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Speaker 1: Pushkin. I'm Jacob Goldstein. This is what's your problem? And

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Speaker 1: my guest today is David Otter. David is a labor

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Speaker 1: economist at MIT, and among other things, he's done really

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Speaker 1: influential work on how new technology affected American workers over

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Speaker 1: the past several decades. In particular, his work makes a

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Speaker 1: compelling case that computers contributed to rising inequality in the

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Speaker 1: late twentieth and early twenty first centuries. So I was interested,

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Speaker 1: and I was pleasantly surprised when I recently read some

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Speaker 1: new work of his that suggested that AI might actually

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Speaker 1: drive inequality down. It's a nuanced story and it's a

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Speaker 1: really interesting case that he makes, And of course we

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Speaker 1: don't actually know what's going to happen, but whatever happens,

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Speaker 1: David Otter's framework is a really useful way for thinking

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Speaker 1: about the way technology affects work, affects who does what,

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Speaker 1: who makes how much money? So to start, I asked

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Speaker 1: him to talk about what happened when computers started to

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Speaker 1: become common in offices back in the nineteen eighties, so you.

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Speaker 2: Know, information technology, The computer was different from many other

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Speaker 2: technologies that preceded, and what made computers different traditional computers

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Speaker 2: like PREAI computers, is that they followed rules and they

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Speaker 2: could you could write a program to accomplish a task

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Speaker 2: that you would previously have thought of as a cognitive procedure.

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Speaker 3: Right.

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Speaker 2: It required all these ifens, all these steps gathering information,

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Speaker 2: doing calculation and making things that we thought of were

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Speaker 2: you know, exclusively in the minds dominion, right, And so

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Speaker 2: that that codification might took out a lot of the

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Speaker 2: routine tasks in the work that people did. How that

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Speaker 2: affected work was really quite nuanced, right, So people who

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Speaker 2: did bookkeeping and accounting, right, they spent much much less

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Speaker 2: of their time on data you know, tabulation, data calculation,

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Speaker 2: and much more on scenario planning right on forecasting, on

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Speaker 2: you know, forensics, trying to you know, find errors and

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Speaker 2: so among technical, professional managerial work, it made work more

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Speaker 2: productive and it made more specialized. It eliminated a lot

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Speaker 2: of the grunt work and enabled people to focus on

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Speaker 2: the abstract problems. But for the other group of people

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Speaker 2: whose work was simplified, whether it was in the kind

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Speaker 2: of you know, uh, stock and inventory clerks, whether it's

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Speaker 2: people who were doing who were working as cashiers who

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Speaker 2: used to have to know, you know, how to be

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Speaker 2: able to make change and you know, keep traffing numbers on.

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Speaker 3: It made the work simpler and less specialized.

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Speaker 2: And in the case of production work, and you know,

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Speaker 2: it eliminated certain types of production jobs, certain types office

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Speaker 2: jobs because they could be more or less fully automated.

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Speaker 2: And that had the effect of taking people with you know,

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Speaker 2: high school degrees and some college and making their skills

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Speaker 2: less valuable in offices, less valuable in you know, in

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Speaker 2: factories and assembly lines. And so it shifted the weight

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Speaker 2: of employment for people without college degrees away from offices,

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Speaker 2: away from factories, and towards a lot of services food service, cleaning, security, entertainment, recreation,

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Speaker 2: home health aids. And that's socially valuable work, but it's

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Speaker 2: poorly paid. And there's a number of reasons why it's

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Speaker 2: poorly paid on oversimplified, but one of them is it's

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Speaker 2: not specialized. Right, people can do it without training your certification.

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Speaker 1: So one example that I find helpful in thinking about

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Speaker 1: the kind of dynamics you're talking about is looking at

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Speaker 1: accountants versus bookkeepers. Accountants focus more on the big pick

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Speaker 1: sure typically have a college degree, often a special license.

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Speaker 1: Bookkeepers focus more on the day to day look at

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Speaker 1: you know, money coming in and out of a business.

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Speaker 1: They don't typically have as much training as accountants, and

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Speaker 1: so I mean, can you just talk about how computers

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Speaker 1: affected accountants versus bookkeepers.

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Speaker 2: So for accountants, it was the story of essentially eliminating

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Speaker 2: the supporting work, the inexpert work, the drudge work, and

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Speaker 2: allowing them to focus on the cognitively demanding stuff right,

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Speaker 2: to do the problem solving, to do the forecasting, to

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Speaker 2: do the scenario planning, to do the forensics of finding

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Speaker 2: you know, errors and issues and so on.

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Speaker 1: Accountants can be better accountants because of computers. They can

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Speaker 1: do figure out more complicated ways to save people money

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Speaker 1: on their taxes or to find errors or whatever. And

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Speaker 1: so it's good for accountants yep, and other similar you

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Speaker 1: know sort of professionals with a high amount of expertise. Right,

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Speaker 1: and then the as a tool that's making them better

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Speaker 1: at their jobs. What is it doing to the bookkeepers?

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Speaker 2: So it's making your work like specialized less specialized, right,

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Speaker 2: and so many more people can do it, and therefore

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Speaker 2: it doesn't have to pay as well. It may grow

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Speaker 2: right because firms like well, look it's cheaper than it

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Speaker 2: used to be. It's more productive than it used to

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Speaker 2: be and lots of people can do it h and

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Speaker 2: we see this often, right, So like another nice another

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Speaker 2: example this is ride hailing hailing software the you know

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Speaker 2: the phone apps right, uber.

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Speaker 3: Uber, uberlyft Right.

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Speaker 2: They changed the work of taxi and chauffeur service in

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Speaker 2: number of ways. They made it much easier to enterance on.

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Speaker 2: But one thing they did is they entirely eliminated the

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Speaker 2: expertise that was required to drive, because you used to

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Speaker 2: have to know your way around.

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Speaker 1: A place to drive, famously in London, Right, there's a

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Speaker 1: test that you had to know every street in London

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Speaker 1: or something to be a cap driver in to get it.

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Speaker 2: Yeah, take about three years of memorization. It was an

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Speaker 2: incredibly rigorous feed Yeah.

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Speaker 1: And then you can now you can just get a

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Speaker 1: map on your phone. Like now, technoledge is just profoundly obsolete.

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Speaker 1: It's such a wild show, exactly.

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Speaker 2: It's it's totally economically stranded, right. It's it's still out there,

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Speaker 2: but it isn't useful. It has no scarcity value, right

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Speaker 2: because everybody else gets it on their phone.

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Speaker 3: So what did this do? It eliminated the.

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Speaker 2: Expertise requirement to do this work, but it made the

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Speaker 2: work very productive. Right, So we have you know, several

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Speaker 2: hundred percent more people in this walk of life in

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Speaker 2: driving show for service than we did twenty years ago,

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Speaker 2: but their pay, the pay of a taxi driver's showpers

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Speaker 2: has fallen relative to the average.

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Speaker 1: Right.

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Speaker 2: So this so this dynamic of simplifying work enabling more

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Speaker 2: people to do it but at lower rates of pay.

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Speaker 2: That's one of the kind of directions that automation can push,

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Speaker 2: but it can push in the other just like those accountants.

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Speaker 1: So you have this phrase, at least I associate the

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Speaker 1: phrase with you that describes this phenomenon, the hollowing out

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Speaker 1: of the middle. And it seems like this is roughly

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Speaker 1: as a sort of crude sure, college degree not college degree,

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Speaker 1: seems like a way to draw the line for who

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Speaker 1: on average benefited from the arrival of computers and who

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Speaker 1: on average was hurt by It is that about, right?

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Speaker 3: Absolutely?

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Speaker 2: I mean that has been over the last forty years

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Speaker 2: has been the huge economic dividing line.

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Speaker 3: Right.

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Speaker 2: The earnings differential between college and on college you know,

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Speaker 2: was always you know, substantial, and then around nineteen eighty

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Speaker 2: started rising and rising, and it rose for thirty years

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Speaker 2: and then has plateaued at that point. So now the

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Speaker 2: college high school differential on average and is about seventy

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Speaker 2: five percent.

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Speaker 1: So people with college degrees make seventy five percent more

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Speaker 1: for our people.

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Speaker 2: On average, Yeah, on average, so huge, huge difference.

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Speaker 1: So okay, now we have set the table for the

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Speaker 1: arrival of AI. Right, this is this is a story,

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Speaker 1: a true story of what's happened over the last several

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Speaker 1: decades because of computers. Now we have this new kind

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Speaker 1: of technology showing up at our doorstep. You've written a

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Speaker 1: story about how AI might arrive quite differently in the

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Speaker 1: labor force than computers did, right, with different distributional effects.

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Speaker 1: Tell me about that.

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Speaker 2: So over the last you know, really over the last

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Speaker 2: four decades, we've become a society dominated by services. You know,

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Speaker 2: we spend twenty percent of GDP on health care and education.

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Speaker 2: It's like, you know, so back in the you know,

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Speaker 2: nineteen forty, right, we spent a lot more money on

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Speaker 2: you know, cars and appliances and food and clothing.

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Speaker 1: Food. Food was a huge at the household budget in

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Speaker 1: nineteen forty and now it's ten percent instead of thirty

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Speaker 1: or forty or something.

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Speaker 2: Right, But why so the mixed news in that is

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Speaker 2: why do we spend less on those things? Partly because

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Speaker 2: we've gotten really productive with them. They've gotten really cheap, right,

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Speaker 2: all these services like healthcare, like education, like law, they've

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Speaker 2: not We've not gotten more productive with them, right, we

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Speaker 2: just have expensive people doing them. And those people have

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Speaker 2: gotten more expensive as the services become more necessary.

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Speaker 3: They've not gotten faster with them.

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Speaker 2: And so this created it's a distribution income that very

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Speaker 2: much favors the professional class and the guilds of people,

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Speaker 2: be they lawyers, be they doctors, they be they professors, right,

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Speaker 2: you know, engineers, architects, you know, contractors, And so that's

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Speaker 2: not a great scenario for most people. You know, if

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Speaker 2: you're a professional, you're on both sides that transaction. Like

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Speaker 2: you know, yes, you have to pay a ton to

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Speaker 2: a lawyer and to send your kid to college. But

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Speaker 2: on the other hand, you're paid a lot to you know,

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Speaker 2: do marketing, whatever it is that you do. But for

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Speaker 2: most people, most people are not in that line of work, right.

