Speaker 1: I'm saying, we have an extraordinary new technology that can
Speaker 1: create greater abundance, and we should stop using it feels
Speaker 1: totally upside down.
Speaker 2: So you released a really fun diving in video this
Speaker 2: week in which you actually looked back in time at
Speaker 2: the Victorian boot making industry.
Speaker 1: Yeah, fascinating period. You might ask, why the heck do
Speaker 1: we care about making boots in the eight and fifties,
Speaker 1: And that's because it had an amazing technological transformation that
Speaker 1: meant the average worker could produce four times as much
Speaker 1: as they could before. Hey, I don't know if that
Speaker 1: reminds you of anything that you might be feeling a
Speaker 1: little bit of anxiety about right now.
Speaker 2: We had some great questions that came out of this
Speaker 2: from our audience, and we're going to dig into those today.
Speaker 3: I'm Megan Connors.
Speaker 1: I'm Justin Wolfers. This is the professor is in. Think
Speaker 1: about this as office hours. Meghan is out there reading
Speaker 1: your comments so she can bring your comments to me.
Speaker 1: The Professor's in. Let's go.
Speaker 2: Yeah, so you've what we found, or what you found,
Speaker 2: is that the number of jobs, total number of jobs
Speaker 2: really didn't budge that much, even though nearly every other
Speaker 2: aspect of boot.
Speaker 3: Making in England at this time did change.
Speaker 1: And when we pick up there, which is, I didn't
Speaker 1: find it. Hillary Verpond a brilliant young economic history and
Speaker 1: has done this great deep dive into boot making in
Speaker 1: the eighteen fifties. If you had told me eight days
Speaker 1: ago I cared at all about boot making England or
Speaker 1: the eighteen fifties, I would have said no. But she
Speaker 1: showed this is I think is where you're going. The
Speaker 1: total amount of employment didn't change even as this incredible
Speaker 1: labor saving technology came along. That's the good news, Megan.
Speaker 1: Do you want to tell people the other side of
Speaker 1: the coin.
Speaker 2: The I mean the tapes of jobs totally changed, So
Speaker 2: you know, certain elements of boot making did kind of
Speaker 2: go extinct and new jobs appeared in their place.
Speaker 1: Absolutely, all right. I know you've got questions about all yeah.
Speaker 2: So on that note, you know, we had a couple
Speaker 2: of different people ask what happened to pay? And I
Speaker 2: know you might not have the specifics in this circumstance,
Speaker 2: but what do we know about technological revolutions in general
Speaker 2: and their impact on wages?
Speaker 1: Right? This particular study didn't have any pay data, so
Speaker 1: I can't say anything about Victorian England in the eighteen fifties.
Speaker 1: And I know I'm going to surprise you, but I'm
Speaker 1: not actually an expert on Victorian England in the eighteen fifties,
Speaker 1: even though I dress in linen. Okay, So what typically happens,
Speaker 1: it's not always the case that wages rise immediately. The
Speaker 1: period after in the Industrial Revolution was when Charles Dickens
Speaker 1: started writing, and Dickensie and Tail is full of young
Speaker 1: children laboring away in factories in rags, barely able to
Speaker 1: make ends meet. So it took the immediate transition is
Speaker 1: not necessarily good news. What happens though, over time is
Speaker 1: if the pie is bigger, and I don't want to
Speaker 1: say there's magic, which means the pie is always genuinely
Speaker 1: distributed in good ways. But if the pie is big
Speaker 1: enough and humans are an important part of producing that pie,
Speaker 1: some of it comes back to roost. So the story
Speaker 1: on wages is there's two stories. One is what's happening
Speaker 1: on average on average, eventually bigger pie bigger servings to
Speaker 1: workers in the short run, that may not be true.
Speaker 1: And then on average was doing a lot of work
Speaker 1: if you were a cord swainer of that word, I
Speaker 1: don't even know what it means, but I love I
Speaker 1: think I might be a cord swaener in my next life.
Speaker 1: That's one of the old artisan or bootmaking jobs. The
Speaker 1: demand for cordswainn has felt dramatically and so it's very
Speaker 1: likely that cord swainers saw their pay go down. If
Speaker 1: you want to to give a modern day interpretation of this,
Speaker 1: the world of translation is changing right now due to AI.
Speaker 1: It's very likely the current translators are seeing lower wages now.
