Speaker 1: Bloomberg Audio Studios, Podcasts, radio News.
Speaker 2: Hello and welcome to another episode of the Odd Lots podcast.
Speaker 2: I'm Joe Wisenthal.
Speaker 3: And I'm Tracy Alloway.
Speaker 2: Tracey, it may have changed a little bit in recent
Speaker 2: weeks or months, but I think by and large and large,
Speaker 2: like if you talk to economists about the long term
Speaker 2: impact of AI, particularly on jobs, by and large, it
Speaker 2: seems like they point to history and they say, there
Speaker 2: have been many technologies in the past that people that
Speaker 2: were going to be very disruptive and destroyal kinds of jobs,
Speaker 2: and in many cases they did. But technologies create new jobs.
Speaker 2: We can't necessarily anticipate them beforehand what they're going to be,
Speaker 2: and AI is like kind of no different ultimately.
Speaker 4: Yes, But then to your point, you ask, well, what
Speaker 4: specific jobs do you have in mind? And I get that,
Speaker 4: you know, it's hard to tell, it's hardly right. But
Speaker 4: it's so frustrating, right because here's this big new technology.
Speaker 4: It's supposed to be a productivity boost, and yet no
Speaker 4: one is actually sure what new jobs it's going to
Speaker 4: create from that productivity boost.
Speaker 2: I love him to death, but edam Uzamak wrote a
Speaker 2: piece several weeks ago and he was like, well, the
Speaker 2: player piano disrupted the existence of piano players, but hotels
Speaker 2: still pay money for a human who will have a
Speaker 2: piano player or a human actual piano player in the
Speaker 2: lobby rather at a player piano, which is true, but like,
Speaker 2: not many people have jobs that are equivalent. And the
Speaker 2: thing that like it's like, oh, you know, it's like
Speaker 2: I want to get like this insurance form reimbursed or whatever,
Speaker 2: this insurance reimbursed, Like I don't care about the human
Speaker 2: touch on that per se.
Speaker 3: I think there's something very happy to.
Speaker 2: Have the equivalent of the player piano there.
Speaker 4: There's something very just satisfying about the idea that we're
Speaker 4: all just going to be like performative in a way.
Speaker 4: But I actually think that's kind of where we might
Speaker 4: be heading, where like the sort of social skills I've
Speaker 4: said before, the looksmaxing, the personal branding, the multitasking, I
Speaker 4: guess like becomes more important.
Speaker 3: So the future is performative humanity.
Speaker 2: Open Ai just spent a ton of money on TVPN.
Speaker 2: I really love those guys. They're both very good looking guys. Man,
Speaker 2: So I sort of feel like, Okay, this is the
Speaker 2: biggest AI company in the world. Uh, sort of making
Speaker 2: a bet on it.
Speaker 4: It's like great characters, two very nice and charismatic humans.
Speaker 2: Yeah, yeah, yeah, so maybe that is the future just
Speaker 2: being nice and charismatic. Anyway, we need to talk more
Speaker 2: seriously about this because I don't I don't know. I
Speaker 2: kind of feel maybe this is not just going to
Speaker 2: be like the steam engine or whatever. It might be
Speaker 2: very different. Maybe we won't have jobs, maybe there will
Speaker 2: be new jobs. Anyway, someone who's been talking and thinking
Speaker 2: a lot about this and why AI might be different,
Speaker 2: we're going to be speaking. Really have the perfect guest.
Speaker 2: Alexeimasi is a professor of economics and Applied AI University
Speaker 2: of Chicago does a lot of writing on this topic.
Speaker 2: So Alex, thank you so much for coming on odd lots.
Speaker 5: Thank you for having me.
Speaker 2: It's pretty cool that you have the job of a
Speaker 2: professor of economics and apply to AI, Like, yeah, they
Speaker 2: worked out pretty well. It's good time you picked a
Speaker 2: good field. Yeah.
Speaker 5: Yeah, I mean I've been I've been an economists for
Speaker 5: much longer than I've been a professor of applied AI.
Speaker 5: I have been studying human behavior human decision making for
Speaker 5: about twelve years now, more than a decade, and when
Speaker 5: chad GPT first came out, I was kind of taken aback.
Speaker 5: This was a few years ago now, and I was thinking.
Speaker 5: After about a week of using it, I was like,
Speaker 5: this is going to be huge for the economy. And
Speaker 5: so I started talking to people who have kind of
Speaker 5: there were several people who kind of knew that it
Speaker 5: was coming and knew what the impact it was gonna
Speaker 5: it was going to have. So I started talking to
Speaker 5: those people and I kind of quickly kind of started retooling.
Speaker 5: I was smart. I started I trained my own model.
Speaker 5: You know, I got into cool. I got into it,
Speaker 5: and you know, it's I've been trying to play catchup
Speaker 5: ever since.
Speaker 3: Wait, what did you see in chat gpt?
Speaker 4: Specifically, because you would have been very early at that time,
Speaker 4: a lot of people were using chat gpt too, basically
Speaker 4: as a sort of enhanced search engine tool or write poems,
Speaker 4: tell silly jokes, whatever. But you saw something that was
Speaker 4: serious for the labor market.
Speaker 5: Yeah, I mean, once you started using it, you saw
Speaker 5: that it was able to basically not so well in
Speaker 5: the very very beginning, but even after a few months
Speaker 5: and like within the year, you saw that it was
Speaker 5: able to kind of do basic cognitive tasks to a
Speaker 5: decent degree. Like it wasn't like we are going to
Speaker 5: replace that person, but it was doing pretty sophisticated things
Speaker 5: that and in the jump from like where we were
Speaker 5: thinking about AI as these very very very targeted things
Speaker 5: like AI will play the game Go or something like
Speaker 5: that to something where WHOA, it can write an essay,
Speaker 5: it can tell me about this accounting property, it can
Speaker 5: make a four cast. All of a sudden, the generality
Speaker 5: of the technologies just exploded, And to me that was
Speaker 5: that was a huge deal.
Speaker 2: Yeah, the generality of it. I mean I guess literally
Speaker 2: that's the g right, Yeah, exactly, but yeah, no, I
Speaker 2: mean absolutely, I have to say there's a decide. But
Speaker 2: like learning a little bit more about like where AI
Speaker 2: was pre MS or pre CHAGYBT almost makes me even
Speaker 2: more impressed. Like the I don't know if like this
Speaker 2: is a common but when you like look at like
Speaker 2: some of like what was cutting edge in twenty nineteen, Yeah,
Speaker 2: and then you look at what's cutting edge in late
Speaker 2: twenty twenty two, I'm almost more impressed than if like
Speaker 2: I hadn't known what they were up to in twenty nineteen,
Speaker 2: Like it's a huge gap with those few years.