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Speaker 2: Only you know, forty percent of us workers have a

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Speaker 2: college degree. Most of them are not highly paid professionals.

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Speaker 2: So most people are only on the side of the

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Speaker 2: transaction where they pay a lot.

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Speaker 3: Of money for those services.

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Speaker 2: So the potential for AI is to enable more people

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Speaker 2: to do that expert work and bring down its price

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Speaker 2: while increasing the set of jobs available to people.

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Speaker 1: Right, So there's two sides of the coin you're sort

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Speaker 1: of packing into that last half of the sentence. Right,

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Speaker 1: So you're saying more people could do jobs that are

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Speaker 1: now done by a small guild of expects. So that's

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Speaker 1: good for the people doing the jobs. And also if

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Speaker 1: that happened, it would bring down the price of those services,

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Speaker 1: So that's good forever.

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Speaker 3: That's good for customers.

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Speaker 1: Like that sounds like a lot to ask for, Like

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Speaker 1: what what is an example?

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Speaker 3: Sure?

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Speaker 1: Like what do you think? Sure?

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Speaker 2: But let me also say, like, not everybody loves that story, right,

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Speaker 2: Who doesn't love that story?

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Speaker 3: The people at the top of that pyramid.

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Speaker 1: Right, right, you know, the people in the guilds. That's

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Speaker 1: why they're in a gilt.

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Speaker 2: Right.

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Speaker 1: But so let's do an example because it'll become much clearer.

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Speaker 1: Like you you've written about doctors and nurse practitioners right

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Speaker 1: in this context, So you.

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Speaker 2: Know nurse practitioners, you know, they're registered nurses who have

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Speaker 2: an additional master's degree, and that enables them to diagnose.

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Speaker 3: Treat and prescribe.

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Speaker 2: In many states they can debscribe, not all of them,

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Speaker 2: it depends, you know, there's different rules, and they now

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Speaker 2: that really encroaches on the territory that used to be

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Speaker 2: wholly owned by physicians, right, and you know this, they

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Speaker 2: have not been welcomed by the American Medical Association, not all.

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Speaker 2: They fought very hard to limit their scope of practice.

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Speaker 2: And in fact, you know, the neurors practitioners basically it's

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Speaker 2: been a you know, they started sixty years ago, like

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Speaker 2: trying to create a credential, a curriculum of scope of practice.

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Speaker 2: They you know, fought their way and that it's only

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Speaker 2: in the last twenty years they've grown, and they expected

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Speaker 2: to grow a lot, but always facing great pushback from

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Speaker 2: the medical establishment. Why because they're encroaching on their expert

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Speaker 2: territory that's very threatened. So I think like the you know,

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Speaker 2: the growth of mirrors practitioners has in many ways been

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Speaker 2: good for the labor market and good for patients.

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Speaker 3: Right, It's brought down costs.

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Speaker 1: And is the evidence I haven't looked at the evidence

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Speaker 1: on this in particular.

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Speaker 2: The evidence is not there's there isn't enough evidence on this, Okay,

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Speaker 2: So I mean I.

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Speaker 1: If there isn't a clear evidence one way or the other,

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Speaker 1: that's right.

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Speaker 2: Okay, there's one paper I know, but it's in a

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Speaker 2: very very limited scope, so I would know it's in

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Speaker 2: the Veterans Administration. But I think like from a from

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Speaker 2: a like, like I brought my kids to the pediatrician.

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Speaker 3: And my began bringing my kids to pediatrician.

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Speaker 2: There were no open nurse practitioners and I would wait

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Speaker 2: hours to see the pediatrician. He was terrific, but then

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Speaker 2: it was like a half day lost by the time

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Speaker 2: my kids were ten, Right, I would just go into

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Speaker 2: the p diatrican's office and boom, I would see a

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Speaker 2: nurse practitioner and it was so great. And they were attentive, right,

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Speaker 2: and they were knowledgeable, they're up to date, and they

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Speaker 2: were accessible.

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Speaker 3: Uh.

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Speaker 2: And of course and that's a that's a high pay

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Speaker 2: job and it's growing rapidly. So now that's not a

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Speaker 2: function of AI clearly, right.

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Speaker 1: So so okay, so now we have a we have

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Speaker 1: this structure, bring AI into the story. Now, how does

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Speaker 1: how does AI bear our story?

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Speaker 2: So? AI gives people tools to accomplish complicated things that

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Speaker 2: might be out of reach without those tools. But let

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Speaker 2: me be clear, it doesn't mean anyone can do anything.

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Speaker 2: Doesn't mean I can just go into met doctors often

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Speaker 2: be a nurse practitioner.

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Speaker 3: Right, you need to have domain knowledge.

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Speaker 2: Right, I have to understand, like what do I do

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Speaker 2: with someone starts bleeding or has a heart attack.

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Speaker 3: I need to.

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Speaker 2: Understand, you know, a ton of things. But given that,

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Speaker 2: with better tools, I can do more.

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Speaker 3: Right.

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Speaker 2: So AI can help with diagnosis, right, it can help

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Speaker 2: figure out how a medical practitioner should allocate their time

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Speaker 2: and attention. Appointment with a nurse practitioner, even in a clinic,

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Speaker 2: maybe twelve minutes, maybe twenty minutes, there's a ton that

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Speaker 2: could happen.

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Speaker 3: Right.

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Speaker 2: Having a tool that says, hey, here's the medical record, right,

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Speaker 2: this is what we saw last time. You know, here's

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Speaker 2: your high priority item, and here's it you know, and

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Speaker 2: here's the right resources.

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Speaker 3: And then you, between the AI.

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Speaker 2: And yourself, you can say, okay, now I can triage

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Speaker 2: this and decide should I send this person home?

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Speaker 3: Should I refer them upward? Can I care for them now?

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Speaker 2: And so you are it's a force multiplier for your

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Speaker 2: expertise right enables you to go further, and that would

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Speaker 2: be true. You know, whether you're if you're doing legal work,

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Speaker 2: if you're doing accounting work, if you're developing software. Software

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Speaker 2: is an area where AI is going to allow lots.

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Speaker 3: And lots of entry.

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Speaker 2: A lot more people can do software using AI because

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Speaker 2: they don't have to understand a much of the kind

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Speaker 2: of engineering of coding. Now as you hear software is

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Speaker 2: going to you knows exist at many levels. Right the

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Speaker 2: people who are training AI models, running data centers, doing

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Speaker 2: enterprise software, they will be very specialized, very highly paid.

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Speaker 2: But there will be a lot more software that's like

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Speaker 2: Uber drivers.

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Speaker 3: Right.

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Speaker 2: Oh, I want some to vibe code, you know, an

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Speaker 2: app for my home to do X, Y and Z.

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Speaker 2: I can easily find someone who can write that software

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Speaker 2: for me because they'll just use an AI. I probably

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Speaker 2: probably you won't do it yourself. Probably you'll still hire someone.

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Speaker 1: I mean, that's not obvious to me. Why do you

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Speaker 1: say probably you won't do it yourself.

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Speaker 2: There's you know, for example, you can easily build a

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Speaker 2: website right using wicx or squarespace things, But most people

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Speaker 2: who have a business, they hire someone to build a

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Speaker 2: website for them. Why, Well, One, there's a lot of

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Speaker 2: design that goes into it. It's not just a matter

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Speaker 2: of getting it running right. And two there's a lot

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Speaker 2: of back end behind it in terms of oh it's

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Speaker 2: going to take payments, Oh it's gonna you know.

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Speaker 1: But these things seem aliable so that.

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Speaker 2: You know it could change. But there are many many

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Speaker 2: things that you could in theory do for yourself.

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Speaker 1: I would not want to be in the website building

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Speaker 1: business right now. I don't know, right, does that not

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Speaker 1: like I'm not sure you can definitely do design and

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Speaker 1: back end work with whatever claude We'll see.

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Speaker 3: Yeah, we'll say.

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Speaker 2: The likelihood is there will be more entry. It will

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Speaker 2: come in at lower cost, but more people will be

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Speaker 2: able to do it, and you know, just like the

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Speaker 2: Uber example, right, So I think they will be more

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Speaker 2: demand for software because it's cheaper. There'll be more people

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Speaker 2: who can produce good software at lower cost. It will

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Speaker 2: be less specialized, and therefore I suspect it it'll be

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Speaker 2: numerically large, but it won't be as well paid as

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Speaker 2: the typical software job.

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Speaker 1: So you'll have more software engineers and the media software

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Speaker 1: engineer will make less than they make today.

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Speaker 3: Yeah, and they won't. They won't have a degree in engineering, right.

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Speaker 1: Right, they'll just be somebody who's good a viating.

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Speaker 2: I mean, like you know, website design now is primarily

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Speaker 2: done by people who are designers, not programmers.

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Speaker 1: Yes, but design is is this is something that AI

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Speaker 1: is getting getting better.

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Speaker 3: Absolutely, that's right.

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Speaker 1: So let what are some other domains. I mean you

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Speaker 1: mentioned the law.

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Speaker 2: Law is a good example, but again laws guilt.

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Speaker 1: I could see law being more hollowing out of the

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Speaker 1: middle though, right, like more, you know, you don't need

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Speaker 1: to hire the junior lawyer. You don't need to hire

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Speaker 1: or at least as many junior lawyers. You don't need

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Speaker 1: to hire as many paralegals. You can have one lawyer

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Speaker 1: with an army of agents.

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Speaker 2: Uh, it's possible. But I think in many cases you

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Speaker 2: want a a knowledgeable, reliable, trustworthy person working with you.

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Speaker 1: Yeah, you the you, the CLO, the person who knows

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Speaker 1: a lawyer. But you would like them to.

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Speaker 3: Be efficient and cheap.

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Speaker 2: Yeah, right, so you'd like them to be able to

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Speaker 2: write a contract for you in an hour rather than

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Speaker 2: a day.

