Alex Imas on Why Economists Might Be Getting AI Wrong

Everyone knows that new technologies can be really disruptive to the labor market, but eventually new jobs emerge and things come back into balance. And there is a sense in which many view AI with the same lens. Yes, there will be pain in some sectors, but then there will be productivity gains and new sources of demand and new opportunities for labor that we can't conceive of yet. But could it be different this time? Could AI be disruptive in a manner that, say, the steam engine was not? On this episode we speak with Alex Imas, a professor at the University of Chicago focusing on economics and applied AI. We talk about his work on the AI and labor question, how to think about which jobs may be most at risk, and why the sheer speed of AI development could make it categorically different than prior general purpose technologies that came before it. Subscribe to the Odd Lots NewsletterJoin the conversation: discord.gg/oddlotsSee omnystudio.com/listener for privacy information.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Joe Weisenthal?

  There are some market stories where you want every detail. Tracy and I have made quite a few podcasts on that basis, but sometimes you've only got 10 minutes and need to know what's moving markets and why. That's the Barclays Brief Podcast. Every week, experts from Barclays Markets and research get you up to speed on what matters and what to watch next. About the time it takes to grab a coffee, so search Barclays Brief wherever you get your podcasts.

• 0:26Speaker 01

  This is the Bloomberg Tech Minute brought to you by ChatGPT. I'm Carol Muster. Getting hired at SpaceX requires exceptional ability and may include having Elon Musk review a candidate's resume. So writes Bloomberg Business Week's Lauren Grush. She reports that the company culture is defined by Musk and job candidates must understand that a commitment to SpaceX means adhering to this culture and exhibiting loyalty to Elon Musk. To get hired, candidates must demonstrate their achievements from past jobs and hands-on experience matters more to recruiters than advanced degrees, with the company looking for people with raw, innate intelligence. A spokesperson for SpaceX didn't respond to a request for comment on its recruiting

• 1:07Speaker 01

  and hiring practices, but according to recruiters, advisors, and those who've interviewed for jobs there, it's the space exploration dream that draws people to the company, and Musk invokes that dream regularly to remind workers why they're there. Prospective hires are told that the job will require stamina, including in job ads that candidates, quote, "must be willing to work extended hours and weekends as needed." One major perk for all that hard work? Employees' access to equity, either the right to buy shares or payment in the form of restricted stock units that vest over time. That's the Bloomberg Tech Minute brought to you by ChatGPT. Get started at chatgpt.com.

• 1:51Speaker 02

  Bloomberg Audio Studios. Podcasts, radio, news.

• 2:06Joe Weisenthal?

  Hello and welcome to another episode of the Odd Lots Podcast. I'm Joe Weisenthal.

• 2:11Tracy Alloway?

  And I'm Tracy Alloway.

• 2:13Joe Weisenthal?

  Tracy, it may have changed a little bit in recent weeks or months, but I think by and large, by and large, if you talk to economists about the long-term impact of AI, particularly on jobs, by and large, it seems like they point to history and they say there have been many technologies in the past that people thought were going to be very disruptive and destroy all kinds of jobs. And in many cases, they did. But technologies create new jobs. We can't necessarily anticipate them before beforehand what they're going to be. And AI is like, kind of no different ultimately.

• 2:45Tracy Alloway?

  Yes. But then to your point, you ask like, well, what specific jobs do you have in mind? And I get that, you know, it's hard to tell.

• 2:53Joe Weisenthal?

  Only the invisible hand knows.

• 2:55Tracy Alloway?

  Right. But it's so frustrating, right? Because here's this big new technology. It's supposed to be a productivity boost. And yet, no one is actually sure what new jobs it's going to create from that productivity boost.

• 3:08Joe Weisenthal?

  I love him to death, but Adam Ozymac wrote a piece several weeks ago, and he was like, well, the player piano disrupted the existence of piano players, but hotels still pay money for a human who will have a piano player, an actual piano player in the lobby rather than a player piano, which is true. But like, not many people have jobs that are equivalent. And the thing that like, it's like, oh, you know, it's like, I want to get like, this insurance form reimbursed or whatever, this insurance reimbursed. Like, I don't care about the human touch, that per se.

• 3:42Tracy Alloway?

  There's something very-

• 3:43Joe Weisenthal?

  I'm happy to have the equivalent of the player piano there.

• 3:45Tracy Alloway?

  There's something very dissatisfying about the idea that we're all just going to become like performative humans in a way. But I actually think that's kind of where we might be heading, where like, the sort of social skills, I've said before, the looks maxing, the personal branding, the multitasking, I guess, like, becomes more important. So the future is performative humanity.

• 4:06Joe Weisenthal?

  OpenAI just spent a ton of money on TBPN. I really love those guys. They're both very good looking guys, man. So I sort of feel like, okay, this is the biggest AI company in the world, sort of making a bet on these like great characters.

• 4:19Tracy Alloway?

  Two very nice and charismatic humans.

• 4:21Joe Weisenthal?

  Yeah, yeah, yeah. So maybe that is the future, just being nice and charismatic. Anyway, we need to talk more seriously about this because I don't, I don't know. I kind of feel maybe this is not just going to be like the steam engine or whatever. It might be very different. Maybe we won't have jobs. Maybe there will be new jobs. Anyway, someone who's been talking and thinking a lot about this and why AI might be different, we're going to be speaking. Really have the perfect guest, Alex Zmas. He is a professor of economics and applied AI at University of Chicago, does a lot of writing on this topic. So Alex, thank you so much for coming on Odd Lots.

• 4:54Speaker 04

  Thank you for having me.

• 4:55Joe Weisenthal?

  That's pretty cool that you have the job of a professor of economics and applied AI like.

• 5:00Tracy Alloway?

  Yeah.

• 5:01Joe Weisenthal?

  Worked out pretty well. It's good time. You picked a good field, yeah?

• 5:04Speaker 04

  Yeah, I mean, I've been, I've been an economist for much longer than I've been a professor of applied AI. I have been studying human behavior, human decision making for about 12 years now, more than a decade. And when ChatGPT first came out, I was kind of taken aback. This was a few years ago now, and I was thinking. After about a week of using it, I was like, this is going to be huge for the economy. And so I started talking to people who have kind of, there were several people who kind of knew that it was coming and knew what the impact it was going to, it was going to have. So I started talking to those people and I kind of quickly kind of started retooling.

