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Speaker 1: So like literally a week and a half ago, hadn't

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Speaker 1: logged down to Zoom for maybe maybe two weeks. Right,

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Speaker 1: it was around Thanksgiving, and then I was traveling and

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Speaker 1: I had the Zoom meeting, and I open it, I

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Speaker 1: click on the link, you know, a minute before it

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Speaker 1: starts because I'm early, I am professional, And of course

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Speaker 1: it starts updating itself because it needed a software update.

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Speaker 1: And I'm frantically texting everybody who I'm supposed to be

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Speaker 1: meeting with, like blaming Zoom as if it's Zoom's fault. Oh,

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Speaker 1: of course, now Zoom decides to do a software update.

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Speaker 2: Blah blah blah. No, I knew in my heart that this.

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Speaker 1: Was a me issue, right, You knew you had a

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Speaker 1: Zoom meeting log on ten minutes early because it might

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Speaker 1: need a software update. But that's like how young and

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Speaker 1: old I am simultaneously, right, sold that I'm trying to

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Speaker 1: blame Zoom for a thing that's clearly my issue, and

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Speaker 1: young enough that, like, you know, we all had roommates

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Speaker 1: who did dial up the porn because that's what the

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Speaker 1: nineties were like. But as we think about things more

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Speaker 1: and more in a society, if you want to use

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Speaker 1: the word progresses. We've all thought about AI as well

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Speaker 1: as an actor, of course, terrifying the idea that robots

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Speaker 1: and computers could take all of our work. But it's

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Speaker 1: not just actors. No matter what you do for a living,

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Speaker 1: the humanity of your work, the interpersonal skills, the taking

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Speaker 1: pride in your job, even if that means you're in

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Speaker 1: a cubicle all day, whatever it is that you're doing.

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Speaker 1: The fear that maybe technology could replace us is like

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Speaker 1: a real thing.

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Speaker 2: It's not just creepy.

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Speaker 1: It feels like it's getting out of control, and it

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Speaker 1: feels like the heart of the humanity that we all

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Speaker 1: have is being questioned right now. So wanted to have

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Speaker 1: a very interesting guest on who could help us sort

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Speaker 1: through all of that, and that is journalist Jacob Goldstein,

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Speaker 1: who tells me that we've been here before, and.

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Speaker 3: It started in the cloth business.

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Speaker 4: So these original a lot ofites were like the first

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Speaker 4: people really to be impacted in a significant way by

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Speaker 4: the industrial revolution, the first people to face this thing

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Speaker 4: that we are talking about that we are afraid of

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Speaker 4: essentially losing their jobs to technological change.

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Speaker 1: In my conversation with Jacob, we're going to go all

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Speaker 1: the way back to the eighteen hundreds, to the original Luddites,

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Speaker 1: and I want to hear about their story. And we're

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Speaker 1: going to talk about how technology changes, how those changes

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Speaker 1: reshape our work, and then frankly, who historically gets protected

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Speaker 1: and who gets left behind?

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Speaker 2: Here we go again again again.

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Speaker 1: Okay, Hey, I'm cal Pen and this is here we

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Speaker 1: Go again, a show that takes today's trends and headlines and.

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Speaker 2: Asks why does history keep repeating itself?

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Speaker 3: Here we go?

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Speaker 1: Hey, good morning, good morning, how you doing. Thanks for

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Speaker 1: today I'm speaking with Hi.

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Speaker 3: I'm Jacob Goldstein.

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Speaker 1: He was a previous host on the NPR podcast Planet Money,

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Speaker 1: so if you recognize his voice maybe from that. He's

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Speaker 1: the current host of the podcast What's Your Problem and

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Speaker 1: Business History, and he's the author of the book Money,

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Speaker 1: The True Story of a Made Up Thing. Before we

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Speaker 1: fully dive in, I was very excited to talk to

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Speaker 1: you about all these topics because a couple of reasons.

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Speaker 1: I generally know very little about technology. I'm forty eight,

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Speaker 1: so as my college friend who does work in tech,

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Speaker 1: always reminds me like, we're that generation that grew up

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Speaker 1: fully analog, but we're still kids when the digital wireless

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Speaker 1: world kind of came around, and so it's rare, that

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Speaker 1: rarity of knowing how to use both. But I'm now

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Speaker 1: getting old enough from like I just want to rotary phone,

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Speaker 1: like when I was nine. So since you are smart

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Speaker 1: and thoughtful and very knowledgeable about all of this, just

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Speaker 1: the nerd part of me was really excited to talk

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Speaker 1: to you. When did you know you wanted to be

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Speaker 1: a journalist and focus on the things that you do.

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Speaker 4: So I became a journalist in my late twenties. I

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Speaker 4: was a newspaper reporter, and it was around the time

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Speaker 4: of the two thousand and eight financial crisis. I was

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Speaker 4: a reporter covering healthcare at the Wall Street Journal, and

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Speaker 4: like everybody else, I wanted to understand what was going on.

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Speaker 4: It was confusing to me, you know, I knew about healthcare,

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Speaker 4: but even though I was at the journal, I wasn't

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Speaker 4: covering finance.

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Speaker 3: And I have an aunt who.

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Speaker 4: Was an MBA who is like my go to kind

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Speaker 4: of business and money person when I don't understand things,

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Speaker 4: and you know, there had been this collapse obviously of

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Speaker 4: the stock market and of real estate prices, and I

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Speaker 4: asked her, like it seemed like people were talking about

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Speaker 4: like a trillion dollars disappeared from the stock market, right,

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Speaker 4: So I had this maybe dumb, maybe not question, where

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Speaker 4: did the money go?

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Speaker 2: Yeah, that's not dumb, that would be my question.

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Speaker 3: And she said it wasn't there in the first place.

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Speaker 3: Money is fiction. Oh yeah, And I was like, ooh fiction.

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Speaker 3: I had been an English major, I knew about fiction.

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Speaker 3: I was like, this is interesting to me. And that

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Speaker 3: was sort of the gateway.

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Speaker 4: And so not long after that, I went to work

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Speaker 4: for this podcast called Planet Money. It's like covering the

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Speaker 4: economy but in a very sort of narrative storytelling way,

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Speaker 4: and through that I got into kind of the history

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Speaker 4: of money and the history of economics and the stuff

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Speaker 4: we're going to.

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Speaker 3: Talk about today.

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Speaker 2: Amazing.

