Speaker 1: Bloomberg Audio Studios, Podcasts, radio News.
Speaker 2: Hello and welcome to another episode of the Odd Lots podcast.
Speaker 3: I'm Jolle Wisenthal and I'm Tracy Alloway.
Speaker 2: You know, not a novel observation here, but I do
Speaker 2: think that if, like in early twenty twenty two or
Speaker 2: late twenty twenty one, you had someone had revealed to
Speaker 2: you all of the amazing things that we could do
Speaker 2: with AI these days, I think you would have expected
Speaker 2: that either the broader economy or society would be more
Speaker 2: different than it has been. Like I am large, you know,
Speaker 2: I think most people's jobs are done roughly the same way.
Speaker 2: Sort of society still seems to operate, although a little
Speaker 2: maybe a little bit worse every day. I don't know,
Speaker 2: But like I just don't think like it's mind blowing
Speaker 2: technology and get by and large, like it hasn't had
Speaker 2: the economic disruption that I think many people would have guessed.
Speaker 3: Maybe I'm cynical on the subject, but I always say,
Speaker 3: never underestimate the human capacity for stasis, I guess, and
Speaker 3: making things much more difficult than they actually need to be,
Speaker 3: and putting up you know, bureaucratic barriers, regulatory barriers and
Speaker 3: things like that. So it's not it's not that surprising
Speaker 3: to me, but it is true. You have a lot
Speaker 3: of economists out there who think there was going to
Speaker 3: be this massive productivity boost, right well, it's certainly.
Speaker 2: Tech people like I don't know, like economists. You know,
Speaker 2: they're like, oh, there were some we're going to have
Speaker 2: a productivity boom. We're raising our estimates from two percent
Speaker 2: too and a quarter percent, and then you have all
Speaker 2: things relative to and then you know, we talked to
Speaker 2: Kathy Wood one time, and I think she what did
Speaker 2: she predict twenty percent real GDP growth for twenty years?
Speaker 2: Or you certainly have these people in Silicon Valley deflationary boom,
Speaker 2: post scarcity any any minute, right now, some real gaps
Speaker 2: I would say, between how lot of economists think about
Speaker 2: some of these things and the numbers, then economists would
Speaker 2: be comfortable using versus maybe literally.
Speaker 3: Everyone else like Kathy would certainly many others. Yeah, you
Speaker 3: know what I think about is how different would your
Speaker 3: career in my career have been had AI existed in
Speaker 3: like two thousand and eight, two thousand and six when
Speaker 3: we were starting to blog.
Speaker 2: Basically, it's a really good question, and I don't know
Speaker 2: the answer because part of me thinks, well, you know,
Speaker 2: that whole path that was like defined my career like
Speaker 2: would not have been there, would not have existed or no. Maybe,
Speaker 2: But on the other hand, maybe in two thousand and
Speaker 2: six I would have been like just like I was
Speaker 2: super early into blogging, super early into experimenting with AI news,
Speaker 2: and it would look different, but maybe I would have
Speaker 2: rowed some different.
Speaker 3: Different but the same.
Speaker 2: Yeah, different but the same, you know, Like I think
Speaker 2: it's a it's pretty hard to tell.
Speaker 3: One thing I know is like back then we were
Speaker 3: writing to optimize Google results, right, that was basic or know,
Speaker 3: like I know social media was disseminating stuff as well,
Speaker 3: but like Google was still the main platform for a
Speaker 3: lot of stuff. I'm sure if we were doing it now,
Speaker 3: we would be optimizing for chat, GPT or perplexity or
Speaker 3: someone like that to actually pick up the content. I mean,
Speaker 3: I think that's the audience.
Speaker 2: Yeah it is. And I think like any especially any
Speaker 2: like commercial publisher, right that's trying to do a scale.
Speaker 2: I think like a sort of niche voice expert could
Speaker 2: still have like their audience that comes to them directly,
Speaker 2: of course, but like it's scale scale for sure every
Speaker 2: publisher is trying to figure this out. Anyway, We really
Speaker 2: do have the perfect guest, someone we should have had
Speaker 2: on the podcast years and years ago. It's almost surprising
Speaker 2: that it's just the first time we've had him on.
Speaker 2: It is crazy, It is crazy, but it is someone
Speaker 2: who I think is like at the intersection of everything
Speaker 2: that we're talking about. Because he's an economist. He knows
Speaker 2: the tech really well. The tech people know him, the
Speaker 2: tech people love him. He may even be one of
Speaker 2: the preferred economists for this. I feel he's like the
Speaker 2: economist that all people in the AI world want to
Speaker 2: get his take. A long time blogger, someone I, you know,
Speaker 2: like both of us sort of gave up on mike blogging.
Speaker 2: Although we have our newsletter. It's like close enough, but
Speaker 2: it's different than blogging. Someone who's stuck with the medium
Speaker 2: for a long time. One of the true original econ
Speaker 2: bloggers that I've been reading for over twenty years. We're
Speaker 2: going to be speaking with Tyler Cowen. Here's the host
Speaker 2: of the Conversations with Tyler podcast. He also, obviously is
Speaker 2: one of the two bloggers that the famous Marginal Revolution blog.
Speaker 2: Economics professor gmu appreciator of ethnic foods all around the
Speaker 2: DC Northern Virginia, Maryland area. Someone known on the internet
Speaker 2: for a long time, Tyler, thank you so much for
Speaker 2: coming on odd Laws. Really thrilled to have you here.
Speaker 4: Hello, happy to be here.
Speaker 2: Amazing. What do you think about my initial assessment? How
Speaker 2: fair do you think it is that, Like, had you
Speaker 2: known in twenty twenty one how powerful these tools would be,
Speaker 2: maybe we'd be a bit surprised that by and large,
Speaker 2: like busines, and this seems to more or less run
Speaker 2: the same, I'm.
Speaker 4: Not surprised at all. So what I see right now
Speaker 4: is people using AI as an add on to their
Speaker 4: pre existing work routines. Oh, you need to write a memo,
Speaker 4: you ask AI how to do it. You need to
Speaker 4: write a column, you ask AI to proofread it or
Speaker 4: fact check it. And that works great, But those are
Speaker 4: marginal gains. What we really need to see a major
Speaker 4: impact is new organizations built around AI, and those will
Speaker 4: be startups. They will come only slowly. It will take
Speaker 4: twenty or more years before they really transform the economy.
Speaker 4: And in the meantime, it's a whole bunch of add ons,
Speaker 4: which are fun and fine, but that's why I think
Speaker 4: it's slow.
Speaker 2: What can you tell us about the history of technology
Speaker 2: such that legacy organizations that existed prior to the invention
Speaker 2: of maybe potentially revolutionary new technology have a hard time
Speaker 2: massively changing their workflows.
Speaker 4: Well, you can take even very simple examples. So Toyota
Speaker 4: starts competing with General Motors in the nineteen seventies, General
Speaker 4: Motors is paralyzed. It cannot really come back and adopt
Speaker 4: the new and superior Toyota methods, and those are not
Speaker 4: really that different compared to say AI. So there's just plenty,
Speaker 4: plenty examples. Old mainstream media could not cope very well
Speaker 4: with the Internet. There are exceptions like The New York Times.
