, it never says do or don't
(41:51):
do something. It says only do them when the benefits
succeed the costs. And so I'm very good at thinking
through non financial benefits and financial benefits and non financial
costs and financial costs and making decisions that I feel
really comfortable with.
Speaker 1 (42:09):
That was a ten of ten answer, Most underrated Economists
of all Time.
Speaker 2 (42:19):
Well, I mean there's a lot I should I Actually
I'm going to give you it. I just wrote the
forward to a book of Australian Women in Economics, Okay,
And actually there were some just absolutely incredible women in
that book because they were using the insights of family
(42:46):
life and bringing it into economics. And you know, you're
like mid eighteen hundred, huh, late eighteen hundreds Australia, so
you know what was really powerful about the book is
it was like, wait, let's just celebrate the people who
succeeded despite the fact that they did it against lots
of odds and maybe they didn't have traditional male success,
but they were there, they did get their degree, they
(43:08):
did their work, and let's learn about them. And so that,
I would say it was a big eye opener to me.
So there's a lot of underrated, particularly women or minority economists,
that we don't think about at all.
Speaker 1 (43:25):
What's one thing your kids have taught you about AI?
Speaker 2 (43:29):
They didn't ask for it and they don't really want it.
Speaker 1 (43:32):
Yeah, my kids are also anti AI, like it's weird.
I'm way more into it than they are.
Speaker 2 (43:38):
Yeah, that's that's where we're at in your household. But
I think what they taught me is, I didn't ask
for this. It's going to upend my life. I don't
know how to live with it. And why why do
I have to do this? You didn't have to Why
do I have to navigate this thing?
Speaker 1 (43:51):
I mean, I also didn't ask for it, and I
also would prefer that it not exists. Uh, but it
does exist, right they did?
Speaker 2 (44:02):
You know? They've also taught me how hard it is
living on spotlight. Yeah, I lived in the spotlight. I
mean when my son was eight, he said no for
social media of me please, mom Oh yeah, never, And
I have not posted a photo of him on social
media since he's maybe he was even seven. It was
(44:25):
just like, no, I don't want this big digital footprint
on a smart kid.
Speaker 1 (44:30):
That's like a that's like a humble brag right there.
Speaker 2 (44:36):
I thought it was really interesting at such a young
I mean, it's worrisome at such a young age to
be so cognizant of what that means.
Speaker 1 (44:43):
It's tough to be a smart kid. Uh okay. Last one,
what's your favorite way to use AI that has nothing
to do with work?
Speaker 2 (44:54):
Plan vacations? Spring break? I was like, I have four days.
I want a NonStop flight. I want to travel no
more than four hours three hours. Where should I go?
And it was like it kept pushing me to the
beach and I was like, no, I don't want to
be just like fine, go to Arizona.
Speaker 1 (45:11):
So we did.
Speaker 2 (45:12):
How was it was amazing. I had never hiked in
the Grand Canyon before, and now I am determined that
I'm going to do the Rim to hike from the
North Rim to the south Rim.
Speaker 1 (45:24):
You do it in a day, you go down and up.
Speaker 2 (45:27):
Well that some people do that, but I'm not insane.
I'm going to do it in two.
Speaker 1 (45:30):
Days and you camp, you camp on the river.
Speaker 2 (45:33):
You camp on the river.
Speaker 1 (45:33):
Yeah that sounds incredible.
Speaker 2 (45:35):
Yeah, I I that's where see, there's a lot of
things to do if we don't work so much.
Speaker 1 (45:42):
Yes, although I'll be honest, I like working better than hiking.
I mean, I like hiking. I love exercise. I'm not
I like birds, But like, if I could do some work,
you know, I mean, I'm interested in some work, you
know what I mean? Like me too, Like if I
could work a few hours a day. The other thing
(46:03):
is like I mean, if there's if we're really dreaming big,
like if there's a universe where like I don't need
worry about money and I can make a podcast and
people like it. That's actually the part that I like, right,
And so uh, you know, I.
Speaker 2 (46:19):
Think aren't we entering that era where we're all just
creating content for each other? So as long as we.
Speaker 1 (46:25):
Go anything else you want to say or talk about, I.
Speaker 2 (46:33):
Think like as a thing to remember is like we
can't actually improve our living standards without an increase productivity,
and so that's the reason why we can't be totally
afraid of AI. But we do need to realize that
we're responsible for how our society responds to it. We
(46:56):
don't need to let it just happen to us. It
is something that we have some control over and we
should take it. And this is where we need to
start to have how should we spend our new wealth?
We should think of it collectively. I'd like to spend
some of it on vacation. I'd like to spend some
(47:17):
of it on national healthcare. But I definitely want us
to get it. And that's what AI, that's its potential
is it's going to give us the wealth that could
get us those things.
Speaker 1 (47:27):
Thank you for being so generous with your time.
Speaker 2 (47:29):
Yeah, it was awesome to talk to you. This was
super fun.
Speaker 1 (47:40):
Betsy Stephenson is a professor at the Gerald R. Ford
School of Public Policy at the University of Michigan. Today's
show was produced by Gabriel Hunter Chang and edited by
Lydia Jean Kott. Our engineer this week was Hansdale Sheet.
We're always looking for ideas for who to talk to
and what to cover on the show. You can email
(48:00):
us at problem at Pushkin dot fm. You can find
me on x at Jacob Goldstein. You can find me
on LinkedIn. I'm Jacob Goldstein. Thank you very much for
listening to the show, and we'll be back next week
with another episode.