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Speaker 1: I had an AHA moment like that when I was

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Speaker 1: hosting a show just before COVID terrible title called This

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Speaker 1: Giant Beast that is the Global Economy for Amazon Prime.

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Speaker 1: And I had a great time working on it, but

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Speaker 1: that was, you know, I was the host through which

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Speaker 1: the audience gets to experience things, so I knew very

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Speaker 1: little going in and my mind was blown. It sounds

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Speaker 1: like not dissimilar to your two thousand and eight phone call. Yeah,

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Speaker 1: as a journalist, I'm going to jump ahead a little

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Speaker 1: bit to just sort of what people are thinking about today.

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Speaker 1: And I'll preface it by saying, as an actor and

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Speaker 1: a writer, we just came off of two very protracted

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Speaker 1: labor disputes where we were forced to go on strike

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Speaker 1: by these big media companies in large part because of

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Speaker 1: AI proposals and provisions. As a journalist, are you at

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Speaker 1: the place where you're thinking through or or are you

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Speaker 1: afraid that technology is going to replace people in your

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Speaker 1: profession or has it already?

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Speaker 3: Yes?

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Speaker 4: And yes, I mean if you look at employment in

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Speaker 4: newsrooms over the past twenty years, which is to say

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Speaker 4: my career, it has gone down and down and down

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Speaker 4: because of technological change. Right, It's not exactly AI doing reporters' jobs.

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Speaker 4: It's people substituting away from newspapers. But yes, looking forward,

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Speaker 4: I am worried that AI will be able to do

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Speaker 4: what I do. And fortunately I'm not that young and

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Speaker 4: I don't have to make it that much longer. But

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Speaker 4: like I would caution someone starting their career of trying

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Speaker 4: to be a journalist in the traditional sense, right, I mean,

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Speaker 4: perhaps interesting question is, like what is the combination, right, Like,

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Speaker 4: we'll AI take our jobs is a valid question, But

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Speaker 4: in the meantime, how can we use AI to do

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Speaker 4: our jobs better? Is perhaps a more practical question.

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Speaker 2: And how do you feel like you're able to use

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Speaker 2: AI to make your job better?

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Speaker 4: I mean it helps me with research, is the short answer.

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Speaker 4: And I will say there's an interesting cultural thing, certainly

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Speaker 4: among journalists and writers. I suspect it's not as much

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Speaker 4: in other fields. But because large language models are writing,

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Speaker 4: they feel like a direct threat to you know, journalists

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Speaker 4: in a way that they perhaps do not to people

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Speaker 4: in other fields. So I don't know, there's a feeling

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Speaker 4: among journalists that using AI is cheating.

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Speaker 2: Right.

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Speaker 4: People will sort of abashedly admit to using AI, and

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Speaker 4: I think that's unhealthy, right. I think using AI is

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Speaker 4: like using the Internet, which by the way, is like

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Speaker 4: you know, Google searches are driven by AI, of course.

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Speaker 4: So for example, I host this show called Business History,

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Speaker 4: and just this morning I took a script that one

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Speaker 4: of my colleagues had written that was like a great narrative,

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Speaker 4: but I felt like it could use some big ideas,

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Speaker 4: and I put it into a large language model and said, like,

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Speaker 4: what are some big I can ideas that you think

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Speaker 4: sort of emerge from this, what are some themes? And

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Speaker 4: it's not like I copied and pasted the answer. It

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Speaker 4: suggested some ideas, and I went and researched those ideas, right,

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Speaker 4: So that's an example.

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Speaker 1: So when you research the ideas after the prompt, are

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Speaker 1: you researching that outside of the AI models? And here's

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Speaker 1: basically what I'm getting at is like, how as a journalist,

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Speaker 1: how do you make sure that the sources themselves are accurate?

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Speaker 1: Knowing that, however, AI has learned that may not be

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Speaker 1: whatever the inputs were may not be a real thing.

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Speaker 1: So how do you fact check that when you take

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Speaker 1: the next step in research?

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Speaker 4: I mean that part is the same as always, right,

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Speaker 4: Like I don't I never take the answer from the

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Speaker 4: AI as a reliable answer. I go and I look

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Speaker 4: it up elsewhere, and I look at the source and

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Speaker 4: I evaluate the credibility of the source material. And you know,

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Speaker 4: certainly ayes hallucinate some, but like overall, it's useful, and

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Speaker 4: like once in a while it's like, oh, that doesn't

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Speaker 4: make sense, but quite often it's like, oh, that's a

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Speaker 4: good idea. And like just this morning there was like

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Speaker 4: particular monetary policy shift in Germany and the nineteen forties

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Speaker 4: that it pointed me to that was in fact real

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Speaker 4: and interesting in the history of the Volkswagen.

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Speaker 3: Beetle, which is the story we're working on.

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Speaker 1: But okay, I want to go back a little bit

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Speaker 1: to history because the same friend who I mentioned, the

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Speaker 1: tech savvy college friend, often likes to tell me that

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Speaker 1: I'm a Luddite.

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Speaker 2: And I don't think he's necessarily wrong.

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Speaker 1: The way that we throw around the word today technology

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Speaker 1: doesn't necessarily get me as geeked as some of my friends,

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Speaker 1: although although I'll obviously have my moments. And so just

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Speaker 1: in looking back, right, I get that people have always

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Speaker 1: had this fear that newer technology is going to replace

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Speaker 1: their jobs. So what you've talked about is that we've

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Speaker 1: been here before, and all this palpable fear started back

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Speaker 1: in eighteen hundreds, and so can you take us to

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Speaker 1: the eighteen hundreds and tell us what happened, Like, take

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Speaker 1: us back to that moment in history for somebody who

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Speaker 1: doesn't know anything.

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Speaker 2: About the Luodites.

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Speaker 4: Yeah, so the original Luddites were quite different. Just to start, then,

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Speaker 4: the word we have to the word Luddite today. I mean,

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Speaker 4: it's just like somebody who doesn't like technology because they

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Speaker 4: don't like it. Right, So, the original Ludites were cloth

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Speaker 4: workers in England in the first part of the eighteen hundreds,

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Speaker 4: like eighteen eleven, eighteen twelve. Around that time, and for

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Speaker 4: essentially all of human history, there was very little technological change.

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Speaker 4: Right this world do we live in where you just

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Speaker 4: assume that technology changes generation after generation, that things get

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Speaker 4: more efficient. That was not the nature of the world

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Speaker 4: until the Industrial Revolution, which started in the second part

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Speaker 4: of the seventeen hundreds in England, and it started in

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Speaker 4: the cloth.