Speaker 4: Odd Lot's podcast would be another thank you, But it's
Speaker 4: the norm. So we're seeing it again with AI. And
Speaker 4: again you need a complete turnover of who and what
Speaker 4: is doing business for it to really matter on a
Speaker 4: big scale.
Speaker 3: If you need that complete turnover, and you need some
Speaker 3: time for AI to become fully embedded in a business
Speaker 3: model or for a business model to form around AI,
Speaker 3: what industry or what part of the economy would you
Speaker 3: expect it, I guess to show up first in that
Speaker 3: sort of revolutionary way.
Speaker 4: Well, we have obvious data on this, and it's programming.
Speaker 4: You will hear people who do programming claim that say
Speaker 4: eighty percent of the work is now done by AIS.
Speaker 4: I suspect that's an overstatement, but there's no doubt at
Speaker 4: all that there's simply a lot of programming already done
Speaker 4: by AIS. When you have low fixed costs, a competitive sector,
Speaker 4: immediate feedback, you know, the revenue has to flow programming.
Speaker 4: And also New York City finance, there's been quantston finance
Speaker 4: for a long time. Those quants are now you could say,
Speaker 4: more AI equipped than they used to be, and those
Speaker 4: areas already are being revolutionized.
Speaker 2: I've heard them and got some pitches to do episodes,
Speaker 2: which we should do at some point. But I've heard
Speaker 2: about like some law firms that are being like new
Speaker 2: AI law firms where there are lawyers, et cetera. But
Speaker 2: from the very ground up, the idea is perhaps there
Speaker 2: is some way to just get way more productivity if
Speaker 2: they start from the very beginning with some combination of
Speaker 2: lawyers plus AI models. It seems like that could be
Speaker 2: the kind of thing where maybe the legacy law firms
Speaker 2: are seeing some productivity gains from AI. There's probably some
Speaker 2: evidence you could find but that a new one with
Speaker 2: a totally different approach could deliver that big productivity boost
Speaker 2: that actually ends up changing the industry.
Speaker 4: It's already the case, say, mid tier associates are much
Speaker 4: less needed. But there's one big problem with law in particular,
Speaker 4: and that is the way large language models work. Now
Speaker 4: you have to send your queries somewhere else. You can't
Speaker 4: just own and control and hold the whole thing on
Speaker 4: your hard drive. Now. I think within a few years
Speaker 4: time that will be very different. But until then, major
Speaker 4: law firms are extremely skittish about just typing in their
Speaker 4: questions and sending it, you know, to San Francisco. I
Speaker 4: don't actually think there's a risk, but when you think
Speaker 4: about how fiduciary responsibility works, they just don't want to
Speaker 4: do it.
Speaker 3: Talk more about I guess privacy concerns and regulation, because
Speaker 3: this seems to be an area where if you are
Speaker 3: in a heavily regulated industry, or if you're just in
Speaker 3: an industry that tends to be full of paranoid people
Speaker 3: like lawyers, it does seem like there's going to be
Speaker 3: a natural tendency to be very very cautious. When it
Speaker 3: comes to sharing data with AI, you're going to be
Speaker 3: worried about actual data ownership the queries that you're sending
Speaker 3: to San Francisco, as you say, are those industries just
Speaker 3: inevitably going to be slow to adapt.
Speaker 4: They'll be slow to adapt again until the point where
Speaker 4: they just control their own model and they hold it
Speaker 4: within the firm and no one else really can get
Speaker 4: at it. So what you need is cheaper models where
Speaker 4: a law firm can afford to have its own model.
Speaker 4: And I think that's a few years away. It's not
Speaker 4: a very long time away, but it won't come in
Speaker 4: six months. Sam Altman I just did a podcast with
Speaker 4: him and he said a privacy problem is AI queries
Speaker 4: or subject to subpoena, and he thinks they should have
Speaker 4: as much protection as say, your conversation with your lawyer
Speaker 4: or your doctor or your therapist. I think that's a
Speaker 4: good idea, but that hasn't happened yet, and until it happens,
Speaker 4: or you get the whole thing on your own hard drive,
Speaker 4: progress in law is going to be slow. But once
Speaker 4: the progress comes, that's one of the areas where I
Speaker 4: think AI has the most promise it's just very very
Speaker 4: good at mastering a large corpus of text and organizing
Speaker 4: it for you.
Speaker 2: It is interesting, isn't it. Where it's like, Okay, if
Speaker 2: you are my lawyer, you and I could have a
Speaker 2: conversation and it would be not it would not be uh.
Speaker 3: It'd be privileged.
Speaker 2: Yeah, it would be privileged. Or you know, one can
Speaker 2: have conversations with anyone, and as long as you're doing
Speaker 2: it on the phone, or if you're doing it person
Speaker 2: to person, it's much more difficult to get that, you know,
Speaker 2: in discover or I always think about this with you know,
Speaker 2: public public records of employees that sometimes you can get
Speaker 2: their emails through Floyers, et cetera, but you can't get
Speaker 2: the cons content of conversations. It does feel I hadn't
Speaker 2: really thought about this dynamic though, that when you're using
Speaker 2: an AI it is sort of like a conversation and
Speaker 2: yet sort of from an evidentiary basis, it would be
Speaker 2: much more like an email.
Speaker 4: I think when it comes to medical issues, there are
Speaker 4: many more people willing to share their data. Not everyone
Speaker 4: by any means. Some medical conditions are secretor people just
Speaker 4: don't want others to know. But I see many, many
Speaker 4: people I know typing in all kinds of things about
Speaker 4: their medical history to say GPT five and getting what
Speaker 4: are on the whole very good answers. It's like medical
Speaker 4: diagnosis for free spreading now to the whole world. A
Speaker 4: lot of countries where people just don't even have access
Speaker 4: to good doctors at all, and I think that will
Speaker 4: be important more quickly than the law innovations.
Speaker 3: Just as a thought experiment, what does all of this
Speaker 3: mean for insurers? Because I kind of think, you know,
Speaker 3: I think about a bunch of people typing in their
Speaker 3: medical information. I think about basically the explosion in data
Speaker 3: that we have nowadays, and it seems like the insurance
Speaker 3: industry would be one place that would really benefit from
Speaker 3: all this trove of additional information if they could access
Speaker 3: it well.
Speaker 4: This is one of my worries about AI in general.
Speaker 4: I'm quite positive on what's happening, But as insurers get
Speaker 4: better and better information on their customers, this is just
Speaker 4: through big data more generally, it doesn't have to be
Speaker 4: current large language models. They know exactly how to write
Speaker 4: the risk and how to price the premium, and in
Speaker 4: a sense for the buyer, it's not insurance anymore. So,
Speaker 4: if we know your house is going to burn down
Speaker 4: with high probability and you have to pay the super
Speaker 4: high premium, you don't really have the benefits of the insurance.