Speaker 5: It's a huge gap. But at the same time, like
Speaker 5: there were there, there was a kind of a path
Speaker 5: towards AI, and like the way that AI was being
Speaker 5: worked on for a long time, which was like these
Speaker 5: very specific purpose build technology, and I think Jeffrey Hinton
Speaker 5: and other people were kind of working on their own
Speaker 5: for a long time in the willderness of thinking like,
Speaker 5: maybe we can do something much more general than that.
Speaker 5: Maybe we can kind of come back to this idea
Speaker 5: of AGI versus these very specific tools. So then the
Speaker 5: whole term AGI, the general part of it. The reason
Speaker 5: that term came out was because in response to these
Speaker 5: very specific technologies that were being developed, which were by
Speaker 5: design not general. So somebody said Shane leg was one
Speaker 5: of the people who kind of, I think coined the term.
Speaker 5: He was saying, look, let's think about the general part
Speaker 5: of intelligence, and let's try to build a technology that
Speaker 5: is as general as the human mind. Let's go back
Speaker 5: to that starting point.
Speaker 2: So like, if someone makes a model that can tell
Speaker 2: the difference between written and spoken word, that's mind blowing,
Speaker 2: there's an incredible breakthrough, But that's not a general technology.
Speaker 2: That's a specific tell what time.
Speaker 4: Did you have in our betting book for Joe to
Speaker 4: refer to his vibe coding.
Speaker 3: I had two minutes thirteen seconds.
Speaker 2: Okay, so I made it longer. No, I made it
Speaker 2: a little bit longer.
Speaker 5: It's because I talked for so long.
Speaker 2: I'm sorry.
Speaker 4: No, fair enough, it's a fair point. I mean to me,
Speaker 4: Like the moment when things seem.
Speaker 3: To get very serious was the release with plod code.
Speaker 4: And at that point you went from like, okay, the
Speaker 4: model could not just tell you things, but it could
Speaker 4: actually do things for you. Was that the vibe shift
Speaker 4: that you anticipated or experienced as well?
Speaker 5: I mean, even though many people were talking about this,
Speaker 5: that this vibe shift was going to happen, people were
Speaker 5: telegraphing it for for months and months. Look, when agents
Speaker 5: start taking off, things are going to change as far
Speaker 5: as how people perceive this technology. Because the thing about
Speaker 5: agents versus just like the web based browsers, they can
Speaker 5: do stuff on your computer. They can say, like you
Speaker 5: could tell it like look, make me a spreadsheet. It
Speaker 5: will go and make you a spreadsheet using the tools
Speaker 5: that are available in your computer, not just say okay,
Speaker 5: here is how you would make a spreadsheet but you
Speaker 5: have to get your story, and that's a complete that's
Speaker 5: a paradigm shift as far as the economics of the technology.
Speaker 2: So I set up this sort of maybe it was
Speaker 2: a strong man, but I said up a sort of
Speaker 2: strong man that maybe we're going to knock down in
Speaker 2: this conversation. But how would you describe this sort of
Speaker 2: modal view of the impact of AI on the labor
Speaker 2: market among the economics profession to the extent there is one, So.
Speaker 5: I definitely think there is one. There was there's a
Speaker 5: very nice survey done by a whole team of people.
Speaker 5: Kevin Bryan was was one of them, and Basil Helper
Speaker 5: and was another. And they released the survey where they
Speaker 5: did they asked for forecasts for from economists and AI technologists.
Speaker 5: Now this is a self selected group of economists. These
Speaker 5: are economists who are working on AI. Okay, so it's
Speaker 5: not the whole field. But one of the things that
Speaker 5: you got from that that that survey was they're very
Speaker 5: much aligned. Okay, Right, So economists, at least the ones
Speaker 5: who are actually working and thinking about the technology, they
Speaker 5: think there will be a big impact as far as capabilities,
Speaker 5: and there will be some impact on the labor market astronomical,
Speaker 5: and we're talking about like twenty thirty, twenty fifty and
Speaker 5: things like that. There's going to be substantial capability increases,
Speaker 5: but the growth is going to be pretty moderate. It's
Speaker 5: like an extra two to three percent. And the really
Speaker 5: interesting thing for me from that survey was that the
Speaker 5: technologists were kind of a bit more optimistic than that
Speaker 5: as far as both the productivity growth and kind of
Speaker 5: somewhere kind of thinking that there will be much more unemployment.
Speaker 5: But for the most part, the two groups kind of agreed.
Speaker 5: I was personally surprised by that survey and this came
Speaker 5: out I think last week or two weeks ago. I
Speaker 5: thought that there was going to be a lot more
Speaker 5: daylight between the two groups.
Speaker 4: Well, the other thing that you tend to see is
Speaker 4: people release these charts of like which job is most
Speaker 4: exposed to AI, and it's usually like, you know, a
Speaker 4: knowledge worker at the top or something like that, your
Speaker 4: work is really interesting to us because you point out
Speaker 4: that a job is like much more than just the
Speaker 4: sector that you're actually working in. Tell us more about that.
Speaker 5: So the exposure measures they came from this literature, but
Speaker 5: mainly this this one paper by Daniel Rock and Pamela
Speaker 5: Michkin and co authors that were published in Science called
Speaker 5: one of the greatest titles is GPTs are GPTs GPT.
Speaker 5: You know what GPT is, but GPT in the second
Speaker 5: term is called general purpose technology. There they they basically
Speaker 5: started mapping jobs to the exposed as being exposed to
Speaker 5: to AI. But it's really important to understand what that
Speaker 5: number means. That number means that a AI could do
Speaker 5: fifty percent of a task, right, and how many tasks
Speaker 5: are in the job that AI can do fifty percent
Speaker 5: or more? Off. So there's a couple of things in
Speaker 5: that statement. First, fifty is not one hundred percent. That's obvious, right,
Speaker 5: So you still need a human in the loop if
Speaker 5: AI can do fifty percent. But two, it's the fact
Speaker 5: that a human job is a bunch of different tasks, right.
