Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News.
Speaker 2: Hello and welcome to another episode of the Odd Lots Podcast.
Speaker 1: I'm Joe Wisenthal and I'm Tracy Alloway.
Speaker 2: Tracy, I feel like AI is a great thing for
Speaker 2: anyone who wants to have an opinion on anything. It's
Speaker 2: like this blank canvas out there in which any idea
Speaker 2: you have it. It's just a great moment for pontificators
Speaker 2: in general.
Speaker 1: Well, not only can you hang a bunch of different
Speaker 1: opinions on it, but it can generate those opinions for you.
Speaker 3: Yeah, you can, that's right.
Speaker 2: Like you can just go to chat GPT and say
Speaker 2: which jobs are going to be lost thanks to you,
Speaker 2: and it'll like spew some answer forward based on the
Speaker 2: collective wisdom of trillions of words that people have typed
Speaker 2: over the years.
Speaker 1: I do think, though, if we're talking about one opinion,
Speaker 1: in particular, the dominant opinion at this point in time,
Speaker 1: it does feel like there's a lot of nervousness about
Speaker 1: this new technology and what exactly it means for the economy,
Speaker 1: what specifically it means for jobs. And so you see
Speaker 1: all these headlines that AI is going to lead to
Speaker 1: a bunch of job losses, that it's going to basically
Speaker 1: be a new technological revolution that plays out very similarly
Speaker 1: to the computer revolution that led to the destruction of
Speaker 1: a bunch of sort of middle office jobs, or the
Speaker 1: industrial revolution that led to a loss of skilled artisan jobs.
Speaker 1: And we've seen some hints of that, to be fair,
Speaker 1: So I'm thinking back to last year. I think it
Speaker 1: was in the summer, maybe in June, and the Challenger
Speaker 1: Jobs Report came out and for the first time ever,
Speaker 1: they included a line about job losses stemming from AI.
Speaker 2: Yeah, although I'm just going to say right here then,
Speaker 2: I think when a company lays off workers and says
Speaker 2: it's due to AI, I still have this assumption that
Speaker 2: it's like, we're doing badly, so we're gonna put a
Speaker 2: positive spin on it by making it seem as though
Speaker 2: our layoffs are the result of some internal productivity breakthrough
Speaker 2: that we're getting from a chatbot. So, like, I don't
Speaker 2: quite believe it, but I think.
Speaker 1: That's totally fair. That's totally fair. But I think clearly
Speaker 1: this is something people are paying attention to. You are
Speaker 1: starting to see some of the economic reports sort of
Speaker 1: break this down. At least the Challenger report if not
Speaker 1: like the BLS and things like that. So there is
Speaker 1: this idea hovering over the economy at the moment, which is, Okay,
Speaker 1: maybe AI will be great for productivity, we'll get that boost,
Speaker 1: but what does it mean for jobs? Right?
Speaker 2: Basically everyone in any realm loses their job and is
Speaker 2: on the UBI drip, and only Sam Altman is the
Speaker 2: last person who is employed, is it? But like, I
Speaker 2: don't know, I get freaked out, Like it's pretty good,
Speaker 2: Like there are many I use AI almost chatbots all
Speaker 2: the time in my work, and it's like, well, maybe
Speaker 2: it could one day be a better host than myself
Speaker 2: for a podcast. It seems possible to me. I am anxious.
Speaker 2: Of course, people also like to project onto their perceived
Speaker 2: ideological enemies. That's like, oh, all you English majors are
Speaker 2: going to lose your jobs hahaha, And then the English
Speaker 2: majors all go, all you coders are going to lose
Speaker 2: your jobs, and you're going to need English majors there.
Speaker 3: It's just an endless thing.
Speaker 2: And actually I think I tune most of it out
Speaker 2: because it's so ambiguous in my view where this technology
Speaker 2: is going that there are very few people I even
Speaker 2: want to hear from on the topic, because I think
Speaker 2: it's just so there's so much extreme uncertainty.
Speaker 1: Still extreme uncertainty. As you mentioned, people kind of harness
Speaker 1: it to further their own biases or arguments. But you're right,
Speaker 1: there are people who are good on this topic, and
Speaker 1: we're about to speak to one of them.
Speaker 3: That's exactly right.
Speaker 2: So last month, there was this really interesting headline that
Speaker 2: I saw in a Nuema magazine and it sort of
Speaker 2: felt like this sort of like provocative, maybe clickbaity type
Speaker 2: headline that said AI could actually help rebuild the middle class,
Speaker 2: which is very counterintuitive, very the opposite of what we're
Speaker 2: talking about. But then I noticed who the author of
Speaker 2: the piece was, and it's someone whose work is very
Speaker 2: strongly associated with forces in the past and forces in
Speaker 2: technology that have been destructive to the middle class and
Speaker 2: have caused great labor market upheaval. And so if someone
Speaker 2: who has sort of been watching this exact topic, the
Speaker 2: intersection of labor market upheaval and technological change, is saying,
Speaker 2: actually this could be good, and this person is a
Speaker 2: track record in this area, I'm like, Okay, this is
Speaker 2: an argument maybe I'll pay more attention to than the
Speaker 2: random person doing a Twitter threat.
Speaker 1: I'm into it. As you mentioned, we're speaking to someone
Speaker 1: who is an expert on this particular topic and specifically
Speaker 1: has has written a lot and researched a lot about
Speaker 1: previous labor market shocks, including the China Shock. So competition
Speaker 1: from China in the realms of manufacturing in the sort
Speaker 1: of nineteen nineties, early two thousands. So I'm very excited
Speaker 1: for this conversation. I am interested to hear an argument
Speaker 1: that's not just AI is terrible and it's going to
Speaker 1: take all of our jobs.
Speaker 2: Absolutely well, I'm really excited. We do, in fact have
Speaker 2: the perfect guest. We are going to be speaking with,
Speaker 2: David Otter. He's a professor of economics at MIT and
Speaker 2: co director of the MIT Shaping the Future of Work Initiative,
Speaker 2: and he's really known for his work on the China
Speaker 2: Shock and the devastating impact that China's boom in tradeable goods,
Speaker 2: particularly after its essension to the WTO, had on various
Speaker 2: communities within the United States that were sort of dependent
Speaker 2: on a sort of regional manufacturing. So, David, thank you
Speaker 2: so much for coming on odd LATS.
Speaker 4: Thank you so much. Joe and Tracy for inviting me.
Speaker 4: I'll try not to be clickbaity.
Speaker 3: That's okay. It's okay.
Speaker 2: It's okay to be clickbait if it delivers. And the
Speaker 2: other thing about this article, by the way, is that
Speaker 2: it wasn't like a paragraph thought piece like this is
Speaker 2: clearly some serious work which we obviously appreciated and made
Speaker 2: me take it seriously. But before we get into this
Speaker 2: or even the China shock or general work or AI
Speaker 2: in general, what is your like, what do you tell
Speaker 2: us like what has been the thrust of your career
Speaker 2: over time?
Speaker 1: Like?
Speaker 2: What is what is sort of the main interest of
Speaker 2: yours that spans from the effects of globalization to now AI,
Speaker 2: et cetera.