Speaker 4: So some insurance markets might unravel if through big data
Speaker 4: the insurers learn too much about what's likely to happen.
Speaker 2: Economists seem to be very consistent about the effects of
Speaker 2: technology on labor demand, which is that in the end
Speaker 2: it washes out right, some people, Okay, there's a disruption,
Speaker 2: but I'm gonna save money because I use the I.
Speaker 2: But that means I have more spending power, and then
Speaker 2: I'm gonna buy something else that I wouldn't have bought
Speaker 2: had it not been for paying wages, and then that'll
Speaker 2: create demand for labor elsewhere. And so in the end,
Speaker 2: the idea that you could really have tech driven unemployment
Speaker 2: at scale that is not transitory or not temporary is hard.
Speaker 2: A lot of economists seem to be intuitively skeptical of
Speaker 2: this idea. Whereas you have people in the ifields, fifty
Speaker 2: percent of the people aren't gonna have jobs we need
Speaker 2: ubi otherwise there's gonna be a permanent underclass. Could there
Speaker 2: be something different about AI such that it doesn't have
Speaker 2: the same labor market effects that past technologies have had.
Speaker 4: I would say, you understand me. Well, So the energy sector,
Speaker 4: there's going to be a lot of new jobs taking
Speaker 4: care of older people. I think as AI produces more potential,
Speaker 4: medical innovations will need to test them, So the biomedical
Speaker 4: sector testing clinical trials, there'll be a lot new jobs.
Speaker 4: I'm not worried about mass unemployment, and most economists are not,
Speaker 4: and I agree with their perspectives, which I think you
Speaker 4: outlined pretty clearly.
Speaker 2: What do you say, though, because I have a feeling
Speaker 2: that when you're out in San Francisco, they don't see
Speaker 2: it that way, and they talk a lot, and some
Speaker 2: of them are more in you know, UBI talk and
Speaker 2: permanent underclass talk and all of this stuff. Do they
Speaker 2: see your perspective when you make this case? What do
Speaker 2: they say or what are they What are a lot
Speaker 2: of them seem to be missing about the logic that
Speaker 2: you spell out.
Speaker 4: Well, I think more and more they're coming around to
Speaker 4: the economist's point of view. So Andre Carpathi, who was
Speaker 4: at open AI, you know, in the most important years,
Speaker 4: he just did a podcast saying he thinks adjustment will
Speaker 4: be slow. Things will be fine, will grow at two
Speaker 4: points something percent. There won't be mass unemployment, and you
Speaker 4: wouldn't I have heard that, say two years ago. But
Speaker 4: I think as people see the models rolling out, and
Speaker 4: as you mentioned well, the real world impact it is
Speaker 4: stretched out in time, right, it's not all immediate. Earlier
Speaker 4: on people had more the sense that AI was a
Speaker 4: kind of god box that you just talked to it
Speaker 4: and it can magically do anything and convert that into
Speaker 4: results in the real world. But if you think about
Speaker 4: your actual job, even if it's a highly intellectual job,
Speaker 4: so much of what you do is the interaction between
Speaker 4: your intellect, your physical presence, your interactions with others, your travel,
Speaker 4: many other things. And until we get to some far,
Speaker 4: far off world where the robots are perfect copies of you,
Speaker 4: which I don't think ever will come, jobs will be fine,
Speaker 4: but they will change a lot, and I'm actually worried
Speaker 4: about who will be the biggest losers. I think poor
Speaker 4: people will do great, the very wealthy will do well,
Speaker 4: but people who are sort of upper upper middle class
Speaker 4: will find this automatic ticket to a law or consulting
Speaker 4: job that assured they would be upper upper middle class
Speaker 4: for the rest of their lives. I think a lot
Speaker 4: of that is going away already.
Speaker 3: I think also no one would have expected plastic surgery,
Speaker 3: I guess, to be a beneficiary of the AI revolution.
Speaker 3: But if you think that what's going to matter in
Speaker 3: the future is like your personal presence and your network
Speaker 3: of social contacts, then I guess we should all be
Speaker 3: working on on our looks Maxing, Yeah, absolutely right, charisma. Okay, noted,
Speaker 3: everyone work on their respective charisma. One thing I was
Speaker 3: wondering is the impact of AI on public finances. I
Speaker 3: am not very good at tax policy. Joe knows this
Speaker 3: because I've complained about taxes to him repeatedly. But I'm thinking,
Speaker 3: but if you're thinking about where the value add of
Speaker 3: AI actually shows up in the economy, So you know,
Speaker 3: presumably you got a productivity boost. We're not entirely sure
Speaker 3: how much that's going to be. But where does that
Speaker 3: additional output actually show up in terms of revenue for governments?
Speaker 3: How is that collected? And how would you expect the
Speaker 3: distribution to vary across the world.
Speaker 4: Well, I think in the United States medium term, they'll
Speaker 4: just be much much more. Healthcare will be new drugs,
Speaker 4: new medical devices, will have to test all these things,
Speaker 4: will have to produce them, and that will be It
Speaker 4: was already the case that those sectors were growing, but
Speaker 4: that growth will be accelerated. So that's where I think, God,
Speaker 4: they'll be the biggest difference longer term, and people will
Speaker 4: live longer because we'll fix at least partially various diseases
Speaker 4: and maladies. So if you live to be ninety four
Speaker 4: across a lifetime, you spend way more on healthcare than
Speaker 4: if you live to be seventy seven. And that's yet
Speaker 4: further growth for the healthcare sector. But some things like
Speaker 4: medical diagnosis that's already very cheap, like you know, a
Speaker 4: good large language model probably outperforms your current doctor at
Speaker 4: least if you type in what's wrong with you properly.
Speaker 3: But does that additional productivity or or the output generated
Speaker 3: by AI, does that actually show up an additional taxation
Speaker 3: for the government.
Speaker 4: Well, the healthcare sector generates an enormous amount of taxation revenue.
Speaker 4: I do think we'll have some sectors that maybe just
Speaker 4: become free in the same way that Wikipedia is free.
Speaker 4: So I could imagine say ten or twenty percent of
Speaker 4: the music sector is music you create at home using
Speaker 4: your own AI and it's a customized song for you,
Speaker 4: and maybe you paid a subscription for the service. But
Speaker 4: rather than spending more money on Spotify or a streaming service,
Speaker 4: you just build the music and that's a partial substitute
Speaker 4: for some human created music. I don't think human created
Speaker 4: music will go away at all. People want to enjoy
Speaker 4: the human touch, the feeling that you're a fan of Taller,
Speaker 4: Swift or whatever. But there's going to be a lot
Speaker 4: of AI generated music and art and many other areas,
Speaker 4: and some of it will be free. But that's not
Speaker 4: a problem from a revenue standpoint. So instead of spending
Speaker 4: money on safe buying a picture, you create one digitally
Speaker 4: at home with your AI. You'll spend that money on
Speaker 4: something else.
Speaker 3: I got to ask, now, are you a swiftie.