Speaker 1: The thing we learned from Victorian bootmaking is cord swain
Speaker 1: has went away, but the price of boots fell, so
Speaker 1: therefore the number of boots we make went up, and
Speaker 1: that created more demand for folks to work in factories,
Speaker 1: you know, foreman's and foremen and machinists and riveters and
Speaker 1: so on, which would be the equivalent of today. What
Speaker 1: we're seeing is the rise of the prompt engineer. And
Speaker 1: I want to be clear, prompt engineers almost too glib
Speaker 1: for what's happening in a current moment. The reason I
Speaker 1: use that is that's a job title. You and I
Speaker 1: both know. One of the points that's really important to
Speaker 1: understand is there's going to be occupations we've never heard of,
Speaker 1: and if we follow it all the path of the
Speaker 1: boot makers, I'm not sure we will. Those other occupations
Speaker 1: are going to grow. Part of the reason they're going
Speaker 1: to grow is they're highly productive. That means is going
Speaker 1: to be high wages. Those high wages are also going
Speaker 1: to draw new and younger workers into it.
Speaker 3: Well, it was Gary.
Speaker 2: I mean, have any other evidence from like countries that
Speaker 2: have industrialized more recently. I'm thinking, like China, do we
Speaker 2: know what's happened? What happened with wages? My sense is
Speaker 2: that we saw the benefits of increased wages more quickly
Speaker 2: than maybe Victorian England did.
Speaker 1: Yes, I think that's right. So you know, so China
Speaker 1: famously has moved from largely a rural agricultural economy to
Speaker 1: an urban manufacturing economy. It is still the case there
Speaker 1: is immense poverty in the rural areas. But what's happened
Speaker 1: is there's been opportunities in the cities that have pulled
Speaker 1: workers out, which is why the average Chinese person's living
Speaker 1: standards have risen dramatically. And a very human reaction is
Speaker 1: won't these technological changes just make the rich rich and
Speaker 1: leave everyone else behind. In order to tell that story,
Speaker 1: given how much bigger the pie gets, that story is
Speaker 1: implicitly suggesting that the returns to the share of the
Speaker 1: pie that capital gets must be extraordinary or must shift
Speaker 1: the more almost implausibly. Right, So, the average income in China,
Speaker 1: I don't have a number in front of me. I'm
Speaker 1: going to guess that over a period of decades it
Speaker 1: rose literally tenfold.
Speaker 3: I think so.
Speaker 1: And you know, if the average wage of the work
Speaker 1: arose sixfold or eightfold, could have risen tenfold, that's all plausible.
Speaker 1: Saying that it all went to capital is just literally implausible.
Speaker 1: I don't want to be mistaken for saying trickle down economics,
Speaker 1: but this here is where we push the technological frontier.
Speaker 1: We learned to produce more, and you can't produce more
Speaker 1: without people engaged in the production process. That was true
Speaker 1: of bootleather, it was true of manufacturing, and it is
Speaker 1: still true of AI, at least at the current moment.
Speaker 1: Someone needs to ask, you know, AI puts us in
Speaker 1: a world where the price of questions has fallen to zero.
Speaker 1: But sorry, the price of answers has fallen to zero.
Speaker 1: But someone's got to figure out the interesting questions. That's
Speaker 1: the human.
Speaker 2: Okay, I want to dig into the demand side a
Speaker 2: little bit more, right, So, cheaper boots meant more boots
Speaker 2: and kind of we had a funny comment of someone asking,
Speaker 2: like how many AI images can I actually consume? And
Speaker 2: so I'm just curious kind of that idea of like
Speaker 2: demand saturation, Like is that a thing? Like how might
Speaker 2: that play under this story?
Speaker 1: Yeah, Megan, let's get personal about this. How many pairs
Speaker 1: of leather boots do you wind me?
Speaker 2: I tend to do one at a time, and I
Speaker 2: replace it every one to two years.
Speaker 1: Wow, you're really good at throwing out your old boots.
Speaker 2: Well, they usually fall apart, and that's what I know.
Speaker 2: I have to get rid of them.
Speaker 1: Okay, So I'll tell you about me, which is several
Speaker 1: years ago. I just signed my textbook contract and I
Speaker 1: wanted to treat myself, and I bought a beautiful pair
Speaker 1: of brown leather boots to go up just below the knee.
Speaker 1: You can decide whether that's masculine or not. I don't care.
Speaker 1: Then we're in Spain and I bought a pair that
Speaker 1: sort of looked part Spain, part Texas, and it's partly
Speaker 1: because I loved the Bob Dylan song boots of Spanish leather,
Speaker 1: And every time I put on those boots and I
Speaker 1: had a chance to wear them recently. What was the
Speaker 1: big country music star who came to Michigan recently.
Speaker 3: Zach Bryan. Last year, I wore.
Speaker 1: Those boots to a Zach Bryan concert. I wanted to
Speaker 1: wear them with cut off shorts, but Betsy wouldn't let me.
Speaker 1: So we got that pair of boots. And then, because
Speaker 1: I'm an Australian, I've got to have a pair of Blundstones.
Speaker 1: And then the first pair of Blundstones I brought over
Speaker 1: from Australia thirty years ago. Honestly, I still have them.
