AI exposure is narrowing entry-level hiring

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 27:18

   is interesting, it's like quite hard to actually see the labor market effects in jobs data. And one component of this is that people aren't being like fired and replaced with AI so much as young people aren't being hired as much in jobs that are more AI exposed. At least in the paper I go into in the book called Canaries in the Coal Mine, these Stanford economists use more fine-grained economic data than what the government publishes. And previously economists were like, ah, we can't see any effects of AI in the labor market. But then when you use this more fine-grained data and create subsets of workers by occupation and then age group, like 22 to 25, 26 to 30, in the most...

2. 28:06

   AI Expos age group of youngest people and AI Expos jobs, they found a close to 20% relative decline in job openings or jobs. And this is controlling for interest rate changes and COVID and education effects of COVID and all these things. And it's possible they're missing things and there's critiques of this from economists and grains of salt and all of that. um but it also just like jives with a lot of people's recorded experience of the labor market as young people today um and so the idea here is like you know these are just the easiest jobs to automate and you could have you know experienced engineers doing the work of like multiple engineers uh programming all kinds of stuff um and if you just hire one really good experienced engineer for half as much as four junior engineers, and then they can do more work. That's what people

3. 29:03

   will tend to do. This creates problems, though. The apprenticeship gap is one term people have used where you don't have young people who are learning the trade anymore. And at some point, all of your experienced engineers will retire. The industry and maybe the companies using these models are maybe just banking on the AI getting better at doing the job faster than humans are being replaced by it. And I use this analogy in the book of like, if deep learning, the training technique for all modern AI systems, if it were a person, it would have been born in 2012. Back when that was like when deep learning first showed promise at beating other types of AI techniques because we had enough data and computing power. And that means it would be like 14 years old right now and so going from like not being able to string together a sentence or even see very well

4. 30:00

   to seeing as well as humans to uh passing tons of tests passing the bar exam at like the age of 11 or something or 12 and now at 14 being superhuman at hacking um and my question is like well where will it be by the time it would be graduating college in 2034 because so much of this ai conversation is focused on like today's capabilities with a denial that they'll get any better despite them having gotten better for like 14 years in a row so where do you expect this to be in in 2034 depends a lot on what we do about it but i think by default i do not expect humans that were born in 2012 by the time they're graduating college to be able to compete for white-collar remote capable jobs with AI systems. And that's assuming humans are still around. And robotics is lagging