Speaker 4: No, it's worrying for me. It's a little too predictable,
Speaker 4: so I have to say I'm not, but I'm glad
Speaker 4: other people are. Let's put it that way.
Speaker 2: Do you have a theory let's talk, let's just talk
Speaker 2: about music. Do you have like a theory of the
Speaker 2: swift phenomenon? Like, is there a reason? What is it?
Speaker 2: Because it's just so, doesn't I don't know what's your
Speaker 2: what's your meta take on the tailor, the swifties and
Speaker 2: culture and society.
Speaker 4: Well, it's super polished and because of the Internet, the
Speaker 4: very biggest of celebrities can be much bigger than before,
Speaker 4: and someone will fill a few of those slots. But
Speaker 4: I think also how she presents herself. She has the
Speaker 4: guise of being attractive without feeling threatening to other women. Uh,
Speaker 4: and there's something all American about her and quite generic.
Speaker 4: She doesn't rule out the fandom of that many people
Speaker 4: m hm. And she's the one who's filled that slot.
Speaker 4: She's been brilliant at managing her career and seems to
Speaker 4: just stick at it and has an incredible work ethic.
Speaker 4: People say the shows are incredible. As more and more
Speaker 4: life goes online, who can give a good show? It's
Speaker 4: the charisma and looks. Point Well, she seems a pleasant
Speaker 4: that I've never been to one, but I hear plenty
Speaker 4: of reports. And you put all that together and she's,
Speaker 4: you know, the megastar of the music world.
Speaker 2: Do you think like culture, Like there's this popular idea
Speaker 2: that culture is sort of dead, and I do think
Speaker 2: that is probably overstated, But you know, you look at
Speaker 2: movies and people have people have observed this for a
Speaker 2: long time. It's just rehashes of franchises that have been
Speaker 2: around for thirty years. And I think if you look
Speaker 2: at Spotify's streaming, there's still this overwhelming tyranny of the
Speaker 2: boomer rock et cetera, and this feeling that culture in
Speaker 2: many respects is that's rehash that is very hard for
Speaker 2: new things to to break out. I mean, Taylor Swift
Speaker 2: at this point is decades old phenomenon. Is that real
Speaker 2: in your view? Or is that just people in the
Speaker 2: pundit class who've been lazy and not discovering new things
Speaker 2: and not actually putting in the effort because they're not
Speaker 2: young anymore and they're not going out and they say
Speaker 2: nobody listens to new music.
Speaker 3: Joe, are you just talking about yourself?
Speaker 2: I have talked about I'm trying. I'm doing a little
Speaker 2: introspection here. Is this just me because I don't go
Speaker 2: to shows like I did when I was twenty or
Speaker 2: is something changed?
Speaker 4: But a lot of it is the pundit class. So
Speaker 4: you look at movies, I think it's perfectly correct to
Speaker 4: say the most popular movies today are pretty dreadful, and
Speaker 4: it used to be the case that the most popular
Speaker 4: movies were God, The Godfather in Star Wars. Yeah, that's
Speaker 4: a big change. But if you look at movie making
Speaker 4: around the world and in a given year list say
Speaker 4: the twenty five best movies from all places, which may
Speaker 4: not even come to your multiplex every year, you have
Speaker 4: an incredible list. I don't think they're worse than the
Speaker 4: movies of earlier times. I do think mainstream hollywo is
Speaker 4: much worse. So in many areas you just have quality
Speaker 4: moving more into unises.
Speaker 2: By the way, I just want to say, the first
Speaker 2: time I ever encountered your work, prior to even having
Speaker 2: stumbled across Marginal Revolution, was at the bookstore in Austin
Speaker 2: finding a copy of In Praise of Commercial Culture, and
Speaker 2: I just feel like that book is held up so well.
Speaker 2: I mean in the specific sense that there is so
Speaker 2: much mass culture these days, whether it's high end Netflix
Speaker 2: TV shows, et cetera, whether it's A twenty four films,
Speaker 2: whether it's Beyonce or Taylor Swift or some of these
Speaker 2: other big names who are simultaneously incredibly popular. And I
Speaker 2: get that you're not as swifty, neither mine anymore, but
Speaker 2: people take this yeah, I liked their country for once
Speaker 2: she left country. But like where people take these popular
Speaker 2: culture things extremely seriously as art and don't dismiss these
Speaker 2: outputs as sort of being trash.
Speaker 4: Right now, we're in a golden age for country and
Speaker 4: western music and also horror movies. Neither is really like
Speaker 4: my taste in particular, but it's easy to see what
Speaker 4: has gotten worse, and especially for critics, harder to see
Speaker 4: what has been getting better.
Speaker 3: That's my sweet spot. Country and horror is perfect for me.
Speaker 3: But just on the culture point, I mean, I think
Speaker 3: the lack of culture argument, the one I hear the
Speaker 3: most is it's a lack of shared culture.
Speaker 4: Right.
Speaker 3: So you do have like some giant monolists like a
Speaker 3: Swift or a Beyonce that everyone knows about, and you know,
Speaker 3: they do have these large audiences, but broadly we're not
Speaker 3: all experiencing the same media that we used to, right, Like,
Speaker 3: no one is gathering around the TV to watch the
Speaker 3: finale of you know, some show that airs like once
Speaker 3: a week and has been going on for five years.
Speaker 2: The exception is NFL football, which I don't really get it,
Speaker 2: but yeah, but it's not cultural.
Speaker 3: Sports is outside of my experience.
Speaker 4: And the Super Bowl is cultural, right, it's a cultural event.
Speaker 4: You care about the advertisements, But the biggest of YouTube stars,
Speaker 4: which again I would say is not personally my thing,
Speaker 4: but they can have bigger audiences than those older TV shows. Right.
Speaker 3: Well, So what I'm getting at is it does feel
Speaker 3: like nowadays there's an ability because of tech, to serve
Speaker 3: up very specific content and like niche content in streams.
Speaker 3: And the analogy that I like to use is, you know,
Speaker 3: everyone knows if you download Netflix for the first time,
Speaker 3: the first movie you watch is incredibly important because whatever
Speaker 3: you watch, you know, if it's a rom com, you're
Speaker 3: going to be served up Kate Hudson films for the
Speaker 3: rest of your life. Right, Like, the algorithm looks at
Speaker 3: what you're watching and then it serves up that additional content.
Speaker 3: What does that mean for society? The idea that you
Speaker 3: have people you know, basically funneled into smaller and smaller
Speaker 3: streams in some respect, well.
Speaker 4: A lot of the Netflix algorithm it just directs you
Speaker 4: to slop. True, people have always wanted slop. Like people
Speaker 4: listen to music way back when it was quite common,
Speaker 4: or they would just list in the top forty, which
Speaker 4: in some years was very good, but often was pretty terrible,
Speaker 4: even in the nineteen sixties. So what you can do
Speaker 4: today is basically watch not any movie out there, but
Speaker 4: you have movie You can still buy DVDs and Blu rays.
Speaker 4: You have access to more cinema today than you ever have.
