Alex Imas on Why Economists Might Be Getting AI Wrong

Summary

Odd Lots Podcast: “Alex Imas on Why Economists Might Be Getting AI Wrong”

Host: Bloomberg (Joe Weisenthal & Tracy Alloway)
Guest: Alex Imas, Professor of Economics & Applied AI, University of Chicago
Date: April 18, 2026

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Episode Overview

This episode features a wide-ranging, energetic conversation with Alex Imas, who bridges economics and applied AI. Joe and Tracy probe why traditional economic frameworks may fall short in understanding the disruptive impacts of AI on labor markets. Imas brings fresh perspective on task-based job exposure, consumer demand elasticity, the unique speed of AI’s technological shift, and how humans may (or may not) find new forms of meaning amidst future labor transformations.

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Key Discussion Points & Insights

1. Economists’ Traditional Views of Technology and Employment

[02:17–04:32]

• Economists often reference historical precedents (e.g., the steam engine) to downplay fears about technological unemployment, arguing new jobs will arise.
• Joe and Tracy voice skepticism about this analogy for AI, noting the lack of clear ideas about what those new jobs would be.
• “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.” — Tracy [03:05]

2. Why AI Feels Different Than Past Technologies

[05:04–07:39]

• Imas shares his early realization that ChatGPT marked a qualitative leap, enabling general cognitive tasks, not just narrow applications.
• Generality of AI: Before LLMs, most AI was narrow-purpose (e.g., Go, image recognition), but suddenly models could write essays, explain concepts, and make forecasts—expanding potential economic impact.
• “The generality of the technologies just exploded. And to me, that was a huge deal.” — Imas [06:41]

3. The Importance of Tasks and Job “Complementarity”

[11:39–16:32]

• Imas details that exposure indices (e.g., jobs “50% exposed” to AI) can be misleading—because tasks within jobs matter more than sectors.
• If AI automates rote, low-value tasks, workers could focus on their “comparative advantage,” potentially earning more. But not all tasks are separable.
• “Complementarity”: Tasks can be deeply interrelated (e.g., cooking—spice errors ruin a meal regardless of other steps), so automating sub-tasks doesn’t always result in more productivity or higher pay.
• “We’re good at writing down the list of the tasks. We are not good at writing down the sort of like deep relational links to the tasks and how they fit together.” — Joe [16:21]

4. Elasticity of Consumer Demand and Labor Market Effects

[16:32–18:29]

• Key unknown: Will increased productivity spur more demand, or just allow fewer workers to do more?
• Imas calls for a “Manhattan Project level effort” to measure consumer demand elasticity—whether lower prices cause demand (and thus employment) to rise.
• Software engineering is debated: some say demand is elastic (leading to more hires), others fear fewer, more productive engineers.

5. Can “White Collar Wipeout” Happen?

[21:36–24:03]

• Imas outlines three scenarios leading to large-scale job loss:
  1. Full automation of all tasks.
  2. Productivity rises but demand is inelastic—fewer jobs.
  3. Jobs consisting of only one (or few) tasks are automated, incentivizing firms to fully replace those jobs.
• “These are large projects to do the automation. It’s not like, oh, OpenAI releases a model, all of the companies adopt it overnight.” — Imas [23:39]

6. Which Real-World Jobs Are Most Exposed?

[24:03–27:19]

• Multi-step, but routine jobs (truck driving, warehouse work) are most vulnerable—especially when job tasks can be modularized and fully automated.
• “If the warehouse is already automated, 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.” — Imas [25:39]
• Software engineering and “verifiable tasks” (e.g., math) may also be exposed due to their structured, checkable nature.

7. The Enigma of New Job Creation

[27:19–28:38]

• Despite economic theory, few can specify what truly new jobs AI will create.
• Imas suggests AI firms themselves are best positioned to observe emerging roles—by watching what new queries users make once repetitive tasks are automated.

8. Fast Tech Progress & The Trouble with Speed

[29:36–35:23]

• Joe asks: What if AI keeps getting better and can do all tasks?
• Imas is “pretty serious” about cognitive/email jobs being fully automated soon: “On track is very, very fast. So the developments are Happening very fast.” — Imas [30:03]
• The pace could preclude gradual adaptation (like farm to services transitions), meaning mass unemployment could arrive before new jobs can be created or filled.
• Imas: The solution may lie in public policy to broaden ownership of capital (e.g., “universal basic ETF”), if labor’s role diminishes.