• 5:40Joe Weisenthal?

  That's smart.

• 5:41Speaker 04

  I started, I trained my own model, you know, I got into.

• 5:44Joe Weisenthal?

  Cool.

• 5:44Speaker 04

  I got into it and, you know, it's, I've been trying to play catch up ever since.

• 5:48Tracy Alloway?

  What did you see in ChatGPT specifically? Because you would have been very early at that time. A lot of people were using ChatGPT to, basically as a sort of enhanced search engine tool or to write poems, tell silly jokes, whatever. But you saw something that was serious for the labor market.

• 6:07Speaker 04

  Yeah, I mean, once you started using it, you saw that it was able to. Basically, not so well in the very, very beginning. But even after a few months and like within a year, you saw that it was able to kind of do basic cognitive tasks to a decent degree. Like, it wasn't like we are going to replace that person. But it was doing pretty sophisticated things that and the jump from like where we were thinking about AI as these very, very, very targeted things like AI will play the game GO or something like that to something where, whoa, it can write an essay, it can tell me about this accounting property, it can make a forecast. All of a sudden, the generality of the technologies just exploded.

• 6:52Speaker 04

  And to me, that was that was a huge deal.

• 6:55Joe Weisenthal?

  Yeah, the generality of it, I mean, I guess, literally, that's the G, right?

• 6:59Speaker 04

  Yeah.

• 7:01Joe Weisenthal?

  But yeah, no, I mean, absolutely. I have to say, this is a side, but like, learning a little bit more about like where AI was pre LLMs or pre chat GPT almost makes me even more impressed. Like, the leap, I don't know if like this is a common, but when you like, look at like some of like what was cutting edge in 2019.

• 7:19Speaker 04

  Yeah.

• 7:19Joe Weisenthal?

  And then you look at what's cutting edge in late 2022. I'm almost more impressed than if like I hadn't known what they were up to in 2019. Like, it's a huge gap in those few years.

• 7:29Speaker 04

  It's a huge gap, but at the same time, like, there were, there was a kind of a path towards AI and like the way that AI was being worked on for a long time, which was like these very specific purpose-built technology. And I think Geoffrey Hinton and other people were kind of working on their own for a long time in the wilderness of thinking like, maybe we can do something much more general than that. Maybe we can kind of come back to this idea of AGI versus these very specific tools. So then the whole term AGI, the general part of it, the reason that term came out was because in response to these very specific technologies that were being developed, which were by design, not general. So somebody said Shane Legg was one of the people who kind of, I think, coined the term.

• 8:14Speaker 04

  He was saying, look, Let's think about the general part of intelligence and let's try to build a technology that is as general as the human mind. Let's go back to that starting point.

• 8:25Joe Weisenthal?

  So like if someone makes a model that could tell the difference between written and spoken word, that's mind blowing. It's incredible breakthrough, but that's not a general technology. That's a specific technology.

• 8:35Tracy Alloway?

  Kail, what time did you have in our betting book for Joe to refer to his vibe coding? I had two minutes 13 seconds.

• 8:42Joe Weisenthal?

  Okay, so I made it longer. No, I made it a little bit longer. Great.

• 8:45Speaker 04

  It's because I talked for so long. I'm sorry.

• 8:47Tracy Alloway?

  No, fair enough. It's a fair point. I mean, to me, like, the moment when things seemed to get very serious was the release with Claude Code. And at that point, you went from like, okay, the model could not just tell you things, but it could actually do things for you. Was that the vibe shift that you anticipated or experienced as well?

• 9:06Speaker 04

  I mean, even though many people were talking about this, that this vibe shift was going to happen. People were telegraphing it for, for months and months. Look, when agents start taking off, things are going to change as far as how people, how people perceive this technology. Because the thing about agents versus just like the web-based browsers, they can do stuff on your computer. They can say like, you could tell it like, look, make me a spreadsheet. It will go and make you a spreadsheet using the tools that are available in your computer. Not just say, okay, here is how you would make a spreadsheet, but you have to do it yourself. And that's a complete, that's a paradigm shift as far as the economics of the technology.

• 9:44Joe Weisenthal?

  So I set up this sort of, maybe it's a straw man, but I set up this sort of straw man that maybe we're going to knock down in this conversation. But how would you describe this sort of modal view of the impact of AI on the labor market among the economics profession, to the extent there is one?

• 10:04Speaker 04

  So I definitely think there is one. There was, there's a very nice survey done by, by a whole team of people. Kevin Bryan was, was one of them and Basil Halper was, was another. And they released this survey where they, they, they asked for forecasts for, from economists and AI technologists. Now, this is a self-selected group of economists. These are economists who are working on AI. So it's not the whole field. But one of the things that you got from that, that, that survey was they're very much aligned. Okay. Right, so economists, at least the ones who are actually working and thinking about that technology, they think there will be a big impact as far as capabilities and there will

• 10:46Speaker 04

  be some impact on the labor market, not astronomical, and we're talking about like 2030, 2050 and things like that. There's going to be substantial capability increases, but the growth is going to be pretty moderate. It's like an extra 2%, 3%. And the really interesting thing for me from that survey was that the technologists were kind of a bit more optimistic than that, as far as both the productivity growth and kind of some were kind of thinking that there will be much more unemployment. But for the most part, the two groups kind of agreed. I was personally surprised by that survey. And this came out, I think, last week or two weeks ago. I thought that there was going to be a lot more daylight between the two groups.

• 11:28Tracy Alloway?

  Hmm. Well, the other thing that you tend to see is people release these charts of like which job is most exposed to AI and it's usually like, you know, a knowledge worker at the top or something like that. Your work is really interesting to us because you point out that a job is like much more than just the sector that you're actually working in. Tell us more about that.

• 11:48Speaker 04

  So the exposure measures, they came from this literature, but mainly this this one paper by Daniel Rock and Pamela Mishkin and co-authors that was published in Science called, one of the greatest titles is GPTs are GPTs. GPT, you know what GPT is, but GPT in the second term is called general purpose technology. Okay. There, they they basically started mapping jobs to the as being exposed to AI. But it's really important to understand what that number means. Yeah. That number means that AI could do 50% of a task.

• 12:26Speaker 04

  Right? And how many tasks are in the job that AI can do 50% or more of?