Speaker 4: So people will sort themselves. And I think it's from
Speaker 4: the point of view of cultural consumption. I don't think
Speaker 4: there's ever been a better time to be alive than
Speaker 4: right now. Like, well, a lot of people abuse that
Speaker 4: and go for slop. Of course that's sad, but it's
Speaker 4: hardly new.
Speaker 2: So changing gears a little bit. Tracy and I write
Speaker 2: almost every day because we have a daily newsletter that
Speaker 2: forces us to and I really like having that because
Speaker 2: I don't know if I would write every day if
Speaker 2: I did not have that obligation to deliver something in
Speaker 2: people's inbox that they pay for as partler Bloomberg dot
Speaker 2: Com subscription. I love writing, but I don't know if
Speaker 2: I would do it every day if I didn't have
Speaker 2: this sort of requirement. I might just tweet. How do
Speaker 2: you You've been blogging for over twenty years, how do
Speaker 2: you avoid the temptation to just fire off all your
Speaker 2: ideas via tweet and actually commit to the blog.
Speaker 4: I'm never tempted to do that. I like to think
Speaker 4: things out.
Speaker 2: I don't do.
Speaker 4: I just don't more when I write properly. I've actually
Speaker 4: blogged every single day for over twenty two years.
Speaker 2: That's amazing. It's amazing. Most people gave up, and so
Speaker 2: what's the difference.
Speaker 4: I don't feel it requires any discipline. For me. The
Speaker 4: discipline is not writing more like I have to restrain myself.
Speaker 4: So I guess I'm just weird. I don't think I
Speaker 4: have any neat little trick or formula. It's one of
Speaker 4: these niches that you can do now that you couldn't
Speaker 4: do before. And I found my niche, as have the
Speaker 4: two of you.
Speaker 3: Here's a slightly different question playing on that theme, but
Speaker 3: going back to the intro, How different do you think
Speaker 3: you're blogging career would have been had chat bots existed,
Speaker 3: you know, ten or twenty years ago when you were
Speaker 3: starting out.
Speaker 4: I don't think we know yet. My intuition is that
Speaker 4: people still want to read human writers simply because they're human,
Speaker 4: and if the bot is as good as you, most
Speaker 4: of the world doesn't care. But that has not truly
Speaker 4: been tested yet. I think we'll see in the next
Speaker 4: two years, but that's what I'm expecting. Just I think.
Speaker 4: I think in music, there'll be plenty of AI music.
Speaker 4: It might be say ten or twenty percent of the
Speaker 4: music sector, but listeners will still want that human to
Speaker 4: human connection.
Speaker 2: Do you think you know, when Twitter came out, they
Speaker 2: called it a micro blogging site, as if it were
Speaker 2: just blogging but on a shorter basis. But I think
Speaker 2: it's fundamentally different. You know, in the early, the glory
Speaker 2: days of blogs, which we'll be talking about forever when
Speaker 2: we're all very old people, how good it was, you know.
Speaker 2: I think there was this sort of spirit of you know,
Speaker 2: liberal linking with each other and idea exploration, whereas Twitter
Speaker 2: strikes me as much more conflictual and one upmanship and
Speaker 2: so forth. Do you think there are like fundamental I
Speaker 2: don't know if political is the right word, but like
Speaker 2: new communication paradigms like sort of have their own terroir,
Speaker 2: so to speak, in terms of the impulse towards collaboration
Speaker 2: or conflict, etc. And does that change society?
Speaker 4: Yeah, I still like blogging, and I'm sad people have
Speaker 4: moved away from it Twitter. To me, it seems too
Speaker 4: meme heavy, and meme heavy media have more potential for racism,
Speaker 4: which of course is a big negative. And I see
Speaker 4: so many people who are driven crazy by being on Twitter.
Speaker 4: Whether it's because they're writing on it or reading it,
Speaker 4: I'm not sure, maybe both. I don't want to name names,
Speaker 4: but it's a lot of people, and I bet you
Speaker 4: see the same ones that I do.
Speaker 3: Also very sexist nowadays, I would just add in unappreciated
Speaker 3: ways in many ways speaking of sexism, the impact of
Speaker 3: AI on economics. Talk about that economic institutions, you know,
Speaker 3: famous for modeling, spend a lot of time with numbers
Speaker 3: and things like that. Is that all just going to
Speaker 3: be replaced by AI?
Speaker 4: Not all? So. I think what human economists will do
Speaker 4: is put more and more time into gathering data and
Speaker 4: feeding it to the AIS. The returns to doing that
Speaker 4: will be very high. But the actual econometrics, statistics humans
Speaker 4: will maybe set up part of the problem, but the hard, boring,
Speaker 4: routine work will be done by the machines. As to
Speaker 4: some extent it was already the case, and this will
Speaker 4: be a way to make a lot of progress rather
Speaker 4: quickly do the.
Speaker 3: Actual economic statistics or data points that economists collect. Do
Speaker 3: some of those need to be changed or thought of
Speaker 3: differently in light of the AI era.
Speaker 4: Well, I don't know. Eventually they will need to be,
Speaker 4: but I would say any period of radical change in history,
Speaker 4: your statistics are less useful. It's not that the people
Speaker 4: creating the statistics are making some mistake. You just cannot
Speaker 4: capture every way the world is changing, and index number
Speaker 4: comparisons require the basket of goods be relatively close to constant,
Speaker 4: and at some point that doesn't hold anymore, and we'll
Speaker 4: be faced with that. We'll deal with it. I would
Speaker 4: say the current statistics we have, they're more underrated than overrated,
Speaker 4: so they're actually pretty good. I'll be glad when we
Speaker 4: get them back again.
Speaker 2: There's another thing. The tech people you talk to, they
Speaker 2: must think like GDP is terrible. Doesn't capture all this stuff,
Speaker 2: all this value that we can't price. I'm sure you've
Speaker 2: had conversations explaining to many AI workers that GDP is
Speaker 2: not the worst statistic in the world. I want to
Speaker 2: go back though, to them. So you know you mentioned
Speaker 2: like on Twitter, right, you say something someone so it's
Speaker 2: a meme. They dunk on you, they make fun of you,
Speaker 2: they whatever. Your chatbot won't do that. Like if I'm
Speaker 2: having a conversation with Chad GBT, it's never going to
Speaker 2: respond to me with the meme sort of indicating one
Speaker 2: day of my ad that I'm.
Speaker 3: A moral you'll learn your language, Joe.
Speaker 2: Yeah, But if anything, it is too obsequious, right, I mean,
Speaker 2: the issue is like the online world has become this
Speaker 2: very like sort of competitive, conflictual world, and then I
Speaker 2: go to the chatbot and my complaint is literally the opposite.