Speaker 1: I then bought another pair of Bludstones. Someone else gave
Speaker 1: me another pair of pluts. I got a lot of boots,
Speaker 1: and I live in a cold weather area. I need
Speaker 1: a lot of pairs of bits. That is so there
Speaker 1: are lots of ways you could look at that. You
Speaker 1: could say that's the lunacy of the current moment. How
Speaker 1: many pairs of boots do you need? Or what a
Speaker 1: joy it was to have a special outfit for my
Speaker 1: Zach Bryant concert and honestly did give me a smile,
Speaker 1: and lots of people add a good laugh at me
Speaker 1: and with me, and those smiles of what economics is
Speaker 1: all about. So I don't feel like I'm done with
Speaker 1: leather boots yet. So next big revolution, I'm more for it.
Speaker 1: Let's come back to AI. AI is doing cognitive work.
Speaker 1: I understand people might object to the word cognitive, but
Speaker 1: cognitive adjacent. How many AI images can you consume? Well,
Speaker 1: I will tell you I remember ancient history. I think
Speaker 1: it was four years ago. Whenever I would want to
Speaker 1: write a column for the New York Times and we
Speaker 1: want to have art with a column, and they would
Speaker 1: actually have to call someone from the art department, and
Speaker 1: there is something visually pleasing about space on the page
Speaker 1: and so on. We're now writing on substack, and substack
Speaker 1: has a vast image library. We do have access to AI.
Speaker 1: Anyone can now produce somewhat something semi professional. The point
Speaker 1: that I think a comment is making that's really important
Speaker 1: is the value of the last image I created isn't
Speaker 1: that high? But the first doesn't I created? Were they
Speaker 1: were images that went into my textbook, for instance, or
Speaker 1: images I put into a substack and so yeah, the
Speaker 1: last one's not that valuable, but all the ones in
Speaker 1: between were quite valuable. You know, if there's too much
Speaker 1: AI images in your life, cry uncle, demand be artisanally drawn.
Speaker 1: But also remember that there are still people all around
Speaker 1: the world doing without, and when we create more, we
Speaker 1: help those who do without.
Speaker 2: Yeah, I think one thing that I've been thinking about
Speaker 2: is kind of where we're seeing AI show up first.
Speaker 3: And I think in the US.
Speaker 2: It's in a lot of media to the point where
Speaker 2: it's for some people it's not actually like adding to
Speaker 2: their lives.
Speaker 3: In some ways, it can be.
Speaker 2: Detracting, right AI slot, Whereas I've heard that like China
Speaker 2: has been focused a little bit more on robotics side
Speaker 2: of AI and things that are actually like helping people
Speaker 2: with domestic chores, like things that are a little maybe
Speaker 2: have seemed to have a little more value, And so
Speaker 2: I don't know, I think I think a lot of
Speaker 2: our conversation has been really focused on what where we're
Speaker 2: seeing AI now, which is maybe arguably not the best
Speaker 2: use case for it, and it's making it hard to
Speaker 2: think about kind of the wider applications.
Speaker 1: Let me try something brave. I want to connect this
Speaker 1: to Victorian England and boot making. So people complain that
Speaker 1: when the cost of producing an image gets low, people
Speaker 1: produce low quality images, and we call that aislop or
Speaker 1: low quality text. Well, it's also the case you've been
Speaker 1: to a shoe store. There is such a thing as
Speaker 1: boot slop. Right back in the day, in the eighteen fifties,
Speaker 1: boots were artisanal though are handmade. They were workers boots,
Speaker 1: They were made to last. They were a major investment.
Speaker 1: Not quite as important as a car, but closer to
Speaker 1: that than how we think about it today. All technologies
Speaker 1: create slot. I have seen some horrendous pairs of boots
Speaker 1: in the world. You're broader question of like, where's the
Speaker 1: best use case for this? This is a moment of
Speaker 1: immense transition. The only way to figure out the future
Speaker 1: is to try a bunch of things. And if people
Speaker 1: don't like AI generated images, and the verdict seems to
Speaker 1: be that they don't, we're going to try using fewer
Speaker 1: of them. You know, you try some things, they work out,
Speaker 1: You try others they don't. I think there's a much
Speaker 1: deeper question here, and this is where a lot of
Speaker 1: the discussions in Silicon Valley go. There's one occupation where
Speaker 1: it's come to absolutely dominate the workflow, which is it
Speaker 1: turns out that AI can do the work of a
Speaker 1: highly trained coder. A bunch of coders right now are
Speaker 1: going to scream at me in the comments and say, no,
Speaker 1: they can't do what I do. But I will literally
Speaker 1: tell you Bill Gates told me that it can code
Speaker 1: as well or better than him, and that's been his
Speaker 1: life's work. How is that for a little just name
Speaker 1: dropping in there? The deeper question is how much are
Speaker 1: we all like coders. There's lots of reasons to think
Speaker 1: coding is special, Like it's literally codified, it's a very
Speaker 1: simple language. At its heart, coding is things like if
Speaker 1: then statements, it's loops and so on, And so the
Speaker 1: ability for a computer to learn a constrained set of
Speaker 1: tasks that to some people involve constrained imagination, we shouldn't
Speaker 1: be surprised, and then the rest of us should be fine, because,
Speaker 1: of course AI could never do economics. Of course, it
Speaker 1: could never paint a landscape. Of course it could never
Speaker 1: sing an opera. But let's come back to singing an opera.