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Speaker 1: Yeah. There are the sort of software first versions of

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Speaker 1: this already. Right, there's legal Zoom, right has existed since before.

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Speaker 1: It's right actually for kind of routine things like a

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Speaker 1: will or whatever.

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Speaker 2: I mean, So in many cases something is not legal

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Speaker 2: unless it's done by a lawyer, Like if a lawyer

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Speaker 2: doesn't sign it, it doesn't count.

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Speaker 1: This is the guild part, right.

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Speaker 3: That's right.

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Speaker 2: But even so, you could say, well, look, you know

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Speaker 2: more lawyers can offer some of these services at lower

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Speaker 2: prices if they can do them effectively.

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Speaker 1: Yeah, or you could have one lawyer with one hundred

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Speaker 1: call them paralegals plus ais, and the lawyer is signing

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Speaker 1: it and the paralegal is talking to the customer and marketing. Okay,

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Speaker 1: But that.

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Speaker 2: Even so, that would in many ways would be a

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Speaker 2: better world. Right, legal services be cheaper, and there'd be

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Speaker 2: this whole layer of people who are doing that intermediate role, right,

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Speaker 2: as opposed to going to one very expensive person. And

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Speaker 2: hopefully in the good scenario, there's a layer of people

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Speaker 2: who are not just doing grunt work. They have some

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Speaker 2: specialized knowledge, they're.

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Speaker 3: Using the tools. Right often to use these tools, well,

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Speaker 3: you have to know something.

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Speaker 1: So this is an important point, right, because it's easy

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Speaker 1: for me to imagine in the world where you actually

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Speaker 1: don't have to know much to use the tool well, right,

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Speaker 1: and that this sort of current moment we're in is

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Speaker 1: just the you know, the foothills of real AI, and

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Speaker 1: it'll look like some terrible scratchy TV broadcasts from ninety

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Speaker 1: years ago in ten years, do you know what I mean?

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Speaker 1: And so it seems totally likely that the AI could

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Speaker 1: get good enough that you wouldn't need a paralegal like

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Speaker 1: that the paralegal wouldn't add value because the AI would

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Speaker 1: just figure out what you needed better than a paralegal would.

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Speaker 3: Uh, I don't you know.

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Speaker 2: I don't want to dismiss that possibility, but I do

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Speaker 2: think in many cases, you're ultimately going to want another

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Speaker 2: person involved in what you're doing, whether for its medical care,

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Speaker 2: whether it's for law, whether design, and so you will

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Speaker 2: want a person who has better tools and some specialized knowledge,

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Speaker 2: but they will not be as expensive, they will be

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Speaker 2: more productive. And let me say that, you know, and

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Speaker 2: there's something else to bear in mind, right, many of

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Speaker 2: these services that we're talking about about law, design, contracting,

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Speaker 2: like you know, even like restaurants, right, they don't lend

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Speaker 2: themselves to a superstar type model where one person does

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Speaker 2: all the work, right, like in entertainment. Right, we have

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Speaker 2: you know, Taylor swim Right, she can entertain two billion

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Speaker 2: people at a time, and so it actually crowds out

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Speaker 2: like there, it's hard you know, in movies, in music, right,

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Speaker 2: the superstars crowd out everybody else. Right, And that didn't

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Speaker 2: used to be true in a time when we didn't

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Speaker 2: have broadcasts, we didn't have television.

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Speaker 1: Right, there was an opera house in every face in

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Speaker 1: your town.

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Speaker 2: Right. Yeah, but this can't be true in medicine. It

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Speaker 2: can't be true in law, It can't be true in restaurants.

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Speaker 2: It can't be true in contracting, you know, in many

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Speaker 2: of these services because they're congestible people, people have finite capacity. Right,

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Speaker 2: there's no doctor who can serve two billion people simultaneously.

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Speaker 1: Did you see the movie Her? You're describing the part

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Speaker 1: right before big reveal in that movie.

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Speaker 3: The I'm Meanna.

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Speaker 2: There's a lot of hands on work, and there's a

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Speaker 2: lot of a lot of communication. And I think, so

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Speaker 2: someone's going to see the best doctor, someone's gonna see

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Speaker 2: the median doctor, someone's gonna see the twenty fifth percentile doctor, right,

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Speaker 2: and if those doctors have better tools and better you know,

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Speaker 2: supports and by and decision supports they will be better doctors.

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Speaker 1: Well should That part is the easy part, But I

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Speaker 1: feel like that's neither here nor there with respect to

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Speaker 1: what we're talking. No, no, no, that's.

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Speaker 2: That's very critical because it means that you know, it

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Speaker 2: means it just because some doctor has AI, right, that

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Speaker 2: doctor does not crowd out all the other workers.

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Speaker 1: Yeah, I mean doctor is like a weird case that

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Speaker 1: is not like most things, right, Like doctor, like you

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Speaker 1: care about having a person in the room at least

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Speaker 1: for now when it's a doctor, more than you care

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Speaker 1: about it with almost anything else, right Like like with

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Speaker 1: most legal stuff. I mean, unless it was some huge thing,

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Speaker 1: as long as you, like everybody use the AI and

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Speaker 1: you know it was fine, you would just use the AI,

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Speaker 1: do you know what I mean? Like for sure at

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Speaker 1: least in a lot of cases. Yes, some people want

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Speaker 1: a person, but.

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Speaker 3: Do you know what I mean, I'm following what you're saying.

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Speaker 1: Somebody who's a teenager now, like they don't want to

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Speaker 1: talk to a person, Like they would rather not talk

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Speaker 1: to a person if they could. I know teenagers, Yeah,

439
00:21:24,876 --> 00:21:25,996
Speaker 1: I mean live in my house.

440
00:21:28,156 --> 00:21:31,916
Speaker 2: Yeah, I think I think medicine is maybe is the

441
00:21:31,996 --> 00:21:32,996
Speaker 2: strongest example.

442
00:21:33,116 --> 00:21:35,436
Speaker 1: So, yeah, medicine is the strongest example. Yes, you want

443
00:21:35,476 --> 00:21:37,476
Speaker 1: you want a person, you want a doctor or a nurse,

444
00:21:37,556 --> 00:21:39,436
Speaker 1: and it's you're scared.

445
00:21:39,276 --> 00:21:41,636
Speaker 2: And well, I mean, let's first let's remember that, you know,

446
00:21:41,676 --> 00:21:44,076
Speaker 2: medicine is a huge part of employment. It's also about

447
00:21:44,116 --> 00:21:47,036
Speaker 2: twenty percent of US employment healthcare, and it's growing.

448
00:21:47,716 --> 00:21:47,916
Speaker 3: Right.

449
00:21:48,116 --> 00:21:51,316
Speaker 2: Health is sort of the ultimate luxury good. Right, It's

450
00:21:51,356 --> 00:21:54,196
Speaker 2: the one thing that as you get more and more affluent,

451
00:21:54,756 --> 00:21:57,236
Speaker 2: you want to spend more and more on, right, because

452
00:21:57,276 --> 00:21:59,436
Speaker 2: you know, you don't want more and more food you

453
00:21:59,436 --> 00:22:01,196
Speaker 2: can have. You you know, some people can have one

454
00:22:01,236 --> 00:22:03,836
Speaker 2: hundred cars, but most people are good with two. But

455
00:22:03,956 --> 00:22:07,436
Speaker 2: your longevity and your well being, right, those are kind

456
00:22:07,436 --> 00:22:10,356
Speaker 2: of fundamental to everything else that you do. And so

457
00:22:10,676 --> 00:22:12,476
Speaker 2: and you look at all these billionaire bros right whore

458
00:22:12,516 --> 00:22:15,116
Speaker 2: you know, they have their blood boys and there you know,

459
00:22:15,276 --> 00:22:17,436
Speaker 2: and they're spending infinite you know why are they doing that?

460
00:22:17,556 --> 00:22:17,716
Speaker 1: Right?

461
00:22:17,756 --> 00:22:20,556
Speaker 2: Why is why is healthcare becoming a larger and larger

462
00:22:20,596 --> 00:22:22,356
Speaker 2: part of expenditure over time?

463
00:22:22,636 --> 00:22:22,796
Speaker 3: Right?

464
00:22:22,836 --> 00:22:25,956
Speaker 2: Because we're getting more affluent h and so it matters

465
00:22:25,996 --> 00:22:28,316
Speaker 2: more h And they're getting more effective.

466
00:22:29,036 --> 00:22:29,316
Speaker 3: Right.

467
00:22:29,436 --> 00:22:31,036
Speaker 2: You know, there was a one hundred years ago, it

468
00:22:31,076 --> 00:22:32,636
Speaker 2: wasn't clear it was good for your health. Nego see

469
00:22:32,636 --> 00:22:33,756
Speaker 2: a doctor, right, No.

470
00:22:33,756 --> 00:22:35,956
Speaker 1: It quite possibly bad for you, exactly.

471
00:22:36,036 --> 00:22:38,476
Speaker 2: Now there's just an amazing number of things and that

472
00:22:38,556 --> 00:22:42,356
Speaker 2: number is expanded, so we will spend more on health services,

473
00:22:42,436 --> 00:22:44,196
Speaker 2: it will be a larger part of employment. And of

474
00:22:44,236 --> 00:22:46,756
Speaker 2: course we're aging, right, I mean, you know our population

475
00:22:46,996 --> 00:22:49,996
Speaker 2: is getting older, so it's not at all crazy to

476
00:22:49,996 --> 00:22:52,916
Speaker 2: think this will be remain And I don't care how

477
00:22:52,996 --> 00:22:55,476
Speaker 2: much AI you throw in there. It's not going to

478
00:22:55,516 --> 00:22:58,516
Speaker 2: shrink in terms of employment. It's going to be ill,

479
00:22:58,636 --> 00:23:01,796
Speaker 2: provide more services, ideally at lower cost, and make it

480
00:23:01,836 --> 00:23:05,676
Speaker 2: more accessible. It's not going to become small. So although

481
00:23:05,756 --> 00:23:10,276
Speaker 2: it is a special case, it's not like a ledge cases.

482
00:23:10,356 --> 00:23:13,036
Speaker 2: So it's a big special, big special special exactly.