Speaker 2: It doesn't challenge me enough. It's too obsequious. Every question
Speaker 2: I ask, it's a great question. Sometimes I wish it
Speaker 2: would call me a moron a little bit more. But
Speaker 2: what does it change about the world? And we know
Speaker 2: that like many people's brains have been broken by social
Speaker 2: media that probably has downstream effects on how our politics
Speaker 2: operates these days. What does it do to the world
Speaker 2: if we started inhabiting these chat environments where they're just
Speaker 2: very sort of polite, and every time you say something,
Speaker 2: it says, yes, great thought, Tyler, great thought, Joe, would
Speaker 2: you like to expand it? You asked the perfect question,
Speaker 2: what do you see that having sort of second order
Speaker 2: effects on how society operates.
Speaker 4: Well, that's the four to zero model that does that.
Speaker 4: The newer models like Claude four point five and GDP five,
Speaker 4: they're more objective, and that's better.
Speaker 2: But they never make fun of you. They never will
Speaker 2: like say, you're a moron? How could you possibly how
Speaker 2: could you possibly ask such a dumb question? You're obviously
Speaker 2: so out of touch for having the ax ask this.
Speaker 2: This is like a very in some respects. This is
Speaker 2: a very positive change from many of the conversations that
Speaker 2: I've had from typing into a computer.
Speaker 4: Oh it's great. I think people should be nicer to
Speaker 4: each other. And I think they're the most objective media
Speaker 4: source the human race ever has had. If you're ask
Speaker 4: good about, say vaccines or conspiracy theories, it basically gives
Speaker 4: you the right answers.
Speaker 3: Well, one thing that they don't do, and I mean
Speaker 3: I do think they can be trained to be mean
Speaker 3: to you and to insult you to a certain degree,
Speaker 3: and you can.
Speaker 2: Could bring but I never account of them.
Speaker 4: Try market that more readily on PAP right.
Speaker 3: So we spoke to the chief business officer of Perplexity,
Speaker 3: Dmitri Shevalenko. We spoke to him recently, and he was
Speaker 3: saying that one thing chat models can't do is express
Speaker 3: a natural curiosity, which I thought was kind of weird
Speaker 3: coming from him, because perplexity is the only model I
Speaker 3: know that actually throws out those additional questions if you
Speaker 3: query it and then it comes up with would you
Speaker 3: like to have more information on this point or are
Speaker 3: you thinking about this now? But there does seem to
Speaker 3: be an element of creativity perhaps that is lost in
Speaker 3: some of these lms. How much does that change things
Speaker 3: in media? The idea that you know, the models are
Speaker 3: going to spit out something that's sort of predestined in
Speaker 3: many ways.
Speaker 4: Well, that's what most humans do, to be clear, But
Speaker 4: it's now the case that on a regular basis, the
Speaker 4: models say can prove new theorems and math or discover
Speaker 4: new potential drugs. And keep in mind, you know, a
Speaker 4: year ago these things thought the word strawberry had too
Speaker 4: and now they're winning gold medals and math Olympias. So
Speaker 4: a year or two from now, maybe we don't know
Speaker 4: how much better they'll be, but I don't think they're
Speaker 4: gonna have any problems being creative, certainly more creative than
Speaker 4: humans on average.
Speaker 3: So now I have to ask. Since we're talking about
Speaker 3: being mean or nice to the models and them being
Speaker 3: mean or nice to you, do you say please and
Speaker 3: thank you in your LLM queries?
Speaker 4: You know? I used to, and then Sam Altman said, well,
Speaker 4: it costs us just a little bit of money because
Speaker 4: of the extra tokens. And then I thought, I'll hold
Speaker 4: off on this. But I have this pre existing record
Speaker 4: of saying please, and it knows that. And then it
Speaker 4: knows I stopped when Sam said to stop. And I
Speaker 4: think I'll get points for both of those decisions.
Speaker 3: See, I actually find if you're slightly meaner to the
Speaker 3: models in your queries, they perform slightly better, much like
Speaker 3: interacting with Joe.
Speaker 2: No comment on that. I you know what I do.
Speaker 2: I have said luchadal like, I'll have a query and
Speaker 2: at a respet bond and I'll say that was a
Speaker 2: little on the nose, wasn't it? Like like it over.
Speaker 3: I'll say, do better.
Speaker 2: Yeah, I've seen that it does. Yeah, I've said like bad,
Speaker 2: I've said things like that. It's like, this is really
Speaker 2: on the nose. This response was a little bit trite,
Speaker 2: don't you think, et cetera. Like I do feel like
Speaker 2: I've gotten more comfortable at uh, let's be let's be
Speaker 2: real here. You're not doing You're not doing your best
Speaker 2: job here. What do you think is? Like, it's interesting
Speaker 2: they like I get that they win the gold medals
Speaker 2: and the math and et cetera. Like I've had so
Speaker 2: many conversations with the chatbots that are on some level
Speaker 2: like mind blowing what the capability is. I've never seen
Speaker 2: a chatbot query that is like interesting, like that is like, oh,
Speaker 2: that is like a really maybe one I could think of,
Speaker 2: but there was like a really interesting thought. I feel
Speaker 2: like my children still say on a daily basis more
Speaker 2: like interesting things they get me thinking than I've ever
Speaker 2: gotten from a chat Does that resonate to you at all?
Speaker 4: I don't know.
Speaker 2: I feel like you've said so many more things in
Speaker 2: this hour than any than any interesting thing, like actually
Speaker 2: like interesting ideas than I've ever gotten from the hours
Speaker 2: I've spent playing with chat chipet your claude.
Speaker 4: You know, I use mine a lot for music. So
Speaker 4: if I'm going to listen to Sibelius's Fifth Symphony, I'll
Speaker 4: just ask it what should I listen for? And I'll say,
Speaker 4: this is Tyler Kwan asking which I hope raises the
Speaker 4: quality of the answer. It knows a lot about me. Yeah,
Speaker 4: and what it gives me to listen for I find
Speaker 4: is better than any human source I can access readily better.
Speaker 2: I that I agree with, but like, does it make
Speaker 2: a connection, does it like tell you something about Sabilias's
Speaker 2: music that is like, oh, that's a very interesting that's
Speaker 2: a novel way of thinking about what makes it profound?
Speaker 2: These are the things that I rarely ever encounter something
Speaker 2: I was like, oh, that is an interesting thought, Whereas
Speaker 2: I feel like if we're talking to a music cologist
Speaker 2: for an hour, I would get infinitely more like actual
Speaker 2: insight into something that makes the music special. So I
Speaker 2: think I hadn't heard before.
Speaker 4: I don't know if it's novel because I don't really
Speaker 4: know the Sibelius literature, but most musicologists I find pretty boring.
Speaker 4: And I find say, GPT five on a classical symphony
Speaker 4: quite to the point. And whether or not it's original,
Speaker 4: it's not that important to me. It helps me listen
Speaker 4: to the music better. Yeah, and it's certainly original relative
Speaker 4: to the other sources at my disposal, say Wikipedia or
Speaker 4: what I could google to So I think for almost
Speaker 4: all purposes, that's enough. Does it have a truly original
Speaker 4: idea in the sense that Einstein's theory of relativity when
Speaker 4: he came up with it, was original to him? I
Speaker 4: don't think so. That may come in some number of years,
Speaker 4: but again, for almost all purposes, that's not what we need.
