Alex Imas

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.

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.

Or 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.

vibe coding an app for voice.

Yeah, exactly right.

So...

A lot of parts of that question.