Pushkin. I'm Jacob Goldstein, and this is what's your problem?
My yesterday is Betsy Stevenson. Betsy is a labor economist
(00:36):
at the University of Michigan. She's worked in Washington. She
was the chief economist at the Department of Labor. She
was on President Obama's Council of Economic Advisors, and I
wanted to talk with her because she's recently written a
couple of really interesting papers about AI. The problem Betsy
is thinking about ultimately boils down to how can we
(00:58):
create a world where the benefits of AI are broadly shared,
where lots of people benefit, almost everybody benefits. In our conversation,
Betsy and I talked about a few day different things.
We talked about what history economic history has to teach
us about what's happening now with AI. We talked about
the effects of AI that she is seeing as a
(01:19):
college teacher, and we also discussed the public policies that
Betsy thinks might help us live better in an AI world.
I appreciate the way Betsy focuses on meaning as well
as on money when she thinks about AI and its implications.
Speaker 2 (01:37):
And I also.
Speaker 1 (01:37):
Appreciate the way that she balances her worries about AI
with hopes about how AI might make life better.
Speaker 2 (01:44):
I'm not anti AI at all. I'm very pro AI.
The pro is that AI will make us so productive
that we can all have much higher living standards than
we have right now. That AI will give us enormous bounty,
that it will cure diseases, that it will solve problems.
So the fear is not the productivity gains. The fear
(02:07):
is always around does one person get the productivity gains
and all the income and other people just lose work.
Speaker 1 (02:14):
Did you have a moment yourself when you realized that
AI generative AI is a big deal.
Speaker 2 (02:23):
I think when chat TPT came out. That's what then
led me to think about it as Okay, we have
had an utterly transformative technological change before, and that was
when human strength got made completely redundant by machine strength.
Speaker 1 (02:42):
So let's talk about the industrial revolution. You have walked
us up to the Industrial revolution, and in particular, let's
talk about Engles pause name for Friedrich Engels Robin to
Karl Marx as batman. What was Engles pause?
Speaker 2 (03:00):
So engles pause is a period of time, and I
should say that phrase was coined by the economic historian
Robert Allen. I love that phrase that he coined, so
I want to make sure he gets his credit. And
then I want to explain to you what happened is
that we got this amazing technology that allowed us to
(03:21):
produce a lot more, and the workers who were living
in that era, who were born at the dawn of
this revolution, got nothing. Yeah, right, so real what do
I mean by got nothing? Real wages in England, in
the UK where this technology was being developed stagnated, so
(03:43):
they did not grow for the majority of workers, and
actually life expectancy fell.
Speaker 1 (03:51):
They got less than nothing. They got nothing would have
been great compared to what they got.
Speaker 2 (03:55):
Well, that created a lot of agitation and frustration. And
so what did we get? What we get out of
that is descriptions of this era by my great authors
like Charles Dickens. And then we get writers like and
Engels who start talking about how this just doesn't work.
It doesn't work if real people, the workers are being
(04:20):
you know, treated as an appendage of a machine dragged
under the juggernaut of capitalism.
Speaker 1 (04:25):
And so this this period is like basically what eighteen
hundred to eighteen fifty or something right.
Speaker 2 (04:31):
Seventeen sixty to eighteen forty. And the reason the Industrial
Revolution came to the UK was because it was the
most advanced economy around and the workers were most expensive
and the returns to capital were highest. So that's actually
really an important part of the story. It didn't go
to France. Those peasants weren't as expensive.
Speaker 1 (04:52):
As there wasn't as much of an incentive to create
machines that could do the work people could do because
the people were cheaper.
Speaker 2 (04:59):
Exactly, yeah, exactly. So this is now you're starting to
like see the dots I'm going to connect here, like
where are workers right now expensive? Yeah, in the United States?
Speaker 1 (05:13):
And even which workers are expensive?
Speaker 2 (05:15):
Which workers are expensive? Where are the incentives to replace
workers with machines? And it's American white collar workers.
Speaker 1 (05:27):
Yes, so right, so the incentives are aligned. The cautionary
tail in angles pause is you can have huge productivity
gains without wages going up. And I should add that
in the long run, wages have gone up a lot, right,
Like Marx and Engels came along at a moment where
it was like, look, there's the Industrial Revolution and wages
(05:49):
don't go up, and the rich guys won the capitol,
get all the money, but in the long run, that
in fact has not happened. And like wages today are
higher than they have ever been. Nobody will believe me
if I say it, and people will get mad if
I say it, But it's true.
Speaker 2 (06:02):
Of course it's true. I mean the at the end
of the day, wages can't go up unless we get
technological change.
Speaker 1 (06:09):
Well, right, But they don't have to, is the point
of the story. And yet they have in the long.
Speaker 2 (06:14):
Run exactly, so they don't have to. So that's why
Angeles Pause is a cautionary tale. Yeah, And so then
the question is what are the institutional and societal frameworks
we need to make sure that they do.
Speaker 1 (06:29):
Right to make sure that productivity gains are shared by
workers as well as by the owners of capital, which
in this case is basically the people who own the
four Frontier AI companies.
Speaker 2 (06:41):
If we're being reductive at it, exactly.
Speaker 1 (06:44):
Okay, So that's one. I want to do. One more
historical story that you tell that frankly was more surprising
to me that I hadn't heard before in this context
before we get to the present and then to the future,
and that one is about labor saving devices in the
home in the mid twentieth century, the story of sort
(07:04):
of you know, the a washing machine and the dishwasher
and what that meant for economics and for meaning in
people's life, which you talk about in a recent paper.