Speaker 4: We need something better than our pre existing state of knowledge.
Speaker 4: And on that I think it just cleans up.
Speaker 3: So I just ask perplexity what music I should recommend
Speaker 3: to Tyler Collan, and it said that in order to
Speaker 3: recommend effectively to Tyler Cowen, I need to look for
Speaker 3: underappreciated recordings and obscure things that no one accept him
Speaker 3: might have ever heard about. And it recommended. I mean,
Speaker 3: boy genius seems pretty on the on the nose, right,
Speaker 3: reggae acts like Toots and the May talent.
Speaker 1: This is so is that.
Speaker 2: Tyler Price records.
Speaker 3: So it's just scraping stuff that you've already talked to it.
Speaker 2: It's not even trying.
Speaker 3: Yeah, all right, well you.
Speaker 4: Need to make the prompt more exacting. Rule out anything
Speaker 4: Tyler has talked or written about. Yeah, give me something
Speaker 4: he doesn't know and try GPT five in the pro mode.
Speaker 4: And I think it will succeed well.
Speaker 3: So on this note. This is something I've been asking everyone,
Speaker 3: but like, what is an example in your mind of
Speaker 3: a really good prompt or one that sticks out to
Speaker 3: you that has generated something that you know, maybe you
Speaker 3: didn't expect.
Speaker 4: You know, dwark Esh Patel once wrote a very good
Speaker 4: prompt and he shared it with me. When I interview
Speaker 4: some podcast guests, It's really a long prompt. It's in
Speaker 4: hundreds of words, and it asks what questions should I
Speaker 4: ask them? Then it goes into great detail. It should
Speaker 4: be a unique question, it should be a question they
Speaker 4: were not asked anywhere else. Then it's given what you
Speaker 4: think their answer might be, and what would be my
Speaker 4: follow up question? And yeah, give you give me some
Speaker 4: cases where you think their answer might be wrong. And
Speaker 4: it goes on and on, and you run that through
Speaker 4: the very best models. I think you get good results. Yeah.
Speaker 2: No, I've you know, I've run transcripts of the podcast
Speaker 2: before and I say, like, well, where should I have
Speaker 2: pushed the guests harder on? What were the weak answers
Speaker 2: that they what were the inconsistencies that the guests had
Speaker 2: over the time? And I've found it to be a
Speaker 2: very useful exercise for things like that. So I do
Speaker 2: think like that's the thing which is first it is
Speaker 2: it is objectively impressive on many of these from and
Speaker 2: I would say objectively useful if you do sort of
Speaker 2: a detailed prompting. I'm just curious, what do you what's
Speaker 2: your like as a professor from the professor perspective, what
Speaker 2: do you think is the right way to think about
Speaker 2: how students will be using chadib I mean, I know
Speaker 2: that there's a million opinions in academia about well, what's
Speaker 2: the right way to test, now, what's the right way
Speaker 2: to deal with essays, et cetera. How are you thinking
Speaker 2: about some of these challenges.
Speaker 4: We should devote one third of all higher education to
Speaker 4: teaching students how to use AI, and right now that's
Speaker 4: close to zero, so we don't have the faculty who
Speaker 4: can teach it. As part of the problem, often the
Speaker 4: students no more than the professor.
Speaker 2: Yea, I'm sure.
Speaker 4: You need to restructure ratically what we do because future
Speaker 4: work will be done with AIS, so that's the thing
Speaker 4: to teach.
Speaker 2: But like so, just to play devil's advocate, like intuitively,
Speaker 2: I still feel like there's value in long periods of
Speaker 2: time cut off reading where you're not looking at devices,
Speaker 2: where you're training your body to sort of be disciplined
Speaker 2: and pay attention and focus. I still think memorization of
Speaker 2: facts and numbers and dates and places and names is
Speaker 2: very useful in actually having them in your head, et cetera.
Speaker 2: Does that seem right to you or is that sort
Speaker 2: of retro thinking on my part?
Speaker 3: No?
Speaker 4: Strong agree? And most of all writing, But that's the
Speaker 4: other two thirds of higher ed right, I said, one third?
Speaker 2: Oh yeah, so tell us about the other two thirds.
Speaker 4: We should with or without AI just teach students much
Speaker 4: more and much better how to write. Most people can't write.
Speaker 4: Writing is thinking we should do much more to teach
Speaker 4: writing and test writing, and now with AI, that has
Speaker 4: to be face to face in a controlled environment where
Speaker 4: people are just going to cheat, So that we should
Speaker 4: have doubled down on to begin with.
Speaker 2: Yeah.
Speaker 4: So that and just numeracy and basic issues like how
Speaker 4: to manage a portfolio, what kind of mortgage to take out.
Speaker 4: There are classes that cover those things, but I think
Speaker 4: they ought to be front and center of any curriculum.
Speaker 4: Basic finance, basic life decisions like how to choose a doctor,
Speaker 4: how to prompt the AI you know, for diagnosis whatever,
Speaker 4: are relatively neglected in a lot of education. That to
Speaker 4: me just seems crazy.
Speaker 2: I want to.
Speaker 3: Ask one market question before we go, which is obviously
Speaker 3: there's a lot of talk about an AI bubble at
Speaker 3: the moment, and I think the concern from a lot
Speaker 3: of people is when you start talking about in new
Speaker 3: technology as revolutionary, when you start talking about how you
Speaker 3: know the effects are basically going to be infinite, and
Speaker 3: the market size is hypothetically the entire world, there's a
Speaker 3: risk that expectations overshoot reality, right and we have seen
Speaker 3: some people voicing their worries about that right now, and
Speaker 3: a little bit of nervousness creeping into the market. In
Speaker 3: terms of valuations, Where do you stand on the AI bubble?
Speaker 3: Do you see signs of frauth or do you think
Speaker 3: most of the CAPEX spending is justified at this point?
Speaker 4: I don't like the word bubble. I would point out
Speaker 4: that tech sector earnings are exceeding tech sector capital expenditure.
Speaker 4: This is not mostly debt financed, so we're in less
Speaker 4: trouble than many people think. It wouldn't shock me if
Speaker 4: a lot of these efforts lost money. That was the
Speaker 4: case with the railroads, the case with the internet, case
Speaker 4: with most things humans have done. But I think it
Speaker 4: will endure. It's not like pets dot com, where the
Speaker 4: thing just gets swept away. These are incredibly well capitalized,
Speaker 4: highly skilled companies where the CEOs and or founders are
Speaker 4: quite committed to doing this and they're going to see
Speaker 4: it through and they're going to succeed. But does that
Speaker 4: mean every share value will go up or Nvidia ends
Speaker 4: up being worth ten trillion dollars. I don't know. I
Speaker 4: wouldn't necessarily predict that. There's always ups and downs, but
Speaker 4: this is clearly a very useful thing and we're going
Speaker 4: to we as Americans, we're going to make it work.
Speaker 4: And we're way ahead of the rest of the world.