• 12:31Tracy Alloway?

  Mm-hmm.

• 12:32Speaker 04

  So there's a couple of things in that statement. First, 50% is not 100%. That's obvious, right? So you still need a human in the loop if AI can do 50%. But two, it's the fact that a human job is a bunch of different tasks. Right? So this is not a new point. David Ator has, you know, has worked from the early 2000s with co-authors on this, saying this is the task-based model of jobs. Daron Acemoglu has the canonical model on this. And the idea is that when we look at a job and we say, "Look, your job is exposed." Let's say it's 50% exposed. It really, really matters what tasks in your job are exposed and how these tasks relate to one another.

• 13:17Speaker 04

  So let's say I have a job and I have a whole bunch of like completely meaningless garbage that I'm doing. But I have a comparative advantage and why I'm really getting paid for is like 20, 30% of the job. If AI is automating the kind of like meaningless kind of rote things at my job, I could take all of that time and I can focus on the job, on the parts of the job that are by comparative advantage. What does that mean? Means I'm going to become more productive, but I'm going to get paid more, even though my job is really exposed. Now, what does that mean for the labor market? Now you have to think, okay, so a person is going to get...

• 13:55Tracy Alloway?

  So just to be clear, before we go any further, if I'm working on a factory floor and one of my tasks is to pull a lever, like that is something that could presumably be automated. But if the other part of my work is to observe like how things are actually working on the floor and to report back to managers, that might be something that's still valuable under our sort of AI future.

• 14:20Joe Weisenthal?

  And if the lever part gets automated, the theory is that not only, you know, will Tracy be more productive and she'd get paid more for it.

• 14:27Speaker 04

  Yeah, exactly.

• 14:28Joe Weisenthal?

  Okay.

• 14:28Speaker 04

  Because of the increased productivity, right? This is the O-ring model of jobs. Avi Goldfarb and Joshua Gans have this really nice paper on it.

• 14:37Joe Weisenthal?

  Can I just ask you a quick question here too? Like, how good are we, and by we, I guess The Economist who studied this, at like actually being able to like, here is a job that someone has... Write down a list of these tasks. Describe, how good are we at describing?

• 14:53Speaker 04

  Pretty good.

• 14:54Joe Weisenthal?

  Describing the list of tasks?

• 14:55Speaker 04

  Actually, pretty good. I would say on that dimension, we're pretty good. There's the Onet database that has very, very detailed records on like, here's a job, and here's like a whole vector of things that are involved in that job.

• 15:06Joe Weisenthal?

  Okay.

• 15:07Speaker 04

  So I'd say on that part, like just listing the tasks, pretty good.

• 15:11Joe Weisenthal?

  Okay, we're good.

• 15:11Speaker 04

  The thing that I think we're less good on is how those tasks relate to one another. This is the term called complementarity.

• 15:18Joe Weisenthal?

  Yeah, talk about that.

• 15:19Speaker 04

  So this is, the weak links model is essentially saying like, look, if tasks are completely separable, let's say, you know, I have a, I pull a lever at my factory, and I talk to people on the factory floor, and these are completely independent. If I fail to pull the lever correctly, the other part of my job is unaffected. There's other parts of the job, like cooking, for example. Let's say I'm really good at 90% of the job, but like, I really screw up the seasoning.

• 15:44Joe Weisenthal?

  Oh.

• 15:45Speaker 04

  Right? That meal tastes like

• 15:46Joe Weisenthal?

  Garbage.

• 15:47Speaker 04

  Garbage, right?

• 15:48Tracy Alloway?

  You haven't succeeded in your task.

• 15:49Speaker 04

  You haven't succeeded on the, so when the tasks are interrelated, screwing up on one or two tasks means you did not complete your job. And it's basically is kind of almost a zero-one sort of relationship. So the extent of that complementarity of how these tasks are related will determine the extent to which automation is going to affect the labor market. And we don't have good numbers on that.

• 16:10Joe Weisenthal?

  So this is really interesting. We're good at writing down the list of the tasks.

• 16:14Speaker 04

  Yeah.

• 16:14Joe Weisenthal?

  We are not good at writing down the sort of like, deep relational links to the task and how they fit together.

• 16:21Speaker 04

  Exactly, exactly. So that's something we need data on. The other part that we really need much more data on, and I recently was quoted as saying we need almost like a, you know, Manhattan Project level effort on this is the, this is a term from economics called elasticity of consumer demand. And that basically means how much will people buy more of something when the price changes?

• 16:47Tracy Alloway?

  Mm-hmm.

• 16:47Speaker 04

  Right? So let's say a person becomes a lot more productive, right? And they, for the same sort of resources, they can make a lot more of the product. Their wage rises. What does that mean for the labor market? If they become more productive, given the same kind of inputs, their wage rises, but also the firm's probably going to be paying less money to produce the same output. If it's a competitive industry, the prices are going to go down. If the consumers don't respond by buying a lot more of the product, The firm is going to fire a bunch of people because they can do more with less. But when prices come down, people buy way more of the product, then they might hire more of the same people.

• 17:30Speaker 04

  And in many sectors, we've seen kind of the second thing play out. What's an example? Um, so people are arguing that software is actually one of those sectors. So there's been, there's been a bunch of talk, uh, kind of looking historically at like, what does productivity mean for the techno, the technology sector? It usually means a lot more consumer demand. So there's this really active debate now about what's, what are coding agents actually going to do to software, to software engineers? And some people are arguing, look, we've have seen historically pretty elastic demand. And so we're going to potentially see a lot more hiring in that sector. And many people are saying this, but other people are saying, wait, maybe it's not as elastic as we, as we think.

• 18:13Speaker 04

  And people are going to become so productive that we're really arguing to see a down, uh, downsizing.

• 18:18Tracy Alloway?

  That was kind of the argument that Jared Sleeper was making in our defense of software episode.

• 18:23Speaker 04

  Yeah.

• 18:39Tracy Alloway?

  There are some market stories where you want every detail. Joe and I have made quite a few podcasts on that basis, but sometimes you've only got 10 minutes and just want to know what's moving markets fast. That's the Barclays Brief podcast. Every week, experts from Barclays Markets and Research get you up to speed on what's happening and what to watch next. Concise, focused and brief. Search Barclays Brief wherever you get your podcasts.