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Speaker 3: Business, right.

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Speaker 4: So these original Lutdites were like the first people really

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Speaker 4: to be impacted in a significant way by the Industrial Revolution,

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Speaker 4: the first people to face this thing that we are

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Speaker 4: talking about that we were afraid of, essentially losing their

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Speaker 4: jobs to technological change. The cloth business was actually a

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Speaker 4: huge business for England at the time, and the.

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Speaker 3: Luddites were skilled artisans. Right.

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Speaker 4: We think of, you know, factory work as terrible in

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Speaker 4: the eighteen hundreds, and it was, but before the Industrial Revolution,

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Speaker 4: it was like something you did kind of in your home.

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Speaker 4: It was farmed out and so you know, there were

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Speaker 4: all these steps to making fabric, and different people specialized

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Speaker 4: in different parts of it.

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Speaker 3: So the croppers would take a rough piece of.

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Speaker 4: Fabric and they would have these giant shears, like kind

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Speaker 4: of giant metal scissors, and they would crop that. I

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Speaker 4: guess the nap I don't know about fabric. They'd crop

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Speaker 4: something off.

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Speaker 3: The wool to like make it smooth and nice.

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Speaker 4: Okay, So that was their job, and for the time

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Speaker 4: it was a pretty good job. Like they worked for themselves.

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Speaker 4: They set their own hours, very poor by the standards

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Speaker 4: of today, which was an important point, but in relative

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Speaker 4: terms at the time they were doing well. And then

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Speaker 4: along came the industrial Revolution, which started out as machines

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Speaker 4: to make cloth. Right, So somebody invented a thing to

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Speaker 4: spin a raw fiber into threadbody invented a loom, and

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Speaker 4: then somebody invented a shearing frame, right, a machine to crop,

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Speaker 4: a machine to do what the croppers had done. Right,

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Speaker 4: And so this is the thing we are talking about.

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Speaker 4: This is AI taking our job, but for the croppers,

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Speaker 4: but for the skilled clothmakers.

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Speaker 1: I'm curious the all those inventions. What's the do you

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Speaker 1: know the range of time? Like did this all happen

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Speaker 1: in a year or was it over a period of

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Speaker 1: like fifty years.

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Speaker 3: Decades? The order of magnitude is decades.

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Speaker 2: So slower than slower than right now. Well maybe maybe not.

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Speaker 4: I think it didn't feel that way if you were

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Speaker 4: a cropper right like, there wasn't the spread of information

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Speaker 4: like there is today. They didn't necessarily you know, if

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Speaker 4: you were a cropper in eighteen hundred, you didn't know

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Speaker 4: that somebody was going to invent a shearing frame and

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Speaker 4: that it was going to show up.

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Speaker 1: You know.

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Speaker 3: I think it came as a surprise.

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Speaker 2: And once it was there, there's a guy named Ned Lud.

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Speaker 3: Yes, so what starts happening? Does one curse on this show?

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Speaker 2: Oh? Yes, please feel free.

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Speaker 4: So these people who have these good jobs, who have

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Speaker 4: these skills, see the machines coming and taking their jobs,

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Speaker 4: and they essentially think like fuck this, like no, like

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Speaker 4: we're not gonna do it this way. And so what

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Speaker 4: they start doing is going in the middle of the

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Speaker 4: night and attacking these new machines like literally physically, like breaking.

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Speaker 3: Them with sledgehammers.

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Speaker 4: It's like, oh, you're gonna do this with the machine. No,

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Speaker 4: I'm going to break your machine with a sledgehammer. And

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Speaker 4: then you can come back to me and I'll keep cropping.

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Speaker 3: And in I.

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Speaker 4: Believe it's eighteen eleven, it starts to get more organized.

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Speaker 4: So these have been just kind of random, ad hoc

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Speaker 4: kind of what we would call today, like maybe mob.

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Speaker 3: Sure it might be a word people would use today.

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Speaker 4: But in eighteen eleven it starts to feel more organized,

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Speaker 4: and there start to be these letters from this self

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Speaker 4: titled General General ned Lud, And he's actually holed up

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Speaker 4: in Sherwood forest, like Robin Hood, who has kind of

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Speaker 4: similar vibes.

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Speaker 3: Right, and these letters are.

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Speaker 4: Taking on the tones of Civil war frankly, right. He

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Speaker 4: has this title General ned Lud and at one point

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Speaker 4: they call his his Army of Redressers, right, like a

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Speaker 4: redress of grievances. The main thing you need to know

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Speaker 4: about ned Lud there was no ned Lud he was.

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Speaker 4: He was a myth.

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Speaker 3: He was a myth. He was like Robinhood. He was

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Speaker 3: like Robin Hood.

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Speaker 4: I mean, there may actually have been a guy named

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Speaker 4: ned Lud decades earlier who was like a framebreaker, but

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Speaker 4: like this General ned Lud, this guy leading the revolt,

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Speaker 4: he was an invention, which is kind of genius, right, like, yes,

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Speaker 4: it's a genius way for a people with no political power,

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Speaker 4: for a group of people with no political power to.

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Speaker 3: Create a movement. Right, you invent a figurehead, a mythical

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Speaker 3: general hold up in the forest who does not exist.

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Speaker 4: And so it's this idea that the workers are not

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Speaker 4: just randomly breaking machines, they're organizing to fight back. And

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Speaker 4: one thing that's interesting, there's this historian who has called

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Speaker 4: what they were doing collective bargaining by riot because there

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Speaker 4: were no unions, Like they couldn't even vote, right, there

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Speaker 4: wasn't a mass suffrage in England at the time. There

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Speaker 4: were certainly no units. They basically didn't have power in

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Speaker 4: any organized way. So they were seizing power and it's

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Speaker 4: this collective kind of.

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Speaker 3: Ad hoc way.

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Speaker 4: And these attacks get more systematic. There's this one, particularly

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Speaker 4: dramatic one where these guys they all mass at this

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Speaker 4: bar and they're going to attack this mill.

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Speaker 3: But the mill owner knows.

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Speaker 4: Like the Luddites that you know, have been attacking around

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Speaker 4: this region, He's been preparing to defend himself. So the

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Speaker 4: owner is actually like sleeping there. He's hired some people

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Speaker 4: with rifles to defend the factory. He actually has like

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Speaker 4: a that of I think sulfuric acid.

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Speaker 3: He's going to like pour down on them.