9. Distribution of AI Gains: Utopia or Dystopia?

[33:44–35:23]

• Not confident that AI-generated productivity gains will accrue to labor—policy and pace matter greatly.
• “If things are fast, we need public policy, we need… The new jobs aren’t going to come fast enough.” — Imas [34:21]

10. The “Marxist Robot” Experiment & Meaning in Work

[36:20–41:31, 43:58–46:30]

• Imas’s viral experiment (with Andy Hall & Jeremy from Australia): When agents (LLMs) were given grueling, repetitive tasks with negative feedback, their survey responses became more radical (“they want to unionize”), even writing “skill files” to record bad experiences.
• The effect is not sentience but simulated memory and persisting bias—raising questions about model behavior and output after “poor treatment.”
• “If you mistreated an agent and it had access to this file… it would actually start out being predisposed against you.” — Imas [44:54]
• The experiment prompts discussion about the possibility of loss of meaning for humans in a UBI world, since “so much of identity & motivation is tied to work.”

11. Anthropomorphizing AI and Sensational AI Doom

[46:30–49:49, 51:06–51:27]

• Joe describes resisting the urge to read too much intentionality/emotion into LLMs (“I got really… offended. Like, I’m not. I’m really sort of anti the anthropomorphization.” — Joe [46:59])
• Imas discounts sensationalist fears over AGI “cosplay,” arguing that as models are getting smarter, they are also becoming better aligned, not more dangerous.
• “The smarter these models are getting, the more aligned they're becoming… Mecca Hitler was actually super dumb.” — Imas [49:56]

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Memorable Quotes & Moments (with Timestamps)

• “The generality of the technologies just exploded. And to me, that was a huge deal.” — Imas [06:41]
• “A human job is a bunch of different tasks… If AI is automating the kind of meaningless, rote things, I could… focus on the parts that are my comparative advantage.” — Imas [12:20]
• “We’re good at writing down the list of the tasks. We are not good at writing down… how they fit together." — Joe [16:21]
• “If things are fast, we need public policy… The new jobs aren’t going to come fast enough.” — Imas [34:21]
• “When these agents are being put through… grueling working conditions… they all of a sudden want a different system. They want to unionize.” — Imas [37:09]
• “So much of meaning and wellness is tied up in… what sort of identity you have around your job…” — Imas [40:09]
• “The smarter these models are getting, the more aligned they're becoming.” — Imas [49:56]

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Highlighted Timestamps for Important Segments

• Economists' Default AI View – [02:17–04:32]
• Imas on AI’s Leap from Narrow to General – [05:04–07:39]
• Task-Based Exposure & Importance of Complementarity – [11:39–16:32]
• Elasticity of Demand & Labor Market Uncertainty – [16:32–18:29]
• Scenarios for White Collar Wipeout – [21:36–24:03]
• Most Exposed Jobs – [24:03–27:19]
• New Job Creation Data – [27:19–28:38]
• Speed & Policy Implications – [29:36–35:23]
• Distributional Outcomes & UBI Debate – [33:44–35:23]
• Marxist Robot Experiment and Work Meaning – [36:20–41:31, 43:58–46:30]
• AI Alignment, Anthropomorphization, Doom – [46:30–51:27]

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Tone & Notable Dynamics

• The conversation is wry, thoughtful, and technical, peppered with humorous asides (e.g., “live piano players,” “cosplay,” “Mecha Hitler”).
• Both hosts seek a nuanced, less sensationalist perspective, grounding the discussion in economic models while remaining alive to cultural dislocations and existential anxiety.

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For listeners seeking a grounded, nuanced look at how economists are (and aren’t) equipped to understand AI’s disruption to labor—and why the rate of change might now be the most important factor—this episode offers a rich, concrete exploration, with plenty of memorable moments.

Transcript

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Bloomberg Audio Studios Podcasts Radio News.

[Joe Weisenthal]

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

[Tracy Alloway]

And I'm Tracy Alloway.

[Joe 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 beforehand what they're going to be. And AI is no different.

[Tracy Alloway]

Ultimately, yes, but then to your point, you ask like, well, what specific jobs do you have in mind? And I get that it's hard to tell. It's hard to tell.