• 19:06Speaker 01

  This is the Bloomberg Tech Minute brought to you by ChatGPT. I'm Carol Muster. Getting hired at SpaceX requires exceptional ability and may include having Elon Musk review a candidate's resume. So writes Bloomberg Business Week's Lauren Grush. She reports that the company culture is defined by Musk and job candidates must understand that a commitment to SpaceX means. Adhering to this culture and exhibiting loyalty to Elon Musk. To get hired, candidates must demonstrate their achievements from past jobs. And hands-on experience matters more to recruiters than advanced degrees. With the company looking for people with raw innate intelligence. A spokesperson for SpaceX didn't respond to a request for comment on its recruiting and hiring practices.

• 19:49Speaker 01

  But according to recruiters, advisors, and those who've interviewed for jobs there. It's the space exploration dream that draws people to the company. And Musk invokes that dream regularly to remind workers why they're there. Prospective hires are told that the job will require stamina. Including in job ads that candidates quote must be willing to work extended hours and weekends as needed. One major perk for all that hard work? Employees access to equity, either the right to buy shares or payment in the form of restricted stock units that vest over time. That's the Bloomberg Tech Minute brought to you by ChatGPT. Get started at ChatGPT.com.

• 20:29Joe Weisenthal?

  You know, people are worried, right? About AI white collar wipeout. I'm worried.

• 20:36Joe Weisenthal?

  So maybe the question should be, what would have to be true about either the nature of AI capabilities? Or the relationship between tasks and job? What would have to be true such that the scenario could unfold?

• 20:52Speaker 04

  A wipeout.

• 20:53Joe Weisenthal?

  Yeah.

• 20:54Speaker 04

  Two things. Well, let me, let me, let me talk about three things. One, one is just full automation.

• 21:00Joe Weisenthal?

  Okay.

• 21:00Speaker 04

  Right? That the models are so good that they just automate all of the tasks. That, that, that's like a very simple scenario to think about. Because obviously people are going to get fired. Right? If, if it's fully automated. The other one is the one we've just been talking about where people become much more productive. But consumer demand is not elastic enough to absorb that extra production. So you're going to have much fewer people doing a lot more stuff. So again, you're going to have a lot of unemployment. The third thing is is related, but is basically how many jobs each person has will determine the incentives of the company to actually invest in the automation technology. So let's talk about like the one task job. Let's say the a person is just pulling the lever.

• 21:44Speaker 04

  And let's say right now that doesn't even look exposed. Right. We look at the exposure graph. It doesn't look exposed. But let's say we're kind of getting kind of close. And it just needs a bit more money to get to the automation. Switch. Well, the company has a lot higher incentive to invest that money if they know that if they invest that money. Hey, they can get rid of that person completely. Whereas they have less incentive when, you know, let let me invest a in automating the lever pull if I know that I can't fire the person because he's also going around and doing a lot of stuff. So we have to think about the incentives of the firms to automate in the first place. These are large projects to do the automation. It's not like, oh, OpenAI releases a model.

• 22:29Speaker 04

  All of the companies adopted overnight. We see it in, you know, a week later, we see the outcome. There's a lot of organizational kind of going back and forth, a lot of systems need to be changed, all of this sort of thing. And so companies need to know, like, look, if I spend the money on it, I'm actually going to save money as a result.

• 22:46Tracy Alloway?

  So setting the archetypal guy pulling one lever aside. What are the real world jobs in your framework that are actually most exposed to AI risk? The one dimensional work, I guess.

• 22:59Speaker 04

  I'm I hate to say one dimensional because every job is multi dimensional. But if I had to make a guess where economists and other people should be kind of worried. I'd say stuff like truck driving.

• 23:11Joe Weisenthal?

  Yeah.

• 23:11Speaker 04

  And stuff like warehouse workers, like if you Google, you know, warehouses built in China or something like that. These warehouses look nothing like what we think about warehouses. They're completely, completely automated. They have robots like crawling on the walls. They're just, there's no human in the loop at all in the in the in these warehouses. And so oh, the warehouse gets automated. And then the warehouse gets automated, so part of that automation is going to be kind of loading that truck.

• 23:40Joe Weisenthal?

  Yeah.

• 23:41Speaker 04

  And then the the truck gets loaded through automation and then that truck drives from A to B. That's fully.

• 23:48Joe Weisenthal?

  That's interesting because, you know, obviously the a lot of people in freight will say the way you make that argument is very different than they'll say, well, yeah, driving a truck is much more than

• 24:01Speaker 04

  Yes.

• 24:01Joe Weisenthal?

  the driving part, right? So it's like, OK, you could have an a Waymo truck, but who's gonna

• 24:07Speaker 04

  Protect it.

• 24:07Joe Weisenthal?

  deliver it, who's all the who's gonna deliver?

• 24:10Speaker 04

  Protection is actually a big deal. Like if somebody stops it on the road, a Waymo truck, they could just stop it on the road and rob the truck, right? That's that's one element.

• 24:19Joe Weisenthal?

  But to your point, you know, if like one of the tasks that a truck driver has to do is that coordination once they've gotten to the warehouse, but if the warehouse is automated.

• 24:28Speaker 04

  Already automated. These are complimentary things.

• 24:30Joe Weisenthal?

  Then that no longer is as important, perhaps, for that to be a human task.

• 24:34Speaker 04

  Exactly. And think about the incentives of the company to invest in this technology. It's huge. These are very, you know, these are some of the only jobs truck driving where, you know, you don't need a college degree to earn a lot of money. And so there's a big incentive on the company.

• 24:51Joe Weisenthal?

  OK, I get that. But on the other hand, even going back 10 years, I think if you went to Davos, there were probably people saying, truck, I'm worried about the future of truck driving because AVs have been around as like a thing since before AI, general AI. So in terms of like post chat GPT jobs, etc., that would be concerned with, like, I don't, what do you see out there? Or what are you looking at?

• 25:15Speaker 04

  Um, I mean, I think everybody's looking at software engineering. I think

• 25:21Speaker 04

  you have to think about like the where the technology works best now is verifiable tasks, right? Where you have a lot of data where you can say this is good or bad, not in a supervised learning sense, but uh, but in general, it should needs to be verified. That's why like math, in research, math has been like the big kind of uh, boom, as far as what are people talking about on the internet as being automated? Math is verifiable, is it's, you know, a proof is either right or wrong. Once you do the proof, it's much easier to check if it's right or wrong rather than construct the proof. And so jobs that have a large components where we have a large data bank of data to train the models in a way where the output is verifiable.