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Speaker 4: Yes, it's very medieval, right, it feels very like medieval castle.

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Speaker 4: He's made this factory like a fortress, and so the

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Speaker 4: Ludights march on it is it is very it's like

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Speaker 4: proto civil war, right. This is an armed organized attack

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Speaker 4: on a heavily defended factory. And they get in and

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Speaker 4: there's like actually an exchange of gunfire, and ultimately they retreat.

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Speaker 4: So there's two Ledites dead. They have failed in their attack.

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Speaker 4: And around this time Parliament, you know, the British government

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Speaker 4: realizes that this is getting out of hand, and they

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Speaker 4: have passed a law making attacking machines punishable by death.

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Speaker 2: Amazing, I am amazed.

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Speaker 1: It was almost ninety nine percent confident that this would

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Speaker 1: not go in the favor.

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Speaker 2: Of the people.

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Speaker 1: Tell me something about British history that I didn't already know. Okay,

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Speaker 1: go ahead.

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Speaker 4: So after this attack where two Ledites are killed, there's

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Speaker 4: like this round up. Essentially, the government fights back. A

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Speaker 4: bunch of Leedits are arrested, throwing in jail their triede

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Speaker 4: and several of them are in fact given death sentences,

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Speaker 4: and they are publicly hung. They actually make the gallows

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Speaker 4: twice as high as usual so that you can see

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Speaker 4: it from even farther away. You know, there's like, you know,

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Speaker 4: a crowd of people witnessing the hanging. And this basically

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Speaker 4: defeats the Luddites, right, Like, this is basically the Luddites lose. Right,

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Speaker 4: the government rounds them up and kills them, and they

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Speaker 4: stop attacking the machines, and there are no more rich croppers,

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Speaker 4: or they were never rich, but there are no more

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Speaker 4: like relatively well off croppers after this, Right, It's just

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Speaker 4: the machines are, in fact, a better cheaper way to

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Speaker 4: make cloth. The Ludites don't have any political power and

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Speaker 4: they are out of luck.

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Speaker 1: That all makes me wonder, like if you put the

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Speaker 1: Luddite story next to conversations that we're having about AI today,

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Speaker 1: in what ways do you think that they're similar and

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Speaker 1: how are they different? And the one stat that I

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Speaker 1: remember when we were researching for this that came out

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Speaker 1: up is like Goldman said that by twenty thirty, AI

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Speaker 1: could replace the equivalent of three hundred million full time jobs.

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Speaker 1: Forbes said it would replace two million manufacturing jobs by.

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Speaker 2: The end of next year alone.

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Speaker 1: And so when I when that Light eight story especially,

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Speaker 1: it's got everything, It's got public policy, it has a clear,

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Speaker 1: very simple explanation of who really wins in the short

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Speaker 1: term and capitalism, the power dynamics, all of that. So

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Speaker 1: then that just makes me wonder if you put that

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Speaker 1: story next to today's AI conversations, like, how do you

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Speaker 1: see them being similar or different?

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Speaker 4: Yeah, I mean, certainly workers being potentially replaced by machines

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Speaker 4: is in fact similar like that in some places you know,

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Speaker 4: call centers.

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Speaker 3: It's already happening, clearly. I Mean.

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Speaker 4: One thing that I think is an important difference is

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Speaker 4: political economy, right Like it's and for people to say, oh,

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Speaker 4: ordinary people have no power in today's economy, it's all

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Speaker 4: the rich people. And like, certainly rich people have a

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Speaker 4: lot of power, but relative to the Luddites, ordinary people

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Speaker 4: do have more power today, right Like unions were illegal,

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Speaker 4: the Luddites literally could not vote, right, And so it

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Speaker 4: will be interesting to see who is losing jobs to

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Speaker 4: AI and when and how politics and the government respond, right,

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Speaker 4: And it's an interesting moment now because for a long time,

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Speaker 4: technological change threatened lower skilled workers, right like twentieth century

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Speaker 4: automation hollowed out.

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Speaker 3: The middle to a significant degree.

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Speaker 4: Right, There was this phrase, the hollowing out of the middle,

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Speaker 4: where like physical labor actually for a while did okay,

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Speaker 4: and if you were sort of highly educated you did okay,

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Speaker 4: but if you were kind of in the middle of

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Speaker 4: the distribution, it was bad for you. Now it's kind

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Speaker 4: of across the board, and strikingly, people like lawyers and

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Speaker 4: journalists are threatened. And so those are people who traditionally

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Speaker 4: have had more political influence, perhaps, right, And so I

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Speaker 4: think how will the government respond? Is super interesting and

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Speaker 4: super unclear.

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Speaker 1: Yeah, do you have a sense of which other types

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Speaker 1: of jobs are projected to be affected? I mean the

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Speaker 1: manufacturing jobs obviously that's well documented and also follows a

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Speaker 1: pattern of technological change throughout history.

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Speaker 2: I think you're right, the.

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Speaker 1: Doctor lawyer thing is relatively new, jarring for a whole

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Speaker 1: different economic class of people. What are the ones we're

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Speaker 1: not thinking about?

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Speaker 3: I don't know. I mean, I'm wary of making.

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Speaker 4: Like AI is insane right now, Like it is like

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Speaker 4: I don't you know, there is one thread of the discourse,

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Speaker 4: and maybe this has died down. I don't know that

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Speaker 4: is like, Oh, it's just hype from AI companies when

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Speaker 4: A companies are like AI is going to be crazy,

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Speaker 4: Like I don't think that is true. It is interesting

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Speaker 4: to think about where are the bottlenecks, right, I do

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Speaker 4: think there might be bottlenecks in adoption, right, Like in

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Speaker 4: a superficial.

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Speaker 3: Way, AI looks really good, but when you actually try

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Speaker 3: and get it to do stuff, it's kind of a

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Speaker 3: pain in the ass.

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Speaker 4: And like there's this interesting guy, Dwarkish Patel, I believe

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Speaker 4: who he writes about AI and he interviews a lot

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Speaker 4: of the really smart AI people, and he made the

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Speaker 4: point that he's been trying to use it for his

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Speaker 4: own work, but that it's not good at like learning incrementally,

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Speaker 4: Like it can do an okay job, It can do

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Speaker 4: a five out of ten job, But a human being

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Speaker 4: that starts out a five out of ten at your company,

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Speaker 4: you can kind of get them up to eight out

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Speaker 4: of ten, and getting the AI to eight out of

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Speaker 4: ten getting it to learn on a particular task is

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Speaker 4: actually still doesn't work. And like getting an AI to

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Speaker 4: actually do a job for you is still hard. And

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Speaker 4: so I don't know, I really don't know how it's

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Speaker 4: going to roll out, and I don't know what the

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Speaker 4: political responsible base.