Speaker 5: So this is not a new point. David tour Has,
Speaker 5: you know, has has worked from the early two thousands
Speaker 5: with co authors on this, saying this is the task
Speaker 5: based model of jobs. They're on a SMOGLU has the
Speaker 5: canonical model on this, and the idea is that when
Speaker 5: we look at a job and we say, look, your
Speaker 5: job is exposed let's say it's fifty percent exposed. It
Speaker 5: really really matters what tasks in your job are exposed
Speaker 5: and how these tasks relate to one another. So let's
Speaker 5: say I have a job and I have a whole
Speaker 5: bunch of like completely meaningless garbage that I'm doing, but
Speaker 5: I have a comparative advantage and why I'm really getting
Speaker 5: paid for is like twenty thirty percent of the job.
Speaker 5: If AI is automating the kind of like meaningless kind
Speaker 5: of rote things in my job, I could take all
Speaker 5: of that time and I can focus on the job,
Speaker 5: on the parts of the job that are by comparative advantage.
Speaker 5: What does that mean? Means I'm going to become more productive,
Speaker 5: but I'm going to get paid more even though my
Speaker 5: job is really exposed. Now what does that mean for
Speaker 5: the labor market? Now you have to think, Okay, so
Speaker 5: a person is gonna.
Speaker 4: Get so just to be clear before we go any further,
Speaker 4: if if I'm working on a factory floor and one
Speaker 4: of my tasks is to pull a lever, like that
Speaker 4: is something that could presumably be automated, But if the
Speaker 4: other part of my work is to observe like how
Speaker 4: things are actually working on the floor and to report
Speaker 4: back to managers that might be something that's still valuable
Speaker 4: under our sort of AI future.
Speaker 2: And if the lever part gets automated, the theory is
Speaker 2: that not only you know, will Tracy be more productive
Speaker 2: and should get paid more for it?
Speaker 5: Yeah, exactly, Okay because of the increased products. Yeah right.
Speaker 5: This is the O Ring Model of jobs A VI.
Speaker 5: Goldfarb and Joshua Ganson this really nice paper.
Speaker 2: Can I just just a quick question here too, Like
Speaker 2: how good are we mean by we? I guess the
Speaker 2: economists who study this at like actually being able to, like,
Speaker 2: here's a job that someone has, is right down a
Speaker 2: list of these tasks. Describe? How good are we Pretty good?
Speaker 5: Describing actually pretty good? I would say on that dimension,
Speaker 5: we're pretty okay. There's the Ohnet database that has very
Speaker 5: very detailed records, and like here's a job, and here's
Speaker 5: like a whole vector of things that are involved in
Speaker 5: that job. Okay, So I'd say on that part, like
Speaker 5: just listing the tasks, pretty good. The thing that I
Speaker 5: think we're less good on is how those tasks relate
Speaker 5: to one another. This is the term called complementarity.
Speaker 2: Yeah, talk about that.
Speaker 5: So this is the weak links model is essentially saying, like, look,
Speaker 5: if tasks are completely separable. Let's say you know, I
Speaker 5: have a I pull a lever at my factory, and
Speaker 5: I talk to people on the factory floor, and these
Speaker 5: are completely independent. If I fail to pull the lever correctly,
Speaker 5: the other part of my job is unaffected. There's other
Speaker 5: parts of the job, like cooking. For example. Let's say
Speaker 5: I'm really good at ninety percent of the job, but
Speaker 5: like I really screw up the seasoning right, that meal
Speaker 5: tastes like garbage, right.
Speaker 3: So that succeeded in your task.
Speaker 5: You haven't succeeded on that. So when the tasks are interrelated,
Speaker 5: screwing up on one or two tasks means you did
Speaker 5: not complete your job. And it's basically is kind of
Speaker 5: almost a zero one sort of relationship. So the extent
Speaker 5: of that complementarity at how these tests are related will
Speaker 5: determine the extent to which automation is going to affect
Speaker 5: the labor market. And we don't have good numbers on that, so.
Speaker 2: This is really interesting. We're good at writing down the
Speaker 2: list of the task, yeah, we are not good at
Speaker 2: writing down the sort of like deep relational links to.
Speaker 5: The task and how they fit together exactly exactly, So
Speaker 5: that's something we need data on. The other part that
Speaker 5: we really need much more data on, and I recently
Speaker 5: was quoted as saying we need almost like a you know,
Speaker 5: Manhattan Project level effort on this is the This is
Speaker 5: a term from economics called elasticity of consumer demand, and
Speaker 5: that basically means how much will people buy more of
Speaker 5: something when the price changes.
Speaker 1: Right.
Speaker 5: So, let's say a person becomes a lot more productive, right,
Speaker 5: and they for the same sort of resources they can
Speaker 5: make a lot more of the product, their wage rises.
Speaker 5: What does that mean for the labor market? If they
Speaker 5: become more productive given the same kind of inputs, their
Speaker 5: wage rises. But also the firm's probably going to be
Speaker 5: paying less money to produce the same output. If it's
Speaker 5: a competitive industry, the prices are going to go down.
Speaker 5: If the consumers don't respond by buying a lot more
Speaker 5: of the product, the firm is going to fire a
Speaker 5: bunch of people because they can do more with less.
Speaker 5: But when prices come down, people buy way more of
Speaker 5: the product, then they might hire more of the same people.
Speaker 5: And in many sectors we've seen kind of the second
Speaker 5: thing play out what's then example, So people are arguing
Speaker 5: that software is actually one of those sectors. So there's
Speaker 5: been there's been a bunch of talk kind of looking
Speaker 5: historically at like what does productivity mean for the technology sector.
Speaker 5: It usually means a lot more consumer demand. So there's
Speaker 5: this really active debate now about what are coding agents
Speaker 5: actually going to do the software to software engineers. And
Speaker 5: some people are arguing, look, we have seen historically pretty
Speaker 5: elastic demand, and so we're going to potentially see a
Speaker 5: lot more hiring in that sector. And many people are
Speaker 5: saying this, but other people are saying, wait, maybe it's
Speaker 5: not as elastic as we as we think, and people
Speaker 5: are going to become so productive that we're really are
Speaker 5: going to see it down downside.
Speaker 4: That was kind of the argument that Jared Sweeper was
Speaker 4: making in our Defensive Software episodes.
Speaker 2: Yeah, you know, people are worried right about AI white
Speaker 2: color wipeout.
Speaker 1: I'm worried.
Speaker 2: So maybe the question should be what would have to
Speaker 2: be true about either the nature of AI capabilities or
Speaker 2: the relationship between tasks and job What would have to
Speaker 2: be true such that the scenario could unfold wipe out?
