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Speaker 1: Pushkin.

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Speaker 2: If you look back at the history of technology, there

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Speaker 2: is this very very very long period of time, like

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Speaker 2: thousands of years, where not much happened for humanity, at

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Speaker 2: least not by modern standards. There were certainly some advances,

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Speaker 2: there were periods of technological ferment here and there, Song

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Speaker 2: Dynasty China as one famous example, But then those moments

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Speaker 2: would pass, and things in terms of technology at least

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Speaker 2: would go on largely as before people traveled. When they

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Speaker 2: traveled at all, by foot or by animal or by

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Speaker 2: sailing ship, and century after century, most of the people

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Speaker 2: on earth worked as subsistence farmers, trying to grow enough

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Speaker 2: food to survive. And then everything changed. It started in

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Speaker 2: England about two hundred and fifty years ago. The steam engine,

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Speaker 2: a modern factories all emerged in this moment, this moment

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Speaker 2: that came to be called the Industrial Revolution, that was

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Speaker 2: in many ways the beginning of the world we're living

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Speaker 2: in today, This world where continuous technological breakthroughs, not just

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Speaker 2: year after year, but generation after generation make workers more

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Speaker 2: efficient and more productive. And this world where generation after

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Speaker 2: generation the material conditions of humanity keep improving, the economic

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Speaker 2: pie gets bigger. The changes that began with the industry

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Speaker 2: of revolution are the reason that most people alive today

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Speaker 2: are profoundly richer than their ancestors in the centuries since

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Speaker 2: the industry of revolution. That link between new technologies and

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Speaker 2: increasing broad based prosperity has been true for most people

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Speaker 2: over the long run. But and this is a very

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Speaker 2: important but this has not been true forever everybody. And

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Speaker 2: sometimes that long run takes a very very long time.

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Speaker 2: In other words, just because the pie gets bigger, it

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Speaker 2: doesn't mean that everybody gets more pie. I'm Jacob Goldstein,

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Speaker 2: and this is what's your problem. My guest today is

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Speaker 2: Simon Johnson. He's an MIT economist and the co author

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Speaker 2: of a new book called Power and Progress, Our thousand

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Speaker 2: year Struggle over Technology and Prosperity. Simon's problem is this,

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Speaker 2: how do you create the conditions for technological change to

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Speaker 2: benefit everybody, or at least almost everybody, instead of only

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Speaker 2: a powerful few.

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Speaker 3: The basic argument is that it is wrong historically and

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Speaker 3: also not sensible economics to assume that technological change always

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Speaker 3: brings immediately and broadly shared prosperity. In fact, sometimes it

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Speaker 3: does the opposite. Sometimes it helps just a few people,

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Speaker 3: and sometimes it's really good for a lot of people.

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Speaker 3: But the conditions under which you get a lot of

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Speaker 3: benefits for a lot of people from technological change require

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Speaker 3: some thought, and particularly thinking about today in America. Do

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Speaker 3: we have the right conditions, for example, for an artificial

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Speaker 3: intelligence revolution, if that's what we're facing, for that to

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Speaker 3: really deliver lots of benefits lots of people, or is

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Speaker 3: it going to be like some of those previous historical

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Speaker 3: episodes where only a few people gain or hardly anybody gains,

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Speaker 3: and a lot of people lose.

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Speaker 2: Simon and I talked a lot about the development of

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Speaker 2: AI and how to maximize the chances that AI delivers

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Speaker 2: lots of benefits to lots of people. That's basically the

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Speaker 2: second half of the interview. But I also really wanted

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Speaker 2: to talk with him about the Industrial Revolution because I

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Speaker 2: truly think it has deep lessons that are really useful

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Speaker 2: in thinking about technology and the world today. And the

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Speaker 2: Industrial Revolution, Simon said, really started with textiles, in particular,

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Speaker 2: one kind of fabric.

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Speaker 3: The real breakthrough, Jacob, is about cotton. So cotton is

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Speaker 3: a material that the British did not invent. It's been

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Speaker 3: in long use in many parts of the world. The

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Speaker 3: Indian spinners were the leading spinners of cotton, and the

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Speaker 3: export of spun cotton from India was a big deal.

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Speaker 3: But the British figure that they can spin cotton and

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Speaker 3: then subsequently weave cotton more efficiently than Indian artisans by

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Speaker 3: applying machinery to this problem, and that became this cotton

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Speaker 3: industry became the powerhouse of the English.

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Speaker 2: So you have this the birth of technological innovation as

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Speaker 2: we know it is happening right by the early eighteen hundreds.

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Speaker 2: You're having these incredible productivity gains. This new technological era

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Speaker 2: is just charging forward. Productivity is increasing. What's happening with

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Speaker 2: wages for most workers in England at this point.

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Speaker 3: In terms of what's happening in England's and what seemed

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Speaker 3: to happen what seems to happen in terms of industrial

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Speaker 3: wages as a whole, is they really don't progress. More

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Speaker 3: so speaking quite broadly here anywhay near like what you

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Speaker 3: would imagine given the pro tivy advances, and there is

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Speaker 3: some of them, suggests broadly, not without its exceptions, but

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Speaker 3: broadly there was wage stagnation in that early period, which

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Speaker 3: is incredible and weird and pretty unsettling. Given how much

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Speaker 3: productivity transformation, given how much technological change was underway, Well.

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Speaker 1: That seems like.

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Speaker 2: Sort of the key point from this period for the

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Speaker 2: argument of your book to be a little.

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Speaker 1: Reductive about it.

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Speaker 2: And yes they're asterisks and caveats, but the big idea

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Speaker 2: is you have this call it fifty this year period,

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Speaker 2: the first half of the eighteen hundreds, where there's incredible

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Speaker 2: productivity gains in England and overall workers don't seem to

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Speaker 2: be capturing any of those gains, right, So what's going on?

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Speaker 1: Why aren't workers' wages going up in this period?

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Speaker 3: So the core issue Jacob in this moment, and it

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Speaker 3: exists across all of technological transformation, but here we do

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Speaker 3: see a nasty version here is that when you automate things,

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Speaker 3: you obviously make some people more productive than the people

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Speaker 3: who are run the machines, but you're also replacing other workers. Now,

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Speaker 3: once we start to automate weaving, that's two hundred and

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Speaker 3: fifty thousand weavers who are going to lose their jobs.