Speaker 1: At a fairly deep level, it's got a lot in
Speaker 1: common with coding. It's a fairly constrained language, a set
Speaker 1: of notes on the page. We can look to a
Speaker 1: long history to figure out what works well and what
Speaker 1: works poorly. On and on it goes. You might think
Speaker 1: that's a useful analogy. You might not. The point is
Speaker 1: neither of us knows. And so what we are going
Speaker 1: to see is, you know, the question is, look, codeers,
Speaker 1: their lives are changed forever, same as cord sweiners to
Speaker 1: you right now, megan to me, to our viewers, they
Speaker 1: like chord swiners and coders or are they more like
Speaker 1: jobs that continue? Because that wildly human aspect is so important.
Speaker 1: And when I stayed it that way, lots of people
Speaker 1: have a particular view, But I really think it's an
Speaker 1: open question.
Speaker 3: Okay.
Speaker 2: That leads us well into the next question. So the
Speaker 2: video also stressed the importance of peace, and I think
Speaker 2: you said Hillary Vpon stressed that the moderate piece of
Speaker 2: change in Victorian boot making really helped shield incumbent workers.
Speaker 2: They were able to retire in their roles. It was
Speaker 2: just new people didn't enter. So what do you take
Speaker 2: away from that, like, should policy makers be trying to
Speaker 2: slow the peace of AI? That seems difficult to do
Speaker 2: in a global economy, I mean, should they be trying
Speaker 2: to direct AI to certain goals?
Speaker 3: Like, what do you take away from that?
Speaker 1: Yeah, let me throw out a few of Hillary's facts
Speaker 1: first because I think they're really interesting. So what you
Speaker 1: saw is, over you know, a period of several decades,
Speaker 1: literally two thirds of the jobs in what you call
Speaker 1: old occupations, the called swayiners and the like, disappeared. The
Speaker 1: number of people working in bootmaking didn't change. Two thirds
Speaker 1: of the occupy, the folks who are in the old occupations,
Speaker 1: their jobs disappeared, a new set of jobs turned up.
Speaker 1: They ended up taking two thirds of the new jobs.
Speaker 1: So it really is the total level employments the same,
Speaker 1: but the tasks being done all literally are just fundamentally different,
Speaker 1: wildly different. And what was interesting about this case is
Speaker 1: a majority of the folks who had the old artisanal
Speaker 1: bootmaking roles ended up as old artisanal bootmakers. What happened
Speaker 1: was they slowly retired and died and their sons didn't
Speaker 1: enter the same trade. And I mean sons literally, because
Speaker 1: that's how this worked. There's an interesting side story, by
Speaker 1: the way, which is the women who worked as bootmakers,
Speaker 1: they did leave bootmaking altogether. They were the ones whose
Speaker 1: professional ambitions had to give way to the economic reality.
Speaker 1: The bootmaker couldn't employ mum and dad, and so then
Speaker 1: what you had was the sons of the bootmakers either
Speaker 1: went into new boot making occupations or went into something
Speaker 1: different altogether. And so there was a lot of numbers
Speaker 1: to say what you said, which is the old bootmakers
Speaker 1: retired in their jobs, but no one else was hired
Speaker 1: as a kordswaanner after that. So where does that bring
Speaker 1: us in the current moment. So the headline is Dario Amode,
Speaker 1: the CEO of Anthropic, says that within five years, was it,
Speaker 1: half of all white collar work will be done by AI.
Speaker 1: So first thing is he's a technologist, not an economist.
Speaker 1: Economists have studied history. When you look at history, things
Speaker 1: never happened quite that fast. And so what he should
Speaker 1: have said is he believes the code will be capable
Speaker 1: of doing half of the work within five years. And
Speaker 1: if he says it that way, by the way, I
Speaker 1: actually think that's true, or it's in the neighborhood of true.
Speaker 1: But organizations are run by people. Organizations run on people.
Speaker 1: Organizations adapt and adopt slowly. There's no way it happens
Speaker 1: at quickly, but to the extent that happens quickly, it
Speaker 1: means that every member of our audience is wearing a
Speaker 1: white collar right now should be terrified that their job's gone.