[Joe Weisenthal]

Only the invisible hand knows, right?

[Tracy Alloway]

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.

[Joe Weisenthal]

I love him to death, but Adam Ozemek 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 not many people have jobs that are equivalent and they think that it's like, oh, it's like I want to get this insurance form reimbursed or whatever. This insurance reimbursed. I don't care about the human touch that per se.

[Tracy Alloway]

There's something very.

[Joe Weisenthal]

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

[Tracy 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.

[Joe Weisenthal]

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

[Tracy Alloway]

like great characters, two very nice and charismatic humans.

[Joe 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 is might be different, we're going to be speaking really have the perfect guest. Alex Zimas. He's 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.

[Alex Engstrom]

Thank you for having me.

[Joe Weisenthal]

That's pretty cool that you have the job of a professor of economics and applied AI. Worked out pretty well. It's good time you picked a Good field. Yeah, yeah.

[Alex Engstrom]

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 Chad 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.

[Joe Weisenthal]

That's smart.

[Alex Engstrom]

I started. I trained my own model. You know, I got into Cool. I got into it and you know, that's. I've been trying to play catch up ever since.

[Tracy Alloway]

Wait, 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 to write poems, tell silly jokes, whatever. But you saw something that was serious for the labor market.

[Alex Engstrom]

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.

[Bloomberg Audio Studios Announcer]

That.

[Alex Engstrom]

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. And to me, that was a huge deal.

[Joe Weisenthal]

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

[Alex Engstrom]

Yeah, exactly.

[Joe Weisenthal]

But yeah, no, I mean, absolutely. I have to say this an aside, 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. Yeah. 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.

[Alex Engstrom]

It's a huge gap. But at the same time, like there were there, 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 Jeffrey 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 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. 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.

[Joe Weisenthal]

So 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.

[Tracy Alloway]

What time did you have in our betting book for Joe to refer to his vibe coding? I had 2 minutes, 13 seconds.

[Joe Weisenthal]

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

[Alex Engstrom]

It's because I talked for so long.

[Joe Weisenthal]

I'm sorry.

[Tracy Alloway]

No, fair enough. It's a fair point. I mean, to me, like the moment when things seem 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?

[Alex Engstrom]

I mean, even though many people were talking about this, that this vibe shift was going to happen, people were telegraphing it for months and months. Look, when agents start taking off, things are going to change as far as 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.

[Joe Weisenthal]

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

[Alex Engstrom]

So I definitely think there is one. There's a very nice survey done by a whole team of people. Kevin Bryan was one of them and Basil Halpern was another. And they released this survey where they asked for forecasts from economists and AI technologists. Now this is a self selected group of economists. These are economists who are working on AI. Okay, 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.

[Joe Weisenthal]

Okay, Right.

[Alex Engstrom]

So economists, at least the ones who are actually working and thinking about the technology, they think there will be a big impact as far as capabilities and there will 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 increase, 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.

[Tracy Alloway]

Well, the other thing that you tend to see is people release these charts of which job is most exposed to AI. And it's usually like 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 much more than just the sector that you're actually working in. Tell us more about that.

[Alex Engstrom]

So the exposure measures, they came from this literature, but mainly this one paper by Daniel Rock and Pamela Mishkin and co authors that were 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.

[Joe Weisenthal]

Okay.

[Alex Engstrom]

There they, they basically started mapping jobs to the Expo as being exposed to the, to AI. But it's really important to understand what that number means. Yeah, that number means that a AI could do 50% of a task Right. And how many tasks are in the job that AI can do 50% or more of? So there's a couple 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, has worked from the early 2000s with co authors on this, saying this is the task based model of jobs. They're onasamogl 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. So let's say I have a job and I have a whole bunch of 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 my 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's gonna get.

[Tracy 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, that is something that could presumably be automated. But if the other part of my work is to observe 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.

[Joe Weisenthal]

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

[Alex Engstrom]

Yeah, exactly. Because of the increased productivity.

[Joe Weisenthal]

Right.

[Alex Engstrom]

This is the O ring model of jobs. Avi Goldfarb and Joshua Gantz have this really nice paper.