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Speaker 3: About another fifty thousand people who are auxiliary service workers

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Speaker 3: around that hand loom sector. So three to thousand people

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Speaker 3: losing their jobs. Where are they going to work.

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Speaker 2: Two stories that I feel like people have as kind

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Speaker 2: of heuristics for this, right, as sort of toy models

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Speaker 2: in their head. The sort of popular version is, oh,

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Speaker 2: machines throw people out of work and we have technological unemployment. Right,

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Speaker 2: everybody always says that, and yet here we are in

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Speaker 2: twenty twenty three with an incredible amount of technology and

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Speaker 2: unemployment at historic cloths right, So clearly that heuristic is

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Speaker 2: not right. And then the other, the kind of economics

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Speaker 2: heuristic is no, no, technological innovation makes things cheaper and

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Speaker 2: so well, people can buy more stuff, and in buying

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Speaker 2: more stuff goods and services, we create new jobs. But

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Speaker 2: plainly that second one was not happening in the eighteen twenties.

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Speaker 1: Why not.

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Speaker 3: So there's a third possibility, actually, Jacob, which is people

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Speaker 3: lose their jobs. They don't become unemployed because they can't

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Speaker 3: afford to be unemployed. There's no uneployment assurance. They have

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Speaker 3: to go to work at a very very low wage,

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Speaker 3: and that wage may be you know, essentially at or

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Speaker 3: slightly below subsistence. And I think that's also what we've

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Speaker 3: experienced in the United States. To flash forward, which is

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Speaker 3: since the digital transformation of the nineteen eighties, we've not

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Speaker 3: had the technological unemployment that people were afraid of, but

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Speaker 3: we have had a polarization of the job market. So

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Speaker 3: some people have done well and a lot of people

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Speaker 3: previous middle class, middle skill jobs have disappeared and people

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Speaker 3: have got pushed down to low wage jobs. And that

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Speaker 3: parallel I think we see in the early Industrial Revolution, Jacob,

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Speaker 3: That's what I think absolutely happened to the weavers, for example.

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Speaker 2: So, okay, that's the first half of the eighteen hundreds

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Speaker 2: in England. In England, that changes in the second half. Right,

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Speaker 2: in the latter part of the eighteen hundreds, we just

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Speaker 2: do go up for ordinary people. These productivity gains that

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Speaker 2: have been happening for decades and decades, they finally sort

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Speaker 2: of pay off in a broader way. Right, Why what changes?

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Speaker 2: Why does that happen?

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Speaker 3: Then? So many of the frustrations of the first half

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Speaker 3: of the nineteenth century, including the lack of representation for

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Speaker 3: people living in these newly industrial areas that had bubbled

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Speaker 3: up after the eighteen thirties, and there was much more

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Speaker 3: awareness of anger and also very difficult living standards in

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Speaker 3: these big industrial cities, and that leads to approcess of

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Speaker 3: partial but ongoing democratization. So there's better rights for workers,

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Speaker 3: including ultimately the right to form trade unions, and those

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Speaker 3: trade unions are demanding higher wages by the eighteen eighties,

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Speaker 3: and high wages in return to match the higher productivity.

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Speaker 3: That's what starts to build much more broadly shared prosperity.

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Speaker 2: So there's the political piece, trade unions being a notable

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Speaker 2: piece of it that you have by the second half.

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Speaker 2: You also in the book talk about a technological piece,

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Speaker 2: how the nature of the technologies that are emerging and

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Speaker 2: spreading more than emerging spreading by the second half of

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Speaker 2: the eighteen hundreds, are also driving broader wage gains.

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Speaker 1: Talk a little bit about that, right.

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Speaker 3: So it's interesting that in the eighteen fifty one Great

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Speaker 3: Exhibition that was held in London, there was almost no

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Speaker 3: American technology on exhibit. The exceptions were some stuffed wild

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Speaker 3: animals and some guns. Actually seems very American even today,

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Speaker 3: but that was eighteen fifty one. By eighteen ninety, the

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Speaker 3: US is the leading industrial power in the world. And

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Speaker 3: that transformation was a lot about movement west. It was

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Speaker 3: a lot about developing technology using agriculture. People were leaving

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Speaker 3: farms in the Midwest to go to live in Chicago

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Speaker 3: and work for McCormick Reaper's company to make equipment that

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Speaker 3: would let more people leave the farms. And as these

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Speaker 3: companies expanded, Singer Sewing Machine Company is another one, they

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Speaker 3: look to European markets and they brought over their factories

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Speaker 3: and their business models which were very or into making

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Speaker 3: effective productive use of relatively unskilled labor. And when they

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Speaker 3: brought that into Europe, that helped boost the demand for

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Speaker 3: unskilled workers. And it was unskilled workers, make them highly productive,

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Speaker 3: pay them a decent wage. That put the industrial development

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Speaker 3: onto a different and we would argue much better try

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Speaker 3: and they.

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Speaker 1: Pay them a decent wage.

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Speaker 3: Piece.

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Speaker 1: I mean, it's that just a market dynamic.

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Speaker 2: Because there are these new technologies like sewing machines, there

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Speaker 2: is more demand for unskilled labor across the board, and

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Speaker 2: therefore it's just a competitive equilibrium. Well, I got to

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Speaker 2: hire somebody, and the guy at the factory nextdoor is

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Speaker 2: going to hire this unskilled worker if I don't, so

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Speaker 2: I have to offer them more money. Is it just that?

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Speaker 1: Yeah?

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Speaker 3: And I think it's it's part of that. It's the

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Speaker 3: demand for labor. It's also the arrival of trade union.

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Speaker 3: So the people I'm employing, I don't have a union

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Speaker 3: in my factory, but two factories down they have a UNI,

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Speaker 3: and my guys can and move down there and get

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Speaker 3: the union wage if I don't pay them enough money.

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Speaker 3: And I do think personally that railways were really important

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Speaker 3: in this entire process, Jacob, because prior to railways it

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Speaker 3: was very hard to move between places. It could take

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Speaker 3: you two or three days on a stagecoach from London

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Speaker 3: to Manchester, for example, very uncomfortable, quite expensive. Labor mobility

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Speaker 3: was not very high in the sense of you know,

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Speaker 3: are you going how far would you billing to move

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Speaker 3: to get a better job. Once railways arrive, and once

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Speaker 3: you have this unified market, there's a lot more possibility

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Speaker 3: of moving to boom town's and a lot more understanding

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Speaker 3: what's going on in you know, fifty miles away. And

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Speaker 3: I think that was a very important part of critian

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Speaker 3: national market and spreading ideas and boosting the demand for labor.