Tell me about that.
Speaker 2 (07:14):
You know, women have already gone through a transition where
their work was completely replaced by machines, and they had
to adapt. And that's because women played big roles inside
the home as homemakers who baked food from scratch, who
kept houses clean, who did all the things somebody needed
(07:38):
to do at home. And then they got vacuum cleaners
and dishwashers and prepackaged foods. And the first thing that
happens is the standards and expectations rise. So the first
thing that happens is the houses have to be cleaner,
the food has to be nicer, everything has to be
(08:00):
done better. So they use all of this technological improvement,
these productivity gains to just produce more home stuff. And
that lasts for a little while, but it starts to
really gnaw at some women. And that's when we get
a problem that has no name. And Betty Fergan basically
(08:24):
says the problem is women really have nothing to do
at home.
Speaker 1 (08:28):
Huh. This is the feminine mystique who published in what
nineteen sixty or so?
Speaker 2 (08:33):
Right, why are the housewives so bored? They're bored because
robots can do all their jobs and they need something more.
And you know, if you go back to your you know,
nineteen seventies eighties feminist literature, you see that some of
them are advocating for women getting into the workplace, creating
(08:55):
new identities as you know, breadwinners as career women.
Speaker 1 (09:00):
Which happened at a profound level, right, I think that
shift is underappreciated, like the I mean, people know at
a superficial level, oh yes, women to the workforce, But
when you look at a macro level, it is a
profound transformation that happened in a relatively short period of time.
Speaker 2 (09:16):
A profound transformation not only in what women do, but
how they see themselves that happened very quickly. Over like
you know, one to two generations, women went from you know,
seeing themselves as you know, potentially happy housewives to wanting more.
(09:37):
They expect more from their marriages, they expect more from
their life, they expect more from themselves as independent intellectual creatures.
Speaker 1 (09:46):
Yeah.
Speaker 2 (09:46):
And one of the things I found interesting to tie
it to this current moment is not every feminist thought
or things that women need to enter the labor force
to have this richer identity.
Speaker 1 (10:00):
Yeah, there's this amazing quote in your paper. You know what,
it's going to be your smind you say it.
Speaker 2 (10:06):
I know it is like my favorite quote. I feel
like I need to my office bulletin board, which is
I didn't fight to get women out from behind the
hoover just to get them on the board of Hoover.
Speaker 1 (10:19):
Yes, Hoover, the vacuum cleaner.
Speaker 2 (10:21):
Yes, right, and Hoover was a big brand of vacuum
cleaner back in the day. And that's your main greer.
And she's pointing out, no, that's not. The point is
not to swap one form of drudgery for another.
Speaker 1 (10:32):
Yeah, which is profound and different. Right, Like I guess
because of my age or you know whatever, the universe
I live in is the is the universe of like, oh, yes,
women have jobs like men, and men help out around
the house, and like this is the happy new equilibrium.
But this quote is something entirely different and interesting in
(10:52):
the context of AI. Right, what we're walking toward I
mean the sort of these two. I like these two
stories in parallel because the the Engles pause story is
the one we're always thinking about and talking about, which
is money and essentially labor versus capital. Right, So that's
every day everybody's talking about. The other one is meaning, right,
(11:12):
like the problem that has no name, the housewife whose
skills are are devalued by automation. It's not a money problem,
it's a meaning problem, right, And this idea that like, no, no,
the point is not just to go to work. The
point is find some new source of meaning, like, oh,
this is interesting when one thinks about ai.
Speaker 2 (11:32):
Yeah, it's a meaning and identity to you know. The
the Japanese have this concept of meaning called eke guy Yeah,
that I pay a lot of attention to. And I
just was in Japan about a year ago discussing a
paper where they were using in their national survey. They're
surveying people on their their life satisfaction, their subjective well being, happiness,
(11:57):
and on their eke guy.
Speaker 1 (11:59):
Yeah. And just to be clear, just define ekey guy.
Speaker 2 (12:02):
Eke guy is your purpose for living, Okay, your your
purpose for living. And so it's kind of what is
the thing that drives you. What is it that you
care about every day? What do you up and do?
And so for people, their purpose could be really small,
like tending their garden. They get it up every day.
And I tend my garden because my garden's important to me,
(12:24):
and my purpose of maintaining a beautiful garden is contributing
to this world in some way.
Speaker 1 (12:28):
Yeah, And and we have these sort of set ones. Well,
the big one for lots of us is work, right
or a big one and a relevant one in the
context of what we're talking about is work. So we've
set the table now in a nice way. And so
I want to talk. I want to talk a little
bit about the present, and then I want to talk
(12:49):
about the future. I want to return to money and meaning.
But the present is really interesting to me, and I
feel like kind of under discussed in the context of AI.
Like everybody mostly talks about what's going to be like
if whatever the eye takes our jobs. But a lot
is happening right now. And so I'm curious about your
experience of AI the present, both as a as a
(13:11):
teacher and as a as an economist. Right, how has
AI changed being a college teacher.
Speaker 2 (13:19):
Oh, so there's so many things in there. So one
thing that's obvious is that the students are struggling with
the idea of productive struggle. And I think the reason
for that is cognitive offloading is too easy.
Speaker 1 (13:43):
So there's a lot of You've said a lot of
semi jargon things in there, like productive struggle is interesting.
I get it. I think what's productive struggle?