Speaker 4: My focus has always been on forces that shape opportunity,
Speaker 4: particularly for workers without for your college degrees, the majority
Speaker 4: of workers in the United States and of course elsewhere,
Speaker 4: and that have been so buffeted by computerization, by globalization,
Speaker 4: by changes and institutions, including the unionization, the fall the
Speaker 4: mid and wages the United States. And so that is
Speaker 4: the common focus of my work, and that has included,
Speaker 4: you know, a lot of work on technological change, computerization,
Speaker 4: the China trade shock, and many other angles to that.
Speaker 4: But that kind of unifies that's you know, I think
Speaker 4: the labor market is the most important thing in the world.
Speaker 4: I think that's where people drive most of their income,
Speaker 4: spend most of their time, derive identity from, and so
Speaker 4: things that affect the quality of jobs, the opportunities that
Speaker 4: people have are just quintessentially important and are going to
Speaker 4: shape the structure of their lives, you know, more than
Speaker 4: the quality of entertainment, more than the ease of transportation,
Speaker 4: more than you know, what fashion is available. This is
Speaker 4: really a big ee.
Speaker 1: So in the spirit of this discussion, I asked to
Speaker 1: chat GPT to poke intellectual and logical holes in this article.
Speaker 1: So let's just start there. Number one. No, I'm joking.
Speaker 1: I did actually do that, and some of them, some
Speaker 1: of them are quite good, and I will get to
Speaker 1: them later. But maybe just to begin with, could you
Speaker 1: talk about the current discourse on AI and why there
Speaker 1: seems to be this distrust of new technology. What is
Speaker 1: that predicated on. I mean I kind of referred to
Speaker 1: it in the intro, but there is past history, obviously
Speaker 1: with major technological advances and booms that have led to
Speaker 1: certain outcomes in the labor market. How does that inform
Speaker 1: the current discussion.
Speaker 4: Sure, so people are understandably very concerned about all of
Speaker 4: these technological forces because they are disruptive and they create
Speaker 4: winners and losers. There's no sense in which everyone is
Speaker 4: better off because of a new technology. So you mentioned
Speaker 4: the industrial era, and the Luddites rose up against the
Speaker 4: introduction power looms and smashed them, and they're often derided historically,
Speaker 4: but they were right. The industry revolution, the mechanization of
Speaker 4: weaving wiped out the careers of artisans and made their
Speaker 4: work non tenable, and wages didn't rise for decades into
Speaker 4: the Industrial Revolution. So that was very displacing. Ultimately at
Speaker 4: raised living stairs, but it took a long time and
Speaker 4: the beneficiaries were not workers. The computer revolution has raised productivity,
Speaker 4: but it's been very unequal and polarizing. It's automated a
Speaker 4: lot of middle skill, middle class work in factories and
Speaker 4: in offices. It's been great for professionals, but for many
Speaker 4: other people it's just meant that because they can no
Speaker 4: longer do. Those middle skill jobs are often found in
Speaker 4: food service, cleaning, security, entertainment, recreation, and those are valuable,
Speaker 4: laudable activities, but they don't pay well. Because they don't
Speaker 4: use specialized expertise in training, so most people can do
Speaker 4: that work almost right away, so it tends to be
Speaker 4: low paid. So I think there's many reasons to take
Speaker 4: this very very seriously and think carefully about what the
Speaker 4: implications are.
Speaker 2: Before we even get to AI talked to us more
Speaker 2: about the computer revolution, because, like I said, I saw
Speaker 2: your piece and I'm like, uh, and I first thought
Speaker 2: China Shock and your work on that. It's like, okay,
Speaker 2: this is interesting, but actually, just like I feel like
Speaker 2: there actually has not been a lot of general conversation
Speaker 2: about the sort of unequalizing effects of the computer revolution,
Speaker 2: like how did that happen? What does the research show
Speaker 2: about the timing of the introduction of the computers, And
Speaker 2: then this sort of like I don't know, maybe Barbelle
Speaker 2: or fragmentation of what happened to workers.
Speaker 4: So you know, this really begins in the nineteen eighties
Speaker 4: and it can continues through the over at least thirty
Speaker 4: five years. And you know, a very simple way to
Speaker 4: boil it down and say, look, what are computers useful for.
Speaker 4: They're useful for following rules and procedures, right, they don't
Speaker 4: think they're not creative. They're not problem solvers. They don't improvise,
Speaker 4: they follow codified rules and procedures. But that describes a
Speaker 4: lot of middle skill work. Right, whether you're in an
Speaker 4: office or you're doing repetitive assembly work. The ability to
Speaker 4: accurately carry out codified procedures is a valuable skill. It
Speaker 4: requires off in literacy and numeracy and training, and so
Speaker 4: the ability to automate that was a really big deal,
Speaker 4: and that had the effect of displacing many people who
Speaker 4: were doing what I would call these mass expertise jobs
Speaker 4: where they were following codified procedures. Right, it takes education
Speaker 4: to be a typist or a bookkeeper or someone who
Speaker 4: does filing an organization, keeps track of accounts, and so
Speaker 4: the fact that a lot of that work takes real skill.
Speaker 4: To do high quality work on assembly line, you have
Speaker 4: to understan the tools, who have to understand the product
Speaker 4: and so on. So the fact that that work could
Speaker 4: be automated was not unambiguously good. It was good for
Speaker 4: you know, it was good for productivity, it was good
Speaker 4: for consumers, it was good for firms. But for the
Speaker 4: workers who had invested their careers in those activities, that
Speaker 4: was definitely a negative. And on the other hand, if
Speaker 4: you were a professional or you know, a manager or
Speaker 4: a designer, researcher, doctor, having access to information and quick calculation,
Speaker 4: that's not your main job. Those are just inputs into
Speaker 4: your decision making. So computers were very complimentary to people
Speaker 4: who are decision makers, which is really the bulk of
Speaker 4: the professions making high stakes decisions about you know, important
Speaker 4: one off cases. You know how to care for a
Speaker 4: cancer patient, or you know how to design a building,
Speaker 4: or how to do a marketing plan. Right, computerizations extremely
Speaker 4: helpful for that doesn't displace your main job, it just
Speaker 4: makes you more efficient add it. But for people who
Speaker 4: did not have the opportunity to get degrees and move
Speaker 4: upward into that work, what remained was a lot of
Speaker 4: work that's very hard to automate. You to mention these
Speaker 4: a lot of these hands on manual jobs, so you know,
Speaker 4: food service and cleaning, could be a transportation and many
Speaker 4: of those jobs. Not all of those jobs are open
Speaker 4: to many many people. They don't require much training or experience,
Speaker 4: and you don't get a great deal better at them
Speaker 4: over time. And so because of that, because they're non
Speaker 4: expert work, they tend to be low paid in all
Speaker 4: industrialized countries. Now, I want to be clear that not
Speaker 4: all hands on work is low paid or low skilled
Speaker 4: in any sense. Right, if you're a plumber, electrician, you're
Speaker 4: working the skilled trades, right, if you do skilled repair.
Speaker 4: There are many, many skilled hands on jobs, but the
Speaker 4: ones that have grown so much as the middle has
Speaker 4: hollowed out, have been much more of these personal service
Speaker 4: occupations that have low training and expertise requirements.
Speaker 1: The thing this reminds me of, and I cannot, for
Speaker 1: the life of me remember which guest this was, but
Speaker 1: a previous all Lots guests described this as remember the
Speaker 1: scene from The Producers where Matthew Broderick is like an
Speaker 1: actuary or something working in an office and they're all
Speaker 1: toiling away.