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Speaker 2: I'm glad you mentioned railroads because in the book you

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Speaker 2: write at some length about the rise of railroads, which

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Speaker 2: is a little bit earlier than the period we're talking

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Speaker 2: about here, right it's the first half of the eighteen hundreds,

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Speaker 2: and in particular you write about this one guy named

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Speaker 2: George Stephenson, who is sort of representative of this new

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Speaker 2: kind of entrepreneur that's emerging around this time. Talk a

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Speaker 2: little bit about George Stephenson.

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Speaker 1: Who was he?

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Speaker 3: So, George Stephens largely self educated. He was a mining engineer,

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Speaker 3: but he didn't have any formal qualifications. He was just

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Speaker 3: a chap who had worked in minds and helped solve

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Speaker 3: problems with the design of the minds underground.

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Speaker 2: Yourew not poor, right, He was not some like gentleman engineer.

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Speaker 3: Absolutely, and in fact that's a common element to a

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Speaker 3: lot of these people who become prominent innovators in the

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Speaker 3: industrial ages. They come from quite modest backgrounds. Now there

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Speaker 3: are gradations of poverty, of course, before the divosy. He's

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Speaker 3: not in the poorest of the poor, but he's not

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Speaker 3: middle class. He doesn't grow up in a nice house.

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Speaker 3: He doesn't He can't read and write actually probably until

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Speaker 3: he's an adult. So he figures stuff out by himself.

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Speaker 3: And he's a tinkerer with machines, and his big breakthrough

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Speaker 3: came when he went up to the coal mine one

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Speaker 3: day and they were having trouble with a new fangled

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Speaker 3: seam engine was pumping water out of the mine and

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Speaker 3: he said, you know what, you know, give me a

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Speaker 3: couple of days and some of my mates and we

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Speaker 3: can fix this. And he did. And the people who

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Speaker 3: owned the coal mine were you know, they were rich people.

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Speaker 3: They were quite smart for people also, and they say, right,

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Speaker 3: this Stevenson Chap is obviously you know a talent that

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Speaker 3: we should and we should you know, back him and

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Speaker 3: help him solve other problems.

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Speaker 2: So how does Stevenson get from there to becoming a

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Speaker 2: sort of great entrepreneur slash inventor engineer, Well.

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Speaker 3: By solving problems really And then the main main problem

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Speaker 3: he solved was how to move the coal. So there

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Speaker 3: was a sort of a mechanical issue what's the best

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Speaker 3: way to transport coal along the railways? But there was

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Speaker 3: also an organization issue. So initially they ran their rails.

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Speaker 3: This is in the northeast of England, near Newcastle. They

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Speaker 3: ran them like we run roads today, which is, you know,

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Speaker 3: somebody's in charge of the road, but anybody who's got

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Speaker 3: a licensed car can put it on the road and

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Speaker 3: drive somewhere and so there was an enormous amount of

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Speaker 3: confusion and many hilarious stories, with some of them with

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Speaker 3: quite tragic endings about people not giving way to each

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Speaker 3: other on these limited railways. Stevenson had this vision, if

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Speaker 3: you like, that there was a better way to do this,

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Speaker 3: and that was to have one company owned the rail

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Speaker 3: run the trains. People could provide freight, and of course

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Speaker 3: passengers could step up or not to ride the trains,

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Speaker 3: but you'd have an integrated control over this railway system.

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Speaker 3: And that's what he persuaded some people to adopt for

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Speaker 3: the Liverpool and Manchester Railway, and in what I think

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Speaker 3: is one of the most amazing moments of human history

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Speaker 3: of nginuity, certainly, he ran a combined Nobel Prize slash

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Speaker 3: bakeoff show to determine who had the best railway engine

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Speaker 3: to run on the rails that he designed and that

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Speaker 3: he had planned. And he was also the winner of

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Speaker 3: that competition. So there's some interesting conflicts of interest at

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Speaker 3: the beginning of the Industrial Revolution, but his stuff worked.

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Speaker 3: That was a huge event and it brought massive amount

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Speaker 3: of attention to this industry and kicked off what became

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Speaker 3: known as a railway mania, which was the building of

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Speaker 3: railways and the development of the railway system across first

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Speaker 3: England and then Europe and.

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Speaker 1: Then the world.

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Speaker 2: Mostly on this show I interview founders of tech companies

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Speaker 2: more or less, and when I was reading about Stevenson,

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Speaker 2: I was like, oh, this guy's like a like a

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Speaker 2: tech founder, He's.

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Speaker 1: Like people I interview on the show. I mean, do

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Speaker 1: you think that's fair to some extent? Not fair? Like,

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Speaker 1: am I just dreaming?

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Speaker 3: I think no. I think this parallel. So here's a

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Speaker 3: man with vision. He makes mistakes, he learns the hard way.

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Speaker 3: A lot of these engines blew up, by the way,

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Speaker 3: several of his relatives died in the in the in

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Speaker 3: the shop where they were making engines because it was

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Speaker 3: extremely dangerous business. So he figures stuff out, he learns

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Speaker 3: by doing. He fails fast. I mean these are all

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Speaker 3: buzzwords of today, of course I think there are. And

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Speaker 3: he gets he gets people with money to back him

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Speaker 3: and proves results and then gets more money and so on.

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Speaker 3: I think the social gap between Stevenson and the elite

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Speaker 3: was was enormous, and there was a famous hearing to

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Speaker 3: review whether or not they could build this Liverpool and

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Speaker 3: Manchester railway in which a top barrister who went on

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Speaker 3: to become a prominent lawyer in the UK working for

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Speaker 3: the government. This rarester just ripped into pieces and Stevenson

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Speaker 3: was in articulate and he couldn't explain himself. And I

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Speaker 3: think that social gap, that's the size of it. We

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Speaker 3: don't see that today. I think most of the entrepreneurs

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Speaker 3: we come across are well educated, they've gone to college,

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Speaker 3: they're articulate people, and I don't see people rising up

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Speaker 3: from essentially nowhere like Stevenson did to the same extent

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Speaker 3: today as in the early industrial revolutionship.