Speaker 2 (13:55):
So productive struggle? I mean I talk about this with
my thirteen year old. Productive struggle is sitting in the
math class and not knowing how to do the problem
and just sitting with that and trying to figure out
how you would solve it. How are you going to
figure out how to do the math problem? And you don't.
(14:17):
It feels frustrating, but you can stay in that moment
of frustration and you can work through it, and you
know what, it's super satisfying when you get to the
other end. The productive struggle is that it's awful.
Speaker 1 (14:34):
I don't know.
Speaker 2 (14:34):
I bet you've had this in your career. Have you
ever sat there writing something and been like, this is
the worst thing happen?
Speaker 1 (14:39):
Oh? Yeah, I hated right. I wrote a book and
that was my experience of writing a book. It was
I was. I was unhappy for a year. Basically, you're
I'm happy that I wrote the book. I'm happy that
I wrote the book, and I would do it again exactly.
Speaker 2 (14:53):
But you would do it again. You would do it
again because actually it's probably meeting that ekey guy.
Speaker 1 (15:00):
Yeah it was.
Speaker 2 (15:01):
It wasn't fun, yeah, but it was purposeful. You did
it with purpose, and it made you feel like you
we're on a journey to somewhere good in your brain
and your heart and your soul. But you gotta have
enough incentives to want to be on that path because
it's not actually fun. Yeah, And that's like I see that.
(15:25):
As I said, I got a thirteen year old in
seventh grade. We talk about productive struggle a lot.
Speaker 1 (15:31):
I'm bringing that one home. I'm bringing that I got
two teenagers. It's putting it in my dad tool belt.
Speaker 2 (15:38):
It productive struggle is like, this isn't fun. I'm not
gonna tell you it's fun. I'm not gonna tell you
it's not that hard, buddy. Just you know, I'm gonna say, oh, yeah,
this sucks, but we're gonna go through it and then
we're gonna get to the other side. We're gonna be
glad because now you're gonna know how to do you know,
(15:59):
a multi equation algebra problem. Yeah, and a lot of
like how we process the world as a place we
want to be, it is through something's hard and then
we achieve it and then we feel pride, and that
is an important set of emotions and I think that
(16:21):
AI is robbing students of that.
Speaker 1 (16:24):
So okay, So that's an interesting sort of macro ish
college teacher problem. So basically they don't have any incentives
to do the work because they can just get chet
Gpt to do it for them, Claud to do it
for them.
Speaker 2 (16:34):
Yes, And so then when you're asking them to do things,
you know they're struggling, but they also are not having
fun learning. I had a student who presented I taught
an AI policy class, and she actually presented on this
topic and she said, school's not as fun as it
used to be.
Speaker 1 (16:54):
Wow.
Speaker 2 (16:55):
And she was a graduate student who was doing a
master's and she was one of those students who'd been
a great student in high school, grade student in college
and she was doing great in her master's degree. But
she really described it as I no longer have productive
struggle with the aha moment.
Speaker 1 (17:14):
It's the problem that has no name.
Speaker 2 (17:16):
Yes, we're already getting to the problem that has a name.
Speaker 1 (17:20):
Yeah, do you feel it in your own work?
Speaker 2 (17:23):
So I feel like I have to be very careful,
and I definitely see myself where I get lazy. So
I use the word cognitive offloading. Yeah, there's a problem
I could wrestle with, or chatchipt could wrestle with it,
and then I could just much more easily absorb it.
Research does show when you stop the struggle, you stop
(17:43):
the learning.
Speaker 1 (17:44):
I mean the dream, right, the sort of Pollyanna ish
version is the model. The AI allows you to struggle
at a higher level, right, Like, it gets somewhere, It
gets through some work that you could have done but
that wouldn't really push any frontiers, and it gets you
to this new place. And then you struggle, but you
struggle better, or you struggle farther, or something like. Yep,
(18:07):
I think I plus AI can write better still than
just a I. I think I can write a better podcast
script for me than Claude can. And so maybe not forever,
but at least for now. And so then how can
I use it optimally? How can I use it to whatever,
be more efficient or be smarter.
Speaker 2 (18:27):
So I'll give you a couple of examples of how
I've used it recently. So I was writing, I was
participating in a little debate, and I had to have
my five minute opening statement, and I just went back
and forth and asked a ton of questions to chat GPT,
and then I followed the links to the sources it found.
And then that made me come back with more questions
(18:49):
and ask about more sources. And it was it was like,
basically I would have given that to research assistant. Yeah,
I would have come back and said, here, look at
these five you know sources, and that would really helpful.
But I'll tell you the other funny thing was I
drafted my five minute opening and then I pasted it
in and I said fact check this and te h,
(19:10):
and it immediately gave me back just like a rewrite,
and I wrote, that was sloppy. I want you to
go line by line and fact check it. I'll do
my own tightened things. Yes, And it was like, whoops, Okay.
Speaker 1 (19:24):
You're right to push back. It gives you your right
to push back.
Speaker 2 (19:29):
Yeah. It then complimented me on my Yes, my great job.
But the line by line fact checking was amazing, Like
it gave me an external source for everything I said,
and then it said, oof, this Clayton seems a little
maybe over the top. Do you really want to make it? Yeah?
So then it finally gave me some like negative feedback
(19:50):
because I made it go line by line and tell
me where's the external source that justifies it? So it
is on how we use it. But I don't think
we're training young people right now properly on how to
use it.
Speaker 1 (20:04):
Are you getting better at helping your students use it?