483
00:23:17,156 --> 00:23:29,196
Speaker 1: Will be back in just a minute. I want to

484
00:23:29,236 --> 00:23:32,036
Speaker 1: go back to the macro idea. Right, So you have

485
00:23:32,116 --> 00:23:37,716
Speaker 1: this argument, this story about how computers before AI essentially

486
00:23:37,756 --> 00:23:39,876
Speaker 1: contributed to rising at a quality. Right, Like, if you

487
00:23:39,876 --> 00:23:42,036
Speaker 1: were in the top whatever, twenty percent of the distribution,

488
00:23:42,436 --> 00:23:44,436
Speaker 1: computers are good for you. If you were in the middle,

489
00:23:44,436 --> 00:23:45,076
Speaker 1: they were bad for you.

490
00:23:45,116 --> 00:23:45,516
Speaker 3: That's correct.

491
00:23:45,556 --> 00:23:51,476
Speaker 1: In short, how what's the macro story for AI.

492
00:23:52,036 --> 00:23:56,516
Speaker 2: So I don't want to oversimplify. I think it's quite nuanced.

493
00:23:56,676 --> 00:24:02,716
Speaker 2: It's possible that it will enable war competition in these

494
00:24:02,836 --> 00:24:05,276
Speaker 2: high end services that we can spend so much money on,

495
00:24:05,556 --> 00:24:09,196
Speaker 2: and that will could contribute to lowering inequality somewhat and

496
00:24:09,276 --> 00:24:13,116
Speaker 2: sort of growing back a different middle, right, middle set

497
00:24:13,196 --> 00:24:16,716
Speaker 2: of software engineers, a middle set of kitchen designers and contractors,

498
00:24:17,116 --> 00:24:19,436
Speaker 2: you know, middle set of very skilled repair people who

499
00:24:19,516 --> 00:24:22,796
Speaker 2: you know, there's a ton of knowledge and expertise involved.

500
00:24:22,996 --> 00:24:25,716
Speaker 2: It's not all you know comes from college degrees, right,

501
00:24:25,836 --> 00:24:27,676
Speaker 2: people who are in the trades, who do electrical work,

502
00:24:27,676 --> 00:24:29,796
Speaker 2: who do plumbing, who do you know construction?

503
00:24:30,076 --> 00:24:32,556
Speaker 1: I mean people the trades have been doing pretty well, right, exactly.

504
00:24:32,596 --> 00:24:34,396
Speaker 1: I avoided this hollowing out of the middle because a

505
00:24:34,436 --> 00:24:38,916
Speaker 1: computer can't whatever, fix your fix the wiring in your house.

506
00:24:38,996 --> 00:24:41,156
Speaker 2: Yeah, absolutely not. That's right, And I think AI can

507
00:24:41,156 --> 00:24:43,756
Speaker 2: actually be helpful, and we already see examples of this, right,

508
00:24:43,796 --> 00:24:46,316
Speaker 2: you know, you can solve a harder problem. So that

509
00:24:46,356 --> 00:24:49,116
Speaker 2: would create a better a better middle, a different middle.

510
00:24:49,276 --> 00:24:52,396
Speaker 2: It wouldn't be the same middle, and so it's somewhat

511
00:24:52,436 --> 00:24:53,316
Speaker 2: expertise leveling.

512
00:24:53,396 --> 00:24:53,516
Speaker 3: Right.

513
00:24:53,556 --> 00:24:57,716
Speaker 2: It creates competition where we desperately need it and creates

514
00:24:57,716 --> 00:25:02,796
Speaker 2: opportunity for people who have appropriate skills foundational expertise. Right,

515
00:25:02,796 --> 00:25:04,316
Speaker 2: it's got to be got to know something about medicine.

516
00:25:04,316 --> 00:25:05,676
Speaker 2: You gotta know something about the trades, You got to

517
00:25:05,676 --> 00:25:06,956
Speaker 2: know something about the law, and you need to know

518
00:25:06,996 --> 00:25:07,836
Speaker 2: something about software.

519
00:25:07,916 --> 00:25:11,116
Speaker 3: But maybe not as much. Right. You could imagine we

520
00:25:11,196 --> 00:25:11,556
Speaker 3: have a.

521
00:25:11,476 --> 00:25:14,276
Speaker 2: Four year you know, healthcare degree, right where you learn

522
00:25:14,356 --> 00:25:15,996
Speaker 2: a bunch of stuff and then you're ready to do

523
00:25:16,036 --> 00:25:17,796
Speaker 2: a lot of services, a lot of health services.

524
00:25:18,836 --> 00:25:20,036
Speaker 3: So that would be that would be.

525
00:25:20,036 --> 00:25:23,436
Speaker 2: A world where there's more opportunity for middle skilled workers

526
00:25:23,436 --> 00:25:26,716
Speaker 2: and lower prices, and so that would have less inequality.

527
00:25:27,596 --> 00:25:29,836
Speaker 2: But I want to say that even if that's true

528
00:25:29,836 --> 00:25:32,156
Speaker 2: my scenario and there's less, there's gonna be a ton

529
00:25:32,196 --> 00:25:35,436
Speaker 2: of disruption, right, because we're talking about expertise and sometimes

530
00:25:35,476 --> 00:25:36,116
Speaker 2: being vacated.

531
00:25:36,156 --> 00:25:36,276
Speaker 3: Right.

532
00:25:36,316 --> 00:25:39,316
Speaker 2: If you're a medical transcriptionist, that job is almost gone.

533
00:25:39,476 --> 00:25:39,676
Speaker 3: Right.

534
00:25:39,716 --> 00:25:42,476
Speaker 2: If you're a language translator, some will remain and they'll

535
00:25:42,476 --> 00:25:45,316
Speaker 2: be highly highly skilled, right, but there'll be few of them.

536
00:25:45,476 --> 00:25:47,956
Speaker 2: I'm very worried about people who work in call centers, right,

537
00:25:48,716 --> 00:25:52,996
Speaker 2: you know, there's several million of them. It's very disruptive

538
00:25:54,196 --> 00:26:00,036
Speaker 2: psychically damaging, economically scarring for people to have their expertise

539
00:26:00,676 --> 00:26:02,676
Speaker 2: suddenly devalued. The thing that you know how to do.

540
00:26:02,916 --> 00:26:04,676
Speaker 2: For most most people are doing the best paid thing

541
00:26:04,716 --> 00:26:07,556
Speaker 2: they can do. Not everybody, right, some people are podcasting,

542
00:26:08,396 --> 00:26:13,236
Speaker 2: but out our professors, right, many professors, yes, could you know.

543
00:26:13,236 --> 00:26:15,036
Speaker 3: There are higher paying things they could do. They love

544
00:26:15,076 --> 00:26:15,396
Speaker 3: what they do.

545
00:26:15,676 --> 00:26:18,636
Speaker 2: So most people are doing you know, they don't have

546
00:26:18,636 --> 00:26:21,756
Speaker 2: that luxury, right, They're doing the job that pays them

547
00:26:21,756 --> 00:26:23,836
Speaker 2: the best income they can get. And if that work

548
00:26:23,876 --> 00:26:26,676
Speaker 2: becomes less valuable, there's not an equally good job, right,

549
00:26:26,716 --> 00:26:28,476
Speaker 2: They're gonna have to conness like WHI is loss of

550
00:26:28,516 --> 00:26:32,796
Speaker 2: manufacturing work so devastating makings. It's often specialized work that

551
00:26:32,916 --> 00:26:35,916
Speaker 2: you know is relatively highly paid, and the alternative work

552
00:26:36,316 --> 00:26:38,516
Speaker 2: for someone with that education, you know, it's not going

553
00:26:38,596 --> 00:26:41,556
Speaker 2: to be as good. So there's gonna be a lot

554
00:26:41,556 --> 00:26:44,876
Speaker 2: of change, and I expect AI to devalue some forms

555
00:26:44,836 --> 00:26:48,276
Speaker 2: of expertise really rapidly. Others will become more valuable, and

556
00:26:48,596 --> 00:26:50,756
Speaker 2: it's not the same people who are on the upside

557
00:26:50,796 --> 00:26:52,836
Speaker 2: and downsid You could say the average is great, but

558
00:26:52,916 --> 00:26:56,076
Speaker 2: like no one experiences the average, And so even if

559
00:26:56,076 --> 00:26:57,716
Speaker 2: you say, oh, yeah, that could be a better world.

560
00:26:57,916 --> 00:27:00,156
Speaker 2: It's like, yeah, but we don't get to that world overnight,

561
00:27:00,836 --> 00:27:02,996
Speaker 2: and there's a lot of change that happens along the way,

562
00:27:03,076 --> 00:27:05,196
Speaker 2: and we should be investing and preparing for that.

563
00:27:05,756 --> 00:27:08,116
Speaker 1: So let's talk about that, I mean, and that we

564
00:27:08,236 --> 00:27:11,076
Speaker 1: can be like there's the policy WII and then there's

565
00:27:11,196 --> 00:27:14,356
Speaker 1: like I'm some guy, what should I do? So, like

566
00:27:14,676 --> 00:27:16,436
Speaker 1: what do we do on both of those levels?

567
00:27:16,596 --> 00:27:19,356
Speaker 2: Let's start with the policy week? Yeah, okay, So there

568
00:27:19,356 --> 00:27:22,436
Speaker 2: are three buckets of things that I think are very important.

569
00:27:22,676 --> 00:27:25,676
Speaker 2: One of them is to help people transition between jobs.

570
00:27:25,676 --> 00:27:28,476
Speaker 2: So the first of those is wage insurance. Wage insurance

571
00:27:28,596 --> 00:27:31,916
Speaker 2: is like you're working a manufacturing job. You're making fifty

572
00:27:31,956 --> 00:27:34,476
Speaker 2: thousand dollars a year, twenty five dollars an hour. You

573
00:27:34,516 --> 00:27:37,796
Speaker 2: lose that job and you know manufacturing is going down.

574
00:27:37,996 --> 00:27:40,276
Speaker 2: There's non an equivalent job, and the next job you

575
00:27:40,276 --> 00:27:42,236
Speaker 2: can get is fifteen bucks an hour, thirty thousand dollars

576
00:27:42,236 --> 00:27:44,996
Speaker 2: a year, and you say, I'm not gonna take that job.