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Speaker 2: So, oh, okay, so we've done this story. It's like

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Speaker 2: kind of a little more than one hundred years, right,

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Speaker 2: starting in the seventeen hundreds, going up to the late

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Speaker 2: eighteen hundreds.

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Speaker 1: What are the.

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Speaker 2: Lessons of this story about technological change, political power, and

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Speaker 2: how economic gains are shared or not shared.

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Speaker 3: So I think the main lesson is there's nothing automatic

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Speaker 3: that links technological change, improvements in technology with better outcomes

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Speaker 3: for workers. Certainly that linkage can develop, and it did

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Speaker 3: up in the later nineteenth century. And you know, it

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Speaker 3: was great that that happened, but that was a struggle

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Speaker 3: that had to be one as opposed to something that

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Speaker 3: happened just because technology, just because technology was changing. So

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Speaker 3: I do think the political dimension of the second half

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Speaker 3: of the nineteenth century, the spreader of democratization in Europe,

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Speaker 3: the US becoming more democratic for example, in the progressive era,

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Speaker 3: those were really important elements linking a technological change to

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Speaker 3: better outcomes from people. And if that linkage that political

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Speaker 3: language becomes weaker, which it has in the past forty

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Speaker 3: fifty years, then you should be more concerned about what

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Speaker 3: do you only get from potential future technological transformations.

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Speaker 2: In a minute, Simon and I will talk about what's

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Speaker 2: happening now, what may be coming, and what we can

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Speaker 2: do to maximize the chances that AI will benefit lots

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Speaker 2: of people instead of only a few.

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Speaker 1: Now back to the show.

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Speaker 2: By the end of the eighteen hundreds and into the

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Speaker 2: twentieth century, at least in parts of the world, certainly

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Speaker 2: in the United States and in England and in Western Europe,

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Speaker 2: you do have this period when technological innovation, productivity gains

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Speaker 2: are going along with broad based prosperity gains. Not everywhere,

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Speaker 2: not all the time, but certainly, you know famously in

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Speaker 2: the middle of the twentieth century in the US and

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Speaker 2: in Europe at least, you have this era that is

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Speaker 2: sort of the dream of like things are getting more

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Speaker 2: efficient and workers are getting richer, and it's the version

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Speaker 2: of technological progress that we like because it seems to

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Speaker 2: be good for everybody, or at least very large groups

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Speaker 2: of people. It is broadly shared prosperity. When, in your view,

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Speaker 2: does that break down that link between technological progress and

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Speaker 2: broadly shared you know, prosperity.

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Speaker 3: Well, in retrospect, it came under a lot of pressure

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Speaker 3: in the nineteen seventies, and I think it began to

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Speaker 3: break down really seriously in the nineteen eightiest. Like a

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Speaker 3: lot of these things, I don't think it became clear

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Speaker 3: to people that there was a problem until the nineteen nineties,

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Speaker 3: but by the nineteen nineties there were definitely there was

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Speaker 3: a lot of analysis that said, you know, why approtaivity

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Speaker 3: gain is not becoming higher wages like they used to,

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Speaker 3: and what exactly is broken about this the form of innovation?

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Speaker 3: And I think that's a problem we've grappled with for

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Speaker 3: twenty five years and not yet found.

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Speaker 1: A solution to.

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Speaker 2: And you know, it feels like The answer to that

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Speaker 2: is not entirely clear, but I know you make an

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Speaker 2: argument about it in the book, like why do you

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Speaker 2: think it happened?

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Speaker 3: Well, a combination of factors where automation, the form of

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Speaker 3: automation has become really is really important. Automation is being

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Speaker 3: used primarily to replace people. At the same time, we're

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Speaker 3: not generating a lot of new jobs, new opportunities in

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Speaker 3: the way that we did in the early twentieth century,

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Speaker 3: for example. So we've continued to automate, people have lost

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Speaker 3: those jobs. We've not created a lot of new tasks.

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Speaker 3: And that's partly about added UDEs of them and corporate leadership.

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Speaker 3: It's partly about the way digital technology has been developed

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Speaker 3: and deployed. It's also, unfortunately about globalization and the interaction

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Speaker 3: between automation and how we've outsourced work to lower income

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Speaker 3: country well.

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Speaker 2: So globalization is an interesting piece, right because over that period,

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Speaker 2: global inequality has fallen over the last several decades, right

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Speaker 2: to some extent, globalization has led to great wage gains

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Speaker 2: for people who who live in parts of the world

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Speaker 2: that were formerly very, very very poor.

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Speaker 1: You know, namely China.

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Speaker 2: The media in person in China is now much richer

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Speaker 2: than they were thirty years ago.

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Speaker 1: Like that seems good.

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Speaker 3: Yes, right, So the primary change is about China. There

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Speaker 3: are many poor parts of the world that participated in this,

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Speaker 3: And of course what's also happened in China is productivity

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Speaker 3: gains of outstrip wage gains, so that that wedge has

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Speaker 3: benefited some of the people in the Chinese system, not everybody,

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Speaker 3: but I completely agree that wages have gone up and

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Speaker 3: poverty has declined in China as part of this. So, yes,

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Speaker 3: the Chinese and other countries have benefited from the global

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Speaker 3: trading system, and that's good. But I think there are

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Speaker 3: better ways to arrange that system, and better ways that

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Speaker 3: would have more inclusion for more people, including the US.

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Speaker 1: Let's talk more about the US.

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Speaker 2: Well, what's your worry if things don't change, what do

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Speaker 2: you think the US will be like in five years

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Speaker 2: or ten years? Like, what's the sort of Yeah, what's

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Speaker 2: your prediction if the status quo persists?

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Speaker 3: So Vonnegut wrote, I think it's his first novel, play

402
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Speaker 3: a Piano, which he, as I understand it, really wrote

403
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Speaker 3: and developed in the late nineteen forties. Well, and he

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Speaker 3: imagined a society in which there are a few eats

405
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Speaker 3: people who have high status who run the machinery, and

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Speaker 3: a lot of people who have make work projects at

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Speaker 3: relatively low wages, low stand living, no prospects, And I

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Speaker 3: think that that inequality of status, inequality of opportunity, inequality.

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Speaker 3: The people on the public works projects, by the way,

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Speaker 3: are not starving, they are provided for, but that they

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Speaker 3: don't have any future. That stark and rather rigid system,

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Speaker 3: I think is a real possibility in this country. And

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Speaker 3: I think we already have some elements of that, and

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Speaker 3: I think it's problematic and maybe even least to worse

415
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Speaker 3: social outcomes than Vonnegut you imagined.

