James Manyika

So care work is, you know, hard to fully automate because it turns out that, hey, it's actually harder to automate somebody doing physical mechanical tasks than, say, somebody doing analytical work.

But it turns out the person doing analytical work where you can probably automate what they do a lot easier also happens with the person who's

earning a little bit more than the person doing the physical mechanical tasks.

But by the way, that person is one that we don't pay much in the first place.

So you end up with physical mechanical activities that are hard to automate, also growing and being demanded, but then we don't pay much for them.

So the wage effects are something to worry about.

And even in the example I gave you of complementing work,

That's great from the point of view of people and machines working alongside each other.

But even that has interesting wage effects too, right?

Because at one end, which I'll call the happy end, and I'll come back to the challenged end, the happy end is when we automate some of what you do, Lucas, and the combination of what the machine now does for you and what you

you still do yourself as a human.

Both are highly valuable, so the combo is even more productive.

And this is the example that's often given with the classic story of radiologists, right?

So machines can maybe read some of those images way better than the radiologist, but that's not all the radiologist does.

There's a whole other value added activities and tasks that the radiologist does that the machine reading

That's understanding that MRI doesn't do.

But now you've got a radiologist partnered up with a machine.

The combination is great.

So that's a happy example.

Probably the productivity goes up, the wages of that radiologist go up.