Speaker 2: And then Stuart Butterfield.
Speaker 1: That's right, and then all of those people eventually get
Speaker 1: replaced by an Excel spreadsheet, right, Like that's the function
Speaker 1: that became Excel. So, David, I want to kind of
Speaker 1: press you on this point because I think it's a
Speaker 1: really interesting one, and I think it's essential to understanding
Speaker 1: your overall argument. But you make the distinction between information
Speaker 1: and decision making. So the idea that people can have
Speaker 1: access to a lot of information. In fact, plenty of
Speaker 1: people would argue that people are drowning in information at
Speaker 1: the moment.
Speaker 4: Information.
Speaker 1: Yeah, but they're not necessarily using that to make the
Speaker 1: best decisions. Decision making is sort of a separate skill.
Speaker 1: Can you talk a little bit more about that aspect
Speaker 1: of your argument?
Speaker 4: Absolutely so. Let me so, I want to draw a
Speaker 4: sharp line between AI and traditional computing, which is what
Speaker 4: we've been discussing, because they're quite different. But before you
Speaker 4: do that, let me kind of make a kind of
Speaker 4: a meta argument that I think is useful our discussion.
Speaker 4: So the concern we should be having is not about
Speaker 4: the quantity of jobs. We are not running out of jobs.
Speaker 4: And in fact, you know, all the Western world right
Speaker 4: now is in full or overemployment, and even during the
Speaker 4: whole computer revolutions on, we didn't run out of jobs.
Speaker 4: It's not the quantity that matters. In fact, we're all
Speaker 4: facing a demographic crunch. It's the quality. Right A world
Speaker 4: in which everyone's waiting tables is very different from the
Speaker 4: world in which everyone is doing medical care. And so
Speaker 4: what matters is not simply whether there is work, but
Speaker 4: whether it's expert work that requires real skills. If it's
Speaker 4: non expert work, work that anyone can do with no
Speaker 4: training your certification, unfortunately it will be low paid. On
Speaker 4: the other hand, if it's work that requires specialized knowledge
Speaker 4: and that is made more productive by uses of tools,
Speaker 4: and computers are to and AI as a tool, then
Speaker 4: that's good for labor, that's good for earnings, that's good
Speaker 4: for the quality of careers. And so we should be
Speaker 4: thinking about expertise. Just to give you, like a very
Speaker 4: stylized example, you know, think of the job of crossing
Speaker 4: guard and air traffic controller. These are basically the same job.
Speaker 4: The job is to prevent things from crashing into other things, right,
Speaker 4: airplanes from crashing to airplanes, cars from crashing into children
Speaker 4: on their way to school. But air traffic controllers United
Speaker 4: States are paid, you know, four and a half times
Speaker 4: as much as crossing guards, and the reason is expertise.
Speaker 4: Almost anyone can become a crossing guard in the United
Speaker 4: States with no trainer certification, whereas to become an air
Speaker 4: traffic controller requires years of school and thousands of hours
Speaker 4: of practice. And so even though those jobs do the
Speaker 4: same thing, because of the difference in skill requirements, they
Speaker 4: pay very different wage levels, and so we want to
Speaker 4: have jobs where expertise is valuable, not just where physical
Speaker 4: presence is the primary requirement. So that's what we should
Speaker 4: be thinking about. Having said that, let me talk about
Speaker 4: how AI relates to that. So, you know, a traditional computerization,
Speaker 4: as we've been talking about, is really about automating well
Speaker 4: understood procedures and rules, right, what we call formal knowledge.
Speaker 4: You know how to do math, how to reproduce a
Speaker 4: document or check for spelling errors. And it's very limited
Speaker 4: because it cannot do what people do fairly effortlessly, which
Speaker 4: is learn from kind of tacit knowledge. Tacit knowledge is
Speaker 4: all the things that you implicitly understand that you infer
Speaker 4: from your environment, but you never formalize. Right. So, you
Speaker 4: know how to ride a bicycle, but you couldn't explain
Speaker 4: how it's done. Right. You couldn't sit up and explain
Speaker 4: that you know the gyroscopic physics of a bicycle. You
Speaker 4: know how to make a funny joke, but you don't
Speaker 4: know the rules for making a funny joke. You know
Speaker 4: how to recognize the face of someone after you haven't
Speaker 4: seen them for thirty years, right, But that's actually a
Speaker 4: hard problem, and we do it, but we do it
Speaker 4: based on some tacit understanding. And this has always been
Speaker 4: a barrier to computerization because we couldn't code up the
Speaker 4: things that we understood only acidly. We had to understand
Speaker 4: them explicitly, informally. So AI overcomes that barrier. AI essentially
Speaker 4: infers tacit information from large bodies of data. It learns
Speaker 4: the associations between you know, words and phrases and sentences
Speaker 4: between pictures and words. It can look at a scan
Speaker 4: of a patient's lungs and make predictions or you know,
Speaker 4: guesses about whether that patient has an endema or other
Speaker 4: medical disorders. It does that not because someone has written
Speaker 4: a program that says, these things tell you whether you have,
Speaker 4: you know, a lung issue. It's because it learns from
Speaker 4: the patterns it's trained on that data, and so that
Speaker 4: gives it a really different set of capabilities. It gives
Speaker 4: it the ability to do what a lot of us do,
Speaker 4: or at least to supplement a lot of what we do,
Speaker 4: which is to sort of make decisions based on lots
Speaker 4: and lots of inputs and educated guesses. Right, So let's
Speaker 4: say you know, you're a medical doctor, right, when you
Speaker 4: see a patient, you're not simply essentially reading from your
Speaker 4: textbook in your mind about what to do. You understand
Speaker 4: bodily systems, you understand the biology and so on, but
Speaker 4: then you've had lots and lots of experience. So when
Speaker 4: you've see an individual patient, you're going to make a
Speaker 4: decision based on a kind of translation from this formal
Speaker 4: body of knowledge plus all the experience you've had to
Speaker 4: make a good judgment. And the stakes are really high
Speaker 4: because obviously if there was just a simple rule book
Speaker 4: for it, you wouldn't need a doctor. You need a
Speaker 4: person who can make a judgment about how to care
Speaker 4: for this patient and their individual needs.
Speaker 3: It's so funny.
Speaker 2: I was just talking to Tracy in a different context,
Speaker 2: and I was like, I was talking about the TV
Speaker 2: show House, which I'm really into, and like, you know,
Speaker 2: even though it's probably hyper dramatized, this idea of like
Speaker 2: how still today like doctors don't really know a lot
Speaker 2: and they have to like they debate, well, what's actually
Speaker 2: going on here? And of course the show has some
Speaker 2: very entertaining depictions of what those debates among doctors of
Speaker 2: what's really going wrong with the patient and what's the
Speaker 2: proper treatment. So I guess you can go from there
Speaker 2: and just say, well, House was like the most brilliant
Speaker 2: and he had seen thousands of patients over the course
Speaker 2: of several seasons of that show, and so he had
Speaker 2: the best like intuitions. But basically it sounds like, thanks
Speaker 2: to AI, someone can harness those same intuitions without having
Speaker 2: seen thousands of patients before, like doctor House did.
Speaker 4: I think that's a nice way to put it, is
Speaker 4: that what AI can do is provide kind of guidance
Speaker 4: and guardrails for decision making. So what do I mean
Speaker 4: by guidance and guardrails. By guidance, I mean, you know,
Speaker 4: had you considered this set of possibilities, these potential diagnoses,
Speaker 4: guardrails were like, you know, don't prescribe these two drugs together.