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Speaker 1: And so how do we change that?

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Speaker 3: So our argument is that artificial intelligence could be beneficial

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Speaker 3: to more people, that it could be empowering, that we

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Speaker 3: could look to waste emphasise more ways to enhance human capabilities.

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Speaker 3: This is not our understanding of the current technological priority

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Speaker 3: from the people who have the dominant visions, people running

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Speaker 3: Google and Microsoft, for example, but we absolutely talk to

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Speaker 3: them and urge them to move in this direction. We'd

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Speaker 3: like to have more competition for ideas in that market

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Speaker 3: for the same reason, and look for ways to use

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Speaker 3: technology to help humans as opposed to continuing down the

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Speaker 3: road where we've become a little bit too obsessed with

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Speaker 3: how many workers can I fire this quarter by hiring

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Speaker 3: new machines on new algorithms. That I think is a

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Speaker 3: bad dynamic for society.

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Speaker 2: I mean, unemployment is that is still below four percent.

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Speaker 2: Wages have been going up. They've been going up more

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Speaker 2: rapidly in the past few years, for workers at the

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Speaker 2: lower end of the income distribution, right, I mean, the

435
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Speaker 2: facts don't seem exactly like you're describing at this moment.

436
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Speaker 3: Well, we definitely had a bump up from COVID. Now,

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Speaker 3: COVID was not.

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Speaker 1: Something before COVID.

439
00:24:01,236 --> 00:24:04,356
Speaker 2: Right by the late twenty teens, we were essentially at

440
00:24:04,396 --> 00:24:08,156
Speaker 2: full employment. Wages were rising across the income distribution. I mean,

441
00:24:08,156 --> 00:24:10,076
Speaker 2: it's clearly true that it took too long to get

442
00:24:10,076 --> 00:24:13,076
Speaker 2: to full employment after the financial crisis, but whatever, that's

443
00:24:13,156 --> 00:24:15,076
Speaker 2: like a fiscal policy story that we don't need to

444
00:24:15,076 --> 00:24:18,436
Speaker 2: get into here. In terms of technology and labor demand,

445
00:24:18,556 --> 00:24:21,196
Speaker 2: it feels like there is robust labor demand now, even

446
00:24:21,276 --> 00:24:23,356
Speaker 2: for relatively unskilled workers.

447
00:24:24,956 --> 00:24:27,436
Speaker 3: Labor demand is stronger now than it has been in

448
00:24:27,476 --> 00:24:29,716
Speaker 3: some recent periods. I absolutely agree. But if you look

449
00:24:29,716 --> 00:24:32,716
Speaker 3: at that the divergence in incomes of the higher earning

450
00:24:32,916 --> 00:24:36,356
Speaker 3: and the lower earning. Over the past forty years, twenty

451
00:24:36,396 --> 00:24:38,116
Speaker 3: percent of that gap has been closed, perhaps a little

452
00:24:38,156 --> 00:24:40,796
Speaker 3: bit more with the COVID bump, but it's not clear

453
00:24:41,396 --> 00:24:44,196
Speaker 3: and that look, if the problem solved. Jacob Gray happy

454
00:24:44,276 --> 00:24:47,916
Speaker 3: to have been a little too worried. But I think

455
00:24:47,956 --> 00:24:51,276
Speaker 3: the concern is that the underlying dynamics of technological adoption

456
00:24:51,356 --> 00:24:54,436
Speaker 3: and what we're doing with technology hasn't changed much. But

457
00:24:54,516 --> 00:24:56,916
Speaker 3: our view is that the dynamic of deployment of AI

458
00:24:57,036 --> 00:24:59,596
Speaker 3: and what companies are talking about using AI to do

459
00:24:59,796 --> 00:25:04,956
Speaker 3: replace customer service, replace workers in various customer facing roles,

460
00:25:05,436 --> 00:25:09,076
Speaker 3: replace workers in back office, that is going to can

461
00:25:09,116 --> 00:25:13,756
Speaker 3: continue that previous divergence of real incomes.

462
00:25:14,036 --> 00:25:17,036
Speaker 2: So, I mean, you sort of alluded to a few

463
00:25:17,116 --> 00:25:20,356
Speaker 2: possible responses before, but maybe you could pick a few

464
00:25:20,396 --> 00:25:22,356
Speaker 2: to talk about in a little more depth. So there

465
00:25:22,356 --> 00:25:25,556
Speaker 2: are these potential bad outcomes from AI for sort of

466
00:25:26,556 --> 00:25:29,396
Speaker 2: workers in the middle of the distribution or for lesser

467
00:25:29,436 --> 00:25:32,996
Speaker 2: skilled workers. What do we do to help those people?

468
00:25:32,996 --> 00:25:35,476
Speaker 2: How do we reduce those risks? Like what are a

469
00:25:35,476 --> 00:25:36,716
Speaker 2: few specific things?

470
00:25:37,836 --> 00:25:39,796
Speaker 3: So the thing that people talk about all the time

471
00:25:39,876 --> 00:25:42,996
Speaker 3: and the way you framed it that, Jacob, I think

472
00:25:43,036 --> 00:25:45,156
Speaker 3: pools that direction. You say, well, people have less skill,

473
00:25:45,236 --> 00:25:48,476
Speaker 3: let's give them more skill, right, so more education. We're

474
00:25:48,476 --> 00:25:50,716
Speaker 3: not opposed to that, but we also are trying in

475
00:25:50,716 --> 00:25:53,556
Speaker 3: this book to talk about the direction of technological change.

476
00:25:53,636 --> 00:25:56,636
Speaker 3: Who has the visions, who invents things? So this says

477
00:25:56,676 --> 00:25:58,796
Speaker 3: back to your sort of your your interest in George Stephenson.

478
00:25:58,956 --> 00:26:02,076
Speaker 3: Who was George Stevenson? Where did he come from? How

479
00:26:02,076 --> 00:26:05,116
Speaker 3: did he get this opportunity? Right? And I think what

480
00:26:05,156 --> 00:26:08,716
Speaker 3: we'd like to encourage is more use of all new

481
00:26:08,716 --> 00:26:12,996
Speaker 3: technolog including AI, to bring out more George Stephenson's to

482
00:26:13,036 --> 00:26:17,156
Speaker 3: create more new tasks, to try and become more innovative

483
00:26:17,196 --> 00:26:20,516
Speaker 3: with this technology in a way that creates opportunity and job.