Getting better? Like it seems like what you want is
to figure out a way that your students can use
AI and still have the productive struggle.
Speaker 2 (20:16):
I think like we're really in the Like I would
say that at the end of this semester, I found
that there are changes I'll make for next year.
Speaker 1 (20:27):
So it's tough times. So you're saying it's tough times.
Speaker 2 (20:30):
I'm saying, the students are getting worse quite quickly on
this than and so the speed at which I need
to address it is getting faster.
Speaker 1 (20:39):
Like do you feel like you're going to have to
start making the students do work in class?
Speaker 2 (20:45):
Boy? Like, I definitely think I have to change the
assignments and the way that I assess them because having
them write an essay at home is useless. Now, Yeah,
I like in one of my classes, one of the
best sets of essays I got was from a student
who couldn't answer a single question I asked class about
his essays because he clearly didn't write them, but he
(21:06):
obviously knew how to prompt well. GBT wrote me some
beautiful essays I was telling another instructor. Yeah, I loved
reading those essays says I prepared for class because I
knew it was a really good summary of what I
needed to cover that day, because it was they were
reading summaries, and he was the best at the class
(21:30):
at prompting. But the students who I could see the authenticity,
and students who might have used AI to help them
a little bit, but the actual genuine things that they
were noticing from the reading that they thought were most relevant.
It was clearly authentic to them and their approach. And
then when I have a dozen unique perspectives, now we
(21:51):
have a discussion, we have a dozen beautifully crafted summaries
from AI. We don't have a discussion.
Speaker 1 (22:02):
After the break, Betsy and I talk about the future.
We talk about money and meaning and purpose in an
A I world. Let's go to the long term, right,
you have written this paper where you have you sort
(22:24):
of lay out kind of what your worries are and
what you think we should do in response to your worries.
So one, it's kind of a classic but interesting in
this context is how much we tax capital gains basically
profits from investments, versus how much we tax labor work, right,
(22:45):
income tax? Talk about your view on that.
Speaker 2 (22:48):
So the historic view on taxing labor versus taxing capital
is that people are going to work, and they're going
to work sort of no matter whether you tax them
or you don't. So we tax labor income because we
need revenue. So in contrast, the idea was that people
(23:09):
really like to consume today, and so getting them to
invest their money in something that they're gonna not get
any money for a long time. You know, they invest
in a new factory and that's going to give them
purchasing power in ten years but nothing today. So they're
gonna have to give up purchasing power today in order
(23:30):
to build a factory. Well, we figured that they needed
a lot of incentives to do that because people like
spending today. So how do I get you to give
up spending today in order to save and invest in
that factory. Well, I don't want to tax that behavior.
And so we have traditionally put more of our taxes
on labor than on capital investments. So if I go
(23:55):
out and I work and I make one hundred thousand dollars,
I pay more in taxes than if I, you know,
if I invest and I'm starting to make one hundred
thousand dollars a year off of my investment, and I
think with a it's going to start to change how
we should think about that because it makes capital cheaper
(24:16):
because it's a smaller tax wedge.
Speaker 1 (24:19):
So if you have a choice between employing somebody and
well paying for AI, buying a bunch of AI tokens
that costs the same amount of as the person, it's
the tax system makes you prefer the AI investment to
the human being exactly under our current rules.
Speaker 2 (24:37):
Okay, yeah, so the tax system prefers capital investment over
human labor. And so right now we have a tax
system that is rewarding what is currently happening, which is
the labor share of income is going down, and the
share of income that's generated by investments by capital is
(25:00):
going up, and that also creates a revenue problem. You
might have heard the US is running some debt, but
it's also creating a disincentive to hire people over capital
that we probably want to move the other direction. We
actually probably want to subsidize hiring people and maybe just
(25:22):
slow down a little bit the rate at which companies
are moving towards capital substitution.
Speaker 1 (25:29):
So concretely, does this boil down to raise the capital
gains tax.
Speaker 2 (25:36):
Lower taxes on wages, and make it up by raising
the capital gains tax.
Speaker 1 (25:41):
The revenue neutral version. Yeah, I mean, to be clear,
from the point of view of the employer, lowering the
income tax, not the payroll text, but the income tax.
You don't care about that as the employer. I mean,
it might make workers a little bit more willing to
work at the market, Yes.
Speaker 2 (25:58):
It should make workers a little bit more willing to work. Yeah,
And that means, frankly, you can pay them a little
bit less. And that is in fact what the data
says happens. Okay, so this would show up as savings
and compensation costs to employers.
Speaker 1 (26:13):
So higher income.
Speaker 2 (26:15):
That's not going to be a lot of people are
properly disappointed right now. Wait, wait, you mean the employers
capture a cut in labor income taxes. Yeah, yeah, that's
what happens.
Speaker 1 (26:27):
Okay, So that's one. So you also have this idea,
uh you call the data dividend, which is very good.
Reminds me of your time in Washington. What's the data dividend?
Speaker 2 (26:38):
So, I mean, the thing that's most remarkable about AI
is we've all built it open AI didn't build AI.
Anthropic didn't build AI. They're training on every word I've
ever written, every word, my grandparents wrote, every word, my
grandparents grandparents wrote, and you know all it's not just
(27:00):
you know, I yes, have speeches in the public domain,
but I really mean it more more broadly than that.
All of the knowledge of humanity is is what has
trained these AIS. So when Sam Altman says, I envision
a world where cognition is sold to you by the token, Yeah,
like wait, he's envisioning the world in which he's sucked
(27:21):
up all of human cognition and now he's going to
sell it back to us. Well, I'd like him to
pay us for it first.