577
00:27:45,076 --> 00:27:47,316
Speaker 3: That's like beneath my digity, and so you.

578
00:27:47,276 --> 00:27:49,156
Speaker 2: Don't, and then you spend a long time looking and

579
00:27:49,196 --> 00:27:51,036
Speaker 2: that you know it has its own costs, and people

580
00:27:51,276 --> 00:27:53,356
Speaker 2: the longer they're at a laborports, they're harder, they find Again,

581
00:27:53,436 --> 00:27:56,316
Speaker 2: so wage Insurance says, hey, you're like, we understand that

582
00:27:56,716 --> 00:27:59,396
Speaker 2: we're gonna make up half the difference between your twenty

583
00:27:59,396 --> 00:28:01,076
Speaker 2: five dollars hour job and your fifteen dollar an hour

584
00:28:01,156 --> 00:28:02,996
Speaker 2: job up to a finite amount of time, up to

585
00:28:03,036 --> 00:28:05,796
Speaker 2: two years, up to eight thousand dollars total. And we're

586
00:28:05,836 --> 00:28:07,716
Speaker 2: not telling you stay in a low wage job forever.

587
00:28:08,116 --> 00:28:10,316
Speaker 2: By all means get a better job, and so. And

588
00:28:10,356 --> 00:28:12,396
Speaker 2: we have evidence on it, not enough as much as

589
00:28:12,396 --> 00:28:15,396
Speaker 2: we want. But during the twenty tens, right as part

590
00:28:15,396 --> 00:28:17,396
Speaker 2: of the Trade Adjestment Act, there was an experiment done

591
00:28:17,756 --> 00:28:22,196
Speaker 2: with wage insurance. The result of this policy experiment was

592
00:28:22,796 --> 00:28:24,476
Speaker 2: it got people back to work faster, and it was

593
00:28:24,476 --> 00:28:26,436
Speaker 2: so effective that it paid for itself, in other words,

594
00:28:26,436 --> 00:28:32,596
Speaker 2: by the reductions in unemployment insurance payments and the increase

595
00:28:32,756 --> 00:28:36,596
Speaker 2: in tax revenue from people working. It was self funding. Now,

596
00:28:36,836 --> 00:28:38,996
Speaker 2: that's on a limited base of evidences. Were just placed

597
00:28:38,996 --> 00:28:42,596
Speaker 2: manufacturing workers, they were older. But there are now major

598
00:28:44,196 --> 00:28:47,276
Speaker 2: pilot studies underway working with states. I'm you know, more

599
00:28:47,276 --> 00:28:50,756
Speaker 2: details will be revealed soon. They're quite expensive and they're

600
00:28:50,836 --> 00:28:54,476
Speaker 2: you know, they're funded to try this at a larger scale,

601
00:28:55,076 --> 00:28:55,996
Speaker 2: so wage insurance.

602
00:28:56,036 --> 00:28:57,756
Speaker 1: This is one idea.

603
00:28:58,316 --> 00:29:00,036
Speaker 2: And I'll call it's a no regrets idea in the

604
00:29:00,076 --> 00:29:02,996
Speaker 2: sense that we won't be sorry we did it even

605
00:29:03,036 --> 00:29:04,636
Speaker 2: if there isn't a jobs apocalypse.

606
00:29:04,836 --> 00:29:06,996
Speaker 3: Right, It's a good idea. It was a good idea

607
00:29:07,076 --> 00:29:10,076
Speaker 3: twenty years ago. It's a good idea now. It'll still

608
00:29:10,076 --> 00:29:10,916
Speaker 3: be a good idea.

609
00:29:10,956 --> 00:29:14,316
Speaker 2: And the idea is people are making transitions, and those

610
00:29:14,316 --> 00:29:17,756
Speaker 2: transitions are frictional, they're costly, it's psychically damaging, and when

611
00:29:17,756 --> 00:29:19,356
Speaker 2: people are out of work for a long time that

612
00:29:19,436 --> 00:29:21,996
Speaker 2: has its own costs. You would like to facilitate them

613
00:29:21,996 --> 00:29:24,036
Speaker 2: going back to work, even while they continue to search.

614
00:29:25,276 --> 00:29:31,036
Speaker 1: And like, I feel like subsidizing work, essentially paying people

615
00:29:31,076 --> 00:29:33,796
Speaker 1: to work it politically it feels good. It does, and

616
00:29:33,796 --> 00:29:36,516
Speaker 1: frankly for the people like that. I mean, work is

617
00:29:36,596 --> 00:29:39,116
Speaker 1: valuable in our society, and like, you can feel different

618
00:29:39,116 --> 00:29:41,836
Speaker 1: ways about that, but I basically feel good about that,

619
00:29:41,916 --> 00:29:45,556
Speaker 1: and like subsidizing people that work seems good to me, frankly.

620
00:29:45,236 --> 00:29:47,516
Speaker 2: And I think that fits with the American psyche right.

621
00:29:47,636 --> 00:29:51,676
Speaker 2: Our political system is very hostile towards people who are

622
00:29:51,676 --> 00:29:53,956
Speaker 2: not working. Right, If you're not working and you're not

623
00:29:54,036 --> 00:29:56,236
Speaker 2: a kid, and you're not elderly, what.

624
00:29:56,276 --> 00:29:58,756
Speaker 3: Is wrong with you? Right, is what is the kind

625
00:29:58,796 --> 00:29:59,156
Speaker 3: of message?

626
00:29:59,196 --> 00:30:01,836
Speaker 2: That's why you're constantly we're always putting these work requirements.

627
00:30:02,076 --> 00:30:04,156
Speaker 1: And I mean you're not making a normative statement. Just

628
00:30:04,196 --> 00:30:06,716
Speaker 1: to be clear, you're describing the sort of political vibes.

629
00:30:06,796 --> 00:30:08,476
Speaker 3: Yeah, no, no, clearly, No, no, I do not. I'm not

630
00:30:08,516 --> 00:30:09,756
Speaker 3: I'm not endorsed that view at all.

631
00:30:09,796 --> 00:30:11,516
Speaker 2: I'm just saying that that appears to be the perspective

632
00:30:11,556 --> 00:30:13,716
Speaker 2: of the politicians, right, That's why they said, oh, you can't

633
00:30:13,716 --> 00:30:16,836
Speaker 2: get you know, snap, unless you're working, you can't get medical.

634
00:30:16,956 --> 00:30:19,476
Speaker 2: You know, they're constantly trying to condition things on work.

635
00:30:19,716 --> 00:30:24,076
Speaker 2: So this says it says, okay, like this is about supporting.

636
00:30:23,636 --> 00:30:26,196
Speaker 3: People to work. Okay, that's one thing one okay.

637
00:30:26,276 --> 00:30:26,516
Speaker 1: Two.

638
00:30:26,636 --> 00:30:31,916
Speaker 2: Two is we need to do retraining at scale much

639
00:30:31,916 --> 00:30:32,596
Speaker 2: more effectively.

640
00:30:32,796 --> 00:30:35,076
Speaker 1: Yeah, retraining seems like it never worked.

641
00:30:35,076 --> 00:30:38,156
Speaker 2: Okay, See that's what everybody says. But in fact that's

642
00:30:38,236 --> 00:30:39,556
Speaker 2: kind of based on old wisdom.

643
00:30:39,916 --> 00:30:41,236
Speaker 3: We now know much.

644
00:30:41,076 --> 00:30:43,516
Speaker 1: More about ai really good at retraining.

645
00:30:43,596 --> 00:30:45,916
Speaker 2: Oh no, we don't know that, but we do know

646
00:30:45,956 --> 00:30:49,196
Speaker 2: of really effective training programs. Now, they're small.

647
00:30:49,436 --> 00:30:51,036
Speaker 3: So like Project.

648
00:30:50,716 --> 00:30:55,716
Speaker 2: Quest you're a Jewish vocational services goodwill there what are

649
00:30:55,716 --> 00:30:58,996
Speaker 2: called sexual training programs. They train people for specific occupations

650
00:30:58,996 --> 00:31:02,196
Speaker 2: and activities. Right, so construction, it could be a medical technical,

651
00:31:02,236 --> 00:31:05,316
Speaker 2: it could be diesel repair, et cetera. But we don't

652
00:31:05,316 --> 00:31:07,156
Speaker 2: know how to scale those at a very large scale.

653
00:31:07,196 --> 00:31:09,476
Speaker 2: So what folks are working on now is trying to

654
00:31:09,476 --> 00:31:13,676
Speaker 2: evaluate those things in real time using big data without experiments,

655
00:31:13,676 --> 00:31:16,276
Speaker 2: and so you can actually measure the incremental earnings that

656
00:31:16,276 --> 00:31:18,436
Speaker 2: are coming out of these programs and then recycle some

657
00:31:18,476 --> 00:31:21,676
Speaker 2: of that incremental revenue to feedback into these programs.

658
00:31:22,036 --> 00:31:22,836
Speaker 3: And we because we.

659
00:31:22,876 --> 00:31:26,596
Speaker 2: Need to have a phollanx of options available to you.

660
00:31:26,756 --> 00:31:32,036
Speaker 1: Yeah, right, I mean that one. Maybe I just am

661
00:31:32,116 --> 00:31:33,876
Speaker 1: out of date on it, but you know, like your

662
00:31:33,876 --> 00:31:37,476
Speaker 1: own work on the effect of competition from China on

663
00:31:37,716 --> 00:31:39,756
Speaker 1: you know, workers in the US in the first part

664
00:31:39,796 --> 00:31:41,956
Speaker 1: of the century, Like, it's interesting to me that you

665
00:31:42,076 --> 00:31:44,876
Speaker 1: found more recently that like the towns that were really

666
00:31:44,956 --> 00:31:47,196
Speaker 1: impacted by that, the towns have come back, right to

667
00:31:47,236 --> 00:31:50,196
Speaker 1: some extent, right, but the specific workers who were displaced

668
00:31:50,196 --> 00:31:52,356
Speaker 1: have not, but that we did.