484
00:26:20,676 --> 00:26:23,596
Speaker 2: Nobody's going to be opposed to that, Like sure, everybody,

485
00:26:23,676 --> 00:26:25,636
Speaker 2: When you say that, I'll say sure, But how do

486
00:26:25,676 --> 00:26:27,796
Speaker 2: you do that? Like, what is a concrete thing in

487
00:26:27,796 --> 00:26:29,916
Speaker 2: the world that could cause that to happen?

488
00:26:31,396 --> 00:26:34,916
Speaker 3: Well, for example, almost all of the research and development

489
00:26:34,916 --> 00:26:36,556
Speaker 3: around AI right now, it takes place in a few

490
00:26:36,596 --> 00:26:39,956
Speaker 3: big companies. There's a very little takes place in any

491
00:26:39,996 --> 00:26:42,996
Speaker 3: government institute or actually the universities are losing a lot

492
00:26:42,996 --> 00:26:45,356
Speaker 3: of talent into those companies. What is the priority of

493
00:26:45,396 --> 00:26:48,116
Speaker 3: those companies It is to make money. What is an

494
00:26:48,116 --> 00:26:52,276
Speaker 3: alternative set of priorities is to generate new tasks, new opportunities,

495
00:26:52,396 --> 00:26:53,956
Speaker 3: breakthroughs in technology that we haven't yet.

496
00:26:54,036 --> 00:26:54,796
Speaker 1: Money have to happen.

497
00:26:54,876 --> 00:26:58,876
Speaker 2: For a more public spirited development of AI to happen,

498
00:26:58,956 --> 00:27:00,876
Speaker 2: is it public funding for AI development?

499
00:27:02,316 --> 00:27:04,516
Speaker 3: Public funding would be important and it has been important

500
00:27:04,596 --> 00:27:09,596
Speaker 3: in many previous technologies, including development of computer chips and

501
00:27:09,676 --> 00:27:13,596
Speaker 3: the Internet and modern pharmaceuticals. Often the government does very

502
00:27:13,596 --> 00:27:15,876
Speaker 3: well when it provides a market of It says we're

503
00:27:15,876 --> 00:27:18,596
Speaker 3: going to buy things from you if you develop them right.

504
00:27:18,596 --> 00:27:20,756
Speaker 3: That's what we do with COVID vaccines, for example. So

505
00:27:20,796 --> 00:27:23,116
Speaker 3: there are various ways that you can put public resources

506
00:27:23,196 --> 00:27:26,956
Speaker 3: to work. And in the current conversation in Washington, the

507
00:27:26,956 --> 00:27:29,556
Speaker 3: good news is those possibilities are not off the table.

508
00:27:29,796 --> 00:27:32,516
Speaker 3: But I go to some of these conversations take and

509
00:27:32,556 --> 00:27:35,396
Speaker 3: I wouldn't say it's the top item. It probably struggles

510
00:27:35,436 --> 00:27:38,476
Speaker 3: to get into the top five. Mostly it's large private

511
00:27:38,476 --> 00:27:40,756
Speaker 3: sector companies saying, hey, don't get in our way, don't

512
00:27:40,756 --> 00:27:42,276
Speaker 3: put a lot of rules on us. If you do,

513
00:27:42,436 --> 00:27:45,316
Speaker 3: then China will take over the world. And that is

514
00:27:45,796 --> 00:27:50,156
Speaker 3: unfortunately the thrust of that technological conversation right now, whereas

515
00:27:50,156 --> 00:27:52,356
Speaker 3: we would suggest you start much more. Okay, what are

516
00:27:52,356 --> 00:27:53,916
Speaker 3: you trying to invent here? What do we not have?

517
00:27:54,036 --> 00:27:55,956
Speaker 3: What is the private sector not going to come up with?

518
00:27:56,116 --> 00:27:59,316
Speaker 3: And then what's the system of carents? Various kinds of

519
00:27:59,316 --> 00:28:01,676
Speaker 3: carrots probably no stakes, various kinds of carrots that's going

520
00:28:01,676 --> 00:28:07,076
Speaker 3: to pull innovative people into this better direction where I agree,

521
00:28:07,196 --> 00:28:09,556
Speaker 3: you know, it may be a little cloudy exactly what

522
00:28:09,556 --> 00:28:12,956
Speaker 3: better means, but generally speaking, creating new tasks for people

523
00:28:13,116 --> 00:28:15,636
Speaker 3: and creating new tasks that the people were previously not

524
00:28:15,716 --> 00:28:19,276
Speaker 3: highly skilled can do productively, that's a smart way forward.

525
00:28:19,636 --> 00:28:21,356
Speaker 2: What is an example of the thing you were just

526
00:28:21,396 --> 00:28:25,236
Speaker 2: saying of a carrot from the government that would you know,

527
00:28:25,836 --> 00:28:28,516
Speaker 2: induce people, provide an incentive for people to come up

528
00:28:28,556 --> 00:28:32,516
Speaker 2: with a direction for AI that would benefit you know,

529
00:28:32,596 --> 00:28:34,556
Speaker 2: broadly benefit workers, right, this is what we're looking for.

530
00:28:34,596 --> 00:28:35,436
Speaker 1: What's an example of that?

531
00:28:36,796 --> 00:28:42,276
Speaker 3: Make workers in electrical power grid companies, electrical disputors, make

532
00:28:42,276 --> 00:28:44,756
Speaker 3: them more make them safeer, make them more productive, to

533
00:28:44,836 --> 00:28:50,156
Speaker 3: help healthcare workers become more productive and better able to

534
00:28:50,156 --> 00:28:54,116
Speaker 3: diagnose problems and so on, So a smarter expert advisory

535
00:28:54,116 --> 00:28:56,676
Speaker 3: system that a nurse practitioner can have. And the really

536
00:28:56,716 --> 00:28:58,556
Speaker 3: big one, Jacob, which is this one I think is

537
00:28:58,556 --> 00:29:01,876
Speaker 3: probably worth a bounty, is to actually really finally use

538
00:29:01,916 --> 00:29:09,036
Speaker 3: AI and education to help again kids with less advantage

539
00:29:09,276 --> 00:29:12,236
Speaker 3: and less family resources. And these things have been imagined,

540
00:29:12,276 --> 00:29:15,196
Speaker 3: but they've not been ever developed at scale so that

541
00:29:15,676 --> 00:29:20,676
Speaker 3: teachers can actually help students learn better in more tailored

542
00:29:20,676 --> 00:29:25,716
Speaker 3: fashion using AI type tools, and then get those systems

543
00:29:25,756 --> 00:29:30,116
Speaker 3: deployed and adopted across public education. None of these are,

544
00:29:30,156 --> 00:29:33,636
Speaker 3: of course, are easy problems. That's why they're problems. But education,

545
00:29:34,156 --> 00:29:40,676
Speaker 3: healthcare workers in critical sectors, including around electricity, clean energy,

546
00:29:40,756 --> 00:29:42,476
Speaker 3: those would seem to be sensible priorities.