Speaker 1 (27:28):
Uh huh.
Speaker 2 (27:29):
And that shared resource, that public good that all of
human cognition that has trained these models, that should give
each and every one of us a steak and the benefits.
And that's what the data dividends about. So think about
it like the Alaska Permanent Fund. Alaska's sitting on a
(27:51):
bunch of oil. That oil belongs to the citizens of Alaska,
and every year they get a check from the government
that says, this is your share of what we made
off the oil.
Speaker 1 (28:02):
Yeah, it's such an interesting If I was an economist,
I'd move to Alaska and just study that for my
whole life.
Speaker 2 (28:08):
There are economists who have studied it, and it turns
out it doesn't people still go to work, but they now,
I'm going to get beyond that. I mean, it's more
than economics. So from an economics perspective, that puts a
certain amount of consumption in every Alaskan's hands, a data
dividend could put a certain amount of consumption in everybody's hands.
(28:31):
But it does more than that. It also says you belong,
you're part of this. And so when we developed this,
everybody's probably heard a lot about like universal basic incomes,
but it's not about giving losers a handout. That language
(28:51):
infuriates me. You belong, you're part of this. We built
this together, and we all get to benefit. That's what
we have to recognize. And those are the models that
institutional models and frameworks we're going to have to build
for this to be something that's successful, that levels up everybody.
Speaker 1 (29:15):
Not to bring crass details to the slevated discourse, but like,
in practice, is the thing you're talking about, the sort
of frontier AI companies paying some kind of tax that
has then distributed to everybody in America? Is that fundamentally
the thing you're imagining?
Speaker 2 (29:34):
Yeah, what's wrong with that? I mean, they could have
paid for all the data they've used, But we had
this model where we put a bunch of stuff in
the commons. That's not how we envisioned the commons. We
didn't create the commons so one company could capitalize on
all of it, and that I think it means that
we have to think about how we want to treat
(29:56):
the commons in this new era, because it can't just
be one company sucks the whole thing up and then
we all lose. I think it's an institutional failure to
let people get rich off of what's in the commons
and then try to sell it back to us. I
don't want my own cognition sold back to me.
Speaker 1 (30:16):
So we've been talking mostly about the money piece, although
you started talking about meaning in there. But let's talk
more about meaning. I mean, we sort of framed this
in those two ways, right. The meaning piace in some
ways is harder to figure out for me. I mean,
the money base is like, well, if productivity goes up,
there's gonna be more money, and like that's a good
(30:38):
starting place and then hopefully we could figure it out.
The meaning piece seems squishier and harder, Like, what do
we do about meaning if there is in fact a
lot of unemployment and we have solved money.
Speaker 2 (30:51):
Well, you know, the person who really identified that we
were on a really negative trajectory on meaning was Bob Putnam,
who wrote a book called Bowling Alone in the nineties,
and there what he was saying is we're abandoning our communities.
We don't bowl and bowling leagues anymore, even though we
could pull just as often, we just go into it
(31:12):
by ourselves or in small groups. And what had what
that has meant is that we've loaded too much onto
work so work is becoming our community. Are people refer
to their work family? Right? So we have successful transitions
and unsuccessful. So we talked about women successfully transitioned outside
(31:33):
the home and it developed new identities, new meaning, new purpose.
People successfully transitioned out of agriculture. You know it. You
know even since two thousand, we went from forty percent
of the globe working and agriculture to about twenty five
percent today and there's no agricultural meaning crisis. And what
(31:54):
that meant was people were pulled out of agriculture by opportunity.
Now let's compare that with what happened with manufacturing, where
men have been pushed out of manufacturing, not pulled, not
pulled by opportunity, pushed by closures and factory closures that
have left nothing in its way, no alternative. And that's
(32:17):
what I think is a really important lesson for us
to think about from a meaning and identity perspective, because
you have a ton of men who still are thinking
there are no jobs for me because the only jobs
being created are in health care, and health care jobs
are so feminized, and so that real identity and meaning
(32:40):
crisis exists, and it's spilling into family formation. And having
a lot of negative effects. And I think it is
a warning to us that we have to figure out
how to pull people out of opportunities that are closing
into something that's better, rather than letting them get pushed
(33:00):
off a cliff. But how well, one thing to do
is to start to build more community works, to try
to find ways to reward volunteer work, to reward community involvement.
You know, there are lots of things people do outside
of work that build meaning. That could be being a
(33:22):
member of a club, that can be you know, volunteering
at a particular organization.
Speaker 1 (33:29):
I mean, I know what the things are, but like
how do we do it?
Speaker 2 (33:33):
Yeah?
Speaker 1 (33:33):
I mean, as you pointed out, somebody wrote whatever forty
years ago, thirty years ago, that we're not doing it anymore.
And that was before we had phones and you know,
things to just lock up our brains all day. Like everything,
even with jobs, is going in the opposite direction. People
are becoming less and less and less social.
Speaker 2 (33:54):
Well, this is why I think about putting something together
that creates rewards. Whether it's your data dividend is a
function of logging community service hours.
Speaker 1 (34:06):
Uh huh. You got to leave the house to get
your data divid you gotta get talking to a human
being face to face.
Speaker 2 (34:12):
Right, and you know, people want to interact with people.
I really really believe that.