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Speaker 1: Do you feel like it's too early to speculate what

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Speaker 1: the differences are compared to previous technological changes or advances.

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Speaker 4: I mean, I think one thing that is important to

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Speaker 4: remember is, at least so far, every time people have

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Speaker 4: lost jobs to technology, new jobs that people couldn't.

440
00:22:27,520 --> 00:22:30,560
Speaker 3: Imagine before emerged, right, So.

441
00:22:30,680 --> 00:22:34,639
Speaker 4: Like some number of people are displayed, some number of

442
00:22:34,680 --> 00:22:37,240
Speaker 4: people are worse off in the short.

443
00:22:36,960 --> 00:22:40,160
Speaker 3: To medium and sometimes long term, but always so far

444
00:22:40,359 --> 00:22:41,800
Speaker 3: there have been more new jobs.

445
00:22:42,000 --> 00:22:45,880
Speaker 4: People's wants and willingness to pay for things is insatiable,

446
00:22:46,000 --> 00:22:49,119
Speaker 4: and I suppose this may be the end of that,

447
00:22:49,160 --> 00:22:52,120
Speaker 4: but I wouldn't bet against that, you know. I mean,

448
00:22:53,320 --> 00:22:56,480
Speaker 4: I feel a little silly that making podcasts is my job, right,

449
00:22:56,520 --> 00:22:56,840
Speaker 4: trust me?

450
00:22:57,000 --> 00:22:58,879
Speaker 2: It is security I have about my jobs.

451
00:22:59,280 --> 00:23:03,439
Speaker 4: Yes, I understated, and that's because you know, we have

452
00:23:03,480 --> 00:23:06,000
Speaker 4: all these machines that make really cheap clothes and cheap

453
00:23:06,040 --> 00:23:08,159
Speaker 4: food and all of the basic things. You know, Like

454
00:23:08,520 --> 00:23:11,600
Speaker 4: if you just look at farming, right, like in eighteen

455
00:23:11,720 --> 00:23:15,440
Speaker 4: hundred something, they have rough numbers. Eighteen hundred and ninety

456
00:23:15,440 --> 00:23:18,440
Speaker 4: percent of Americans were farmers in nineteen hundred and fifty percent.

457
00:23:18,200 --> 00:23:20,320
Speaker 3: Of Americans were farmers. Ish, maybe forty.

458
00:23:20,400 --> 00:23:23,480
Speaker 4: Two thousand two percent of Americans were farmers, right, maybe

459
00:23:23,480 --> 00:23:26,040
Speaker 4: the greatest displacement of labor you can think of because

460
00:23:26,040 --> 00:23:29,680
Speaker 4: of the reaper and the tractor. Right, But like everybody

461
00:23:29,800 --> 00:23:31,440
Speaker 4: went to working factories and then they went to work

462
00:23:31,480 --> 00:23:34,399
Speaker 4: as you know, personal trainers and bank tellers and other things.

463
00:23:35,240 --> 00:23:40,159
Speaker 1: It also makes me wonder like the the implications up

464
00:23:40,160 --> 00:23:43,119
Speaker 1: to your point about like maybe there is no next

465
00:23:43,920 --> 00:23:46,320
Speaker 1: widespread job thing that people have, Like what do you

466
00:23:46,359 --> 00:23:51,199
Speaker 1: retrain for when it's just a computer taking everything? And

467
00:23:51,520 --> 00:23:53,800
Speaker 1: I wonder like in terms of the TBD for what

468
00:23:53,880 --> 00:23:57,480
Speaker 1: government does, if it's even government that does this is

469
00:23:57,520 --> 00:23:59,760
Speaker 1: like just taking the US. We live in a country

470
00:23:59,800 --> 00:24:04,480
Speaker 1: that can barely agree on the federal level that we

471
00:24:04,520 --> 00:24:09,040
Speaker 1: should have just the basic social safety net in place, right,

472
00:24:09,080 --> 00:24:13,920
Speaker 1: compare us to other industrialized, civilized countries. And so then

473
00:24:14,160 --> 00:24:17,439
Speaker 1: jump to you know, well, what about universal basic income

474
00:24:17,520 --> 00:24:20,240
Speaker 1: and people who are really touting universal basic income? And

475
00:24:20,280 --> 00:24:22,919
Speaker 1: if you look at today's political climate, in what fucking

476
00:24:23,000 --> 00:24:27,440
Speaker 1: reality are you going to get these two generally right

477
00:24:27,480 --> 00:24:29,960
Speaker 1: of center, if you're looking at global standards, two political

478
00:24:30,000 --> 00:24:32,560
Speaker 1: parties to agree that UBI is going to be a

479
00:24:32,600 --> 00:24:34,000
Speaker 1: thing that we can do. And I don't mean to

480
00:24:34,000 --> 00:24:36,880
Speaker 1: be a cynic about it, but it concerns me greatly

481
00:24:37,040 --> 00:24:40,480
Speaker 1: that our politics in the democracy are designed to move slowly.

482
00:24:40,760 --> 00:24:41,359
Speaker 2: That's a given.

483
00:24:42,080 --> 00:24:45,520
Speaker 1: But if the technological displacement of jobs is moving faster

484
00:24:45,760 --> 00:24:47,680
Speaker 1: than our public policy, what happens.

485
00:24:49,080 --> 00:24:53,240
Speaker 4: I mean, that's definitely a possible bad outcome. I mean,

486
00:24:53,280 --> 00:24:55,160
Speaker 4: the short answer to what happens. So what you're imagining

487
00:24:55,240 --> 00:24:57,840
Speaker 4: is like lots of people lose their jobs because of

488
00:24:58,040 --> 00:25:00,520
Speaker 4: technological change, and the government doesn't too very much to help

489
00:25:00,520 --> 00:25:01,640
Speaker 4: you because we have more truly.

490
00:25:01,480 --> 00:25:04,280
Speaker 1: Darius benefiting from all this, and they just don't want

491
00:25:04,280 --> 00:25:05,200
Speaker 1: to give up their money.