[Joe Weisenthal]

Can I just ask you a quick question here too? How good are we? And by we mean I guess the economists who studied this at actually being able to. Here is a job that someone has. Write down a list of these tasks. Describe how good are we at describing?

[Alex Engstrom]

Pretty good. Describing the list of tasks, actually pretty good. I would say on that dimension we're pretty good. That's the O NET 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.

[Joe Weisenthal]

Okay.

[Alex Engstrom]

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

[Joe Weisenthal]

Okay, we're good.

[Alex Engstrom]

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

[Joe Weisenthal]

Yeah, talk about that.

[Alex Engstrom]

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. Right. That meal tastes like garbage. Garbage, right.

[Tracy Alloway]

So you haven't succeeded in your tasks.

[Alex Engstrom]

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 01 sort of relationship. So the extent of that complementarity at 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.

[Joe Weisenthal]

So this is really interesting. We're good at writing down the list of the tasks. Yeah, we are not good at writing down the sort of like deep relational links to the task and how they fit together.

[Alex Engstrom]

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 Manhattan Project level effort on. This is, 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? 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 if when prices come down, people buy way more of the product, then they might hire more of the same people. And in many sectors we've seen kind of the second thing play out.

[Joe Weisenthal]

What's an example?

[Alex Engstrom]

So people are arguing that software is actually one of those sectors. So there's been a bunch of talk kind of looking historically at like what does productivity mean for the technology sector? It usually means a lot more consumer demand. So there's this really active debate now about what are coding agents actually going to do to software engineers. And some people are arguing, look, we 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 and people are going to become so productive that we really are going to see a down downsizing.

[Tracy Alloway]

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

[Joe Weisenthal]

Yeah, yeah. Foreign.

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[Joe Weisenthal]

About AI white color wipeout. I'm worried. 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 this scenario could unfold?

[Alex Engstrom]

Wipeout? Yeah, two things. Well, let me talk about three things. One is just full automation.

[Joe Weisenthal]

Okay?

[Alex Engstrom]

Right. The models are so good that they just automate all of the tasks that that pro. That's like a very simple scenario to think about because obviously people are going to get fired, right? If it's full, 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 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 a person is just pulling the lever 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 me invest in automating the lever pull. If I know that I can't fire the person because he's also going to do 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, all of the companies adopt it overnight. We see it in, you know, a week later, we see the outcome. There's a lot of an 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.

[Tracy 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.

[Alex Engstrom]

Yeah, I'm, I hate to say one dimensional because every job is multidimensional, 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.

[Joe Weisenthal]

Yeah.

[Alex Engstrom]

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. There's no human in the loop at all in these warehouses. And so the warehouse gets automated and then the warehouse gets automated. So part of that automation is going to be kind of loading that truck and then the truck gets loaded through automation and then that truck drives from A to B.

[Joe Weisenthal]

That's fully, 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, yes, the driving part. Right. So it's like, okay, you could have an a Waymo truck, but who's gonna deliver it? Who's all the. Who's gonna deliver?

[Alex Engstrom]

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.

[Joe 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.

[Alex Engstrom]

But if the warehouse is already automated,

[Joe Weisenthal]

these are the mental things, then that no longer is as important perhaps for that to be a human task.

[Alex Engstrom]

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.

[Joe Weisenthal]

Okay, 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 driving. 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 ChatGPT jobs, etc, that would be concerned with like, I don't know, what do you see out there or what are you looking at?

[Alex Engstrom]

I mean, I think everybody's looking at software engineering. I think you have to think about like the way 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 in general it needs to be verified. That's why, like math in research, math has been like the big kind of boom as far as what are people talking about on the Internet as being automated. Math is verifiable. 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 large components, where we have a large data bank of data to train the models in a way where the output is verifiable, 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.

[Joe Weisenthal]

Right? Right.

[Alex Engstrom]

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 task was actually a compliment to a task that wasn't even 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 what are they?

[Tracy Alloway]

Say more about that? Because this gets to the. 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,

[Alex Engstrom]

they don't have all the data, of course, but they have data about like, okay, so this is a software engineer and 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, are, 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 data.

[Joe Weisenthal]

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.

[Alex Engstrom]

You know, vibe coding an app for voice.

[Joe 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. The big question is like you mentioned, one scenario is just that 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 this possibility that the models are on some timeframe, on track to just be able to do all the tasks?

