Chapter 1: What is the main topic discussed in this episode?
Hello, I'm Stephen Carroll. I'm in Brussels, where many of Europe's biggest decisions get made.
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Chapter 2: What are the historical perspectives on AI's impact on jobs?
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. And to me, that was a huge deal.
Yeah, the generality of it, I mean, I guess literally, that's the G, right? Yeah, exactly. But yeah, no, I mean, absolutely. I have to say, this is an aside, but learning a little bit more about where AI was pre-LLMs or pre-ChatGPT almost makes me even more impressed. Like the leap.
I don't know if this is a comment, but when you look at some of what was cutting edge in 2019, and then you look at what's cutting edge in late 2022, I'm almost more impressed than if I hadn't known what they were up to in 2019. That's a huge gap in those few years.
It's a huge gap, but at the same time, there was a path towards AI and the way that AI was being worked on for a long time, which was these very specific purpose-built technology. And I think Jeffrey Hinton and other people were 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. 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. 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.
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Chapter 3: Which specific jobs are most at risk from AI disruption?
I mean, to me, 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?
I mean, even though many people were talking about this, that this vibe ship was going to happen. People were telegraphing it for months and months. Look, when agents start taking off, things are gonna 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 paradigm shift as far as the economics of the technology.
So I set up this sort of ā 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?
Yeah.
to the extent there is one?
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, so it's not the whole field.
But one of the things that you got from that survey was they're very much aligned. Okay. Right. 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 gonna be substantial capability increases, but the growth is gonna be pretty moderate. It's like an extra two, 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.
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Chapter 4: How does AI's speed of development differentiate it from past technologies?
Let's say a person becomes a lot more productive. 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 gonna 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. And in many sectors, we've seen kind of the second thing play out.
What's an example? 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 think. And people are going to become so productive. that we really are going to see a downsizing.
That was kind of the argument that Jared Sleeper was making in our defense of software episode.
Yeah.
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You know, people are worried, right, about AI white collar 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 the scenario could unfold?
Wipeout. Two things. Well, let me talk about three things. One is just full automation. The models are so good that they just automate all of the tasks. That's like a very simple scenario to think about because obviously people are going to get fired if it's fully automated.
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Chapter 5: What are the implications of AI on productivity and labor market dynamics?
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 one element.
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 already automated, these are complementary things.
Then that no longer is as important perhaps for that to be a human task. 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.
Yeah.
And so there's a big incentive on the company.
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, 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-Chad GBT jobs, et cetera, that would be concerned with like, I don't know, what do you see out there or what are you looking at?
I mean, I think everybody's looking at software engineering. I think you have to think about 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 math, in research, math has been 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.
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Chapter 6: How do economists predict the future of jobs in the age of AI?
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 are 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. Did you see that screenshot I posted? I checked out Meta's new AI. And I was sort of curious because Meta has a lot of social data. And I was like, do you know who I am? Not in like a, do you know who I am? But more like because you're Meta, you know. And it didn't. And it said, who are you? It was like, oh, Joe Weisendahl. And then it said, oh, I'm a big fan of the Odd Laws podcast.
And I got really like offended. I'm really sort of anti the anthropomorphization. So it was like, no, you're not. You're an LLM. And you're like, no. But anyway.
It got sad and it wrote a file about you now.
And it said, I'm a big fan of the Oblaws 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. Yeah. I was like, all right, all right.
That's very strange.
I'll go back to Claude for a while.
You know, you very briefly mentioned mythos earlier in the conversation. And again, we are recording this on April 9th. And 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?
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Chapter 7: What is the relationship between task complexity and AI automation?
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.
I thought you were like, oh, good. We're going to like no longer have the jobs.
I think that's where we're sort of heading. It's like the relationship building.
Yeah.
All of that.
I like that take.
All right. Well, shall we leave it there?
Let's leave it there.
This has been another episode of the Odd Thoughts Podcast. I'm Traci Allaway. You can follow me at Traci Allaway.
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