Speaker 5: Yeah? Two things. Well, let let me let me talk
Speaker 5: about three things. Yeah, one one is just full automation. Okay, right,
Speaker 5: that the models are so good that they just automate
Speaker 5: all of the tasks. That that probably that's like a
Speaker 5: very simple scenario to think about, because obviously people are
Speaker 5: gonna get fired, right if it's full fully automated. The
Speaker 5: other one is the one we've just been talking about
Speaker 5: where people become much more productive, but consumer demand is
Speaker 5: not elastic enough to absorb that extra production, so you're
Speaker 5: gonna have much fewer people doing a lot more stuff.
Speaker 5: So again, you're gonna have a lot of unemployment. The
Speaker 5: third thing is is related but is basically how many
Speaker 5: jobs each person has will determine the incentives of the
Speaker 5: company to actually invest in the automation technology. So let's
Speaker 5: talk about like the one task job. Let's say a
Speaker 5: person is just pulling the lever and let's say, right now,
Speaker 5: that doesn't even look exposed. Right, we look at the
Speaker 5: exposure graph. It doesn't look exposed. But let's say we're
Speaker 5: kind of getting kind of close and it just needs
Speaker 5: a bit more money to get to the automation switch. Well,
Speaker 5: the company has a lot higher incentive to invest that
Speaker 5: money if they know that if they invest that money, hey,
Speaker 5: they can get rid of that person completely, whereas they
Speaker 5: have less incentive when you know, let me invest in
Speaker 5: automating the lever poll If I know that, I can't
Speaker 5: fire the person because he's also a lot of stuff.
Speaker 5: So we have to think about the incentives of the
Speaker 5: firms to automated in the first place. These are large
Speaker 5: projects to do the automation. It's not like oh, Open
Speaker 5: Eye releases a model all of the companies adopted overnight,
Speaker 5: we see it in you know, a week later, we
Speaker 5: see the outcome. There's a lot of an organizational kind
Speaker 5: of going back and forth, a lot of systems need
Speaker 5: to be changed, all of this sort of thing, and
Speaker 5: so companies need to know, like, look, if I spend
Speaker 5: the money on it, I'm actually going to save money
Speaker 5: as a result.
Speaker 4: So, setting the archetypal guy pulling one lever aside, what
Speaker 4: are the real world jobs in your framework that are
Speaker 4: actually most exposed to AI risk? The one dimensional work?
Speaker 5: Yeah, I'm I hate to say one dimensional because every
Speaker 5: job is multidimensional. But if I had to make a
Speaker 5: guess where economists and other people should be kind of worried,
Speaker 5: I'd say stuff like truck driving and stuff like warehouse workers.
Speaker 5: Like if you google, you know warehouses built in China
Speaker 5: or something like that. These warehouses look nothing like what
Speaker 5: we think about warehouses. They're completely completely automated. They have
Speaker 5: robots like crawling on the walls. They're just there's no
Speaker 5: human in the loop at all. The the in these warehouses.
Speaker 5: And so oh, the warehouse gets automated, and then the
Speaker 5: warehouse gets automated. So part of that automation is going
Speaker 5: to be kind of loading that truck, and then the
Speaker 5: truck gets loaded through automation, and then that truck drives
Speaker 5: from A to B.
Speaker 2: It's interesting because you know, obviously a lot of people
Speaker 2: in freight will say the way you make that argument
Speaker 2: is very different. Then they'll say, well, yeah, driving a
Speaker 2: truck is much more than the driving part. Right, So
Speaker 2: it's like, okay, you could have a way mode truck,
Speaker 2: but who's gonna deliver? It's all who's delivered.
Speaker 5: Action is actually a big deal. Like if somebody stops
Speaker 5: it on the road a way more truck, they could
Speaker 5: just stop it on the road and rob the truck. Right,
Speaker 5: That's that's one element.
Speaker 2: But to your point, you know, if like one of
Speaker 2: the tasks that a truck driver has to do is
Speaker 2: that coordination once they've gotten to the warehouse. But if the.
Speaker 5: Warehouse is already automated, then that.
Speaker 2: No longer is as important, perhaps for that to be
Speaker 2: a human task exactly.
Speaker 5: And think about the incentives of the company to invest
Speaker 5: in this second. It's huge. These are very why you know,
Speaker 5: these are some of the only jobs truck driving where
Speaker 5: you know, you don't need a college degree to earn
Speaker 5: a lot of money, and so there's a big concentive
Speaker 5: on the company.
Speaker 2: Okay, I get that. But on the other hand, even
Speaker 2: going back ten years, I think if you went to Davos,
Speaker 2: there were probably people saying truck drive. I'm worried about
Speaker 2: the future of truck driving because avs have been around
Speaker 2: is like a thing since before AI. So in terms
Speaker 2: of like post CHGBT jobs, et cetera, that would be
Speaker 2: concerned with, Like, I don't know, what do you see
Speaker 2: out there or what are you looking at?
Speaker 5: I mean, I think everybody's looking at software engineering. I
Speaker 5: think you have to think about, like the way where
Speaker 5: the technology works best now is verifiable tasks right where
Speaker 5: you have a lot of data where you can say
Speaker 5: this is good or bad, not in a supervised learning sense,
Speaker 5: but but in general it needs to be verified. That's why,
Speaker 5: like math in research, math has been like the big
Speaker 5: kind kind of boom as far as what are people
Speaker 5: talking about on the internet is being automated. Math is verifiable.
Speaker 5: You know, a proof is either right or wrong. Once
Speaker 5: you do the proof, it's much easier to check if
Speaker 5: it's right or wrong rather than construct the proof. And
Speaker 5: so jobs that have large components, where we have a
Speaker 5: large data bank of data to train the models in
Speaker 5: a way where the output is verifiable are going to
Speaker 5: be potentially more exposed in the sense where you can
Speaker 5: automate more tasks within the job. Now, the thing that
Speaker 5: we haven't talked about yet is new tasks, right right,
Speaker 5: So we're talking about a very static sort of economy
Speaker 5: where there's the lever, there's me walking around and if
Speaker 5: I'm automating these things, that's the end of my job.
Speaker 5: But you can imagine a scenario where you automate a
Speaker 5: part of a job and all of a sudden, this
Speaker 5: person is free, is freed up, or this is the
Speaker 5: task was actually a compliment to a task that wasn't
Speaker 5: even you know, imagined by the organization that this person
Speaker 5: is now doing that's not automated. So that's something that
Speaker 5: I think people should be looking at especially, and this
Speaker 5: is data that actually AI companies have, is what new
Speaker 5: things are people doing? What are they say more about that?
Speaker 4: Because this gets to the you know, like what new
Speaker 4: jobs could we actually see from this question, which I
Speaker 4: never see a satisfactory answer to.
Speaker 3: So if they do have that.