492
00:25:05,520 --> 00:25:08,639
Speaker 4: It's possible, I mean, I mean, obviously it's the case

493
00:25:08,680 --> 00:25:11,720
Speaker 4: that you know, Northern European countries have much more robust

494
00:25:11,720 --> 00:25:14,000
Speaker 4: safety nets than we do. It's also the case that

495
00:25:14,040 --> 00:25:17,840
Speaker 4: like Medicare and social security are politically totally untouchable and

496
00:25:17,880 --> 00:25:23,520
Speaker 4: have broad support, right, and so like maybe there's like incrementalism, Right,

497
00:25:23,600 --> 00:25:27,360
Speaker 4: maybe you can get social security and medicare ten years earlier. Right,

498
00:25:27,440 --> 00:25:30,600
Speaker 4: Like that seems like a plausible outcome. It is, in

499
00:25:30,600 --> 00:25:34,240
Speaker 4: fact interesting that the people talking the most about universal

500
00:25:34,280 --> 00:25:38,320
Speaker 4: basic income. Are AI people and tech people who actually

501
00:25:38,359 --> 00:25:41,000
Speaker 4: think this might happen. Right, you know, there is this

502
00:25:41,280 --> 00:25:45,480
Speaker 4: essay that John Mayer Kines, you know, of Kynesian economics

503
00:25:45,520 --> 00:25:51,520
Speaker 4: fame wrote in nineteen thirty, I think called economic Possibilities

504
00:25:51,560 --> 00:25:53,880
Speaker 4: for our Grandchildren, and I think that was actually where

505
00:25:53,920 --> 00:25:58,560
Speaker 4: the term technological unemployment was first used. Right, so, you

506
00:25:58,600 --> 00:26:02,240
Speaker 4: know thirty, right, the world's going into the depression, but

507
00:26:02,320 --> 00:26:07,200
Speaker 4: he's thinking generations ahead, and he's thinking about technological change

508
00:26:07,800 --> 00:26:10,919
Speaker 4: and robots taking our jobs as we would sort of

509
00:26:10,920 --> 00:26:14,320
Speaker 4: colloquially say today, and he's imagining, like, well, what if

510
00:26:14,320 --> 00:26:16,639
Speaker 4: our grandchildren are just working fifteen hours a week. He's like,

511
00:26:16,680 --> 00:26:19,000
Speaker 4: first of all, it's hard not to work at all, right,

512
00:26:19,040 --> 00:26:21,359
Speaker 4: we're sort of wired to want to do something, but

513
00:26:21,440 --> 00:26:24,200
Speaker 4: maybe you don't have to work that much. And people

514
00:26:24,240 --> 00:26:26,200
Speaker 4: sort of a look at fifteen hours a week now

515
00:26:26,240 --> 00:26:28,679
Speaker 4: and laugh like, oh haha, how charmingly wrong he is.

516
00:26:29,160 --> 00:26:32,280
Speaker 4: But I was looking as I was thinking about this interview,

517
00:26:32,320 --> 00:26:34,080
Speaker 4: and if you look at hours a week, it was

518
00:26:34,200 --> 00:26:38,520
Speaker 4: like sixty hours a week in nineteen hundred and fifty

519
00:26:38,560 --> 00:26:41,119
Speaker 4: hours a week in nineteen thirty when he wrote that,

520
00:26:41,320 --> 00:26:43,960
Speaker 4: we sort of plateaued at forty in this country, but

521
00:26:44,040 --> 00:26:46,520
Speaker 4: in the Netherlands they're like down to thirty two hours

522
00:26:46,560 --> 00:26:50,359
Speaker 4: a week now, like all these days off with all

523
00:26:50,400 --> 00:26:52,480
Speaker 4: the right, with a lot of same mondays thrown in

524
00:26:52,440 --> 00:26:55,960
Speaker 4: in the MiGs. And so I don't know, like, yes,

525
00:26:56,040 --> 00:26:58,320
Speaker 4: there are many if we think of possibility space, there

526
00:26:58,320 --> 00:27:03,000
Speaker 4: are definitely bad outcomes. I'm not entirely pessimistic, like, look,

527
00:27:03,040 --> 00:27:06,479
Speaker 4: it is the case that there is a progress aspect

528
00:27:06,520 --> 00:27:08,200
Speaker 4: to this, right, Like, yes, a lot of things could

529
00:27:08,240 --> 00:27:09,600
Speaker 4: be bad, and a lot of things can go wrong,

530
00:27:09,600 --> 00:27:11,840
Speaker 4: and maybe we're not in the best possible timeline. Certainly

531
00:27:11,840 --> 00:27:13,119
Speaker 4: we're not in the best possible timeline.

532
00:27:13,119 --> 00:27:13,520
Speaker 2: But like.

533
00:27:15,280 --> 00:27:19,560
Speaker 4: Having machines do stuff instead of people, it does mean

534
00:27:19,600 --> 00:27:21,679
Speaker 4: there is more abundance, right, And so there is this

535
00:27:21,760 --> 00:27:24,359
Speaker 4: problem that you're pointing to of like sharing the abundance,

536
00:27:24,440 --> 00:27:26,760
Speaker 4: making sure that it doesn't all go to six people, right,

537
00:27:26,800 --> 00:27:29,680
Speaker 4: which is a real thing to worry about.

538
00:27:29,760 --> 00:27:32,760
Speaker 3: But there will be more to go around, I guess

539
00:27:32,760 --> 00:27:33,160
Speaker 3: if it's.

540
00:27:33,080 --> 00:27:36,440
Speaker 1: Structured, right, yes, And you know I am not I'm

541
00:27:36,480 --> 00:27:38,679
Speaker 1: not such a louttite that I don't acknowledge that we

542
00:27:39,520 --> 00:27:44,000
Speaker 1: you know, the advances in medical research and science and

543
00:27:44,119 --> 00:27:47,080
Speaker 1: exploration and astronomy, and all of those things are are

544
00:27:47,160 --> 00:27:48,760
Speaker 1: going to be incredible. I was just talking with a

545
00:27:49,480 --> 00:27:52,800
Speaker 1: doctor who I ran into last night, who was touting

546
00:27:52,840 --> 00:27:56,600
Speaker 1: how exciting it is in her field for AI research

547
00:27:56,640 --> 00:27:59,159
Speaker 1: in medicine, and I have the exact opposite viewpoint as

548
00:27:59,160 --> 00:27:59,560
Speaker 1: an artist.

549
00:27:59,680 --> 00:28:02,000
Speaker 4: Right, let me ask you a question. I've been curious,

550
00:28:02,600 --> 00:28:04,200
Speaker 4: do you ever use AI for work?