Speaker 4: They negatively interact and in decision making work. Having that
Speaker 4: kind of access to support, to a form of expertise,
Speaker 4: not that you should one hundred percent rely upon it,
Speaker 4: but that you can supplement your own judgment is potentially
Speaker 4: very useful. So you know, let me give you a
Speaker 4: concrete example, sticking with medicine. So the job of nurse
Speaker 4: practitioner is pretty prominent right now, they're several hundred thousand
Speaker 4: the United States. They make quite a good living, about
Speaker 4: one hundred and thirty two thousand dollars a year of
Speaker 4: the median. And they barely existed twenty years ago. And
Speaker 4: nurse practitioners are nurses with an additional master's degree who
Speaker 4: could do diagnosing, prescribing, treating things that were only done
Speaker 4: by medical doctors decades earlier. And this new occupation has
Speaker 4: come into existence, and it's terrific for patients in the
Speaker 4: way in that it saves them time, it saves the
Speaker 4: healthcare system money, it creates a good job, and it
Speaker 4: does a very important task. Now, this is not a
Speaker 4: technological creation, socially, the result of nurses recognizing they were
Speaker 4: under used, fighting for a larger role, developing a training
Speaker 4: and certification program, and eventually against over the dead body
Speaker 4: of the American Medical Association, effectively carving out this new role.
Speaker 4: So it's not because of technology. However, at this point,
Speaker 4: nurse practitioners are heavily supported by technology. Right, So electronic
Speaker 4: medical records right provide all the information all that you
Speaker 4: would need or some of the information you would need
Speaker 4: for good decision making, as do extensive diagnostic tests, as
Speaker 4: does software that looks for drug interactions, among other things.
Speaker 4: And it's easy to imagine that as we roll the
Speaker 4: clock forward, the set of tools that will support decision
Speaker 4: making by nurse practitioners will improve dramatically. And as it
Speaker 4: does so, it will allow them to do more of
Speaker 4: the tasks that are currently kind of controlled by more
Speaker 4: expensive professionals. And why is that a good thing? You
Speaker 4: might say, Well, it's not a good thing if your
Speaker 4: doctor necessarily, But we live in a world in which
Speaker 4: a lot of the bottlenecks are expensive decision makers, people
Speaker 4: who are the MBAs and the lawyers and the medical
Speaker 4: doctors and the architects and the engineers, and they all
Speaker 4: do valid work and they deserve what they earn, and
Speaker 4: I'm not disputing that, but it would be great to
Speaker 4: be able to create more people who could do that
Speaker 4: work without them being quite so expensive and the advantage
Speaker 4: of that, So, if an AI can enable more people
Speaker 4: to do good decision making work, it actually can open
Speaker 4: up opportunity for people who are not the elite. Right,
Speaker 4: we have tons and tons of healthcare that needs to
Speaker 4: be done right. It doesn't all need to be done
Speaker 4: by medical doctors, or we have lots of software coding
Speaker 4: that needs to be done. It doesn't all need to
Speaker 4: be done by people from top universities with Bachelors of
Speaker 4: Science degrees in computer science. We have tons of design
Speaker 4: that needs to be done, tons of care, tons of
Speaker 4: legal work. Right, So the potential for an AI is
Speaker 4: to enable people who have training and judgment to go
Speaker 4: further with those skills. So it's not to make them unnecessary,
Speaker 4: but simply to extend their range by supporting decision making. So,
Speaker 4: just to give you another super concrete analogy, take YouTube. Right.
Speaker 4: So YouTube is used all the time by people in
Speaker 4: the trades among other groups to try to figure out
Speaker 4: how to do a specific repair or diagnose the problem
Speaker 4: that they have seen before. Now I'm gonna say, well,
Speaker 4: who is YouTube really for. Well, it's not for the
Speaker 4: frontier experts they already know how to do these things,
Speaker 4: nor is it necessarily for the rank amateur. Right. You
Speaker 4: don't want to go to YouTube and say, well, how
Speaker 4: do I install and wire in a brand new central
Speaker 4: house air conditioning? I've never done anything like that before. Right,
Speaker 4: If you went Toto YouTube for that, you would quickly
Speaker 4: get yourself into trouble because if you don't have some
Speaker 4: foundational skills, that could be a problem. On the other hand,
Speaker 4: if you were handy and you had some experience with
Speaker 4: electrical work, some experience with plumbing, some experience with carpentry,
Speaker 4: but you've never done an ac installation before, well, now
Speaker 4: you could go to YouTube and that would get you further.
Speaker 4: So you could think of YouTube as kind of like
Speaker 4: a mini AI that provides guidance and guardrails.
Speaker 2: I feel like Tracy has watched many youtubes in the
Speaker 2: last year to fix your Connecticut house.
Speaker 1: This example hits home so hard, and I'll give you
Speaker 1: a specific anecdote, which is my husband and I are
Speaker 1: currently building a shed and we're trying to put a
Speaker 1: roof on it. And we thought like, okay, we put
Speaker 1: the plywood on the roof, and then we get some
Speaker 1: joyst tape. We put the joystape down over the edges,
Speaker 1: and then we put on the shingles, and we watched many,
Speaker 1: many YouTube videos on how to do this. It turns
Speaker 1: out that you can't use joyste tape when it's less
Speaker 1: than fifty degrees fahrenheit outside, which it was, which, of course,
Speaker 1: none of the YouTube videos that are filmed down in
Speaker 1: Florida or wherever actually mentioned. And then secondly, it turns
Speaker 1: out that the ability of the joyst tape to actually
Speaker 1: adhere to the plywood varies enormously depending on what plywood
Speaker 1: you're using. So there are all these subtleties and nuances
Speaker 1: that you don't necessarily get from a ten minute YouTube video.
Speaker 1: Maybe that's not that surprising. But on this note, so
Speaker 1: you mentioned training, and you've spoken a lot at this
Speaker 1: point about the idea of AI being able to provide
Speaker 1: guardrails and context around decision making that maybe yeah, can
Speaker 1: resolve the bottleneck of expensive decision makers, as you put it,
Speaker 1: by creating more of them or allowing more people to
Speaker 1: tap that function. I guess my big question is how
Speaker 1: much of this is just going to be Well, we
Speaker 1: add a new layer of training that people have to
Speaker 1: do so you can use AI, but you still have
Speaker 1: to know how to use AI. You still have to
Speaker 1: understand the result that it's spitting out and interpret that.
Speaker 1: You still have to know how to actually apply and
Speaker 1: use that result. Are we basically just replacing one skill
Speaker 1: set with another.
Speaker 4: It's a good question. We want it to require skills,
Speaker 4: right if everyone is expert. No one is expert, right,
Speaker 4: it's important. The question is whether it can be the
Speaker 4: acquisition of expertise or whether it just gets in the way.
Speaker 4: Another thing you have to certify on. We now have
Speaker 4: you know a bunch of evidence on AI and specific
Speaker 4: applications and where it works well and where it doesn't.
Speaker 4: So for example, you know some students of mine, ked
Speaker 4: Noy and Whitney Zang published a paper in Science last
Speaker 4: year where they gave chat gipt three and a half
Speaker 4: to people who were doing advertising writing and marketing plans.