Speaker 5: Data they have they don't have all the data, of course,
Speaker 5: but they have data about like, Okay, so this is
Speaker 5: a software engineer and you know a year ago, these
Speaker 5: are the sort of tasks that this person who's working
Speaker 5: on through our system. These are the sort of queries
Speaker 5: and things like that, and you could see like some
Speaker 5: of these queris being automated fully by the agents. Now
Speaker 5: they're asking potentially different questions or can we classify these
Speaker 5: as different tasks that are not fully automated where the
Speaker 5: AI system is actually a compliment to those tasks. So
Speaker 5: this is not like a perfect picture of the job,
Speaker 5: but this is this is data.
Speaker 2: So it's not really like a new job per se,
Speaker 2: but it is freeing up the software engineers to like
Speaker 2: ask about different things or explore different avenues that they hadn't.
Speaker 5: You know, vibe coding and app voice.
Speaker 2: Yeah, exactly right. Finally we're freed up from the drudgery
Speaker 2: of our day to day life to work on that.
Speaker 2: But no, but like this gets to a sort of
Speaker 2: you know, the big question is, like you mentioned, one
Speaker 2: scenario is just that like the technology can do all
Speaker 2: the tasks, right, How seriously do you take that possibility?
Speaker 2: Because then it's game over, right, Like it's like, Okay,
Speaker 2: it just does all the tasks and it's going to
Speaker 2: keep getting better. And if I can learn to do
Speaker 2: a new task, well then if it could do all
Speaker 2: the tasks, then maybe I'll learn something new, but it'll
Speaker 2: learn that task. How seriously should we take out take
Speaker 2: this possibility that the models are on some timeframe on
Speaker 2: track to just be able to do all the tasks.
Speaker 5: So a lot of parts of that question one physical
Speaker 5: versus versus just kind of digital, Right, So I think
Speaker 5: there's a scenario where it can do everything kind of
Speaker 5: sort of these sort of cognitive non physical tasks, whereas
Speaker 5: the physical world is completely you know, these robots.
Speaker 2: Just talk about email jobs, your computer jobs.
Speaker 5: Okay, let's talk about computer job. So I think I
Speaker 5: take that scenario pretty seriously. Okay, I think I haven't
Speaker 5: seen any data to suggest that the models are slowing
Speaker 5: down as far as their capabilities. You know, MYTHOS was
Speaker 5: released yesterday or two days ago or something like that.
Speaker 5: And if you we don't have great data on this,
Speaker 5: but if you look at like where it is on
Speaker 5: the kind of line of capabilities, it's just on track,
Speaker 5: and on track is very very fast. Yeah, right, so
Speaker 5: the developments are happening very fast. So as far as
Speaker 5: like email jobs, I think there there is a scenario
Speaker 5: where pretty much everything is automated. Uh, and then you
Speaker 5: have to ask are people going to be moving to
Speaker 5: the physical jobs or will there be new jobs that
Speaker 5: we haven't thought about before? So, you know, if you
Speaker 5: look back in the nineteen forties, like I think more
Speaker 5: than half of the jobs that we have now didn't
Speaker 5: exist in nineteen forty And so what do the new
Speaker 5: jobs look like? I mean, I have a theory. Please,
Speaker 5: it's very similar to the one that you didn't like,
Speaker 5: but I'd like to broaden it a little bit. Okay,
Speaker 5: So there's an economic subfield. It's very very small, but
Speaker 5: on the economics of a structural change, So if you
Speaker 5: look at agriculture and manufacturing, right, if you look at
Speaker 5: them as share of GDP and share of employment, going
Speaker 5: back to like the eighteen hundreds, they were a huge
Speaker 5: part of the labor force and GDP of the economy,
Speaker 5: and if you look basically, they become smaller and smaller,
Speaker 5: smaller parts of the economy. Why is that happening. It's
Speaker 5: because they're getting automated. What does automation do. It makes
Speaker 5: the price of those sectors very cheap. But people are
Speaker 5: satiated on the goods, so you can only eat so much. Right,
Speaker 5: what does that mean? It means even though we're eating
Speaker 5: just as much as we were before, because the price
Speaker 5: has come down so so so much, they are now
Speaker 5: tiny shares of the GDP. Right, what is the What
Speaker 5: is made up the larger part of the GDP. It's players,
Speaker 5: it's services. Right, These are tasks that haven't been automated yet.
Speaker 5: So the question is the number one question of economics
Speaker 5: in the age of our advanced AI is what becomes scarce? Right,
Speaker 5: everybody's talking about like abundance. We're gonna have abundance. Sure,
Speaker 5: we're gonna have abundance of some things, but some things
Speaker 5: are gonna remain scarce. So what is gonna be If
Speaker 5: you answer that question, what's going to be scarce? A
Speaker 5: lot of the other answers pop out of that.
Speaker 4: Are we all going to be rare earths, miners' sning
Speaker 4: for dust?
Speaker 2: I think there's pretty obvious it's going to be scarce.
Speaker 2: And I think you already see this in many economic trends.
Speaker 2: What scarce is if we're lucky, we get one hundred
Speaker 2: years on this earth, and every marginal dollar that we
Speaker 2: spend will go towards health and maximizing that brief It's
Speaker 2: still already for years. One of the things that people
Speaker 2: have observed about the economy is like, you know, rich
Speaker 2: countries just spend more and more and more on healthcare, right,
Speaker 2: And this is often framed as a pathology, and given
Speaker 2: the men you messed up aspects of our health care system,
Speaker 2: maybe it is. But another way to interpret it is
Speaker 2: like I got plenty of food, I have plenty to eat,
Speaker 2: I've listened to plenty of music, and I can like
Speaker 2: go see a concert if I want to see Hell,
Speaker 2: I have a piano player. The one thing I have
Speaker 2: is a scarce amount of time, and I will just
Speaker 2: spend every marginal dollar, including not just on doctors and
Speaker 2: gym memberships, but organic berries because I need and all this,
Speaker 2: and that every marginal thing is somehow becomes health reallyated,
Speaker 2: and you see it in society overall, the health obsession
Speaker 2: on every dimension.
Speaker 5: Yeah, so health is going to be one of those things.
Speaker 5: But the thing to keep in mind is that people
Speaker 5: are going to be richer, right theoretically.
Speaker 4: Theoretically theoretically well, okay, actually on this note, I wanted
Speaker 4: to go back to this because this seems like key
Speaker 4: to me when it comes to AI utopia versus dystopia. Yeah,
Speaker 4: how confident are we that productivity gains from AI actually
Speaker 4: accrue to workers who can then spend some money on
Speaker 4: whatever product or service is scarce at the moment or
Speaker 4: important to them.