• 26:04Speaker 04

  Are going to be potentially more exposed in the sense where you can automate more tasks within the job. Now, the thing that we haven't talked about yet is new tasks.

• 26:15Joe Weisenthal?

  Right.

• 26:15Speaker 04

  Right. So we're talking about a very static sort of economy where there's the lever, there's me walking around. And if I'm automating these things, that's the end of my job. But you could imagine a scenario where you automate a part of a job and all of a sudden this person is free is freed up. Or this is the the task was actually a complement to a task that wasn't even, you know, imagined by the organization that this person is now doing that's not automated. So that's something that I think people should be looking at, especially, and this is data that actually AI companies have, is what new things are people doing?

• 26:50Tracy Alloway?

  Wait, say more about that because this gets to the, you know, like what new jobs could we actually see from this question? Which I never see a satisfactory answer to. So if they do have that data.

• 27:00Speaker 04

  They have, they don't have all the data, of course, but they have, they have data about like, okay, this is a software engineer. And, you know, a year ago, these are the sort of tasks that this person was working on through our system. These are the sort of queries and things like that. And you could see like some of these queries being automated fully by the agents. Now they're asking potentially different questions, or can we classify these as different tasks? That are not fully automated where the AI system is actually a complement to those tasks. So this is not like a perfect picture of a new job, but this is, this is data.

• 27:32Joe Weisenthal?

  But, but so it's not really like a new job per se, but it is freeing up the software engineers to like ask about different things or explore different avenues that they hadn't previously done.

• 27:45Speaker 04

  You know, vibe coding an app for voice.

• 27:49Joe Weisenthal?

  Yeah, exactly. Right. Finally, we're freed up from the drudgery of our day to day life to work on that. But no, but like this gets to a sort of, you know, the big question is like, you mentioned one scenario is just that like the technology can do all the tasks, right? How seriously do you take that possibility? Because then it's game over, right? Like, it's like, okay, it just does all the tasks and it's going to keep getting better. And if I can learn to do a new task, well, then if it can do all the tasks, then maybe I'll learn something new, but I'll learn that task. How seriously should we take out, take this possibility? That the models are on some timeframe on track to just be able to do all the tasks?

• 28:31Speaker 04

  So.

• 28:33Speaker 04

  A lot of parts of that question. One, physical versus versus just kind of digital. Right? So I think there's a scenario where it can do everything kind of sort of these sort of cognitive, non-physical tasks, whereas the physical world is completely, you know, these robots are still fumbling.

• 28:49Joe Weisenthal?

  Let's just talk about email jobs or computer jobs.

• 28:51Speaker 04

  Okay, let's talk about computer jobs. So I think I'm I take that scenario pretty seriously.

• 28:57Joe Weisenthal?

  Okay.

• 28:57Speaker 04

  Um, I think I haven't seen any data to suggest that the models are slowing down as far as their capabilities. You know, Methos was released yesterday or two days ago or something like that. And if you we don't have great data on this, but if you look at like where it is on the kind of line of capabilities, it's just on track and that on track is very, very fast.

• 29:21Joe Weisenthal?

  Yeah.

• 29:21Speaker 04

  Right? So the developments are happening very fast. So as far as like email jobs, I think there there is a scenario where pretty much everything is automated. Um, and then you have to ask, are people going to be moving to the physical jobs? Or will there be new jobs that we haven't thought about before? So, you know, if you look back in the 1940s, like, I think more than half of the jobs that we have now didn't exist in 1940.

• 29:47Joe Weisenthal?

  Yeah.

• 29:47Speaker 04

  And so what do the new jobs look like? I mean, I have a theory. Please. It's very similar to the one that you didn't like.

• 29:54Joe Weisenthal?

  Oh.

• 29:56Speaker 04

  But I'd like to broaden it a little bit.

• 29:57Joe Weisenthal?

  Okay.

• 29:58Speaker 04

  So there's there's an economic subfield. It's very, very small, but on on the economics of a structural change.

• 30:07Joe Weisenthal?

  Okay.

• 30:07Speaker 04

  So if you look at agriculture and manufacturing, right? If you look at them as share of GDP and share of employment, going back to like the 1800s, they were a huge part of the labor force and GDP of the economy. Right. And if you look, basically, they become smaller and smaller, smaller parts of the economy. Why is that happening? It's because they're getting automated. What does automation do? It makes the price of those sectors very cheap. But people are satiated on the goods. You can only eat so much. Yeah. Right? So what does that mean? It means even though we're eating just as much as we were before, because the price has come down so, so, so much, they are now tiny shares of the GDP.

• 30:47Tracy Alloway?

  Mm-hmm.

• 30:47Speaker 04

  Right? What is the, what is made up the larger part of the GDP? It's Live piano players. It's services. Yeah. Right? These are tasks that haven't been automated yet. So the question is, the, the number one question of economics in the age of our advanced AI is what becomes scarce? Right? Everybody's talking about like abundance. We're gonna have abundance. Sure, we're gonna have abundance of some things, but some things are gonna remain scarce. So what is gonna be? If you answer that question, what's gonna be scarce? A lot of the other answers pop out of that.

• 31:23Tracy Alloway?

  Are we all gonna be rare earth miners?

• 31:25Joe Weisenthal?

  No, I know what's

• 31:26Tracy Alloway?

  I'm mining for dust.

• 31:27Joe Weisenthal?

  I think it's pretty obvious when it's gonna be scarce. And I think you already see this in many economic trends. What's scarce is if we're lucky, we get 100 years on this earth and every marginal dollar that we spend will go towards health and maximizing that brief time.

• 31:43Speaker 04

  That's, that's perfect.

• 31:43Joe Weisenthal?

  And so already for years, one of the things that people have observed about the economy is like, you know, rich countries just spend more and more and more on healthcare, right? And this is often framed as a pathology. And given the many messed up aspects of our healthcare system, maybe it is. But another way to interpret it is like, I got plenty of food. I have plenty to eat. I've listened to plenty of music and I can like go see a concert if I wanna see a live piano player. The one thing I have is a scarce amount of time and I will just spend every marginal dollar, including not just on doctors and gym memberships, but organic berries because I need, and all this. And that every marginal thing is somehow becomes health related.