Speaker 4: And these were people who were college graduates who do
Speaker 4: this for a living. And one group just used the
Speaker 4: standard tools, so it's basically the Internet word processors. Another
Speaker 4: one actually used the chatbot and this was early enough
Speaker 4: that most people didn't already have it, and there were
Speaker 4: a couple of really nice results. So, first thing, it
Speaker 4: saved everybody time. It cut the time it took people
Speaker 4: to do this work from about thirty minutes to about eighteen.
Speaker 4: The second is it improved the quality on average. So
Speaker 4: the output of the people using this tool was judged
Speaker 4: and by other college graduates who were not confederates in
Speaker 4: the experiment to be more precise, more concise, and more accurate,
Speaker 4: so improve the quality of work and saved time. But
Speaker 4: then the most exciting result was if you looked at
Speaker 4: the quality range of the work people did, it basically
Speaker 4: made the least capable writers using chat GPT were about
Speaker 4: as good as the median writers not using it, So
Speaker 4: it kind of leveled up the bottom. And we've seen
Speaker 4: this in other places as well, folks doing customer support.
Speaker 4: The example I'm thinking of is a kind of an
Speaker 4: enterprise software product and they customers chat in through chat window,
Speaker 4: and then the company installed a tool that suggests responses
Speaker 4: to the customer's chat. You don't have to use them,
Speaker 4: but it will also not just suggest technical responses, but
Speaker 4: polite responses and so on to keep the customer from
Speaker 4: getting overheated. And the result is that it speeds the
Speaker 4: rate at which people learn. So it used to take
Speaker 4: people ten months to reach peak capacity. Now it takes
Speaker 4: them about three months. They're somewhat faster when that's done,
Speaker 4: so it's not that it eliminates the training or learning.
Speaker 4: Everyone starts off bad at this job, but they get faster.
Speaker 4: They converge towards expert level more quickly. But this tool,
Speaker 4: and also really interestingly, people quit a lot less. And
Speaker 4: the reason is, you know, customer service work is actually
Speaker 4: really difficult. It's very heavy emotional lay and you have
Speaker 4: to take a lot of incoming abuse actually from customers.
Speaker 4: It's hard to keep your cool. And the sentiment analysis
Speaker 4: of this tool, of the chats that occurred through it,
Speaker 4: is that it basically reduced the level of hostility from
Speaker 4: customers to workers and from workers to customers. So it
Speaker 4: actually did a lot of the emotional labor. So it
Speaker 4: didn't eliminate the need for skills in doing this work,
Speaker 4: but it enabled people to become more efficient, more rapidly,
Speaker 4: with less stress. And so that's the good scenario. There's
Speaker 4: a lot of work that needs to be done. And
Speaker 4: right now, what are the most expensive things? The things
Speaker 4: that are growing more and more costly all the time
Speaker 4: are education, healthcare, legal services. Why is that? Why are
Speaker 4: those things getting so expensive? Well, during the industrial era,
Speaker 4: we got really efficient and manufacturing goods. Right, so TVs, automobiles, coffeemakers, right,
Speaker 4: mobile phones, these things are actually remarkably good and relatively cheap. Why, well,
Speaker 4: we've automated them and the labor content is relatively low.
Speaker 4: On the other hand, healthcare, education, law, Right, we've not
Speaker 4: gotten any more efficient to those things, and they require
Speaker 4: people who've gotten more and more expensive over time because
Speaker 4: as we've automated the other work, the people who are
Speaker 4: the degreed professionals or have become the bottleneck. So that
Speaker 4: slows the growth of productivity. It makes the cost of
Speaker 4: living higher for the typical person. Right, typical person is
Speaker 4: not a lawyer, it's not a professor, it's not a doctor.
Speaker 4: But they're paying for all those things. So if we
Speaker 4: could enable more people without as much training, and I
Speaker 4: don't mean no judgment, I mean some training. If we
Speaker 4: could allow paralegals to do more legal work, if we
Speaker 4: could allow nurse practitioners to do a larger range of
Speaker 4: medical tasks. If we could enable people who are doing
Speaker 4: working as contractors also to do more design. Right, if
Speaker 4: we're enabling people who don't have computer science degrees to
Speaker 4: do more software development, not only would that reduce the
Speaker 4: cost of these expensive services, but when I prove the
Speaker 4: quality of work that people could do, it allow them
Speaker 4: to take some expertise and make it go further. So
Speaker 4: that's the good scenario.
Speaker 1: Joe, I like the idea of using AI to reduce
Speaker 1: emotional labor. I wonder if I can start automating some
Speaker 1: responses on Twitter to toxic bitcoin maximlist. That's interesting, Tracy.
Speaker 2: The block button is right there. So there's so many
Speaker 2: different questions now that I have in my mind. But
Speaker 2: you know, look, we're only near the beginning. I mean
Speaker 2: chat GPT, which is sort of what's brought us all
Speaker 2: into consciousness, was unveiled to the public in late twenty
Speaker 2: twenty two, so not even two years into that this
Speaker 2: sort of breakthrough that enabled it. As just a few
Speaker 2: years older than that. The concern would be, well, yes,
Speaker 2: at this point some training plus AI enables many people
Speaker 2: to become much more productive and have this sort of
Speaker 2: output that was previously associated with people with years of experience.
Speaker 2: Like the fear would be that in multiple generations down
Speaker 2: the road, you don't even need that initial training first.
Speaker 4: I fully agreed. Word just at the beginning. The tools
Speaker 4: are only so good, they're going to get much better.
Speaker 4: Are Understanding how to use them is also very primitive.
Speaker 4: We often don't know how to interact well with AI.
Speaker 4: In fact, I could give you examples of cases where
Speaker 4: it goes pretty badly, even though the tool is good.
Speaker 4: So I think there are sort of two concerns built
Speaker 4: into what you said. One is it basically, for now
Speaker 4: it's a helper, and then eventually it's just your replacement.
Speaker 4: And the other is that even if it just makes
Speaker 4: everyone more efficient, eventually, well, we just saturate the world
Speaker 4: with whatever that thing is, and then it's super cheap. Right. So,
Speaker 4: there's only so many PowerPoint presentations the world can tolerate,
Speaker 4: and if you get really fast at making them, eventually
Speaker 4: people will pay you to stop.
Speaker 2: Yes, we're there now, maybe, but anyway, keep going.
Speaker 4: So I think that that will occur in some cases.
Speaker 4: There's no question that in some cases the tool will
Speaker 4: initially be a supplement and eventually be a replacement. Right.
Speaker 4: So maybe air traffic controllers would be an example like that,
Speaker 4: right where eventually almost all the air traffic control will
Speaker 4: be done by machines. But I don't think every job
Speaker 4: is like that. I don't think that's the case in medicine.
Speaker 4: Medicine will be a hands on occupation for a very
Speaker 4: long time. So will law, where there's a lot of
Speaker 4: high stakes decision making, so will design. So I don't
Speaker 4: think that we're going to automate everything away. I know
Speaker 4: people think that, and I think it's a valid concern.