Speaker 1 (34:19):
I mean, are there, like, you know, are there successful
examples of this like that, you know, people leaving agriculture
to go into manufacturing. Like that is fundamentally the market
solving the problem, right, and a weird potential outcome we're
looking at is like there is not a market to
solve the problem, and like the notion that you can
(34:41):
have some kind of top down solution to this problem
of meaning.
Speaker 2 (34:46):
Well, we could have one thing we could do is
government could start to subsidize third spaces and draw them in.
So we could choose to make third spaces all tax free,
no property tax on public space.
Speaker 1 (35:03):
That's the Starbucks Act of twenty This is.
Speaker 2 (35:07):
Going to let you in without buying anything. Then they
get they get a property tax reduction. You start to
think of creative ways to pull people into the community
so they do things, and honestly, and afternoon at Starbucks
is a refreshing way to engage with humans. Talking to
(35:27):
the burista, seeing the people who go around the same
time you do every day, Those small moments of connection
actually are really important to people, more important than they
realize or they would prioritize them more.
Speaker 1 (35:40):
So. You teach teenagers and very young adults, and you
are the mother of one teenagers of two teenagers, Like,
what do you tell the kids? As a labor economist
who has studied AI, what is your professional advice for
the children?
Speaker 2 (36:03):
Oh? I mean, I'm going to say, Actually, I find
it really hard. I was at a I gave a
talk at Syracuse and it was a big talk on AI.
And afterwards, a young man came up to me and
he said, I'm risking everything to be here. I borrowed
(36:26):
so much money, and how do I know my investment's
not going to go down the tube? And I almost
cried for him because I can't tell him his investment's
not going to go down the tubes. I don't know,
And I think I want just remember that when you're
talking to young people, remember the amount of risk and
(36:47):
uncertainty they're facing. It's like nothing you and I faced.
And I took all sorts of crazy gambles, but I
didn't have a spotlight on me. First of all, they
didn't have the risk of everything being on camera. Everything
being documented, and I could know with some certainty if
(37:10):
I study the field like economics, things are going to
be okay for me. I knew I was a math
econ major because I wanted to reduce to economic uncertainty
in my life. I'd grown up with a lot of it,
and I was like, these are two majors they got
to payoff. I can work in finance, I can work
(37:31):
at a corporation. I could you grow up to be
an econ teacher or a math teacher. I had a
lot of bases covered, and I don't There's almost nothing
I can tell kids now where I'm like, this will
work for you. But what I do tell them is
adaptability is going to really matter. Curiosity is your friend,
(37:57):
and creativity. Despite everything you hear about AI, your creativity
is unique to you. Everybody's creativity is like a fingerprint.
And I think that we will have a desire for
bespoke human creativity even as the AI starts to do things.
(38:21):
So I think at the end of the day, what
I use to say, ultimately there will be something to
do is to realize all exchange from the beginning of
time to now is two human beings, one of who
wants something that they cannot provide themselves, and the other
(38:42):
one can provide it. And I do not think AI
is going to eliminate all of our needs, and so
we will still be looking to each other to fulfill
those needs. And as long as you learn how to
be attentive to other people's needs, you will find your
space in this world.
Speaker 1 (39:01):
We'll be back in a minute with the lightning round. Ah,
let's finish with the lightning round, all right. What is
something other than family and work that gives you meaning
in life?
Speaker 2 (39:21):
Birds?
Speaker 1 (39:23):
Birds?
Speaker 2 (39:23):
I really like birds?
Speaker 1 (39:25):
You got a list? You like that kind of burder No?
Speaker 2 (39:30):
But I really I enjoy nature and wildlife. And when
we go to Australia, I am very into the Australian birds.
And I really like if you've ever spent time with
like cockatoos or other parents, they're so smart and they're
really like a joy to interact with.
Speaker 1 (39:52):
I should say that you're what do you call justin
your life partner? Your common law husband is Australian?
Speaker 2 (39:58):
Is Australian?
Speaker 1 (40:00):
And like, do do people like hang out with cockatoos
and parrots in Australia? Is that just like a thing?
I don't know, I like, I go down.
Speaker 2 (40:06):
To the park and I feed them all the time,
milk you right out of my hand, and he has
strains or like stop feeding the clocket two's I'm like.
Speaker 1 (40:13):
No, it's too awesome.
Speaker 2 (40:18):
Uh do it away from people's houses, though, because otherwise
they will they will throw a little tanty and knock
on the wood to get you to bring out the nuts.
Speaker 1 (40:27):
Good to know. I'll keep that in mind. So, I
know you've done a fair bit of work on the
economics of family life, and I'm curious if there's any
insights from that that are like useful to you in
your own daily life.
Speaker 2 (40:46):
Well, I guess thinking about the what you should make
versus buy and how to really think hard to opportunity
call us cost benefit analysis to make that decision, and
you might be surprised. Like, you know, I told you
I was on the board a lift and Justin's always like,
why do you act like a lift driver for our
(41:09):
teenager when you could actually pay for a lift. And
I'm like, because they talk more in the car. You know,
every parent heard this thing where your teenager tells you
something that they wouldn't tell you if they weren't in
the counter side by side with you and trapped there
for a little while.
Speaker 1 (41:28):
Yeah.
Speaker 2 (41:28):
So, I mean I spent it Saturday, like two weeks ago,
driving for four hours to get my daughter from like
attract me to a friend's house for prom too. Like
it was just ridiculous. But it's building a connection that
I can't get elsewhere. So if you think about what
cost benefit analysis says, it never says do or don't
(41:51):
do something. It says only do them when the benefits
succeed the costs. And so I'm very good at thinking
through non financial benefits and financial benefits and non financial
costs and financial costs and making decisions that I feel
really comfortable with.