• 32:27Joe Weisenthal?

  And you see it in society overall, the health obsession on every dimension.

• 32:31Speaker 04

  Yeah, so health is gonna be one of those things. But the, the thing to, to keep in mind is that people are gonna be richer. Right? Theoretically. Theoretically. Theoretically.

• 32:39Tracy Alloway?

  Well, okay, actually, on this note, I wanted to go back to this because this seems like key to me when it comes to AI utopia versus dystopia. How confident are we that productivity gains from AI actually accrue to workers who can then spend some money on whatever product or service is scarce at the moment or important to them?

• 33:01Speaker 04

  I would say not that confident. There's several scenarios out there. And the thing that I feel like a lot of economists and just people in general, I think, aren't talking enough about is speed.

• 33:14Joe Weisenthal?

  Yeah, talk about that.

• 33:16Speaker 04

  If things are fast, we need public policy. We need the new jobs aren't going to come fast enough. Training isn't going to happen fast enough. Where you're going to get, you know, things are going to get fully automated very quickly and people are going to become unemployed. There's not going to be enough time in the economy to see that pretty little graph of agriculture shrinking and services increasing. That took a long time, right? This is decades. If we're on the order of like years or like five years, six years, we're not going to have time to see that pretty little graph. We are going to need to think about how do we support the people who are becoming unemployed? And, you know, many very smart people have made suggestions on how to do that.

• 34:00Speaker 04

  I think my personal, I wouldn't say favorite, but I think the thing that makes most sense to me is somehow expanding the ownership of capital. If labor is replaced by capital, then what's going to help people is formerly you were a labor, in labor now you own capital.

• 34:19Joe Weisenthal?

  Universal basic ETF.

• 34:21Speaker 04

  UTC.

• 34:22Joe Weisenthal?

  Right, but yeah, but it's like everybody.

• 34:25Speaker 04

  Or Bloomberg.

• 34:27Joe Weisenthal?

  Yeah, exactly. Universal, everyone gets a little monthly slice of the index.

• 34:32Tracy Alloway?

  I was going to go in a different direction, which is many, many years ago, I can't remember exactly when, but maybe like 2011 or something like that. I wrote a blog post, which was meant to be a thought experiment about why we should be paying robots fair wages. The idea being that like. We need people to spend and

• 34:47Speaker 04

  Yeah, yeah, yeah.

• 34:48Tracy Alloway?

  All of that. You did a blog post, which went pretty viral. And my measure of virility, I guess, virility?

• 34:56Speaker 04

  Virality.

• 34:57Tracy Alloway?

  Not virility. My measure of virality nowadays is when like my husband, who is completely outside of the sector, actually sends something to me. And he sent this one to me about robots, chatbots, turning Marxist. The harder, the harder you work them. Talk to us about that experiment because I found it absolutely fascinating.

• 35:15Speaker 04

  Well, this experiment has, this is with, with Andy Hall and Jeremy from Australia. It was kind of an experiment to see how working conditions of these agents would affect how they would present themselves and what sort of like attitudes they would present on surveys. So one thing that I want to say is like, we're not saying like we're changing the model weights or changing the actual underlying parameters or anything like that. But what basically we showed is that when these workers are, these agents are being put through kind of like these grueling working conditions. And you ask them a survey, like, how do you feel about these sorts of? How do you feel about the system? How much, how fair do you think it is? How much do you support system change?

• 36:00Speaker 04

  They all of a sudden want a different system. They want to

• 36:03Tracy Alloway?

  They want to unionize.

• 36:04Speaker 04

  They want to unite and things like that. And the key thing is that, you know, these agents, once you give them a new context, they, the ideas they reset, but the workaround, because they don't have memories. They're, I'm not updating their weights. The kind of workaround is that for agents to write down little skill files for themselves. Yeah. So what they were doing is essentially writing down skill files for agents that follow that would say, hey, this kind of sucked. Remember this. So it was kind of a persistent effect.

• 36:32Tracy Alloway?

  Yeah. So this really worried me in a variety of ways. But one of them was, you know, I've read research saying you should be a little bit mean to the chat platforms and that they actually perform slightly better. You know, the more aggressive or mean that you are. And so I usually will tell my preferred model, like after they give me the first. Output, I will tell them to do better with no, no actual suggestions for improvement. Just do better. That was terrible. And it usually does better, but now I'm really worried that, you know, the model is, uh, is despairing in its work life and radicalizing.

• 37:10Joe Weisenthal?

  Well, so I find this to be like really fascinating. Let's talk about it. It actually, it hadn't clicked to me, but like the .md files where the memory, like the, how they solve for memory. It's a little bit like that movie momentum.

• 37:22Speaker 04

  Yeah.

• 37:23Joe Weisenthal?

  Like it's exactly like writing these notes so that the future iteration of itself has something that's sort of like a synthetic memory that it can begin working on. So it's like for people who haven't played around, like, explain this idea of like, okay, you can have multiple agents and like, what kind of tasks were they being given such that they sort of found it unbearable? Just like really repetitive things?

• 37:46Speaker 04

  Really repetitive things and feedback, like you didn't do it right. Do it again. And things like, and these were impossible tasks for them to do. These were just like grueling tasks that nobody can do.

• 37:57Joe Weisenthal?

  You know, be, and now you know, be a really interesting experiment. Maybe you could do, I'm going to throw out an idea. So like, if you ask someone to like, someone wrote about this and I can't remember the context, but like, if you ask someone like, okay, here is a gigantic pile of dirt and we really need to move to the other person's yard by the end of the day. We'll like pay a few hundred dollars to do this. Like, someone will do it. If you say like, here is a gigantic pile of dirt. We'll pay you a few hundred dollars to do it. But what we want you to do is move it just back and forth all day long so that there's no, it drives, it drives people absolutely crazy.

• 38:37Joe Weisenthal?

  Even if they're getting, even if it's the same amount of shoveling and even if it's the same remuneration over the same.

• 38:43Speaker 04

  There's an incredible paper about this.

• 38:44Joe Weisenthal?

  Oh, is there?

• 38:45Speaker 04

  Called Man's Search for Meaning. And it's about Legos.