Speaker 4: I don't think that's the most likely scenario. But I
Speaker 4: also want to stress something that's said too little in
Speaker 4: these discussions, which is, when you think about what you
Speaker 4: can do with a new tool, most people think, well,
Speaker 4: what can I automate? What is the thing that I'm
Speaker 4: doing now that I could now have the machine do
Speaker 4: for me? And that's important, and we do a lot
Speaker 4: of automation, but automation is not the primary source of
Speaker 4: how innovation improves our lives. Right. Many of the things
Speaker 4: that we do with new tools is create new capabilities
Speaker 4: that we didn't previously have. Right. So, airplanes did not
Speaker 4: automate the way we used to fly. We just didn't
Speaker 4: fly before we had airplanes. Right. The scanning electron microscope
Speaker 4: didn't automate the way we used to look at subatomic particles.
Speaker 4: We simply couldn't see them without that microscope. Right. So
Speaker 4: think of the thought experiment of automating everything in Asian Greece,
Speaker 4: you know, two thousand years ago. Even if you automated
Speaker 4: everything in ancient Greece, it wouldn't be modern America, right,
Speaker 4: It wouldn't have electricity, it wouldn't have computers, it wouldn't
Speaker 4: have airplanes, it wouldn't have penicillin, it wouldn't have a
Speaker 4: million tools technologies that we take for granted. So the
Speaker 4: most important applications of technology are to enable capabilities that
Speaker 4: didn't previously exist, and I think AI will do that
Speaker 4: as well. So you know, we couldn't be having this
Speaker 4: conversation were it not for our computers. Right. If someone
Speaker 4: took my computer away from me, I couldn't even do
Speaker 4: my job, right, It's just my job wouldn't exist in
Speaker 4: its current form. And so what we do with new
Speaker 4: technology is create new capabilities, and then human expertises often
Speaker 4: needed to support those capabilities. Right, we didn't have pilots
Speaker 4: before we had airplanes, and we didn't have pediatric oncologists
Speaker 4: before we had all kinds of tools and knowledge to
Speaker 4: treat cancer or cancer in children. And so as we
Speaker 4: instantiate these new capabilities, we often require new human skills
Speaker 4: and expertise that are valuable. And so much of what
Speaker 4: we do with these tools is to change our lives
Speaker 4: by pushing out the possibility set, rather than simply just
Speaker 4: automating the things that we already do, and I think
Speaker 4: AI will also be really important for that.
Speaker 1: One thing I wanted to ask you is you are
Speaker 1: very very clear in your piece that this is more
Speaker 1: of an informed thesis than an actual forecast. And here
Speaker 1: I am actually leaning on chat GPT when I asked
Speaker 1: it to poke holes in your argument. One of the
Speaker 1: ones it spat out had to do with this exact question.
Speaker 1: Are there specific measures or policies that we could be
Speaker 1: doing right now to make the probability of this outcome
Speaker 1: better rather than the sort of like destructive AI dooomerism
Speaker 1: outcome that everyone is worried about.
Speaker 4: Yeah, so I appreciate your saying that the future should
Speaker 4: not be treated as a forecasting or prediction exercise. It
Speaker 4: should be treated as a design problem. Because the future
Speaker 4: is not like the weather that we just wait and
Speaker 4: see what happens. Right, We're making our own weather. We
Speaker 4: have enormous control over the future in which we live
Speaker 4: and depends on the investments and structures that we create today,
Speaker 4: whether that's democracies, whether that's you know, education, whether that's
Speaker 4: how we use tools and science that whether we use
Speaker 4: fissionable material to make bombs or to make energy. Right,
Speaker 4: we have lots and lots of agency here. So in
Speaker 4: terms of using AI well, So first of all, let
Speaker 4: me say what would be a metric how would we
Speaker 4: know we were using AI? Well, because it's not like
Speaker 4: carbon dioxide, where you know, we say, oh, we know
Speaker 4: we're reducing carbon dioxide, you can just measure it, right,
Speaker 4: How would we know we're using AI well, I would
Speaker 4: say we know we're using it well when we see
Speaker 4: people who don't have for your college degrees doing work
Speaker 4: that we would think of as expert decision making work,
Speaker 4: whether that's coding, whether that's you know, medical vocational work,
Speaker 4: whether that's design and contracting, or even whether it allows
Speaker 4: skilled repair people to work on a broader range of
Speaker 4: products or tools or engines or whatever. So that's my
Speaker 4: metric of success, that it opens up new job opportunities
Speaker 4: to people who are not at the absolute elite of
Speaker 4: a field. How do we get there? So I think
Speaker 4: that's a super central question. And I think most thoughts
Speaker 4: about you know, policies about AI are about regulating, controlling,
Speaker 4: and some of that has to happen, and I feel
Speaker 4: reasonably confident that it will. This is much more about investing, right,
Speaker 4: So say, look, you know, in the United States, for example,
Speaker 4: about twenty percent of GDP two in ten dollars goes
Speaker 4: to education and healthcare. More than half that money is
Speaker 4: public money, so in fact, we have a lot of
Speaker 4: control over how education and healthcare delivered. So healthcare would
Speaker 4: be the best place to start to say, all right,
Speaker 4: let's redesign the tools or invest in the tools in
Speaker 4: a way that enables more people to deliver this work.
Speaker 4: And not only would that make better jobs, it would
Speaker 4: also improve access to healthcare potentially lower those costs. We
Speaker 4: could do the same in education. How can we make education,
Speaker 4: you know, make better use of teachers, provide better services
Speaker 4: to students, and also make education more accessible, immersive, engaging
Speaker 4: for adults. Right, we have lots of adults who need
Speaker 4: to learn, and traditional classrooms are really not the best
Speaker 4: place to do that. So I do think you have
Speaker 4: to think about these moonshots and governments can invest in them.
Speaker 4: That doesn't mean the government has to run them, but
Speaker 4: you know, governments often fund basic science, governments fund education.
Speaker 4: Most health innovation in the United States is paid for
Speaker 4: by the National Institute of Health, which is much much
Speaker 4: larger than the National Science Foundation, for example, So I
Speaker 4: think that is the biggest chance is to look for
Speaker 4: those opportunities and then design with the intention of creating
Speaker 4: a more effective way to structure work that uses the
Speaker 4: tools and uses human skills better. And let me say,
Speaker 4: you might say, well, you know, why doesn't this apply
Speaker 4: equally well to the last era. So first of all,
Speaker 4: we didn't design, and probably we should have done more.
Speaker 4: But essentially, computers are good at following rules and so
Speaker 4: they could replicate a lot of work that was just that,
Speaker 4: but they weren't good at supplementing skills at enabling people
Speaker 4: to do these high stakes decision making tasks. So it's important. Unsually,
Speaker 4: AI is almost the inverse of traditional computing. Right if
Speaker 4: I told you I have the most advanced technology in
Speaker 4: the world, but you know, it really can't do math
Speaker 4: and it's not reliable with facts and figures, you would say, well,
Speaker 4: what kind of technology is that? And I would say, well,
Speaker 4: that's artificial intelligence. It is really quite the opposite. So
Speaker 4: I think it has quite different capabilities. And in some
Speaker 4: sense you could say traditional computing was really complementary to
Speaker 4: you know, the most elite professionals. And it's quite possible
Speaker 4: that AI will enable more people to compete with them,
Speaker 4: and that's a really good thing because that improves the
Speaker 4: quality of services and improves the quality of jaws for
Speaker 4: people who were not at that leading edge.
Speaker 2: This is I think the key thing because in your
Speaker 2: piece and and other testimony you've given, you've talked about
Speaker 2: this idea of collective decision making, And when I think
Speaker 2: about modern American society or modern society in general, I
Speaker 2: don't necessarily think that collective decision making is something we're
Speaker 2: particularly strong on.