Speaker 1: In five years. That would be an economic disruption larger
Speaker 1: than the global financial crisis, larger than COVID, larger and
Speaker 1: eything would be an economic upheaval that would cause political
Speaker 1: and social change of really an extraordinary measure. So then
Speaker 1: a very common conversation here I'm just reporting, in fact,
Speaker 1: a very common conversation among there's a group of economists
Speaker 1: and technologists and policy wonks who they meet each other
Speaker 1: at the same conference every three weeks and talk to
Speaker 1: each other. A very common conversation is how do we
Speaker 1: slow this down? And actually some of the folks from
Speaker 1: the AI labs are part of that conversation and they're
Speaker 1: saying that out loud. There's a range of policy solutions.
Speaker 1: Is what's called a token tax, which is basically tax
Speaker 1: people for the use of AI. You can require the
Speaker 1: businesses hold on to their current workers even as they
Speaker 1: adopt AI. You can imagine all sorts of regulatory responses
Speaker 1: that are designed to slow things down. You know if
Speaker 1: we had a strong union movement so on. So, first
Speaker 1: of all, want to just acknowledge something, which is this
Speaker 1: echoes the Ludites who were worried machines would take their job.
Speaker 3: Now.
Speaker 1: The funny thing is when economists talk about Luddites, they
Speaker 1: almost always mean it in a majorative way, someone who
Speaker 1: thinks that the future is coming to take their job
Speaker 1: and the future will make their lives worse, but actually
Speaker 1: have a little empathy. The Ludites were right, technology was
Speaker 1: coming for their jobs. Change was coming, and if change
Speaker 1: came quickly, change would be painful. That's true. And the
Speaker 1: economists intuition that if we adopt the best technologies all
Speaker 1: the time, the pie will be as big as it
Speaker 1: can possibly be, and therefore we have the capacity to
Speaker 1: feed more people. That's also true. Of course, having the
Speaker 1: capacity to feed more people doesn't mean we actually do it.
Speaker 1: That's also also true.
Speaker 3: Are other video on recent cuts to snap.
Speaker 1: Yeah. Right, we are currently the richest America has ever been,
Speaker 1: but our safety net is not showing that and people
Speaker 1: are not enjoying the spoils. And so that's where we
Speaker 1: get all these other conversations about universal basics can come
Speaker 1: and so on. But I want to stick to this.
Speaker 1: What you're hearing a lot of is a sense that
Speaker 1: if we could slow things down, the transition costs would
Speaker 1: be a lot lower. Let me point something out why
Speaker 1: this sits quite uncomfortably on the economics. Saying we have
Speaker 1: an extraordinary new technology that can create greater abundance and
Speaker 1: we should stop using it feels totally upside down. It
Speaker 1: requires a language of talking about greater abundance versus transition costs,
Speaker 1: and transition costs is just a language economists have never
Speaker 1: been very good at. If you think about the debate
Speaker 1: about international trade, our view as economists was always will
Speaker 1: we get the pie to be bigger? And what we
Speaker 1: didn't recognize is towns like Flint, Michigan will disappear, and
Speaker 1: that transition cost is very real. And in our economic
Speaker 1: models we assume that the guy who's an auto worker
Speaker 1: becomes a home healthcare aid tomorrow, and the reality is
Speaker 1: he doesn't see our past work on pink color jobs,
Speaker 1: masculinity transitions, and so on. So I think my profession
Speaker 1: hasn't got yet a refined enough language to have this
Speaker 1: conversation coherently. And the problem is if we don't have
Speaker 1: it coherently. It's happening incoherently instead. So I do think
Speaker 1: Hillary of a Pond, who warned me having coffee with It,
Speaker 1: that the pace of change is central to the economic
Speaker 1: and social disruption. I think she's one hundred percent right.
Speaker 1: What's a lot harder is what to do with that observation.
Speaker 2: Okay, So another element of change that you discussed was geography.
Speaker 2: The new technology dramatically changed the geography of where people
Speaker 2: were working. The factories tended to be based in kind
Speaker 2: of two major parts of England and human geography. We
Speaker 2: call that agglomeration. Uh, look at you right bringing it in.
Speaker 2: But with so much of today's work being digital remote,
Speaker 2: I'm curious if you think geography is going to be part.
Speaker 3: Of the AI story. Yeah, what what's your take on that?
Speaker 1: I thought the geography story was fascinating. So basically every
Speaker 1: town had bootmakers. The old school bootmakers went away, we
Speaker 1: moved to factories, and factories agglomerated, excellent use of the
Speaker 1: term in a small number of cities. I am not
Speaker 1: sure that geographies such a central issue for AI. The
Speaker 1: reason I wanted to tell that part of the story
Speaker 1: was to make the case that yes, total employment and
Speaker 1: bootmaking didn't change, But beneath the surface there was dramatic change.