Speaker 4: Like three quarters of all AI compute is in this country.
Speaker 4: That's incredible. Worry what percent of the world's population six
Speaker 4: or I don't know, but way smaller than three quarters.
Speaker 2: GPT five on Thinking Mode says you should listen to
Speaker 2: Michael Gulisian, who does dream like acoustic guitar using an
Speaker 2: open tuning, and it said that given your affinity for
Speaker 2: guitarists like John Fahey and Leo Kottke, you'll appreciate him.
Speaker 4: Send me that answer. It sounds excellent. I haven't heard
Speaker 4: of that person. Have you very much liked guitar with
Speaker 4: open tuning, shure a.
Speaker 2: Monkar, a Hindustani vocal singer. It thinks you'll like sun
Speaker 2: oh md Budrul Hawk. I'll send you this list and
Speaker 2: Katerina Barbieri modern modular, minimist electronic composition.
Speaker 4: So I will buy some of these already.
Speaker 3: Wait, just how about a human recommendation? Oh, give us
Speaker 3: a recommend a human recommendation. Since you said that you
Speaker 3: know country is pretty good right now, but I guess
Speaker 3: you personally aren't that into it. But have you tried
Speaker 3: Orville Peck? I think there's a new album out out
Speaker 3: this week.
Speaker 4: I think, what kind of music is it?
Speaker 3: Country? But like a very modern type of country. I've
Speaker 3: tried to get Joe into it, but uh, I'm still
Speaker 3: working on it.
Speaker 4: I like some country, and I love old country. So
Speaker 4: Hank Williams, Johnny Cash, Jed Atkins period.
Speaker 3: It has a vintage toned to it, but with a
Speaker 3: modern twist. Try Orville Peck and then get back to
Speaker 3: us about which was better in terms of the recommendation.
Speaker 2: Tyler Colln Tyler Collen, thank you so much for coming
Speaker 2: on odd Law's long overdue conversation. I really appreciate you
Speaker 2: taking your time.
Speaker 4: Great to chat with you both.
Speaker 2: Tracy a lot to pull out from conversation. I think
Speaker 2: it's very interesting that early observation he made about sort
Speaker 2: of legacy institutions and whether perhaps some of the sort
Speaker 2: of lack of revolutionary impact yet is just about that
Speaker 2: metabolization process into the types of companies that could theoretically
Speaker 2: absorb them.
Speaker 4: Yeah.
Speaker 3: I mean I think that's exactly it, right, So companies
Speaker 3: are using this mostly as an add on to existing workflow.
Speaker 3: You're not going to get the huge productivity boom until
Speaker 3: companies are sort of centered from yeah, which you know
Speaker 3: is probably going to take people who grew up with
Speaker 3: the technology rather than old people like you and I.
Speaker 2: You and I, you just adopted it.
Speaker 3: The other thing I was thinking about, first of all,
Speaker 3: insurers are sort of a pet interest of mine at
Speaker 3: the moment, but I do think like they are emerging
Speaker 3: as some of the really big winners from a lot
Speaker 3: of like I guess, the data saturation of the world
Speaker 3: right now and the increased sophistication of analytical models and
Speaker 3: things like that, and that'll be very interesting to see
Speaker 3: how it shakes out. And you could if you if
Speaker 3: you took it very, very far as a sort of
Speaker 3: thought experiment, you could start to say that, like, well,
Speaker 3: the insurers are going to be a more important actor
Speaker 3: in terms of setting social standards and regulations in the future,
Speaker 3: because they're the ones with the data, doing all the
Speaker 3: modeling and saying like, you have exactly an ex chance
Speaker 3: of being in a car accident, and therefore you must
Speaker 3: do the following things, right.
Speaker 2: I thought also, like I hadn't really thought about, you know,
Speaker 2: subpoena ability. I think it is a very big issue,
Speaker 2: but it is interesting, right, Like, it is a little
Speaker 2: weird that you and I could have a phone conversation
Speaker 2: now if you're under oath and they say, what did
Speaker 2: you talk about? Joe? I expected you'd probably tell the
Speaker 2: truth unless it's very bad for me, and I would
Speaker 2: hope that you would lie.
Speaker 3: That's right.
Speaker 2: Yeah, I hope that you would. I would hope that
Speaker 2: you that's your hope. I would hope that you myself
Speaker 2: to save Joe. Yeah, I would hope that you would
Speaker 2: perjure yourself, but like you know, theoretically could get away
Speaker 2: with it. So yeah, well we have an email, there's
Speaker 2: no chance. And it's sort of interesting. It's sort of
Speaker 2: it's obviously it seems a little bit arbitraryed me, but
Speaker 2: it is interesting to think about, like, Okay, can we
Speaker 2: have a conversation with these entities? And like why do
Speaker 2: we have to leave a digital record? And where are
Speaker 2: these going to be stored? And I do think in
Speaker 2: areas like health and law, which are obviously not obviously
Speaker 2: but sort of intuitively low hanging fruit for productivity gains,
Speaker 2: how much have we not seen, just in part because
Speaker 2: we're still sort of negotiating the transition process as the society.
Speaker 2: What are going to be the new rules and norms
Speaker 2: about this stuff, where it's gonna be hows, et cetera.
Speaker 2: I think that's actually a sort of very interesting question.
Speaker 2: Or space space, This is the.
Speaker 3: Space to watch, yeah speak, Now that I think about it,
Speaker 3: we probably should have discussed some of the regulatory framework
Speaker 3: around all of this a little bit more, But next time,
Speaker 3: next time. But the other thing I've been thinking about
Speaker 3: lately is economic statistics. Yeah, in a world that's increasingly
Speaker 3: driven by AI, and I know that we had the
Speaker 3: big productivity discussion and technology in relation to technology in
Speaker 3: like the sort of early to mid two thousands. I'm
Speaker 3: not sure that ever actually got settled, but I very
Speaker 3: much expect that the AI economic statistics conversation is going
Speaker 3: to be even like wackier because I'm not sure how
Speaker 3: you do things like quality adjustments for something that like
Speaker 3: suddenly comes with its own brain and stuff like that.
Speaker 2: So no, it's gonna be super weird. I did, I
Speaker 2: don't know. In my mind, I have like a very
Speaker 2: these images of like Tyler getting a tour through the
Speaker 2: you know, chatch EPTE offices and the people ask you
Speaker 2: was like, well, and him having to explain that, you know,
Speaker 2: we're not going to have twenty percent GDP growth at
Speaker 2: maybe two and a half or sorry productivity growth, and
Speaker 2: actually GDP isn't really that bad of a measured more
Speaker 2: or less captures the size of the economy, even if
Speaker 2: a lot of Internet things are free. Like some of
Speaker 2: these classic convers stations, I would like to be a
Speaker 2: fly on the wall for some of those.
Speaker 3: Tyler Cowen in defense of GDP dependent Yeah, all right,
Speaker 3: shall we leave it there.
Speaker 2: Let's leave it there.
Speaker 3: This has been another episode of the Odd Lots podcast.
Speaker 3: 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.
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