669
00:31:52,196 --> 00:31:54,476
Speaker 2: Nothing to retrain them, right, I mean there was no

670
00:31:54,556 --> 00:31:56,436
Speaker 2: policy in place, Right.

671
00:31:56,476 --> 00:31:58,676
Speaker 1: So you think it's a policy. You think you think

672
00:31:58,676 --> 00:32:00,836
Speaker 1: a policy could have helped the person who was somewhat

673
00:32:00,876 --> 00:32:03,756
Speaker 1: fifty to my approximate age, someone got laid off.

674
00:32:03,836 --> 00:32:06,036
Speaker 2: Yeah, okay, yeah, I mean it's not a panacea, but

675
00:32:06,076 --> 00:32:08,436
Speaker 2: it's something. And you know, other countries do this much

676
00:32:08,436 --> 00:32:11,036
Speaker 2: more effectively than we do, right, you know Denmark.

677
00:32:11,676 --> 00:32:13,476
Speaker 1: Right, I was afraid you were going to say Denmark,

678
00:32:13,516 --> 00:32:14,436
Speaker 1: it's always dead Mark.

679
00:32:14,596 --> 00:32:15,556
Speaker 3: I know, I know everyone.

680
00:32:15,596 --> 00:32:17,516
Speaker 2: It's like, you know, don't tell me about Scandinavia, like

681
00:32:17,556 --> 00:32:20,996
Speaker 2: it's not a real places.

682
00:32:19,796 --> 00:32:20,996
Speaker 1: Doesn't map to the US.

683
00:32:21,076 --> 00:32:23,836
Speaker 3: Right, Well, I agree, so city, I think that's absolutely right.

684
00:32:23,836 --> 00:32:28,196
Speaker 2: We can't do the Danish system, but it's an existence

685
00:32:28,196 --> 00:32:30,636
Speaker 2: proof of you can do it. Well, right, they're affected,

686
00:32:30,676 --> 00:32:32,956
Speaker 2: they can they can do it, Yell, it can be right.

687
00:32:32,996 --> 00:32:35,316
Speaker 2: So that's why my main, second major policy is to

688
00:32:35,356 --> 00:32:38,676
Speaker 2: send all unemployed workers to Denmark where they can be reattrained.

689
00:32:38,716 --> 00:32:40,516
Speaker 1: I just think that's going to have an incredible safety

690
00:32:40,596 --> 00:32:42,796
Speaker 1: net and swim in the harbor and co. Exactly, it's

691
00:32:42,796 --> 00:32:44,516
Speaker 1: gonna what's number three?

692
00:32:44,716 --> 00:32:44,956
Speaker 3: Okay?

693
00:32:45,036 --> 00:32:48,276
Speaker 2: Number three is what I call universal basic capital. Other

694
00:32:48,316 --> 00:32:50,276
Speaker 2: people call it that as well. So let me just

695
00:32:50,356 --> 00:32:53,196
Speaker 2: same shate universal basic income. It's a notion that you know,

696
00:32:53,276 --> 00:32:54,876
Speaker 2: whether you're working or not, we just write you check

697
00:32:54,876 --> 00:32:56,756
Speaker 2: every month from the largest.

698
00:32:56,476 --> 00:32:57,796
Speaker 3: Of the tech bros. Or whatever.

699
00:32:58,196 --> 00:33:00,396
Speaker 2: Yeah, I don't think the political economy of that is

700
00:33:00,516 --> 00:33:02,956
Speaker 2: viable for the same reason we just had the conversation

701
00:33:02,996 --> 00:33:05,356
Speaker 2: about we don't like to as a country. We're just

702
00:33:05,396 --> 00:33:08,596
Speaker 2: not supportive of people who are not working, or we

703
00:33:08,636 --> 00:33:12,196
Speaker 2: don't believe even unconditional transfers. So universal basic capital is

704
00:33:12,196 --> 00:33:14,676
Speaker 2: a little different idea. It says, look, at the time

705
00:33:14,716 --> 00:33:16,436
Speaker 2: you were born, we're going to give you an endowment

706
00:33:16,476 --> 00:33:16,956
Speaker 2: of capital.

707
00:33:16,996 --> 00:33:18,116
Speaker 3: It doesn't have to be a lot, a couple of

708
00:33:18,156 --> 00:33:18,916
Speaker 3: that you can call.

709
00:33:18,836 --> 00:33:21,236
Speaker 1: Them because capital means stock stock and we just say stock,

710
00:33:21,316 --> 00:33:21,676
Speaker 1: you know, we.

711
00:33:21,636 --> 00:33:25,436
Speaker 2: Call them Trump accounts, right, which is what exists already, right, Yeah,

712
00:33:25,676 --> 00:33:28,996
Speaker 2: And it's a it's an asset you can you know,

713
00:33:29,156 --> 00:33:31,716
Speaker 2: it's you can't spend it until you're of age.

714
00:33:31,916 --> 00:33:34,076
Speaker 1: You so like, just just to be a little more specific,

715
00:33:34,076 --> 00:33:38,036
Speaker 1: the notion is like the government will buy whatever a fund,

716
00:33:38,076 --> 00:33:39,916
Speaker 1: an ETF, a mutual fund that tracks the S and

717
00:33:39,956 --> 00:33:41,876
Speaker 1: P five hundred. The ob a great way to give

718
00:33:42,156 --> 00:33:44,836
Speaker 1: one thousand dollars to every baby basically, and you now

719
00:33:44,876 --> 00:33:46,796
Speaker 1: own a thousand dollars of S and P five hundred

720
00:33:46,796 --> 00:33:48,476
Speaker 1: stock and you can't touch it till you're eighteen. Is

721
00:33:48,516 --> 00:33:49,596
Speaker 1: that what you're thinking? Exactly?

722
00:33:50,356 --> 00:33:52,556
Speaker 2: And you can invest in it further, right, you just

723
00:33:52,556 --> 00:33:55,276
Speaker 2: like you can you have tax advantage for one K savings, right,

724
00:33:55,316 --> 00:33:55,556
Speaker 2: you have.

725
00:33:55,516 --> 00:33:57,316
Speaker 1: To like a five to nine like a college savings

726
00:33:57,356 --> 00:33:58,316
Speaker 1: stock or something exactly.

727
00:33:58,596 --> 00:34:00,956
Speaker 3: So, like, what what is the purpose of this? It does?

728
00:34:01,156 --> 00:34:02,036
Speaker 3: It does three things.

729
00:34:02,316 --> 00:34:06,276
Speaker 2: One is it it diversifies people, right, So most people,

730
00:34:06,316 --> 00:34:08,236
Speaker 2: you know, most people's lifetime income is bound up in

731
00:34:08,236 --> 00:34:11,156
Speaker 2: the value of their skill and expertise. And that's it's

732
00:34:11,196 --> 00:34:12,796
Speaker 2: it's always vulnerable, it's always risky.

733
00:34:12,996 --> 00:34:15,196
Speaker 1: It's labor. Most most people make most of their money

734
00:34:15,236 --> 00:34:15,956
Speaker 1: from working.

735
00:34:15,756 --> 00:34:18,276
Speaker 2: Exactly from the right, you're saying, it's always that's it's

736
00:34:18,316 --> 00:34:20,676
Speaker 2: always risky to have all your eggs in one basket, right,

737
00:34:21,036 --> 00:34:22,876
Speaker 2: especially if you know you do something specialized and all

738
00:34:22,876 --> 00:34:24,276
Speaker 2: of a sudden that thing becomes obsolete.

739
00:34:24,316 --> 00:34:25,116
Speaker 3: That's really risky.

740
00:34:25,316 --> 00:34:27,396
Speaker 2: So one, is it gives you It's not it's by

741
00:34:27,396 --> 00:34:30,556
Speaker 2: no means a complete diveristication start, but it's a start, right,

742
00:34:30,596 --> 00:34:33,916
Speaker 2: And especially that's one thing. Two if we think we're

743
00:34:33,996 --> 00:34:35,756
Speaker 2: entering a world where capital is going to rise in

744
00:34:35,836 --> 00:34:38,556
Speaker 2: value relative to labor, right, that will be less than

745
00:34:38,676 --> 00:34:41,556
Speaker 2: you especially want to have people have money in capital, right.

746
00:34:41,756 --> 00:34:44,796
Speaker 1: Yeah, meaning if if more of the national income is

747
00:34:44,836 --> 00:34:47,876
Speaker 1: going to go to companies are the owners of shares

748
00:34:47,916 --> 00:34:50,196
Speaker 1: of stocks, and less is going to go to labor, right,

749
00:34:50,196 --> 00:34:52,076
Speaker 1: the two places that can go, then you want more

750
00:34:52,156 --> 00:34:55,316
Speaker 1: people to own stock, if you want everybody to prosperate.

751
00:34:54,956 --> 00:34:56,076
Speaker 3: It, exactly, that's right.

752
00:34:56,156 --> 00:34:59,276
Speaker 2: And you know labor is you know, everyone is born

753
00:34:59,556 --> 00:35:02,556
Speaker 2: in you know the good scenario with one worker themselves, right,

754
00:35:02,556 --> 00:35:03,276
Speaker 2: who work for them.

755
00:35:04,116 --> 00:35:06,516
Speaker 3: But capital is not like that, right.

756
00:35:06,476 --> 00:35:08,396
Speaker 1: Right, some people are born with a lot and most

757
00:35:08,436 --> 00:35:08,916
Speaker 1: people are.

758
00:35:08,796 --> 00:35:11,196
Speaker 2: Born people born and zero that's right. And the third

759
00:35:11,196 --> 00:35:15,076
Speaker 2: thing it does is universal based capital. Again, is it

760
00:35:15,396 --> 00:35:17,956
Speaker 2: gives them an ownership stake, almost like a voting stake

761
00:35:18,436 --> 00:35:19,156
Speaker 2: in the economy.

762
00:35:19,356 --> 00:35:21,916
Speaker 1: Yes, well, I mean most most companies, you have a

763
00:35:22,116 --> 00:35:24,916
Speaker 1: voting voting rights if you're on stock, although not all.