Speaker 1: There was dramatic change by occupation. Out with the cord
Speaker 1: swiner and with a riveter or the machinist. There was
Speaker 1: dramatic change by geography. And so what I just want
Speaker 1: to say is there's going to be dramatic change by things.
Speaker 1: What the things are, I think remains somewhat unknown. Where
Speaker 1: sure there will be by occupation. My guess is there
Speaker 1: will be by education. There's a story that there will
Speaker 1: be dramatic changes by gender. Women are doing much more
Speaker 1: likely to be sitting at a computer during the day.
Speaker 1: Men are much more likely to be split in rocks.
Speaker 1: AI doesn't split rocks. The point I want to make
Speaker 1: is there'll be dramatic changes in our lives. And geography
Speaker 1: was what it was in Victorian England. I don't know
Speaker 1: what it is for now. You then asked me the
Speaker 1: question speculate fair enough.
Speaker 3: If you might meet I can speculate place. Tell me, well,
Speaker 3: I you know it's so.
Speaker 2: I was a geography teacher, so I think a lot
Speaker 2: about a lot of things in terms of a geographic lens.
Speaker 2: My first instinct I think was the same as yours,
Speaker 2: if anything. You know, I think a lot of our
Speaker 2: technology is allowing geographic dispersion.
Speaker 3: In a way that we didn't used to. We saw
Speaker 3: that with COVID.
Speaker 2: One thing that's been very interesting to me. So I
Speaker 2: have friends that have lived in I lived in the
Speaker 2: Bay for a bit.
Speaker 3: I have friends that are in Silicon Valley, lived in
Speaker 3: the Bay.
Speaker 2: And you know, during at the start of COVID, everyone left,
Speaker 2: Almost everyone that I know within at least a couple
Speaker 2: of years left, But with the AI boom, a lot
Speaker 2: of them have been forced to return. You know, a
Speaker 2: lot of these companies, and you know that's people working
Speaker 2: on AI as opposed to using AI. But it's it's
Speaker 2: been interesting to me to see that the companies themselves
Speaker 2: are kind of bringing people back together in person, and
Speaker 2: there's become this kind of re recognition of actually being
Speaker 2: in the same place in space is maybe somewhat useful
Speaker 2: in this time of great technological change. So I think
Speaker 2: it's actually a very open question, and that's obviously that's anecdotal,
Speaker 2: but I have heard that, you know, there has been
Speaker 2: a measurable return to San Francisco, to Silicon Valley of
Speaker 2: in person work.
Speaker 1: So let me pick up on that. So there you're
Speaker 1: I think, I think you're mostly talking about the creation
Speaker 1: of AI.
Speaker 2: I am, I am, but I'm curious if that could
Speaker 2: end up right then happening in other places where people
Speaker 2: are implementing AI.
Speaker 1: So the creation of AI is obviously very geographically concentrated.
Speaker 1: A very simple way of thinking about that is the
Speaker 1: value of the math geniuses at the cutting edge. You
Speaker 1: can figure this out, And I want folks to remember,
Speaker 1: this whole thing started with six nerds at Google figuring
Speaker 1: out some hard math, and they then accidentally changed the
Speaker 1: whole wark. The value of the folks who are working
Speaker 1: on the foundational models is unbelievably high. They're in trillion
Speaker 1: dollar companies with nearly thousands of workers, which means each
Speaker 1: individual some of the geniuses on the inside are really
Speaker 1: worth I always find it funny when people say it's
Speaker 1: incredible some of these folks are getting paid ten million
Speaker 1: a year, and I'm like, what does a baseball player get? Yeah,
Speaker 1: it's crazy.
Speaker 2: Well, real quick on this note, I mean maybe that's
Speaker 2: but where that job creation is going to come from?
Speaker 1: Right from baseball players?
Speaker 2: No, No, the AI company people working on AI themselves.
Speaker 1: Yeah, absolutely, So their value is so high if there
Speaker 1: were any of what you're calling agglomeration externalities. Look, what
Speaker 1: is an agglomeration externality. It's when you go to a
Speaker 1: barbecue and you happen to meet someone who knows something
Speaker 1: that you don't know, and you enjoy chatting with them,
Speaker 1: and you walk away smarter. So, Megan, you and I
Speaker 1: live in ann Arbor, you happen to run into a
Speaker 1: lot of people who are very smart master's students or
Speaker 1: doctoral students across a range of issues. So that's why
Speaker 1: you and I are very very book smart. We don't
Speaker 1: run into a lot of coders. That's why you and
Speaker 1: I are not particularly code smart. But if someone's value
Speaker 1: of what they're doing is so great, if they happen
Speaker 1: to run into another AI nerd at a party and
Speaker 1: they happen to learn one more thing, but that one
Speaker 1: more thing is so intensely valuable, then that means the
Speaker 1: agglomeration externalities, the force to put all those nerds in
Speaker 1: the same city becomes really great. So I think for
Speaker 1: the creation of AI, there's very clearly a geographic story,
Speaker 1: and the Bay Area is going to continue to be
Speaker 1: the home to a huge amount of it. The use
Speaker 1: of AIS a somewhat different story. But I want to
Speaker 1: pause there because what's hard conceptual is to draw a
Speaker 1: circle around what is the creation of AI. So I'm
Speaker 1: not the math genius who came up with the transformer technology.