Speaker 1 (42:09):
That was a ten of ten answer, Most underrated Economists
of all Time.
Speaker 2 (42:19):
Well, I mean there's a lot I should I Actually
I'm going to give you it. I just wrote the
forward to a book of Australian Women in Economics, Okay,
And actually there were some just absolutely incredible women in
that book because they were using the insights of family
(42:46):
life and bringing it into economics. And you know, you're
like mid eighteen hundred, huh, late eighteen hundreds Australia, so
you know what was really powerful about the book is
it was like, wait, let's just celebrate the people who
succeeded despite the fact that they did it against lots
of odds and maybe they didn't have traditional male success,
but they were there, they did get their degree, they
(43:08):
did their work, and let's learn about them. And so that,
I would say it was a big eye opener to me.
So there's a lot of underrated, particularly women or minority economists,
that we don't think about at all.
Speaker 1 (43:25):
What's one thing your kids have taught you about AI?
Speaker 2 (43:29):
They didn't ask for it and they don't really want it.
Speaker 1 (43:32):
Yeah, my kids are also anti AI, like it's weird.
I'm way more into it than they are.
Speaker 2 (43:38):
Yeah, that's that's where we're at in your household. But
I think what they taught me is, I didn't ask
for this. It's going to upend my life. I don't
know how to live with it. And why why do
I have to do this? You didn't have to Why
do I have to navigate this thing?
Speaker 1 (43:51):
I mean, I also didn't ask for it, and I
also would prefer that it not exists. Uh, but it
does exist, right they did?
Speaker 2 (44:02):
You know? They've also taught me how hard it is
living on spotlight. Yeah, I lived in the spotlight. I
mean when my son was eight, he said no for
social media of me please, mom Oh yeah, never, And
I have not posted a photo of him on social
media since he's maybe he was even seven. It was
(44:25):
just like, no, I don't want this big digital footprint
on a smart kid.
Speaker 1 (44:30):
That's like a that's like a humble brag right there.
Speaker 2 (44:36):
I thought it was really interesting at such a young
I mean, it's worrisome at such a young age to
be so cognizant of what that means.
Speaker 1 (44:43):
It's tough to be a smart kid. Uh okay. Last one,
what's your favorite way to use AI that has nothing
to do with work?
Speaker 2 (44:54):
Plan vacations? Spring break? I was like, I have four days.
I want a NonStop flight. I want to travel no
more than four hours three hours. Where should I go?
And it was like it kept pushing me to the
beach and I was like, no, I don't want to
be just like fine, go to Arizona.
Speaker 1 (45:11):
So we did.
Speaker 2 (45:12):
How was it was amazing. I had never hiked in
the Grand Canyon before, and now I am determined that
I'm going to do the Rim to hike from the
North Rim to the south Rim.
Speaker 1 (45:24):
You do it in a day, you go down and up.
Speaker 2 (45:27):
Well that some people do that, but I'm not insane.
I'm going to do it in two.
Speaker 1 (45:30):
Days and you camp, you camp on the river.
Speaker 2 (45:33):
You camp on the river.
Speaker 1 (45:33):
Yeah that sounds incredible.
Speaker 2 (45:35):
Yeah, I I that's where see, there's a lot of
things to do if we don't work so much.
Speaker 1 (45:42):
Yes, although I'll be honest, I like working better than hiking.
I mean, I like hiking. I love exercise. I'm not
I like birds, But like, if I could do some work,
you know, I mean, I'm interested in some work, you
know what I mean? Like me too, Like if I
could work a few hours a day. The other thing
(46:03):
is like I mean, if there's if we're really dreaming big,
like if there's a universe where like I don't need
worry about money and I can make a podcast and
people like it. That's actually the part that I like, right,
And so uh, you know, I.
Speaker 2 (46:19):
Think aren't we entering that era where we're all just
creating content for each other? So as long as we.
Speaker 1 (46:25):
Go anything else you want to say or talk about, I.
Speaker 2 (46:33):
Think like as a thing to remember is like we
can't actually improve our living standards without an increase productivity,
and so that's the reason why we can't be totally
afraid of AI. But we do need to realize that
we're responsible for how our society responds to it. We
(46:56):
don't need to let it just happen to us. It
is something that we have some control over and we
should take it. And this is where we need to
start to have how should we spend our new wealth?
We should think of it collectively. I'd like to spend
some of it on vacation. I'd like to spend some
(47:17):
of it on national healthcare. But I definitely want us
to get it. And that's what AI, that's its potential
is it's going to give us the wealth that could
get us those things.
Speaker 1 (47:27):
Thank you for being so generous with your time.
Speaker 2 (47:29):
Yeah, it was awesome to talk to you. This was
super fun.
Speaker 1 (47:40):
Betsy Stephenson is a professor at the Gerald R. Ford
School of Public Policy at the University of Michigan. Today's
show was produced by Gabriel Hunter Chang and edited by
Lydia Jean Kott. Our engineer this week was Hansdale Sheet.
We're always looking for ideas for who to talk to
and what to cover on the show. You can email
(48:00):
us at problem at Pushkin dot fm. You can find
me on x at Jacob Goldstein. You can find me
on LinkedIn. I'm Jacob Goldstein. Thank you very much for
listening to the show, and we'll be back next week
with another episode.
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