547
00:29:42,636 --> 00:29:44,516
Speaker 2: What else are there things you want to talk about

548
00:29:44,516 --> 00:29:45,436
Speaker 2: that we didn't talk about?

549
00:29:45,996 --> 00:29:48,076
Speaker 3: What's very interesting, Jacob, is you dug deeper into the

550
00:29:48,116 --> 00:29:51,316
Speaker 3: Industrial Revolution pieces of it than many people have, including

551
00:29:51,476 --> 00:29:54,076
Speaker 3: HA been on lots of good podcasts. But I think

552
00:29:54,316 --> 00:29:57,276
Speaker 3: that you're absolutely right that it's sort of to understand

553
00:29:57,316 --> 00:29:59,956
Speaker 3: the sequence of history, to understand these the episodes, and

554
00:29:59,956 --> 00:30:02,996
Speaker 3: then to think, okay, we're obviously living history people going

555
00:30:03,036 --> 00:30:04,956
Speaker 3: to look back and say, Okay, this was an episode,

556
00:30:05,036 --> 00:30:07,356
Speaker 3: but what is this episode? Is this like a continuation

557
00:30:07,356 --> 00:30:10,076
Speaker 3: of the nineteen eighties? Is it something brand you? I

558
00:30:10,116 --> 00:30:13,396
Speaker 3: think that we often exaggerate the moment in which we

559
00:30:13,476 --> 00:30:15,676
Speaker 3: live because that's the moment in which we're living. Yeah, right,

560
00:30:15,756 --> 00:30:19,876
Speaker 3: So I always tend to think but but sometimes the

561
00:30:19,916 --> 00:30:22,156
Speaker 3: crisis of two thousand and eight was different. For example,

562
00:30:22,836 --> 00:30:25,436
Speaker 3: nine to eleven was different. There are these departures, there

563
00:30:25,436 --> 00:30:26,596
Speaker 3: are these forks.

564
00:30:26,876 --> 00:30:30,396
Speaker 2: The question right now I feel like, is are we

565
00:30:30,556 --> 00:30:33,196
Speaker 2: just sort of persisting in the same universe we've been in,

566
00:30:33,476 --> 00:30:38,636
Speaker 2: or is what's happening with AI creating.

567
00:30:39,636 --> 00:30:42,076
Speaker 1: An inflection point? To use an overused term.

568
00:30:42,076 --> 00:30:45,396
Speaker 2: Right like, is something really new happening with AI right now?

569
00:30:46,356 --> 00:30:47,876
Speaker 1: Or are is it going to be more of the same?

570
00:30:49,036 --> 00:30:53,796
Speaker 3: So I don't know how big is AI going to be? Unclear?

571
00:30:54,516 --> 00:30:58,796
Speaker 3: But have we is this a process that is likely

572
00:30:58,876 --> 00:31:04,076
Speaker 3: to develop deliver big shared benefits? I think a bit

573
00:31:04,116 --> 00:31:04,636
Speaker 3: worried about that.

574
00:31:04,876 --> 00:31:06,836
Speaker 2: You're a bit worried that all the benefits are going

575
00:31:06,876 --> 00:31:08,396
Speaker 2: to go to a small number of people, and some

576
00:31:08,556 --> 00:31:12,876
Speaker 2: large number of people will be not helped and potentially harmed.

577
00:31:14,636 --> 00:31:17,236
Speaker 3: Right, And you know we have seen episodes in history

578
00:31:17,236 --> 00:31:19,636
Speaker 3: where the harm can be substantial, it can be widespread,

579
00:31:19,876 --> 00:31:21,236
Speaker 3: so let's not kid ourselves about that.

580
00:31:23,836 --> 00:31:26,036
Speaker 2: We'll be back in a minute with the lightning round.

581
00:31:27,876 --> 00:31:39,796
Speaker 2: M back to the show before we go, I want

582
00:31:39,836 --> 00:31:42,236
Speaker 2: to do a lightning round. I want to ask you

583
00:31:42,276 --> 00:31:44,636
Speaker 2: some fun, somewhat silly questions.

584
00:31:46,036 --> 00:31:47,876
Speaker 3: I use a lightning round in my classes, Jacob. I

585
00:31:47,916 --> 00:31:49,756
Speaker 3: don't forget if I got it from Planetmoni or someone else,

586
00:31:49,756 --> 00:31:51,116
Speaker 3: but I think the lightning round is a great thing.

587
00:31:51,916 --> 00:31:53,516
Speaker 3: Just fire fire away, fire away.

588
00:31:53,596 --> 00:31:53,916
Speaker 1: Okay.

589
00:31:55,116 --> 00:31:58,116
Speaker 2: You are a tennis player, a recreational tennis player. As

590
00:31:58,116 --> 00:32:00,076
Speaker 2: a tennis player, what is your view of pickleball?

591
00:32:03,436 --> 00:32:06,676
Speaker 3: That is a really sensitive question among tennis players. I

592
00:32:06,836 --> 00:32:10,076
Speaker 3: enjoy pickaball, the whole family can play its barriers to entry.

593
00:32:10,596 --> 00:32:12,716
Speaker 3: But I understand that some of my tennis playing friends

594
00:32:12,796 --> 00:32:14,756
Speaker 3: may not be tennis may not want to be my

595
00:32:14,796 --> 00:32:15,316
Speaker 3: friends anymore.

596
00:32:15,316 --> 00:32:18,196
Speaker 2: After I said that, on a slightly less important topic,

597
00:32:18,516 --> 00:32:21,596
Speaker 2: you used to be the chief economist at the IMF,

598
00:32:21,676 --> 00:32:23,156
Speaker 2: the International Monetary Fund.