Speaker 4: So if the.
Speaker 2: Future depends on making good collective decisions, then that makes
Speaker 2: me anxious. But you know, you talk about investment, but
Speaker 2: it sounds like the other element here. And you mentioned
Speaker 2: that the rise of the nurse practitioner had to happen
Speaker 2: over the kicking and screaming of the American Medical Association,
Speaker 2: which represents that a top strata of healthcare professionals, the
Speaker 2: elite doctors. How much of this is going to be
Speaker 2: a political fight ultimately in which the doctor and the
Speaker 2: lawyers and the podcasters collectively resist other people who are
Speaker 2: using these tools to do our jobs. And how much
Speaker 2: is that really like where the collective fight is going
Speaker 2: to happen.
Speaker 4: Yeah, if we have to take on the podcasters.
Speaker 2: I think we're doomed, but yeah, yeah, we're going to
Speaker 2: fight this kicking and screaming for sha.
Speaker 4: The AMA is one thing, yeah, but the podcasters, that's
Speaker 4: a whole different army. Some of that will absolutely be
Speaker 4: terf warfare. Right the professions, we think, oh, you know that,
Speaker 4: you know, oil companies and so on don't like competition
Speaker 4: and they're always trying to rig the market, But in fact,
Speaker 4: the professions rig the markets as well, right they what
Speaker 4: a profession is, actually what it means is an occupation
Speaker 4: that gets to certify its own members and decide who's
Speaker 4: in and who's out right. And so it's the medical
Speaker 4: profession that creates training standards and certification standards. It's universities
Speaker 4: that decide what skills enable you to have a PhD
Speaker 4: and therefore become a professor. So it absolutely is going
Speaker 4: to be a challenge. Like lawyers will try very hard
Speaker 4: say well, that can't be a legal document unless a
Speaker 4: lawyer has signed it someone with a JD and has
Speaker 4: passed the bar. So that will be a source of
Speaker 4: resistance for sure. On the other hand, if there's a
Speaker 4: really good competing alternative, if you can say, look, these
Speaker 4: nurse practitioners can do a lot of this diagnostic work.
Speaker 4: You know, they work well with doctors, but they can
Speaker 4: do some things that doctors would be more expensive doing,
Speaker 4: and you can make that case. Or a paralegal using
Speaker 4: the software can create a lot of routine documents, or
Speaker 4: a software developer using GitHub copilot can go pretty far.
Speaker 4: Then that creates a lot of economic pressure that tends,
Speaker 4: over long periods of time to erode these gills. So
Speaker 4: I think that they will not go quietly into this
Speaker 4: dark knight. But if the models are successful, it does
Speaker 4: create a strong incentive for eventually that to become adopted.
Speaker 1: I think part of the concern around AI has to
Speaker 1: do also with how any productivity gains are actually distributed
Speaker 1: and whether or not people are compensated for doing more.
Speaker 1: And I asked chat GPT obviously to provide a summary
Speaker 1: of dust capital before I came on here. No, I
Speaker 1: do think there is this concern about Okay, in an
Speaker 1: ideal scenario, we're all more efficient in terms of our labor,
Speaker 1: and maybe some types of work are even better to perform.
Speaker 1: Maybe we reduce that emotional labor. But aside from that
Speaker 1: particular benefit, how do we distribute the additional productivity gains?
Speaker 1: Is there any evidence or any reason to believe that
Speaker 1: these benefits are going to go to labor, to actual
Speaker 1: workers and individuals versus to companies in capital.
Speaker 4: Yeah. Good. So let me give you two answers that question.
Speaker 4: One is it really does depend on institutions, not just
Speaker 4: on decentralized labor markets.
Speaker 1: Right.
Speaker 4: So if you compare the US versus Germany versus Scandinavia, right,
Speaker 4: we have so much in common. We have the same technologies,
Speaker 4: we have the same agent population, we have the same
Speaker 4: rising education levels, we have the same China as a competitor,
Speaker 4: we have lots of immigration. And yet these countries have big,
Speaker 4: very different cakes with the same ingredients. Right. The US
Speaker 4: is kind of cowboy capitalism, very high levels inequality and
Speaker 4: disparity and not so much sharing with workers. And if
Speaker 4: you look at Scandinavia or Germany, it's much more cuddly capitalism. Right,
Speaker 4: it's not nearly as unequal. And that's really a question
Speaker 4: of tax regulation, it's a question of the role of
Speaker 4: labor unions and labor voice, and it's a question of
Speaker 4: social norms. And so I guess we should not take
Speaker 4: it as inevitable that the outcomes we have are the
Speaker 4: only ones the market could tolerate. But at the same time,
Speaker 4: we should recognize that without those sort of counterveling forces,
Speaker 4: the outcomes can look pretty bad. So I do think
Speaker 4: you know, I'm happy about the rise of collective bargaining
Speaker 4: again in the United States, although it's from a very
Speaker 4: low level. I'm happy that more states are passing minimum
Speaker 4: wage regulations. I'm happy that the Biden administration is trying
Speaker 4: to sort of beef up the Occtational Safety in Health
Speaker 4: Administration and the Equal Employment Opportunity Commission and so on.
Speaker 4: So I think those things matter a great deal. So
Speaker 4: one should not take it for granted that just because
Speaker 4: productivity rises, workers benefit in many countries. That's true, but
Speaker 4: not so much in the United States.
Speaker 2: But I want to press you right here on this point,
Speaker 2: because why doesn't this undermine much of the argument. If
Speaker 2: these different countries, whether it's Sweden and Germany the US,
Speaker 2: can have very different sort of distributional outcomes with the
Speaker 2: same cake ingredients, with roughly similar technology and labor markets,
Speaker 2: why then take the assumption that it's the technology that
Speaker 2: has the distributional impact rather than just those policies themselves.
Speaker 4: Okay, this is an excellent question. So I think the
Speaker 4: technology provides headwinds and tailwinds with which policy can work.
Speaker 4: So all of these countries I mentioned have become more unequal.
Speaker 3: Okay, all of.
Speaker 4: These countries have seen a decline in middle scale work.
Speaker 4: All of these countries have seen the mean wage rise
Speaker 4: relative to the median, meaning the upper wages have risen
Speaker 4: more than the center. But the degree to which countries
Speaker 4: have pushed back against that is a function of their institutions.
Speaker 4: In the prior era, prior to computerization, all of these
Speaker 4: countries saw their middle classes grow together along with the
Speaker 4: upper class and lower class, and so the industrial era
Speaker 4: prior computerization was very friendly, sort of intrinsically towards the
Speaker 4: middle class. The computer era was much much less so,
Speaker 4: and then policy helped ameliorate those impacts, and much less
Speaker 4: so in the United States. So I do think that
Speaker 4: technology plays a role. I just we should simultaneously believe
Speaker 4: that these underlying forces of technology and globalization creates strong
Speaker 4: pressures in one way or another, and then policy can
Speaker 4: shape how those pressures play out. It won't undo them,
Speaker 4: but it can channel them more or less effectively. So
Speaker 4: you're asking both the right questions, and I think the
Speaker 4: answer is both are true, but we should think it's
Speaker 4: not one or the other. And in some periods those
Speaker 4: forces are very favorable and policy has to do less
Speaker 4: hard work, and other periods they're relatively unfavorable. Policy, if
Speaker 4: it's working well, has to do more work. The other
Speaker 4: point I want to make, and this is why I'm
Speaker 4: so focused on expertise, is expert work is intrinsically well paid.
