Alex Imas

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.

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.

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.

Right.

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 freed up or the task was actually a complement 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?

They don't have all the data, of course, but they have data about like, okay, so 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.