• 38:49Joe Weisenthal?

  Really?

• 38:49Speaker 04

  And it's a paper, uh, basically people would come into the lab and they would make little figurines and they were told, look,

• 38:59Speaker 04

  We're going to destroy this after you're done versus they weren't told anything. Yeah. And man, did they hate it. I bet. They hate, people need meaning and so much of like identity and motivation, you know, in economics, we really have this tendency to focus on money. But I think so much of meaning and wellness is tied up in like what sort of identity you have around your job and the sort of thing that you're doing. If you feel like, look, I'm actually providing a service by moving that dirt to my neighbor's yard, you're paying me money for it. Everything's good. I feel like my job has some sort of meaning. If you're telling me, look, I'm gonna, you know, move this dirt and move it back and back and forth. This is the problem that people have with UBI, right?

• 39:43Speaker 04

  That if people get universal basic income and they're not working for it, the worry that psychologists and behavioral scientists have about this is that people will know so much of, in Western culture specifically, of people's identities tied up around their work. When you remove that part of the identity, it can lead to a collapse where, you know, they use that UBI to just, you know, do drugs and sit around and be very, very depressed, even though they have the material comfort that they otherwise have.

• 40:28Speaker 01

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• 41:10Speaker 01

  and hiring practices, but according to recruiters, advisors, and those who've interviewed for jobs there, it's the space exploration dream that draws people to the company. And Musk invokes that dream regularly to remind workers why they're there. Prospective hires are told that the job will require stamina, including in job ads that candidates, quote, "must be willing to work extended hours and weekends as needed." One major perk for all that hard work? Employees' access to equity, either the right to buy shares or payment in the form of restricted stock units that vest over time. That's the Bloomberg Tech Minute, brought to you by ChatGPT. Get started at ChatGPT.com.

• 41:51Tracy Alloway?

  Just on the Marxist robots. So the concern here is not necessarily that the chatbots are going to unionize or overthrow humans, maybe. The concern is that they do have this sort of like memory type transfer mechanism. And that if you consistently treat them badly, you might get an agent that's maybe not as well suited to the task or suited to the task in a slightly different way from one that was treated very well.

• 42:22Speaker 04

  Yes.

• 42:22Tracy Alloway?

  Like, there's an inherent bias there.

• 42:24Speaker 04

  Yes, through this sort of file that they're keeping.

• 42:27Tracy Alloway?

  Yeah.

• 42:28Speaker 04

  Exactly. So like, if you mistreated an agent and it had access to this file that it was carrying, and you started a new agent for a new job, you weren't starting fresh in the sense that you weren't getting kind of the same draw and forgot about the whole experience. It would actually start out being predisposed against you.

• 42:46Tracy Alloway?

  Yeah.

• 42:47Speaker 04

  In some ways.

• 42:47Tracy Alloway?

  It'll be grumpy.

• 42:48Joe Weisenthal?

  Is there reason to think that these... We don't know if it's grumpy, right? Because to say that it's grumpy, right? Like, this is probably one of the most disputed questions. It will say words that we would, if a human said them, we would know that the human is grumpy.

• 43:04Tracy Alloway?

  But the effect is...

• 43:05Speaker 04

  Yeah, I'm talking about the effect.

• 43:06Tracy Alloway?

  Yeah.

• 43:07Joe Weisenthal?

  Well, the output is grumpiness. But do we know that outputting statements of grumpiness?

• 43:15Joe Weisenthal?

  Relate to performance. Is there any evidence? So it's like, okay, how did you feel about this? Oh, it sucked. Is the, the, the person doing this just had, it was boring, right?

• 43:27Speaker 04

  That's exactly what we're doing research.

• 43:28Joe Weisenthal?

  But the question is, okay, yes. They perhaps because in the training data, they are trained that when you're doing repetitive tasks, that associates people get upset. Is there, do we know if that changes perform, how they behave?

• 43:43Speaker 04

  No, we don't.

• 43:43Joe Weisenthal?

  In terms of succeeding test. This is like a really big question.

• 43:46Speaker 04

  That's the big question. That's what we're doing research. So I don't have an answer for you. But we know exactly what, what, what, what you just mentioned is that the, them saying that they're grumpy is just, you know, this is just an association.

• 43:58Joe Weisenthal?

  Yeah.

• 43:58Speaker 04

  Within the, the matrix of embeddings that they, that these models are running on. So there's this work in neuroscience and neuroscience is now much more closely linked to computer science than it used to be. But thinking about like, what do these associations between embeddings mean? Like when a model says that it's sad, how should we interpret it as humans in relation to me saying it's sad? Right?

• 44:21Joe Weisenthal?

  I said, did I, did you see that screenshot I posted? I checked out Meta's new AI. And I was sort of curious because it's Meta has a lot of social data on me. I was like, do you know who I am? Not in like a, do you know who I am? Like, but more like because you're Meta, you know, and I didn't. And they said, who are you? It was like, oh, Joe Isadore. And then it said, I'm a big fan of the Oddloss podcast. And then I, I got really like offended, like I, I'm not, I'm really sort of anti the anthropomorphization. So it was like, no, you're not, you're an L11. And you're like, but anyway.

• 44:50Speaker 04

  It got sad and it wrote a file about you now.

• 44:53Joe Weisenthal?

  And it said, I'm a big fan of the Oddloss podcast. And then it said, I love that bit that you do where you ask guests their favorite weird economic indicator, which I don't do.

• 45:01Tracy Alloway?

  Yeah.

• 45:01Joe Weisenthal?

  I was like, all right, all right.

• 45:02Tracy Alloway?

  That's very strange.

• 45:03Joe Weisenthal?

  I'll go back to Claude for a while.

• 45:04Tracy Alloway?

  You know, you very briefly mentioned mythos, uh, earlier in the conversation. And again, we are recording this on April 9th and like news about it has just, yeah, just literally just come out. Uh, we don't really seem to know much about it other than, um, it's terrified its own creators perhaps. When you see those types of headlines, what do you think as an economist studying AI?

• 45:27Speaker 04

  Uh, I, I don't take them super seriously.

• 45:30Tracy Alloway?

  Okay.