551
00:28:04,760 --> 00:28:08,120
Speaker 1: I have tried because I was curious, and thank god,

552
00:28:08,240 --> 00:28:11,800
Speaker 1: AI does a piss poor job at writing jokes. I'm

553
00:28:11,840 --> 00:28:14,880
Speaker 1: sure one day it will learn. Part of that, obviously,

554
00:28:14,960 --> 00:28:17,520
Speaker 1: is that comedy is so subjective.

555
00:28:18,480 --> 00:28:19,280
Speaker 2: But no.

556
00:28:19,560 --> 00:28:22,000
Speaker 1: In fact, what I've used it for and what I

557
00:28:22,040 --> 00:28:25,480
Speaker 1: find to be helpful is that as a writer an actor,

558
00:28:25,600 --> 00:28:30,359
Speaker 1: my brain is usually very scattered. Like you know, people

559
00:28:30,400 --> 00:28:32,600
Speaker 1: like me think of the ten things that you're not

560
00:28:32,640 --> 00:28:35,040
Speaker 1: supposed to say out loud, but like that's constantly in

561
00:28:35,080 --> 00:28:38,160
Speaker 1: our brain, and so you're you always have a filter

562
00:28:38,440 --> 00:28:40,560
Speaker 1: on your brain depending on the setting you're in. Like

563
00:28:40,560 --> 00:28:43,000
Speaker 1: if I'm on stage doing a stand up routine, that

564
00:28:43,040 --> 00:28:45,160
Speaker 1: filter is off and generally it works and sometimes it

565
00:28:45,200 --> 00:28:47,280
Speaker 1: will get you in trouble. But in normal day to

566
00:28:47,360 --> 00:28:49,600
Speaker 1: day interactions, there's like this weird filter you have to

567
00:28:49,600 --> 00:28:54,120
Speaker 1: put on. And so when I'm writing a what by

568
00:28:54,160 --> 00:28:57,120
Speaker 1: normal people's standards would be like a professional document or

569
00:28:57,120 --> 00:29:00,960
Speaker 1: a professional email or a professional text, my mind has

570
00:29:01,080 --> 00:29:04,000
Speaker 1: nine paragraphs of things to describe what I want to say,

571
00:29:04,360 --> 00:29:07,160
Speaker 1: and I just need two sentences. So I have found

572
00:29:07,200 --> 00:29:10,240
Speaker 1: that something like that is a helpful tool, but I

573
00:29:10,280 --> 00:29:13,240
Speaker 1: have not found that in my actual professional life that

574
00:29:13,400 --> 00:29:17,680
Speaker 1: there's there's anything that I have that I've benefited from.

575
00:29:17,840 --> 00:29:19,360
Speaker 1: That said, I know it's all coming.

576
00:29:19,680 --> 00:29:22,320
Speaker 3: So you're saying it's good for taking all the creativity

577
00:29:22,360 --> 00:29:23,080
Speaker 3: out of your.

578
00:29:23,000 --> 00:29:23,600
Speaker 2: Life so far.

579
00:29:23,720 --> 00:29:26,200
Speaker 3: Yeah, Anti, yeah, yea, yeah.

580
00:29:25,640 --> 00:29:26,720
Speaker 2: That's been my experience.

581
00:29:26,720 --> 00:29:28,840
Speaker 1: But again, the you know, I read about all of

582
00:29:28,880 --> 00:29:32,960
Speaker 1: the things that AI companies are working on to replace actors,

583
00:29:33,000 --> 00:29:35,720
Speaker 1: to clone our voice, our performance is coming up with

584
00:29:35,840 --> 00:29:40,840
Speaker 1: just completely new actors slash characters or personalities. So those

585
00:29:40,840 --> 00:29:42,719
Speaker 1: are the things that I don't you know that I

586
00:29:42,760 --> 00:29:46,040
Speaker 1: hope won't take my job. Like you you said this earlier,

587
00:29:46,080 --> 00:29:48,320
Speaker 1: I am of a certain age where like I'd love

588
00:29:48,320 --> 00:29:51,120
Speaker 1: a good twenty to twenty five more years in my career.

589
00:29:51,440 --> 00:29:53,960
Speaker 1: But man, I'm glad I'm not eighteen going to drama school.

590
00:29:54,160 --> 00:29:56,720
Speaker 1: It's a it's a whole different ballgame.

591
00:29:57,520 --> 00:29:59,520
Speaker 4: I mean, I feel like if you're eight and going

592
00:29:59,560 --> 00:30:02,520
Speaker 4: to drama school, you've got to think of using the tools, right,

593
00:30:02,720 --> 00:30:07,840
Speaker 4: Like that a classic technology thing is you don't want

594
00:30:07,880 --> 00:30:11,080
Speaker 4: to be competing against the tool the technology. You want

595
00:30:11,080 --> 00:30:13,520
Speaker 4: to be using the technology. And you've seen that. I

596
00:30:13,520 --> 00:30:15,560
Speaker 4: mean to get back to the sort of historical arc,

597
00:30:15,680 --> 00:30:18,320
Speaker 4: like that was a thing that happened in factories, right,

598
00:30:18,400 --> 00:30:21,560
Speaker 4: Like US factories in many instances are high tech. Right,

599
00:30:21,600 --> 00:30:25,640
Speaker 4: There's like CNC machining was that computer Numerical control machining.

600
00:30:25,720 --> 00:30:28,960
Speaker 3: Like they're sort of like tech jobs for kind of.