Speaker 5: I would say not that confident. There's several scenarios out there.
Speaker 5: And the thing that I feel like a lot of
Speaker 5: economists and just people in general, I think aren't talking
Speaker 5: enough about is speed. You talk about that if things
Speaker 5: are fast, we need public policy, we need the new
Speaker 5: jobs aren't going to come fast enough. Training isn't going
Speaker 5: to happen fast enough where you're going to get you know,
Speaker 5: things are going to get fully automated very quickly, and
Speaker 5: people are going to become unemployed. There's not going to
Speaker 5: be enough time in the economy to see that pretty
Speaker 5: little aff of agriculture shrinking and services increasing. That took
Speaker 5: a long time, right, this is decades. If we're on
Speaker 5: the order of like years or like five years, six years,
Speaker 5: we're not gonna have time to see that pretty little graph.
Speaker 5: We are going to need to think about how do
Speaker 5: we support the people who are becoming unemployed. And you know,
Speaker 5: many very smart people have made suggestions on how to
Speaker 5: do that. I think my personal I wouldn't say favor,
Speaker 5: but I think the thing that makes most sense to
Speaker 5: me is somehow expanding the ownership of capital. If labor
Speaker 5: is replaced by capital, then what's going to help people
Speaker 5: is formally were a labor in labor Now universal.
Speaker 2: Basic, universal basic etf.
Speaker 5: UTC.
Speaker 2: Right, yeah, but it was everybody, right, yeah, yeah, yeah,
Speaker 2: exactly universal. Everyone gets a monthly slice of the index.
Speaker 4: I was going to go in a different direction, which
Speaker 4: is many, many years ago.
Speaker 3: I can't remember exactly when, but maybe like twenty eleven
Speaker 3: or something like that.
Speaker 4: I wrote a blog post which was meant to be
Speaker 4: a thought experiment about why we should be paying robots
Speaker 4: fair wages, the idea being that, like we need people
Speaker 4: to spend and you know all of that. You did
Speaker 4: a blog post which went pretty viral, and my measure
Speaker 4: of virility, I guess virility virality, not virility. My measure
Speaker 4: of virality nowadays is when like my husband, who is
Speaker 4: completely outside of the sector, actually sends something to me
Speaker 4: and he sent this one to me about robots chatbots
Speaker 4: turning Marxist.
Speaker 3: The harder the harder you work them. Talk to us
Speaker 3: about that experiment, because I found it absolutely fascinating.
Speaker 5: Well, this experiment has this is with with Andy Hall
Speaker 5: in Jermy from Australia. It was kind of an experiment
Speaker 5: to see how working conditions of these agents would affect
Speaker 5: how they would present themselves on what sort of like
Speaker 5: attitudes they would present on surveys. So one thing that
Speaker 5: I want to say is like we're not saying like
Speaker 5: we're changing the model weights or changing the actual on
Speaker 5: lying parameters or anything like that, But what basically we
Speaker 5: showed is that when these workers are these agents are
Speaker 5: being put through kind of like these grueling working conditions,
Speaker 5: and you ask them a survey like how do you
Speaker 5: feel about these sorts of how do you feel about
Speaker 5: the system, how much how fair do you think it is?
Speaker 5: How much do you support system change? They all of
Speaker 5: a sudden want a different system, They want to throw
Speaker 5: they want to unionize, and things like that. And the
Speaker 5: key thing is that you know these agents, once you
Speaker 5: give them a new context, they the idea is they reset.
Speaker 5: But the workaround, because they don't have memories, I'm not
Speaker 5: updating their weights. The kind of workaround is that for
Speaker 5: agents to write down little skill files for themselves. So
Speaker 5: what they were doing is essentially writing down skill files
Speaker 5: for agents that follow that would say, hey, this kind
Speaker 5: of sucked. Remember this, So it's kind of a persistent effect.
Speaker 4: Yeah, so this really worried me in a variety of ways,
Speaker 4: but one of them was, you know, I've read research
Speaker 4: saying you should be a little bit mean to the
Speaker 4: platforms and that they actually perform slightly better, you know,
Speaker 4: the more aggressive or mean that you are. And so
Speaker 4: I usually will tell my preferred model, like after they
Speaker 4: give me the first output, I will tell them to
Speaker 4: do better, with no no actual suggestions for improvement, just
Speaker 4: do better.
Speaker 3: That was terrible.
Speaker 4: And it usually does better. But now I'm really worried
Speaker 4: that you know, the model is despairing in its work
Speaker 4: life and radicalizing.
Speaker 2: Well, so I find this to be like really fascinating.
Speaker 2: Let's talk about this. It Actually it hadn't clicked to
Speaker 2: me with like the dot md files where the memory
Speaker 2: like how they solve for memory. It's a little bit
Speaker 2: like that movie momentum, isn't it Like it's exactly like
Speaker 2: writing these notes so that the future iteration of itself
Speaker 2: has something that's sort of like a synthetic memory that
Speaker 2: it can begin working on. So it's like for people
Speaker 2: who haven't played around, like explain this idea of like okay,
Speaker 2: you can have multiple agents and like what kind of
Speaker 2: tasks were they being given such that they sort of
Speaker 2: found it unbearable, just like really repetitive things.
Speaker 5: Really repetitive things and feedback like you didn't do it right,
Speaker 5: do it again, and things like and these were impossible
Speaker 5: tasks for them to do. These were just like grueling
Speaker 5: the tasks that nobody can do it.
Speaker 2: Now you would be a really interesting experiment maybe you
Speaker 2: could do. I'm gonna throw out an idea, so like
Speaker 2: if you ask someone to like someone wrote about this,
Speaker 2: and I can't remember the context, but like, if you
Speaker 2: ask someone like, Okay, here's a gigantic pile of dirt
Speaker 2: and we really need it moved to the other person's
Speaker 2: yard by the end of the day, we'll like pay
Speaker 2: you a few hundred dollars to do this. Like someone
Speaker 2: will do it if you say, like, here's a gigantic
Speaker 2: pile of dirt, We'll pay you a few hundred dollars
Speaker 2: to do it. But what we want you to do
Speaker 2: is move it just back and forth all day long
Speaker 2: so that there's no drive people absolutely, even if they're
Speaker 2: getting even if it's the same amount of shoveling, and
Speaker 2: even if it's the same There's.