764
00:35:24,916 --> 00:35:27,716
Speaker 2: Exactly, And so if you know, if you're I'm very

765
00:35:27,756 --> 00:35:31,356
Speaker 2: worried about a society where more people are seen as

766
00:35:31,476 --> 00:35:33,516
Speaker 2: claimants without being seen as contributors.

767
00:35:33,676 --> 00:35:36,716
Speaker 1: So claimant rather than contributor. That's the universal basic income.

768
00:35:36,836 --> 00:35:39,436
Speaker 1: That's the world where's the machines take our jobs and

769
00:35:39,476 --> 00:35:42,156
Speaker 1: people just get a check every month or whatever.

770
00:35:42,476 --> 00:35:44,796
Speaker 2: I don't morally oppose that, let me be clearer. I

771
00:35:44,836 --> 00:35:47,476
Speaker 2: just don't think it's our I don't think our body

772
00:35:47,516 --> 00:35:49,156
Speaker 2: politic tolerates it.

773
00:35:49,676 --> 00:35:52,996
Speaker 1: Yes well, and are like our structures of meaning and

774
00:35:53,116 --> 00:35:55,036
Speaker 1: of value are not well set up.

775
00:35:55,356 --> 00:35:57,516
Speaker 3: That I also agree with. That's even secondary point.

776
00:35:58,276 --> 00:36:01,276
Speaker 2: So that's so University of the Capital says, you know again,

777
00:36:01,276 --> 00:36:02,476
Speaker 2: it's a very small step in the way.

778
00:36:02,476 --> 00:36:02,756
Speaker 1: It says.

779
00:36:02,836 --> 00:36:05,436
Speaker 2: Look everyone, now, hey, I'm not just a claimant. I'm

780
00:36:05,436 --> 00:36:07,036
Speaker 2: a contributor. I own part of the capital.

781
00:36:07,516 --> 00:36:10,316
Speaker 1: And in this universe, is the government just buying shares

782
00:36:10,516 --> 00:36:13,596
Speaker 1: in the in stock and everything and giving it to people.

783
00:36:13,756 --> 00:36:15,236
Speaker 3: Sure, but it's not that hard to do.

784
00:36:15,396 --> 00:36:17,756
Speaker 2: Like, there's only about four million babies born a year

785
00:36:17,796 --> 00:36:19,956
Speaker 2: in the United States, right, so if you had to

786
00:36:19,996 --> 00:36:21,596
Speaker 2: write them each a check for a thousand dollars, that's

787
00:36:21,596 --> 00:36:24,116
Speaker 2: four billion bucks, right, That's less than we spend on

788
00:36:24,276 --> 00:36:28,836
Speaker 2: You know, I don't know anything you can anything, any

789
00:36:28,916 --> 00:36:32,316
Speaker 2: bomb you can drop, so it's not.

790
00:36:32,636 --> 00:36:35,316
Speaker 1: But I means, so one thousand dollars, I suppose compounding

791
00:36:35,356 --> 00:36:36,036
Speaker 1: it becomes non.

792
00:36:35,996 --> 00:36:37,636
Speaker 3: True, right, and you can do more than that.

793
00:36:37,756 --> 00:36:37,836
Speaker 1: Right.

794
00:36:37,916 --> 00:36:39,436
Speaker 2: You could even say to billionaire and say, hey, you

795
00:36:39,476 --> 00:36:41,236
Speaker 2: know what, here's a tax advantage way that you could

796
00:36:41,436 --> 00:36:44,436
Speaker 2: donate to the public. Take your shares and just throw

797
00:36:44,436 --> 00:36:46,236
Speaker 2: them into the baby bond fund and they will be destribed.

798
00:36:46,316 --> 00:36:48,916
Speaker 1: That's nice, right, Yeah, that's interesting. I mean it's sort

799
00:36:48,956 --> 00:36:51,796
Speaker 1: of a distributed sovereign wealth fund. Right, it's sort of

800
00:36:51,876 --> 00:36:53,236
Speaker 1: rhymes with a sovereign wealth fund.

801
00:36:53,356 --> 00:36:56,796
Speaker 2: That's that's exactly exactly what it is. Exactly what it is,

802
00:36:56,876 --> 00:36:58,916
Speaker 2: but a sovereign wealth fund. Right, you don't get to

803
00:36:58,956 --> 00:37:00,836
Speaker 2: choose your own investments. You can't say I need to

804
00:37:00,836 --> 00:37:01,676
Speaker 2: take it out today.

805
00:37:01,876 --> 00:37:04,716
Speaker 1: No, it's not yours in the way, that's what you're proposing.

806
00:37:05,356 --> 00:37:08,076
Speaker 2: So you know, and again, none of these policies, the

807
00:37:08,076 --> 00:37:12,036
Speaker 2: three I'm talking about, you know, uh, wage insurance, you know, modernized.

808
00:37:11,516 --> 00:37:15,876
Speaker 1: Training, they're small ish, right, They're they're small issues, that's right.

809
00:37:15,916 --> 00:37:17,236
Speaker 2: None of them is that, you know, and I think

810
00:37:17,276 --> 00:37:19,956
Speaker 2: we need big you know, we face big challenges. These

811
00:37:19,996 --> 00:37:22,556
Speaker 2: are around the edges. But they're all what I call

812
00:37:22,676 --> 00:37:25,436
Speaker 2: no regrets policies. Right, we won't be sorry we did them.

813
00:37:25,476 --> 00:37:26,596
Speaker 2: Even if the worst doesn't.

814
00:37:26,356 --> 00:37:26,996
Speaker 3: Come to pass.

815
00:37:27,556 --> 00:37:29,796
Speaker 2: These are things that are just you know, they are

816
00:37:30,236 --> 00:37:33,836
Speaker 2: wise in terms of helping people make transitions and giving

817
00:37:33,836 --> 00:37:37,436
Speaker 2: people diversification. You know, in the good scenario, right, if AI,

818
00:37:37,756 --> 00:37:41,316
Speaker 2: if we use it, well, we're going to become more affluent. Right,

819
00:37:41,356 --> 00:37:43,796
Speaker 2: but that doesn't mean we're going to have that will

820
00:37:43,796 --> 00:37:44,516
Speaker 2: be well shared.

821
00:37:44,756 --> 00:37:44,916
Speaker 3: Right.

822
00:37:44,996 --> 00:37:48,036
Speaker 2: We need a society which says, look, if there's gonna

823
00:37:48,036 --> 00:37:50,996
Speaker 2: be an AI dividend, meaning we become more productive, we

824
00:37:51,076 --> 00:37:53,396
Speaker 2: want to make sure that people are brought along, right,

825
00:37:53,476 --> 00:37:56,636
Speaker 2: that if you if you're if your expertise becomes devalued,

826
00:37:56,876 --> 00:37:58,796
Speaker 2: that there's new ways to train that get you back

827
00:37:58,836 --> 00:38:01,396
Speaker 2: in the labor market, that your portfolio, your income sources

828
00:38:01,396 --> 00:38:04,956
Speaker 2: are more diversified. You know, people you know, the science

829
00:38:04,956 --> 00:38:08,236
Speaker 2: fiction writer Ted Shang, you know, said look, when you

830
00:38:08,236 --> 00:38:10,676
Speaker 2: know people are saying, oh, you know, people are scared

831
00:38:10,676 --> 00:38:13,396
Speaker 2: of automation. They're scared of machines. They're not scared of automations,

832
00:38:13,396 --> 00:38:17,316
Speaker 2: they're scared of capitalism. Right, They're scared of their work

833
00:38:17,356 --> 00:38:19,676
Speaker 2: will be automated and there'll be nothing left for them

834
00:38:19,956 --> 00:38:21,236
Speaker 2: to get they won't get paid.

835
00:38:21,556 --> 00:38:21,756
Speaker 3: Right.

836
00:38:21,996 --> 00:38:26,116
Speaker 2: It's not that people are opposed to higher productivity or

837
00:38:26,156 --> 00:38:27,596
Speaker 2: you know, things being more convenient.

838
00:38:27,756 --> 00:38:32,076
Speaker 3: They're opposed to losing their livelihoods. Right, And AI could

839
00:38:32,436 --> 00:38:33,316
Speaker 3: right produce.

840
00:38:32,996 --> 00:38:34,596
Speaker 2: A lot of higher productivity and a lot of you know,

841
00:38:34,596 --> 00:38:36,596
Speaker 2: convenience automation, but put a lot of people out of work,

842
00:38:36,596 --> 00:38:38,076
Speaker 2: at least temporarily during the transition.

843
00:38:38,596 --> 00:38:41,676
Speaker 3: Right. So, no wonder people are afraid of it. Right,

844
00:38:41,756 --> 00:38:43,916
Speaker 3: No wonder people are opposed. No wonder people are angry

845
00:38:43,916 --> 00:38:46,596
Speaker 3: about data centers. They don't trust our.

846
00:38:46,876 --> 00:38:49,956
Speaker 2: Social and political systems to ensure them and make sure

847
00:38:49,956 --> 00:38:51,956
Speaker 2: they're brought along. So it's very understandable.

848
00:38:52,596 --> 00:38:52,716
Speaker 1: Right.

849
00:38:52,956 --> 00:38:54,796
Speaker 2: So I would like to take the edge off of

850
00:38:54,796 --> 00:38:56,836
Speaker 2: some of that, Right, I would like to have policies

851
00:38:57,156 --> 00:39:01,356
Speaker 2: that help support the challenges you help people through those

852
00:39:01,436 --> 00:39:04,076
Speaker 2: challenging things that are some of them are likely to

853
00:39:04,196 --> 00:39:06,236
Speaker 2: ahead even in the good scenarios.

854
00:39:09,836 --> 00:39:12,116
Speaker 1: We'll be back in a minute with the lightning ground.

855
00:39:23,796 --> 00:39:27,596
Speaker 1: Let's finish with the lighting rounds. Okay, what's the most

856
00:39:27,636 --> 00:39:29,716
Speaker 1: peril you've ever been in on a sailboat?

857
00:39:33,516 --> 00:39:33,876
Speaker 3: Actually?

858
00:39:33,876 --> 00:39:36,996
Speaker 2: I went, I went, I took my I went sailing

859
00:39:37,076 --> 00:39:37,916
Speaker 2: during a hurricane.

