Early job data shows slower hiring, not mass unemployment

Transcript

From the transcript The Diary Of A CEO publishes with the episode.

1. 1:00:15

   On the journey towards this potential extinction, there's a lot of sort of nearer term things people are worried about. One of the big subjects that people are concerned about is this sort of near term job apocalypse over the next sort of 10 years. And Anthropic released, Anthropic again are the owners of Claude, released a report the other day modelling out the different cases for unemployment. The US unemployment rate is 4.1% currently.

2. 1:00:35

   They projected it will hit 11.9% overall with up to 30% in extreme modelling subsets where job displacement happens without smooth labour absorption. And in the knowledge worker case, knowledge worker, white collar unemployment specifically spiked to 17.9% by 2030 in their more extreme scenario. The pitchforks would probably be out. if there wasn't some sort of mechanism in place for one in five adults being unemployed in the United States.

3. 1:01:06

   It's remarkable to me how recent the last freakout along these lines was and how little we seem to have learned from it. So I think you all know the first really powerful wave of AI that came across the economy was just good old-fashioned machine learning. And that started to demonstrate its power in about 2012. Yeah. Eric and I wrote The Second Machine Age in 2014. And at that time, I thought that a lot of white-collar workers, radiologists is a really good example, were in trouble because the technology was better than they were at the thing they were getting paid to do.

4. 1:01:41

   So I said some things about job and wage pressure from AI about 10 years ago, and I want to own this. I was dead flat wrong about that. Like you point out, unemployment all around the rich world is at historic lows. By far, the bigger problem is that we can't find qualified people to do the work that needs to get done, not that there's not enough work to go around. The best work about the faint signals about AI and job loss right now comes from the guy that I've written four books and co-founded a company with, Eric Brynjolfsson, who wrote a really nice paper called Canaries in the Coal Mine.

5. 1:02:15

   Here is the most... the strongest evidence he found looking at payroll data about the negative job, about the job losses coming from AI. It is in the most exposed professions. Think about software engineers. It is among the new entrants to the workforce where you've got to teach them before they can become really productive. That's exactly what we'd expect. And it's not that we're hiring fewer of them.

6. 1:02:41

   It's that compared to a world where we don't have AI, we're hiring fewer of them. The rate of growth in employment has slowed down. The overall rate of growth in those professions is still really, really healthy. Do you think unemployment is going to be higher 10 years from now? My guess is that 10 years from now, we're still going to be struggling to find enough people to do the work that needs to be done.

7. 1:03:07

   So unemployment would be roughly the same? Yeah. I don't expect a massive trend break in that period of time. Now, 10 years is a long time in the AI world. I get that. But again, four years has also been a long time in AI world. And it's essentially crickets in the labor picture. I think unemployment will go up. I don't think it's because of LLM's. I think that there is probably some effect on jobs because they've been shoving it everywhere, but I don't think long-term that is what causes the issues.

8. 1:03:36

   Roman, you've been writing a lot of notes. Yes. I'm going to give you this brief. Here's how I think about it. So as long as we use tools, we become more productive, more creative, unemployment will be low. Right now, you can probably start a company. You can have, you know, artificial accountant, web designer, logo designer. You can do things you could never do before. So economy should be blooming.

9. 1:03:58

   The question you're asking is about what happens in 10 years. So there are two possibilities. We build superintelligence and then population is zero. Apply unemployment numbers to that. Or we made smart decision. We didn't. We have really cool tools and unemployment is low because everyone's doing awesome things with those tools. Okay. Now, deployment is very different from capability. The example I used before is video phones.

10. 1:04:22

    Video phones were invented in the 70s. They were not deployed until iPhone. Because market reasons, just because I can automate something doesn't mean I want to automate it. So I absolutely cannot make predictions about customer preferences in terms of what they want, in terms of human service, not human. I will not make those. But once we have capability to automate a job, Unless I have a strong preference for a human to do that, oldest profession, then it doesn't matter.

11. 1:04:50

    I'll go with the cheaper option. So this is what I think we're going to see. We're going to either not have a problem or we're going to have really utopian future. Imagine a bunch of horses looking at the improvement of the car. saying, well, you know, the car actually only has a couple narrow applications. Like right now, cars are sort of, you know, they complement horses, right? And that would have been true as you were developing the car.

12. 1:05:23

    And then there was a time when the car was just better than the horse. And then a lot of horses got sent to the glue factory. Easy. I think we've sort of seen this with AI a lot already, right? People who are paying attention to AI saw the GPTs before ChatGPT existed, before they sort of took off. I don't think OpenAI thought that ChatGPT was going to take off so much, which is why it was called ChatGPT rather than like an actual sensible name.

13. 1:05:53

    The researchers were sort of like watching this going and we could sort of like see it slowly getting better and better until it crossed a point where it was sort of like good enough to do a bunch of people's homework. And then suddenly it's everywhere. I think you can have these effects with AI where the AI slowly improves and at some point it crosses a line. It's another threshold argument. The threshold here is the human capability.

14. 1:06:14

    Literally, just another threshold. He's also describing capability jumps rather than thresholds. No, I don't need any capability. I'm agreeing with you. Yeah, but like, unfortunately, you can't actually just make things not happen by assigning a name to the argument. You know, like a nuclear weapon has, there's a big difference between a nuclear weapon, or there's a big difference between a nuclear device.

15. 1:06:34

    where you put in 100 neutrons and get 99 neutrons out, that get 98 more, they get 97 more. And a nuclear weapon, you put in 100 neutrons and get 101 neutrons out, 102, 103, right? One of these is a hot rock. The other one of these is an explosive that can level a city, right? So like reality is the sort of thing where there can be things that are like slowly continuously improving that cross some line, which is like the line where it's better than humans at doing the job.

16. 1:07:04

    And I think we're going to see that happen in some fields, but not others. It's going to be chaos. I don't know what it's going to do to employment. I think we shouldn't, like if things are moving really fast, you might see a lot of people put out of jobs and then be unable to relocate if things are moving. Like it's going to be chaos. If you ask, what do I think unemployment will look like in 10 years?

17. 1:07:26

    My current state is if we don't stop with this AI stuff, I think we'd be very lucky to have 10 years.