Speaker 1: But I wrote some cod that my textbook publisher Lady
Speaker 1: adapted and used. That means every economics one in one
Speaker 1: student around the country has access to an AI tutor
Speaker 1: that helps them learn economics the way that the Stevenson
Speaker 1: and Wolfers textbook tells them to is that creation of AI,
Speaker 1: adaptation or use. Now, I was able to do that
Speaker 1: from an arbor, but the truth is, if I'd been
Speaker 1: in the Bay Area, I would have run across some
Speaker 1: other guy trying to do something similar. You know, there's
Speaker 1: all these end tech startups over there, and so maybe
Speaker 1: I should have moved to the Bay Area. Okay, that's
Speaker 1: creation of AI, use of AI. One of the extraordinary
Speaker 1: things about this technological revolution is how light it is.
Speaker 1: You can pick it up and put it in your
Speaker 1: pocket way zero grams. So if you thought about the
Speaker 1: industrial revolution mechanization automation, what you needed was a factory.
Speaker 1: You needed a lot of capital, you need the ability
Speaker 1: to ship goods. You needed a lot of organizational infrastructure.
Speaker 1: If though you're in India right now, and I get
Speaker 1: it approached by folks like this all early, all the time,
Speaker 1: they say, I want to make thumbnails for your YouTube channel.
Speaker 1: Justin chances are they using AI and you can be
Speaker 1: in a small, poor Indian village and have access to
Speaker 1: cutting edge global technology and then email people, so you're
Speaker 1: buying and selling all around the world. So that would
Speaker 1: be a case that says the benefits of this technology
Speaker 1: might be very glorible in a way that past technologies
Speaker 1: have not been.
Speaker 2: Yeah, Okay, I have so many more questions, but I
Speaker 2: think I'm going to ask my last one. I know
Speaker 2: you said before that you don't know what to tell
Speaker 2: young people in terms of like what to study or
Speaker 2: what field to go in. But what maybe more general
Speaker 2: advice do you have for them as we enter, you know,
Speaker 2: the future of work.
Speaker 1: Yeah, so for folks who are already working, I can
Speaker 1: tell you what my advice is. Become the most asavy
Speaker 1: person in your workplace. It's something I've done, and I
Speaker 1: didn't say become an AI genius. I said, become the
Speaker 1: most aicavy person in your workplace, which means I have
Speaker 1: to become the most asavvy person at the University of
Speaker 1: Michigan Department of Economics. If that's the case. You know
Speaker 1: the old story, you know, a bear comes at two
Speaker 1: hikers and one of them puts on his running shoes
Speaker 1: and his mate says, why are you doing that? You
Speaker 1: can't outrun a bear, And he says, I don't have
Speaker 1: to outrun a bear. The joke being the bear is
Speaker 1: going to eat his mate. There's something kind of a
Speaker 1: bit mean about the advice saying be the most asavvy
Speaker 1: person at your workplace, but that's basically putting on your
Speaker 1: sneakers you will keep your job. I also think it's
Speaker 1: a good practice anyway to be at the top of
Speaker 1: your craft and the top of your game. And I
Speaker 1: think anyone who doesn't at least adopt that mindset is
Speaker 1: very much at risk of being automated out of the workplace. Now.
Speaker 1: I subsequently heard a much better articulation of this, and
Speaker 1: I was later told it was Jensen Wong's. It was
Speaker 1: attributed to Jensen wom AI will not take your job.
Speaker 1: Someone using AI will take your job, And that's really
Speaker 1: the advice and the idea. So then if that's the case,
Speaker 1: you want to be someone using AI. Okay, that's for
Speaker 1: people who have work. I think you were asking more
Speaker 1: about young people. One answer is if nobody knows what
Speaker 1: the skills of the future are, a diversified portfolio is
Speaker 1: less risky. So in some sense would be a vote
Speaker 1: for an American liberal arts education, whereas in Australia my
Speaker 1: degree is a Bachelor of Economics. I began in first
Speaker 1: year taking economics, microeconomics, macroeconomics, econometrics, computer science, and the
Speaker 1: next year I took microeconomics, macroeconomics, you know, econometrics, and
Speaker 1: computer science. I became very very narrow and if economics
Speaker 1: were to be aied away, I don't have a job.