860
00:39:39,836 --> 00:39:40,436
Speaker 1: Bad idea.

861
00:39:40,516 --> 00:39:44,276
Speaker 2: Yeah, and I got knocked over six times on purpose. Well,

862
00:39:44,316 --> 00:39:45,596
Speaker 2: I know, I tried to stay upright.

863
00:39:46,516 --> 00:39:48,436
Speaker 1: No, no, did you go sailing at a hurricane?

864
00:39:48,476 --> 00:39:51,916
Speaker 2: I went sailing and Hurricane Lee a couple of years

865
00:39:51,916 --> 00:39:53,556
Speaker 2: ago in New Hampshire.

866
00:39:53,036 --> 00:39:55,116
Speaker 1: On a couple of years ago. I was gonna ask

867
00:39:55,156 --> 00:39:56,316
Speaker 1: if you were fourteen.

868
00:39:55,956 --> 00:39:59,756
Speaker 2: No, no, no, I was fifty nine. But I have

869
00:40:00,076 --> 00:40:03,596
Speaker 2: an ultra light RSA. It's like a laser, and it's

870
00:40:03,876 --> 00:40:06,956
Speaker 2: super fast and it's very competent even in high winds,

871
00:40:07,516 --> 00:40:08,916
Speaker 2: and I really want to try it out.

872
00:40:08,916 --> 00:40:09,676
Speaker 3: It was small lake.

873
00:40:10,276 --> 00:40:12,996
Speaker 2: People were like my neighbors were all there with the cameras.

874
00:40:13,036 --> 00:40:16,196
Speaker 2: I assume that if I started to drown, they would have, uh,

875
00:40:17,116 --> 00:40:20,396
Speaker 2: I don't know, sent the footage to see exactly exactly. So, yeah,

876
00:40:20,436 --> 00:40:21,236
Speaker 2: that was perilous.

877
00:40:21,436 --> 00:40:22,556
Speaker 3: That was That was exciting.

878
00:40:23,596 --> 00:40:24,996
Speaker 1: And did you capsize six times?

879
00:40:25,236 --> 00:40:25,676
Speaker 3: Six times?

880
00:40:25,876 --> 00:40:26,356
Speaker 1: Six times?

881
00:40:26,436 --> 00:40:28,076
Speaker 3: Was exhausting. It was completely exhausting.

882
00:40:28,356 --> 00:40:29,956
Speaker 1: It's like a little boat and you can kind of

883
00:40:30,036 --> 00:40:31,916
Speaker 1: lean and ride it is now you have to climb.

884
00:40:31,716 --> 00:40:34,636
Speaker 3: On the daggerboard and pull it up and then throw.

885
00:40:34,676 --> 00:40:36,276
Speaker 2: You have to do what's called the walrusk, where you'd

886
00:40:36,316 --> 00:40:38,396
Speaker 2: like sort of throw yourself out of the water into

887
00:40:38,436 --> 00:40:38,956
Speaker 2: the center of the boat.

888
00:40:38,956 --> 00:40:41,916
Speaker 3: Otherwise it capsize back on you. So that was fun.

889
00:40:43,156 --> 00:40:45,916
Speaker 1: What's it like teaching college students in the age of

890
00:40:45,996 --> 00:40:47,356
Speaker 1: AI at the dawn of AI?

891
00:40:49,076 --> 00:40:51,876
Speaker 2: So the students I teach a MIT students and they're

892
00:40:52,316 --> 00:40:53,436
Speaker 2: it's a great So.

893
00:40:53,476 --> 00:40:57,596
Speaker 1: They're really good at prompt They're really good, they're very.

894
00:40:59,196 --> 00:41:01,996
Speaker 2: There, they're super motivated. It's a it's a real luxury.

895
00:41:02,076 --> 00:41:03,716
Speaker 2: So I don't worry that they great.

896
00:41:03,796 --> 00:41:06,116
Speaker 1: It's perhaps never been a better moment to be an

897
00:41:06,236 --> 00:41:08,236
Speaker 1: MIT student than right and you.

898
00:41:08,236 --> 00:41:10,636
Speaker 2: Were being an MIT per I don't worry that they're

899
00:41:10,636 --> 00:41:12,516
Speaker 2: going to take all the shortcuts and not learn anything.

900
00:41:12,836 --> 00:41:15,036
Speaker 2: They're super hard working, so I feel like they're just

901
00:41:15,116 --> 00:41:15,436
Speaker 2: power to.

902
00:41:15,436 --> 00:41:17,476
Speaker 1: They're building the tools for everyone else to take the

903
00:41:17,516 --> 00:41:19,156
Speaker 1: shortcuts and not learn anything, and.

904
00:41:19,236 --> 00:41:21,436
Speaker 2: They know how to use the tools well, right, So

905
00:41:21,676 --> 00:41:24,196
Speaker 2: I feel like I can push them harder and say,

906
00:41:24,276 --> 00:41:25,916
Speaker 2: go figure this out, right.

907
00:41:26,076 --> 00:41:28,956
Speaker 1: Well, are you building AI into your classes or just

908
00:41:29,036 --> 00:41:30,876
Speaker 1: assume they're going to use AI to figure things out?

909
00:41:30,916 --> 00:41:31,436
Speaker 1: And that's good.

910
00:41:32,036 --> 00:41:36,436
Speaker 2: I my curriculum assumes they're using A like for example,

911
00:41:36,476 --> 00:41:38,636
Speaker 2: I've downweighted out of class exams. Right, I still get

912
00:41:38,636 --> 00:41:41,236
Speaker 2: problem sets and they're hard, but they don't count as much.

913
00:41:41,516 --> 00:41:43,596
Speaker 2: You have to do it on exams or on your papers. Right,

914
00:41:44,276 --> 00:41:46,996
Speaker 2: So I expect they're using it, but they know that

915
00:41:47,116 --> 00:41:49,036
Speaker 2: eventually they will have to demonstrate the knowledge. Right.

916
00:41:49,316 --> 00:41:50,996
Speaker 3: But I can throw the harder stuff with them, say

917
00:41:51,036 --> 00:41:51,556
Speaker 3: you'll solve this.

918
00:41:52,156 --> 00:41:55,756
Speaker 2: I can also say, hey, this can't be as badly

919
00:41:55,796 --> 00:41:57,916
Speaker 2: written as it is right now because you have better tools,

920
00:41:57,916 --> 00:42:00,436
Speaker 2: so go do it again. And of course when I

921
00:42:00,516 --> 00:42:03,516
Speaker 2: building my lectures, I can incorporate new material more quickly

922
00:42:03,556 --> 00:42:06,156
Speaker 2: because I can build slides and you know, master stuff

923
00:42:06,196 --> 00:42:06,956
Speaker 2: faster myself.

924
00:42:07,756 --> 00:42:10,516
Speaker 1: Most peril you've ever been in on a motorcycle.

925
00:42:10,556 --> 00:42:12,796
Speaker 3: But you're always in parallel on a motorcycle.

926
00:42:13,036 --> 00:42:19,036
Speaker 1: Yes, that is the correct answer. Anything else we should

927
00:42:19,076 --> 00:42:19,676
Speaker 1: talk about.

928
00:42:20,996 --> 00:42:23,316
Speaker 2: The uh, you know, I will say, I mean, just

929
00:42:23,596 --> 00:42:24,836
Speaker 2: like you know, I think a lot of young people

930
00:42:24,836 --> 00:42:28,716
Speaker 2: are extremely anxious about AI UH and understand I understand why.

931
00:42:28,756 --> 00:42:31,076
Speaker 2: But I also think they are the group that's gonna

932
00:42:31,916 --> 00:42:33,836
Speaker 2: use it well better than the rest of us and

933
00:42:33,876 --> 00:42:36,156
Speaker 2: figure out what to do with it. And so there's

934
00:42:36,196 --> 00:42:38,956
Speaker 2: never been a technology that's made old people great at

935
00:42:38,996 --> 00:42:41,636
Speaker 2: the at the cost of young people that I'm aware of. Right,

936
00:42:41,636 --> 00:42:44,836
Speaker 2: Almost all new technologies are developed and pioneered and ultimately

937
00:42:45,516 --> 00:42:49,636
Speaker 2: kind of figured out by younger generation that figures out

938
00:42:49,676 --> 00:42:51,356
Speaker 2: what to do with it. And so I think, you know,

939
00:42:51,516 --> 00:42:54,436
Speaker 2: businesses are now looking very hard to find, like you know,

940
00:42:54,516 --> 00:42:58,316
Speaker 2: kind of AI native kids, right, And so I think

941
00:42:58,356 --> 00:43:00,476
Speaker 2: there's a lot of risk. I understand the uncertainty and

942
00:43:00,556 --> 00:43:02,196
Speaker 2: the fear. I don't want to disminish that, but I

943
00:43:02,316 --> 00:43:03,316
Speaker 2: also think there's opportunity.

944
00:43:10,956 --> 00:43:15,076
Speaker 1: David Otter is a labor economist at MIT. Please let

945
00:43:15,196 --> 00:43:17,916
Speaker 1: us know what you think of the show, which you

946
00:43:17,916 --> 00:43:19,156
Speaker 1: want to hear more of, which you want to hear

947
00:43:19,276 --> 00:43:24,156
Speaker 1: less of particular guest ideas. You can email us at

948
00:43:24,236 --> 00:43:26,916
Speaker 1: problem at Pushkin dot fm. I read all the emails.

949
00:43:27,236 --> 00:43:30,836
Speaker 1: You can also find me on x rom LinkedIn.

950
00:43:31,556 --> 00:43:33,956
Speaker 3: Really do appreciate all the messages that we get.

951
00:43:34,676 --> 00:43:37,556
Speaker 1: Today's show was produced by Gabriel Hunter Chang and Trina Menino.

952
00:43:37,716 --> 00:43:40,436
Speaker 1: It was engineered by Hans Dale Sheet and edited by

953
00:43:40,516 --> 00:43:43,636
Speaker 1: Lydia Jan Kott. I'm Jacob Goldstein, and we'll be back

954
00:43:43,716 --> 00:43:45,996
Speaker 1: next week with another episode of What's Your Problem.