Dwarkesh Patel

I wonder with radiologists,

I'm totally speculating.

I have no idea what the actual workflow of a radiologist involves.

But one analogy that might be applicable is when WAMOs are first being rolled out, there'd be a person sitting in the front seat, and you just had to have them there to make sure that if something went really wrong, they're there to monitor.

And I think even today, people are still watching to make sure things are going well.

Robotaxi, which was just deployed, actually still has a person inside it.

And we could be in a similar situation where

If you automate 99% of a job, that last 1% the human has to do is incredibly valuable because it's bottlenecking everything else.

And if it was the case with radiologists where the person sitting in the front of the Uber or the front of the Waymo has to be specially trained for years in order to be able to provide the last 1%, their wages should go up tremendously because they're the one thing bottlenecking wide deployment.

So radiologists, I think their wages have gone up for similar reasons.

If you're the last bottleneck, you're like...

And you're not fungible, which, like, you know, a Waymo driver might be fungible with other things.

So you might see this thing where, like, your wages go, like, whoop, and then until you get to 90%, and then, like, just like that.

And then the last 1% is gone.

And I wonder if we're seeing similar things with radiology or salaries of call center workers or anything like that.

I think there's been evidence that that's already been happening generally in companies that have been adopting AI, which I think is quite surprising.

And I also find what was really surprising, okay, AGI, right?

Like a thing which would do everything and, okay, we'll take out physical work.

It's a thing which should be able to do all knowledge work.

And what you would have naively anticipated that the way this regression would happen is like you would take a little task that –