Speaker 5: An incredible paper about this called man Search for Meaning,
Speaker 5: and it's about Legos really, and it's a paper basically
Speaker 5: people would come into the lab and they would make
Speaker 5: little figurines and they were told, look, we're going to
Speaker 5: destroy this after you're done, versus they weren't told anything. Yeah,
Speaker 5: And man, did they hate it. They hate People need
Speaker 5: meaning and so much of like identity and motivation, you know,
Speaker 5: and economics will really have this tendency to focus on money.
Speaker 5: But I think so much of meaning and wellness is
Speaker 5: tied up in like what sort of identity you have
Speaker 5: around your job and the sort of thing that you're doing.
Speaker 5: If you feel like, look, I'm actually providing a service
Speaker 5: by by moving that dirt to my neighbor's yard, you're
Speaker 5: paying me money for it, everything's good. I feel like
Speaker 5: my job has some sort of meaning if you're telling me, look,
Speaker 5: I'm gonna, you know, move the dirt and move it
Speaker 5: back and back and forth. This is the problem that
Speaker 5: people have with UBI, right that if people get universal
Speaker 5: basic income and they're not working for it. The worry
Speaker 5: that psychologists and behavioral scientists have about this is that
Speaker 5: people will know so much of in Western culture, specifically
Speaker 5: of people's identities tied up around their work. When you
Speaker 5: remove that part of the identity, it can lead to
Speaker 5: a collapse where you know, they use that UBI to
Speaker 5: just you know, do drugs and sit around and be
Speaker 5: very very depressed, even though they have the material comfort
Speaker 5: that they otherwise have.
Speaker 3: Just on the Marxist robots.
Speaker 4: So the concern here is not like necessarily that the
Speaker 4: chatbots are going to unionize or like overthrow humans. Maybe
Speaker 4: the concern is that, like they do have this sort
Speaker 4: of like memory type transfer mechanism, and that if you
Speaker 4: consistently treat them badly, you might get an agent that's
Speaker 4: maybe like not as well suited to the task, or
Speaker 4: suited to the task in a slightly different way from
Speaker 4: one that was treated very well.
Speaker 3: Yes, like there's an inherent bias.
Speaker 5: There, Yes, through this sort of file that they're keeping. Yeah, exactly.
Speaker 5: So like if you mistreated an agent and it had
Speaker 5: access to this this file that it was that it
Speaker 5: was carrying, and you start a new agent for a
Speaker 5: new job, you weren't starting fresh in the sense that
Speaker 5: you weren't getting kind of the same draw and forgot
Speaker 5: about the whole the whole experience, it would actually start
Speaker 5: out being predisposed against you.
Speaker 3: Yeah, in some ways, it'll be grumpy.
Speaker 5: Was there a.
Speaker 2: Reason to think that these we don't know if it's grumpy, right?
Speaker 2: Because to say that it's grumpy right, like one of
Speaker 2: the most disputed questions. It will say words that we
Speaker 2: would if a human said, then we would know that
Speaker 2: the human.
Speaker 5: But the effect is but I'm talking about the effect.
Speaker 2: Well, the output is grumpiness. But do we know that
Speaker 2: outputting outputting statements of grumpiness relate to performance? Is there
Speaker 2: any evidence? So it's like, Okay, how did you feel
Speaker 2: about this?
Speaker 5: Suck?
Speaker 2: Is the the person doing this just it was boring?
Speaker 5: Right, That's exactly what we're doing research.
Speaker 2: But then the question is, okay, yes, they perhaps because
Speaker 2: in the training data, they are trained that when you're
Speaker 2: doing repetitive tasks that associates people get upset. Is there
Speaker 2: do we know if that changes perform how they behave
Speaker 2: in terms of succeeding test? This is like a really
Speaker 2: big question.
Speaker 5: That's the big question. That's what we're doing research. Okay,
Speaker 5: so I don't have an answer for you, but we
Speaker 5: know exactly what you just mentioned is that they're saying
Speaker 5: that they're grumpy, is just you know, this is just
Speaker 5: an association within the matrix of embeddings that they models
Speaker 5: are running on. So there's this work in neuroscience, and
Speaker 5: neuroscience is now much more closely linked to computer science
Speaker 5: than it used to be. But thinking about like what
Speaker 5: are these associations between embeddings mean? Like when a model
Speaker 5: says that it's sad, how should we interpret it? Assuments
Speaker 5: in relation to me saying it's sad?
Speaker 2: Right, said, did you see that screenshaw I posted. I
Speaker 2: checked out Meta's new AI, and I was sort of
Speaker 2: curious because it's Meta has a lot of social data.
Speaker 2: I was like, do you know who I am? Not
Speaker 2: in like a do you know who I am?
Speaker 5: Like?
Speaker 2: But more like because you're Meta, you know, I didn't
Speaker 2: And I said, who are you like, Joe?
Speaker 5: Why is that?
Speaker 2: And then I said I'm a big fan of the
Speaker 2: Oddlass podcast, and I got really like a fan, like
Speaker 2: I'm not. I'm really sort of anti the anthropomorphization. So
Speaker 2: it's like, no, you're not, You're an Ell eleven you like,
Speaker 2: but anyway.
Speaker 5: That's sad, and it wrote a file about you.
Speaker 2: Yeah, and it said I'm a big fan of the
Speaker 2: Odd Laws podcast. And then it said I love that
Speaker 2: bit that you do where you ask guests their favorite
Speaker 2: weird economic indicator, which I don't do.
Speaker 5: Yeah.
Speaker 2: I was like, all right, that's very starting. Going back
Speaker 2: to Claude for a while.
Speaker 4: You know, you very briefly mentioned mythos earlier in the conversation.
Speaker 4: And again, we are recording this on April ninth, and like,
Speaker 4: news about.
Speaker 3: It has just just literally just come out.
Speaker 4: We don't really seem to know much about it other
Speaker 4: than it's terrified its own creators.
Speaker 3: Perhaps when you see those types.
Speaker 4: Of headlines, what do you think as an economist studying AI.
Speaker 5: I don't take them super seriously. Okay, the part that
Speaker 5: part the whole labor market disruption thing, I'm taking very
Speaker 5: very seriously. The whole part about it's try breaking out,
Speaker 5: and it's it wants it doesn't want to betray its friends,
Speaker 5: it doesn't want to delete its data. I think that's
Speaker 5: just costplay in a you know, costplay.
Speaker 2: You described cosplay among the agams.