599
00:32:23,596 --> 00:32:25,876
Speaker 1: It's the IMF overrated or underrated?

600
00:32:28,036 --> 00:32:30,596
Speaker 3: Oh, this sounds like the pickaball question in a different guys.

601
00:32:31,636 --> 00:32:35,436
Speaker 2: Equally slightly less controversial than the pickleball question.

602
00:32:36,916 --> 00:32:40,876
Speaker 3: The IMF is a very important organization that exists. Almost

603
00:32:40,916 --> 00:32:43,156
Speaker 3: the entire life of the IMF exists just to the

604
00:32:43,196 --> 00:32:44,796
Speaker 3: side of the front page of the New York Times,

605
00:32:44,876 --> 00:32:48,516
Speaker 3: by which I mean you very rarely read anything about

606
00:32:48,556 --> 00:32:50,556
Speaker 3: all the important things that happened at the IMF, because

607
00:32:50,636 --> 00:32:52,836
Speaker 3: you know, it's just a little too boring. As far

608
00:32:52,876 --> 00:32:54,596
Speaker 3: as the New York Times editor is a concern. And

609
00:32:54,636 --> 00:32:56,996
Speaker 3: that's good because they can get a lot of things done.

610
00:32:56,996 --> 00:32:59,996
Speaker 3: They can be constructive, and it's enabled by that particular

611
00:33:00,316 --> 00:33:02,836
Speaker 3: by the way in which they were positioned relative to

612
00:33:02,876 --> 00:33:05,676
Speaker 3: the US political system. But they do exist. That they

613
00:33:05,716 --> 00:33:08,356
Speaker 3: exist just slightly. You know how there's something in your

614
00:33:08,356 --> 00:33:12,636
Speaker 3: perple vision you can't ever quite focus on. That's where

615
00:33:12,636 --> 00:33:14,436
Speaker 3: they are with pickball.

616
00:33:15,716 --> 00:33:19,796
Speaker 2: Pickleball's right in the middle, man. Pickleball is front and center.

617
00:33:23,996 --> 00:33:24,956
Speaker 2: What's your favorite novel?

618
00:33:27,236 --> 00:33:31,436
Speaker 3: Oh, snow Crash, Neil Stevenson. That's very easy. I reread

619
00:33:31,476 --> 00:33:33,436
Speaker 3: that every every year or so. So this is a

620
00:33:33,436 --> 00:33:36,676
Speaker 3: book written came out in the early nineteen nineties, and

621
00:33:37,476 --> 00:33:39,636
Speaker 3: I originally read it because Paul Kruman said, an economist,

622
00:33:39,676 --> 00:33:41,476
Speaker 3: if you want to understand the future don't read any

623
00:33:41,476 --> 00:33:44,956
Speaker 3: futurologists read Neil Stevenson, and in terms of globalization, in

624
00:33:45,036 --> 00:33:49,316
Speaker 3: terms of technology, in terms of social impact. It's including

625
00:33:49,356 --> 00:33:51,036
Speaker 3: the metaverse. It's remarkable.

626
00:33:51,956 --> 00:33:55,116
Speaker 1: And Crypto, you know, it's funny.

627
00:33:55,756 --> 00:33:57,236
Speaker 3: I've thought it was a different book. Actually that was

628
00:33:57,236 --> 00:34:00,476
Speaker 3: a different series. Crypto was less just as a new author, right,

629
00:34:00,516 --> 00:34:02,636
Speaker 3: same author. Oh yeah, yeah, just as a Neil Stevenson

630
00:34:02,676 --> 00:34:04,476
Speaker 3: groupie to be clear that that was the Crypto, the

631
00:34:04,476 --> 00:34:05,756
Speaker 3: Crypto series, the Money series.

632
00:34:05,796 --> 00:34:07,676
Speaker 2: This is the second time in this interview I've thought

633
00:34:07,676 --> 00:34:09,396
Speaker 2: of sal Khan. I didn't mention it last, but I

634
00:34:09,476 --> 00:34:11,756
Speaker 2: just interviewed Sel Con of the Con Academy, which you

635
00:34:11,836 --> 00:34:13,396
Speaker 2: might know of, and I thought of him when you

636
00:34:13,476 --> 00:34:18,116
Speaker 2: were talking about education, because they are developing AI an

637
00:34:18,156 --> 00:34:20,596
Speaker 2: AI tutor basically that at the moment is not free,

638
00:34:20,596 --> 00:34:24,436
Speaker 2: but we'll be free, I suspect quite soon. Also, he

639
00:34:24,516 --> 00:34:27,196
Speaker 2: loves Neil Stevenson. There's some Neil Stevenson book set. I

640
00:34:27,236 --> 00:34:29,996
Speaker 2: believe in China where there's like a tablet that has

641
00:34:30,116 --> 00:34:34,076
Speaker 2: education and like the masses get the tablet education. We've

642
00:34:34,116 --> 00:34:36,436
Speaker 2: been trying to book Neil Stevenson, but he doesn't do interviews.

643
00:34:36,436 --> 00:34:37,396
Speaker 2: So if you ever see.

644
00:34:37,236 --> 00:34:39,476
Speaker 1: Him, tell him to call.

645
00:34:41,556 --> 00:34:41,876
Speaker 3: We'll do.

646
00:34:47,716 --> 00:34:50,916
Speaker 2: Simon Johnson is a professor at MIT. His new book

647
00:34:51,076 --> 00:34:55,756
Speaker 2: is Power and Progress. Today's show was produced by Edith Russello.

648
00:34:55,956 --> 00:34:59,436
Speaker 2: It was edited by Sarah Nix and engineered by Amanda K.

649
00:34:59,836 --> 00:35:00,116
Speaker 1: Wong.

650
00:35:00,596 --> 00:35:04,116
Speaker 2: You can email us at problem at Pushkin dot fm.

651
00:35:04,476 --> 00:35:07,196
Speaker 2: You can find me on Twitter at Jacob Goldstein. I'm

652
00:35:07,316 --> 00:35:09,716
Speaker 2: Jacob Goldstein and we'll be back next week with another

653
00:35:09,796 --> 00:35:10,956
Speaker 2: episode of What's Your Problem.

654
00:35:18,276 --> 00:35:18,316
Speaker 1: M