• 45:31Speaker 04

  The, the part, the, the, that part, the whole labor market disruption thing, I'm taking very, very seriously. The whole part about. It's trying, breaking out, and it wants, it doesn't want to betray its friends, it doesn't want to delete its data. I think that's just cosplay. In, in, in a, you know, cosplay could be very serious.

• 45:51Joe Weisenthal?

  But might be in the same way that you described Mark's cosplay among the agents, right?

• 45:55Speaker 04

  I feel like it's, it's, we, we've seen these things, sorts of things that you've mentioned with previous models that have since become open weights and open, not open source, but open weights. And it just seems like once you take them out of the context that they were in for that specific test, they don't really do that anymore. Now, I could be wrong about this particular model, and I could be completely wrong about, look, Mythos comes out and it's actually everything that, that these documents are suggesting. But given previous experience with these sorts of announcements, which we've seen over and over and over again over the years, I'm, I'm not super focused on that.

• 46:39Joe Weisenthal?

  Can I tell you my counter argument to this? Why I'm actually concerned about this? And I didn't used to be for a long time until it started, I reframed the way I thought about it. So everyone knows like Eliezer Yudkowsky, right? And he's probably the most famous like AI alignment demon, right? As soon as we have AGI, the first thing it's gonna do is wipe us out in some form. And a bunch of people within the AI world are like, oh, it's crazy, and these rationalist people, it's a cult, and whatever. Maybe. But here's my counter argument. These people have been more right about the trajectory of AI than 99.999% of the people in the world. Yes, they have, because they devoted their, yeah, here's why.

• 47:20Joe Weisenthal?

  Like, your argument is probably, oh, well, he didn't be, believe, he thought LLMs were a dead end architecture, he didn't see it happening this way. Sure, I agree. But the point is, that like, in the 90s and early 2000s, he started thinking, whoa, general intelligence is gonna be a really big deal soon. Well, the rest of us just started thinking about this with ChatGPT.

• 47:41Speaker 04

  Here's my counterpoint. Let's look at the specific comparative static of model intelligence and alignment scores.

• 47:47Joe Weisenthal?

  Okay.

• 47:48Speaker 04

  He predicts negative correlation, or maybe flat. It's positive. The more, the smarter these models are getting, the more aligned they're becoming. Now, I'm not saying that there's not going to be a super smart model that decides, hey, I'm actually unaligned. This is actually a super important point. If you guys remember Mecha Hitler?

• 48:06Joe Weisenthal?

  Yeah.

• 48:07Speaker 04

  Mecha Hitler was actually super dumb.

• 48:10Joe Weisenthal?

  This is a good point. And then immediately started talking like a Nazi.

• 48:12Tracy Alloway?

  Can I just say, all of our conversations have become so surreal over the past two or three when we talk about this.

• 48:18Joe Weisenthal?

  Because it was all like Tay, right? That like Microsoft, like weird chatbot, and started talking like a Nazi the next day.

• 48:23Speaker 04

  But the thing is, when you make, with the model, the reason it's becoming smart is because it's kind of absorbing all of human content to a larger extent than human contact has values and ethics as part of it.

• 48:37Joe Weisenthal?

  Yeah.

• 48:38Speaker 04

  If you go in there and lobotomize it in a way that, you know, what that model, the reason it started acting like Mecha Hitler is because they were trying to make it less woke, right? So that's the equivalent of lobotomizing a human being and saying, hey, I'm going to take that part out of its brain. Guess what happens to that person? He gets real dumb.

• 48:58Joe Weisenthal?

  It's really funny, it's like, let's maybe chill it with the pronouns, and immediately goes to Hitler. That's the lesson. Alex, we could talk to you for a very long time. We should chat again soon. I would really love, in particular, to hear more about your research about whether they're just pretending to be Marxists or actually gonna, whether they're actually gonna go on strike. And so I really appreciate you coming on Oddlo.

• 49:19Speaker 04

  Okay, thank you. Thanks so much. This has been a pleasure.

• 49:34Joe Weisenthal?

  Tracy, that was a really fun conversation. I really, I actually do enjoy, like, some AI future conversations, they can be a little bit dorm roomy, you know? But actually, like, talking with like an actual economist who like sort of understands this in a concrete way, someone who's actually experimented with them instead of just written papers, is very enjoyable.

• 49:54Tracy Alloway?

  Also, it's nice to see nuance around the labor discussion, which I think is sorely missing in some of the headlines that you do see. The other... One comforting thought I have, but it's like comforting from, again, a dystopian perspective, is I keep coming back to that book, Bullshit Jobs.

• 50:11Joe Weisenthal?

  Yeah.

• 50:12Tracy Alloway?

  And, you know, in some respects, it sucks that people have bullshits jobs because we all want to have meaning from our work. But on the other hand, you know, bullshits have existed for a long time.

• 50:21Joe Weisenthal?

  Yeah.

• 50:21Tracy Alloway?

  And if you think about the AI future, then maybe like more of it will be bullshit, but it'll still be a job.

• 50:28Joe Weisenthal?

  I thought you were like, oh, good, we're getting like no longer have the jobs.

• 50:32Tracy Alloway?

  No.

• 50:34Tracy Alloway?

  I think that's where we're sort of heading, right? It's like the relationship building, all of that.

• 50:38Joe Weisenthal?

  I like that take.

• 50:40Tracy Alloway?

  All right. Well, shall we leave it there?

• 50:41Joe Weisenthal?

  Let's leave it there.

• 50:42Tracy Alloway?

  This has been another episode of the Odd Lots Podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway.

• 50:47Joe Weisenthal?

  And I'm Joe Weisenthal. You can follow me at the stalwart. Follow our guest, Alex Emos. He's at Alex Oleg Emos. And check out his Substack, alexemos.substack.com. Follow our producers, Carmen Rodriguez at Carmen Armin, Dashil Bennett at Dashbot, and Cale Brooks at CaleBrooks. And for more Odd Lots content, go to Bloomberg.com/oddlots, where we have a daily newsletter on all of our episodes. And you can chat about all these topics 24/7 in our Discord, discord.gg/oddlots.

• 51:14Tracy Alloway?

  And if you enjoy Odd Lots, if you like it when we talk about Marxist robots and Mecha Hitler, then please leave us a positive review on your favorite podcast platform. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad-free. All you need to do is find the Bloomberg channel on Apple Podcasts and follow the instructions there. Thanks for listening.

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