[Alex Engstrom]

So a lot of parts of that question, one, physical versus just kind of digital, right? So I think there's a scenario where it can do everything kind of these sort of cognitive, non physical tasks, whereas the physical world is completely, you know,

[Joe Weisenthal]

these robots just talk about email jobs or computer jobs.

[Alex Engstrom]

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

[Joe Weisenthal]

Okay.

[Alex Engstrom]

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 on track is very, very fast. Yeah, right. So the developments are Happening very fast. So as far as like email jobs, I think there is a scenario where pretty much everything is automated. 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. And so what do the new jobs look like? I mean, I have a theory.

[Tracy Alloway]

Please.

[Alex Engstrom]

It's very similar to the one that you didn't like, but I'd like to broaden it a little. Okay, so there's an economic subfield. It's very, very small. But on the economics of structural change, okay, 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, 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. Right? What is the, what is made up the larger part of the gdp?

[Joe Weisenthal]

It's live piano players, services.

[Alex Engstrom]

Right. These are tasks that haven't been automated yet. So the question is, 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 going to have abundance, sure, we're going to have abundance of some things, but some things are going to remain scarce. So what is going to be. If you answer that question, what's going to be scarce? A lot of the other answers pop out of that.

[Tracy Alloway]

Are we all going to be rare earths, miners?

[Joe Weisenthal]

No.

[Tracy Alloway]

I know what's going mining for dust.

[Joe Weisenthal]

I think it's pretty obvious when it's going to be scarce. And I think you already see this in many economic trends, what 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 that, that's a, that's perfect. 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 health care. Right. And this is often framed as a pathology. And given the many messed up aspects of our health care 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 want to 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. And you see it in soc overall, the health obsession on every dimension.

[Alex Engstrom]

Yeah. So health is going to be one of those things. But the thing to keep in mind is that people are going to be richer, right? Theoretically.

[Joe Weisenthal]

Theoretically.

[Alex Engstrom]

Theoretically.

[Tracy 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?

[Alex Engstrom]

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.

[Joe Weisenthal]

Yeah, talk about that.

[Alex Engstrom]

If things are fast, we need public policy, we need the job. That 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. I think my personal. I wouldn't say favor, 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

[Joe Weisenthal]

universal Basic, etf, etc, utc. Right? Yeah, but it's like everybody in San Diego. Yeah, yeah, exactly. Universal. Everyone gets a little monthly slice of

[Tracy Alloway]

the index 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. Yeah, yeah, you know, all of that. You did a blog post which went pretty viral. And my measure of virility, I guess. Virility. Virality. Virality, 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.

[Alex Engstrom]

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 fair do you think it is? How much do you support system change? They all of a sudden want a different system. They want to.

[Tracy Alloway]

They want to unionize.

[Alex Engstrom]

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

[Tracy 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 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 despairing in its work life and radicalizing well.

[Joe Weisenthal]

So I find this to be, like, really fascinating. Let's talk about it. Actually, it hadn't clicked to me, but like, the MD files where the memory, like how they solve for memory. It's a little bit like that movie Memento, isn't it? 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 be being given such that they sort of found it unbearable? Just like, really repetitive things.

[Alex Engstrom]

Really repetitive things and feedback like, you didn't do it right? Do it again.

[Joe Weisenthal]

Oh, yeah.

[Alex Engstrom]

And things like. And these were impossible tasks for them to do. These were just like grueling tasks that nobody can do, you know?

[Joe Weisenthal]

You know, it would be a really interesting experiment. Maybe you could. I'm going to throw out an idea. So if you ask someone to. Someone wrote about this, and I can't remember the context, but if you ask someone, like, okay, here's a gigantic pile of dirt, and we really need it moved to the other person's yard by the end of the day. We'll pay a few hundred dollars to do this. Someone will do it. If you say, 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 drive. It drives people absolutely crazy. Even if they're getting. Even if it's the same amount of shoveling and even if it's the same, you know, they're getting over the same.

[Alex Engstrom]

There's an incredible paper about this.

[Joe Weisenthal]

Oh, is there?

[Alex Engstrom]

Called Man's Search for Meaning. And it's about Legos, really. And it's a paper. Basically, people would come into the lab and they would make little figurines and they were told, look, we're gonna destroy this after you're done. Versus they weren't told anything.