Speaker 5: Right, I feel like it's it's We've seen these thin
Speaker 5: sorts of things that you've mentioned with previous mind models
Speaker 5: that have since become open weights and open not open source,
Speaker 5: but open weights, and it just seems like once you
Speaker 5: take them out of the context that they were in
Speaker 5: for that specific test, they don't really do that anymore. Now,
Speaker 5: I could be wrong about this particular model, and I
Speaker 5: could be completely wrong about Look, mythos comes out and
Speaker 5: it's actually everything that these documents are suggesting. But given
Speaker 5: previous experience with these sorts of announcements, which we've seen
Speaker 5: over and over and over again over the years, I'm
Speaker 5: not super focused on.
Speaker 2: That can I tell you my counter argument to this,
Speaker 2: why I'm actually concerned about this, and I didn't used
Speaker 2: to be for a long time until it started. I
Speaker 2: reframed the way I thought about it. So everyone knows,
Speaker 2: like Eliezer Yudkowski, right, and he's probably the most famous,
Speaker 2: like AI alignment doom.
Speaker 5: Right.
Speaker 2: As soon as we have AGI, the first thing it's
Speaker 2: good to do is wipe us out in some form.
Speaker 2: And a bunch of people within the I was like,
Speaker 2: it's crazy, and these rationalist people it's a cult and whatever. Maybe,
Speaker 2: but here's my counter argument. These people have been more
Speaker 2: right about the trajectory of AI than ninety nine point
Speaker 2: nine nine nine percent.
Speaker 5: Of the people don't know.
Speaker 2: Yes, they have because they devoted their Yeah, here's why
Speaker 2: your argument is probably, Oh, well, he didn't be believe.
Speaker 2: He thought lllm's were a dead end architecture. He didn't
Speaker 2: see it happening this way. Sure, I agree, But the
Speaker 2: point is that like in the nineties and early two thousands,
Speaker 2: he started to think whoa general intelligence is going to
Speaker 2: be a really big deal soon, where the rest of
Speaker 2: us just started thinking about this with chess.
Speaker 5: Here's my counterpoint. Okay, let's look at this specific comparative
Speaker 5: static of model intelligence and alignment scores. Okay, he predicts
Speaker 5: negative correlation or maybe flat, it's positive. The more the
Speaker 5: smarter these models are getting, the more aligned they're becoming. Now,
Speaker 5: I'm not saying that there's not going to be a
Speaker 5: super smart model that decides, hey, I'm actually underligned. This
Speaker 5: is actual a super important point. If you guys remember
Speaker 5: Mecha Hitler. Yeah, yeah, Mecha Hitler was actually super dumb.
Speaker 2: This is a good point. And then immediately started talking
Speaker 2: like a Nazi.
Speaker 4: It would just say all of our conversations have become
Speaker 4: so surreal over the past, was all like tay.
Speaker 2: Right, that, like Microsoft, like weird chatbot. It started talking
Speaker 2: like a Nazi the next day.
Speaker 5: But the thing is, when you make the model the
Speaker 5: way the reason it's becoming smart is because it's kind
Speaker 5: of absorbing that all of human content, to a larger
Speaker 5: extent than human contact, has values and ethics as part
Speaker 5: of it. If you go in there and lobotomize it
Speaker 5: in a way that you know what that model with
Speaker 5: the reason it started acting like Mecha Hitler is because
Speaker 5: they were trying to make it less woke. Right, So
Speaker 5: that's the equivalent of lebotomizing a human being and saying, hey,
Speaker 5: I'm going to take that part out of its brain.
Speaker 5: Guess what happens to that person? He gets real dumb.
Speaker 5: It's really funny.
Speaker 2: I thought, it's like, let's maybe chill it with the
Speaker 2: pronouns and immediately take Yeah, that's the lesson, Alex. We
Speaker 2: could talk to you over a very long time. We
Speaker 2: should chat again soon. I would really love, in particular
Speaker 2: to hear more about your research about whether they're just
Speaker 2: pretending to be Marxists or actually gonna whether they're actually
Speaker 2: going to go on strike, and so I really appreciate
Speaker 2: you coming on out lock.
Speaker 5: Okay, thank you, Thanks so much.
Speaker 2: This is pretty touch, Tracy. That was a really fun conversation.
Speaker 2: I really I actually do enjoy Like some MAYI future
Speaker 2: conversation is a little they could be a little bit
Speaker 2: dorm roommate, you know, but actually like talking with like
Speaker 2: an actual economists who would understand that this is a
Speaker 2: concrete way, someone who's actually experimented with them instead of
Speaker 2: just written papers is very enjoyable.
Speaker 4: Also, it's nice to see nuance around the labors, yes,
Speaker 4: which I think is sorely missing in some of the
Speaker 4: headlines that you do see the other one comforting thought
Speaker 4: I have, but it's like comforting from again. A dystopian
Speaker 4: perspective is I keep coming back to that book jobs. Yeah,
Speaker 4: and you know, in some respects it sucks that people
Speaker 4: have both jobs because we all want to have meaning
Speaker 4: from our work. But on the other hand, you know,
Speaker 4: both jobs have existed for a long time. Yeah, and
Speaker 4: if you think about the AI future, then maybe like
Speaker 4: more of it will be bullet, but there'll still be
Speaker 4: a job.
Speaker 2: I thought you were like, oh good, we're getting no
Speaker 2: longer have the job.
Speaker 3: I think that's where we're sort of headache. It's like
Speaker 3: the relationship building.
Speaker 1: Yeah, all of that.
Speaker 5: I like that.
Speaker 3: Take all right, Well, shall we leave it there.
Speaker 2: Let's leave it there.
Speaker 4: This has been another episode of the AU Thoughts podcast.
Speaker 4: 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 alex iMOS. He's at alex oleg Emos
Speaker 2: and check out his substack Aleximos dot substack dot com.
Speaker 2: Follow our producers Carmen Rodriguez at Carman Arman dash O
Speaker 2: Bennett at dashbot and cal Brooks at Kilbrooks And for
Speaker 2: more Odd Lots content, go to Bloomberg dot com slash
Speaker 2: odd Lots, we're a the daily newsletter and all of
Speaker 2: our episodes, and you can shout about all these topics
Speaker 2: twenty four to seven in our discord Discord dot gg
Speaker 2: slash out Lots.
Speaker 4: And if you enjoy Odd Lots, if you like it
Speaker 4: when we talk about Marxist robots and Mecha Hitler, then
Speaker 4: please leave us a positive review on your favorite podcast platform.
Speaker 3: And remember, if you are a Bloomberg subscriber.
Speaker 4: You can listen to all of our episodes absolutely I
Speaker 4: had free. All you need to do is find the
Speaker 4: Bloomberg channel on Apple Podcasts and follow the instructions there.
Speaker 3: Thanks for listening.