Speaker 4: It's scarce, and it's necessary. And that's why if we
Speaker 4: live in a world where all the work can be
Speaker 4: done by machines, we're completely dependent upon redistribution, right the
Speaker 4: people who own the machines to share with everyone else.
Speaker 4: And I'm not so optimistic about people's excitement about sharing
Speaker 4: with everyone else. And even when people say, oh, we'll
Speaker 4: have universal basic income, they really mean universal basic income
Speaker 4: within the borders of the United States. They don't mean
Speaker 4: universal basic income for the rest of the world. Right,
Speaker 4: So people's notion of sharing is very limited. So I
Speaker 4: do think it's extremely important that labor remains valuable, and
Speaker 4: that's actually an achievement of the industrialized world that so
Speaker 4: many people can make a good reasonable standard living based
Speaker 4: on their skills, and so technologies and tools that make
Speaker 4: human expertise more valuable by allowing to go further are
Speaker 4: really favorable towards income distribution. Technologies that just automate away work,
Speaker 4: even though they raise productivity, are not favorable to its
Speaker 4: income distribution because it means it goes to ownership of capital,
Speaker 4: and ownership of capital is intrinsically more centralized in ownership
Speaker 4: of labor, because in a country that doesn't have slavery
Speaker 4: and doesn't have labor coersion, everyone owns one worker themselves,
Speaker 4: and so that inherently creates some tendency towards equality when
Speaker 4: labor is valuable.
Speaker 2: The efforts of the Biden administration to reindustrialize the US
Speaker 2: and sort of counter some of the effects of the
Speaker 2: last twenty years that you wrote about, do you have
Speaker 2: any optimism that those trends can be reversed. I know
Speaker 2: this is a very simple, straightforward question that you're going
Speaker 2: to answer in about thirty seconds, so good luck.
Speaker 4: I don't think they can be completely reversed, but you
Speaker 4: can stem the tide, right, So it's not that the
Speaker 4: U this has stabilized. The US continues to lose industrial capacity, right,
Speaker 4: whether it's in semiconductors, whether it's automobiles, whether it's an aircraft,
Speaker 4: Thank you, and so on. So I think reinvesting can
Speaker 4: help solidify those sectors, and I think it's very important
Speaker 4: to do so, because now they're not just a question
Speaker 4: of jobs. It really is about leadership of the key
Speaker 4: profit and idea generating activities in the modern world, and
Speaker 4: we don't want to lose a leadership place in those activities.
Speaker 2: Good concise answer to what probably could be multiple future episodes,
Speaker 2: David Otter, Thank you so much for coming on out Laws.
Speaker 2: That really was a fascinating conversation. We probably could get
Speaker 2: multiple episodes out of this conversation with you, but really
Speaker 2: appreciate your time.
Speaker 4: Thank you very much. Nice to speak with both of you.
Speaker 3: Have a good day, Tracy. I'm convinced. I think everything
Speaker 3: will be fun.
Speaker 2: I'm no longer worried.
Speaker 1: Well, first of all, I would say it was nice
Speaker 1: to hear a slightly more optimistic argument from David. There
Speaker 1: were a lot of quotable sentences in there. So I
Speaker 1: like the idea that everyone's their own individual capitalist in
Speaker 1: the sense that we each have one worker to direct
Speaker 1: and get the most money out of So that's how
Speaker 1: I'm going to start thinking cuddly capitalism, which, as our
Speaker 1: producer Klee observes, is a much more appealing name than
Speaker 1: the Swedish model. I like that. What I would say
Speaker 1: is again putting on my cynical journalist hat, and I
Speaker 1: guess I don't have an opinion because I am a
Speaker 1: journalist whose expertise is about to be automated away. But
Speaker 1: my non consensus take here, or my sort of hot
Speaker 1: take here, is that I agree with David that we
Speaker 1: are going to get more jobs out of AI, and
Speaker 1: probably more than a lot of people currently anticipate. I
Speaker 1: guess I'm less convinced about how useful those jobs are
Speaker 1: going to be, So going back to his point about
Speaker 1: how do we measure how well we're using AI, I
Speaker 1: have a feeling that a lot of it is going
Speaker 1: to end up basically creating a whole new layer of
Speaker 1: BS jobs that don't actually do much. So there's going
Speaker 1: to be all these decision making bodies attached to AI.
Speaker 1: There's going to be big discussions about how you implement AI, fairness,
Speaker 1: litigating its results, and things like that. I guess I'm
Speaker 1: a little bit pessimistic about the ability of AI to
Speaker 1: generate additional bureaucracy in addition to additional productivity.
Speaker 2: The other term that was great was when you said
Speaker 2: the future is not like the weather. Yeah, but also
Speaker 2: like I am worried about any notion that to achieve
Speaker 2: the good outcome, the good equilibrium, we have to make good,
Speaker 2: correct collective decisions because I have almost zero confidence in
Speaker 2: whether it's just the US specifically or globally to make
Speaker 2: collective decisions. I do think like going after like these
Speaker 2: guilds like the American Medical Association, which for all of
Speaker 2: the rise of nurse practitioners, it doesn't seem like we're
Speaker 2: doing great on like ben the cost of healthcare jobs
Speaker 2: or really having a healthcare capacity. That's going to be
Speaker 2: like really tough. And those fights are going to be
Speaker 2: really intense, whether it's with lawyers, whether it's with doctors,
Speaker 2: whether it's with teachers, whether it's with podcasters, whether it's
Speaker 2: professional architects, et cetera. Like, those fights are going to
Speaker 2: be extremely intense. But like the basic intuition sounds very
Speaker 2: compelling to me. The other thing is like you know
Speaker 2: this idea of like oh, yeah, some training plus AI,
Speaker 2: like I am worried, like maybe you just won't need
Speaker 2: the training, and maybe it's just AI from the start.
Speaker 3: So I don't know.
Speaker 1: Well, in some respects, I think that would almost be
Speaker 1: a better outcome in terms of democratizing AI. But yeah,
Speaker 1: there are so many questions, uncertainty, as you mentioned in
Speaker 1: the intro, lots of different takes at the moment. I
Speaker 1: guess we'll see how it plays out and whether or
Speaker 1: not you and I have jobs in ten years time.
Speaker 2: We'll see, Well, we'll have David back when we're just like.
Speaker 1: When we're automated voices. Yeah, exactly, all right, shall we
Speaker 1: leave it there for now?
Speaker 3: Let's leave it there.
Speaker 1: Okay. This has been another episode of the Authoughts podcast.
Speaker 1: I'm Tracy Alloway. You can follow me at Tracy Alloway.
Speaker 2: And I'm Joe Wisenthal. You can follow me at the Stalwart.
Speaker 2: Follow our guest David Otter, He's at David Otter. Follow
Speaker 2: our producers Carmen Rodriguez at Carman Arman dashl Bennett at
Speaker 2: Dashbot and kel Brooks at kel Brooks. And thank you
Speaker 2: to our producer Moses Ondam. Form our oddlogs content go
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Speaker 2: There's even an AI room in there where people are
Speaker 2: talking about all these things. So imagine there will be
Speaker 2: some conversation about this there.
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