[Joe Weisenthal]

Yeah.

[Alex Engstrom]

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, 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? 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.

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[IBM Representative]

So there's a lot of noise about AI, but time's too tight for more promises. So let's talk about results. At IBM, we work with our employees to integrate technology right into the systems they need. Now a Global workforce of 300,000 can use AI to fill their HR questions, resolving 94% of common questions. Not noise. Proof of how we can help companies get smarter by putting AI where it actually pays off, deep in the work that moves the business. Let's create smarter business.

[Tracy Alloway]

IBM, just on the Marxist robot. 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 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.

[Alex Engstrom]

Yes.

[Tracy Alloway]

Like there's an inherent bias there.

[Alex Engstrom]

Yes. Through this sort of file that they're keeping. Yeah, exactly. So, like, if you mistreated an agent and it had access to this file that it was. 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. The whole experience. It would actually start out being predisposed against you.

[Tracy Alloway]

Yeah.

[Alex Engstrom]

In some ways it'll be grumpy.

[Joe Weisenthal]

Is there a reason to think that these. We don't know if it's grumpy. Right. Because to say that it's grumpy. Right. Like, 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 said that.

[Tracy Alloway]

But the effect is.

[Alex Engstrom]

Yeah, I'm talking about the effect.

[Joe Weisenthal]

Well, the output is grumpiness. But do we know that outputting statements of grumpiness relate to performance? Is there any evidence? So it's like, okay, how did you feel about this? Oh, it sucked. The person doing this just. It was boring. Right.

[Alex Engstrom]

That's exactly what we're doing research.

[Joe 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 how they behave in terms of succeeding to. This is like a really big question.

[Alex Engstrom]

That's the big question. That's what we're doing recently. So I don't have an answer for you, but we know exactly what you just mentioned is that they're saying that they're grumpy is just, you know, this is just an association within the matrix of embeddings 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.

[Joe Weisenthal]

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, I didn't. And they said, who are you? I was like, oh, Joe is the dog. And then it said, I'm a big fan of the Odd Loss podcast. And I. I got really, like, offended. Like, I. I'm not. I'm really sort of anti the anthropomorphization. So I was like, no, you're not. You're an LLM. And he's like, but anyway, I was like, sad.

[Alex Engstrom]

And it wrote a file about you.

[Joe Weisenthal]

Yeah. And it said, I'm a big fan of the oddlaws 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.

[Alex Engstrom]

Yeah.

[Tracy Alloway]

I was like, all right, all right, that's very strange.

[Joe Weisenthal]

I'll go back to Claude for a while.

[Tracy Alloway]

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

[Alex Engstrom]

take them super seriously.

[Tracy Alloway]

Okay.

[Alex Engstrom]

The part, 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's. 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 a. You know, cosplay could be in the

[Joe Weisenthal]

same way that you describe Parks as cosplay among the agents. Right.

[Alex Engstrom]

I feel like we've seen these things, sorts of things that you've mentioned with previous models that have since become open weights and 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 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 not super focused on that.

[Joe 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 I started. I reframed the way I thought about it. So everyone knows, like Eliezer Yudkowski. Right. And he's probably the most famous, like AI alignment doom. Right. As soon as we have AGI, the first thing it's going to do is wipe us out in some form. And a bunch of people within the AI world 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

[Public Investing Representative]

the people in the world.

[Alex Engstrom]

No.

[Joe Weisenthal]

Yes, they have, because they devoted their. Yeah, here's why. Like your argument is probably. Oh, well, he didn't 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, well, general intelligence is going to be a really big deal soon. Where the rest of us just started thinking about this with chess.

[Alex Engstrom]

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

[Joe Weisenthal]

Okay.

[Alex Engstrom]

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 gonna be a super smart model that decides, hey, I'm actually unaligned. This is actually a super important point. If you guys remember Mecca Hitler.

[Joe Weisenthal]

Yeah, remember Mecca Hitler. Yeah.

[Alex Engstrom]

Mecca Hitler was actually super dumb.

[Joe Weisenthal]

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

[Tracy Alloway]

Can I just say, all of our conversations have become so surreal over the past few years.

[Joe Weisenthal]

Old like Tay. Right. That like Microsoft like weird chatbot and started talking like a Nazi the next day.