Speaker 1: Economics is going to stick around. So always the answer
Speaker 1: to every question is study economics. I think the broader
Speaker 1: thing is what are the set of skills that we're
Speaker 1: going to need in the future. I gave you a
Speaker 1: hint at something earlier, and I want to repeat it,
Speaker 1: and I hope folks at home can help me with this.
Speaker 1: The following is either profoundly deep or utterly superficial, and
Speaker 1: I can't figure out what it is AI can answer
Speaker 1: most of our questions accurately, which means the price of
Speaker 1: answers got really low. But what's not getting priced away
Speaker 1: is questions. And so to the extent that you'd organized
Speaker 1: your professional life around answers, you just invested in a
Speaker 1: set of skills whose price is going down, and that's
Speaker 1: a very bad idea. So you might think of some
Speaker 1: parts of lawyering as being answers driven, and we're seeing
Speaker 1: that profession change very, very rapidly right now. You might
Speaker 1: think of some parts of medicine as being answers driven.
Speaker 1: Now you might think there are some parts of both
Speaker 1: of those occupations that are not, and those would be
Speaker 1: the skills. So I think there's going to be some
Speaker 1: sort of softer skills. I've been really amazed at how
Speaker 1: quickly and how smart young people are. Megan, I don't
Speaker 1: know if you remember, but I'm going to share this
Speaker 1: with our audience. You and I were on campus and
Speaker 1: we talked to a brilliant young eighteen or nineteen year
Speaker 1: old computer science major. She was a freshman, and she
Speaker 1: asked me some question about AI, and then I asked her, like, well,
Speaker 1: how do you feel going into coding? You know, given
Speaker 1: that computers can do it better than you, and she
Speaker 1: just said, it's a completely different skill set. I am
Speaker 1: a personnel manager, except I'm not managing people on managing agents.
Speaker 1: And I in that moment, I thought, are all young
Speaker 1: people as smart. If so, they get the moment and
Speaker 1: they're going to be okay, And if not, then they
Speaker 1: need to do a lot of very hard thinking because
Speaker 1: the future of work will be really transformationally different.
Speaker 3: Yeah.
Speaker 2: I just want to give my extra plug for the
Speaker 2: don't be too quick to specialize route. I know, like
Speaker 2: there's some education research that shows that the US high
Speaker 2: school system, the fact that we weren't apprentice base, that
Speaker 2: we focused on giving as many students as possible, as
Speaker 2: broad of a skill set as possible, might have really
Speaker 2: helped pull the US pool ahead of places like Germany
Speaker 2: and England in the twentieth century. And I think that's
Speaker 2: going to be the same here. We don't know what
Speaker 2: jobs will and will not exist, so as and I
Speaker 2: have a feeling that people might have multiple careers in
Speaker 2: their lifetime, so as you know, adaptable, they can be,
Speaker 2: as open they can be to learning new skills. And
Speaker 2: then on top of that, like double down on the basics.
Speaker 3: Though.
Speaker 2: You know, as someone in education, I get very worried
Speaker 2: about kind of the erosion of basic skills, and I
Speaker 2: think now is not the time to move away from them.
Speaker 3: They need to be doubled down in.
Speaker 2: The early years and then I love an interdisciplinary education,
Speaker 2: I always have, but I think now is the time
Speaker 2: for it for sure.
Speaker 1: I want to reinforce what you said about basic skills,
Speaker 1: which is IO as a replacement for lots of things,
Speaker 1: just like the calculator replaced. Doing written to can your hit,
Speaker 1: but it's a compliment for a lot of things can
Speaker 1: help you be better. It's a kite that can help
Speaker 1: you fly, but you have to be able to do
Speaker 1: something before I can make you better at something. Look,
Speaker 1: one of the stories of past technological revolutions is we
Speaker 1: do discover that the old ways of doing things are
Speaker 1: on the trip that they're extraordinary new ways. I hit,
Speaker 1: and you want to be able to adapt to those
Speaker 1: new ways. And those new ways usually take some set
Speaker 1: of skills and mike and even stronger, and that's the
Speaker 1: winning formula here.
Speaker 3: Awesome.
Speaker 2: Well, thank you as always to our audience for your
Speaker 2: great questions and comments. We hope you enjoyed this extra
Speaker 2: long episode of the professors in. If you want your
Speaker 2: question answered in the future segment, just leave a comment
Speaker 2: wherever you're watching or listening to this and as always,
Speaker 2: make sure to like and subscribe to both Justin and
Speaker 2: plotupus Economics on YouTube, substack, and anywhere you can find
Speaker 2: us on social media.
Speaker 1: Well, next time, stay curious. M