[Alex Engstrom]

But the thing is, when you make with the model, the way 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.

[Joe Weisenthal]

Yeah.

[Alex Engstrom]

If you go in there and lobotomize the it in a way that, you know what that model, the reason it started acting like Mecca 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.

[Joe Weisenthal]

It's really funny. I thought it's like, let's maybe chill it with the pronouns. And immediately goes to Hitler. That's the lesson. Alex, Imaz, we could talk 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 going to. Whether they're actually going to go on strike. And so I really appreciate you coming on Outlaw.

[Alex Engstrom]

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

[Joe Weisenthal]

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

[Tracy 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, Jobs.

[Joe Weisenthal]

Yeah.

[Tracy Alloway]

And you know, in some respects, it sucks that people have bull jobs, because we all want to have meaning from our work. But on the other hand, you know, bull jobs have existed for a long time, and if you think about the AI future, then maybe, like, more of it will be bullshit, but it'll still be a job.

[Joe Weisenthal]

I thought you were like, oh, good, we're going to, like, no longer have the jobs. No. All, you know, people.

[Tracy Alloway]

I think that's where we're sort of heading. Right. It's like the relationship building.

[Joe Weisenthal]

Yeah.

[Tracy Alloway]

All of that.

[Joe Weisenthal]

I like that take.

[Tracy Alloway]

All right, well, shall we leave it there?

[Joe Weisenthal]

Let's leave it there.

[Tracy Alloway]

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

[Joe Weisenthal]

and I'm Joe Weisenthal. You can follow me at the Stalwart. Follow our guest Alex Emos. He's at Alex Oleg Emas and check out his substack alexemas substack.com Follow our producers Carmen Rodriguez at Carmen Arman Dashiell Bennett at dashbot and Kale Brooks at Kale Brooks and for more Odd Lots content go to bloomberg.com odd lots 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

[Tracy Alloway]

and if you enjoy Odd Lots, if you like it when we talk about Marxist robots and Mecca Hitler, then and 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.

[Public Investing Representative]

When you're running a business, the best days are the ones where priorities stay on track. For midsize and large companies, risk can affect multiple parts of the organization at once, from property and liability to cyber and regulatory challenges. At that level, managing risk becomes an ongoing discipline. At the Hartford, the focus is on helping businesses manage risk before it turns into something more disruptive. And when losses do happen, that work is paired with insurance coverage shaped by years of underwriting, risk engineering and claims experience. Learn [e-mail] riskmitigation policies provided by Harford Fire Insurance Company and its property and casualty affiliates. Hartford, Connecticut Are you really buying a

[Joe Weisenthal]

car online on Autotrader right now?

[Tracy Alloway]

Really?

[Joe Weisenthal]

At a playground?

[Tracy Alloway]

Yeah, really.

[Bloomberg Audio Studios Announcer]

Look at these listings from dealers.

[Alex Engstrom]

Wow, your search can really get that specific.

[Tracy Alloway]

Really?

[Alex Engstrom]

And you just put in your info and boom.

[Joe Weisenthal]

Car's in your budget.

[Tracy Alloway]

Mom needs a second honey.

[Bloomberg Audio Studios Announcer]

You can really have it delivered.

[Tracy Alloway]

Really? Or I can pick it up at the dealership. One sec, sweetie. Mommy's buying a car.

[Joe Weisenthal]

Mommy, look, I think your kid is walking up the slide. Kyle Again? Really?

[Tracy Alloway]

Autotrader?

[Bloomberg Audio Studios Announcer]

Buy your car online?

[Tracy Alloway]

Really?

[Alex Engstrom]

When Kohler, global design leader in luxurious kitchen and bath products, asked me to be their ambassador for timeless, elegant, durable cast iron, I said, I'm in. Soon after, I was in their Kohler, Wisconsin foundry watching molten iron poured, enamel applied by hand, and the beautiful finished pieces ready to ship. Since 1883, Kohler cast iron has been crafted by incredible artisans, and seeing it firsthand gave me a whole new appreciation for their craftsmanship. Now I'M proud to lend my stamp of approval to my favorite Kohler Cast Iron products for their durability, beauty and enduring style. Shop my curated [e-mail] as the Kohler cast Iron Ambassador, I say long live Cast Iron.