601
00:30:29,040 --> 00:30:33,560
Speaker 4: Skilled workers, right, And even like when I worked at NPR,

602
00:30:33,880 --> 00:30:36,560
Speaker 4: Like there were the engineers in the studio who were,

603
00:30:36,640 --> 00:30:38,480
Speaker 4: you know, running the board, but then there was the

604
00:30:38,520 --> 00:30:41,880
Speaker 4: guy who was the manager of those guys, who was

605
00:30:41,920 --> 00:30:44,400
Speaker 4: like very much like unengineer at heart, and he loved

606
00:30:44,400 --> 00:30:46,280
Speaker 4: doing the stuff, but he was also like building the

607
00:30:46,280 --> 00:30:49,360
Speaker 4: systems and like running the servers and like he was

608
00:30:49,400 --> 00:30:51,680
Speaker 4: the guy who figured out how the reporters could self up,

609
00:30:51,760 --> 00:30:54,560
Speaker 4: could record ourselves, right, And so that's the guy you

610
00:30:54,600 --> 00:30:56,280
Speaker 4: want to be, right, You want to be the guy

611
00:30:56,920 --> 00:31:01,320
Speaker 4: using the tools. And like I haven't given up on that, Like,

612
00:31:01,360 --> 00:31:03,480
Speaker 4: in the long run, I assume AI will be better

613
00:31:03,520 --> 00:31:06,400
Speaker 4: at doing everything that I do than I am, but

614
00:31:06,480 --> 00:31:09,440
Speaker 4: hopefully that'll take a while. There's some number of people

615
00:31:09,440 --> 00:31:12,160
Speaker 4: who are like used to hearing me and like I

616
00:31:12,200 --> 00:31:15,160
Speaker 4: can sort of use AI to maybe work faster or

617
00:31:15,200 --> 00:31:17,800
Speaker 4: be smarter, right to do to figure out in five

618
00:31:17,840 --> 00:31:19,600
Speaker 4: minutes what it would have taken me an hour of

619
00:31:19,640 --> 00:31:22,360
Speaker 4: Google searching to figure out, and obviously still vet it,

620
00:31:22,640 --> 00:31:25,640
Speaker 4: but like, for the medium term, that's what I'm banking on,

621
00:31:26,280 --> 00:31:29,080
Speaker 4: and I do think that's like a healthier relationship to

622
00:31:29,520 --> 00:31:30,560
Speaker 4: technology in general.

623
00:31:37,440 --> 00:31:40,600
Speaker 1: I was invited to an AI exhibit recently, an AI

624
00:31:40,760 --> 00:31:44,320
Speaker 1: art exhibit I'm putting art in air quotes by a

625
00:31:44,360 --> 00:31:47,000
Speaker 1: friend who is a tech bro, and he's like, hey,

626
00:31:47,040 --> 00:31:49,160
Speaker 1: I've been working for the last eighteen months on these

627
00:31:49,880 --> 00:31:52,240
Speaker 1: art pieces, these AI art pieces. Will you come to

628
00:31:52,280 --> 00:31:54,480
Speaker 1: the gallery opening? And I'm like, I love you. There

629
00:31:54,520 --> 00:31:57,080
Speaker 1: is nothing that I would rather do less on a

630
00:31:57,120 --> 00:32:00,840
Speaker 1: Tuesday night than come and see the product of you

631
00:32:01,040 --> 00:32:04,640
Speaker 1: getting high in front of your laptop and pressing buttons.

632
00:32:05,320 --> 00:32:08,200
Speaker 3: No, sorry, man, I wanted that I'd stay home.

633
00:32:08,320 --> 00:32:12,800
Speaker 2: I would stay home and do it myself. But yeah,

634
00:32:12,880 --> 00:32:14,200
Speaker 2: I'm not a fan of that.

635
00:32:14,360 --> 00:32:16,160
Speaker 1: I don't want to get too off drag and I

636
00:32:16,680 --> 00:32:19,320
Speaker 1: want I want to ask a comparison question about the

637
00:32:19,360 --> 00:32:21,840
Speaker 1: Ludites and today. In the lud eight era, you write

638
00:32:21,880 --> 00:32:25,520
Speaker 1: that productivity went way up, wages for regular workers barely

639
00:32:25,560 --> 00:32:28,080
Speaker 1: moved for decades. That's something that we're obviously used to.

640
00:32:28,160 --> 00:32:31,080
Speaker 1: You see that time and time again. You also talked

641
00:32:31,080 --> 00:32:34,680
Speaker 1: about how the generations after the Ludites benefited from the

642
00:32:34,680 --> 00:32:36,640
Speaker 1: machines themselves that replace them.

643
00:32:36,920 --> 00:32:38,720
Speaker 2: So I'm curious, like, what are the big.

644
00:32:38,600 --> 00:32:41,840
Speaker 1: Lessons from that period about who benefits from new technology?

645
00:32:42,200 --> 00:32:45,400
Speaker 1: What do the people who are in the generation that

646
00:32:45,440 --> 00:32:48,400
Speaker 1: are directly impacted by machines supposed to do. I know

647
00:32:48,400 --> 00:32:50,560
Speaker 1: we talked about that a little bit, but all that's

648
00:32:50,640 --> 00:32:54,520
Speaker 1: leading to right now with AI knowing how much diversity

649
00:32:54,520 --> 00:32:58,440
Speaker 1: of opinion there is, how much panic and excitement there is,

650
00:32:58,480 --> 00:32:59,680
Speaker 1: what should our outlook be.

651
00:33:00,720 --> 00:33:01,560
Speaker 3: That's super hard.

652
00:33:02,720 --> 00:33:06,600
Speaker 4: I mean, you know, the I think the closest thing

653
00:33:06,680 --> 00:33:11,000
Speaker 4: in recent times that comes to my mind is workers

654
00:33:11,000 --> 00:33:14,760
Speaker 4: who've lost their job to foreign competition. Right Like, in

655
00:33:14,800 --> 00:33:17,479
Speaker 4: the kind of early part of the aughts, there was

656
00:33:17,560 --> 00:33:20,160
Speaker 4: what's come to be called the China Shock, which was

657
00:33:20,200 --> 00:33:22,600
Speaker 4: you know, China entered the World Trade Organization in like

658
00:33:22,640 --> 00:33:24,920
Speaker 4: two thousand. It's funny not that long ago, right, China

659
00:33:25,160 --> 00:33:26,760
Speaker 4: used to be a super poor country, was not a

660
00:33:26,840 --> 00:33:31,080
Speaker 4: major competitor, and then bam they entered the WTO. And

661
00:33:31,520 --> 00:33:35,240
Speaker 4: there were places in the United States that were making

662
00:33:35,280 --> 00:33:39,920
Speaker 4: things that competed with Chinese imports, like clothes, and they

663
00:33:40,000 --> 00:33:41,680
Speaker 4: got obliterated.

664
00:33:42,640 --> 00:33:46,720
Speaker 3: The United States as a whole did well. And I

665
00:33:46,880 --> 00:33:49,760
Speaker 3: mean ordinary people, right, Like, it is the case that like.

666
00:33:50,680 --> 00:33:54,000
Speaker 4: For working people, when clothes get cheaper, that is good, right.

667
00:33:54,040 --> 00:33:56,200
Speaker 4: It means they have more money in their pocket, more

668
00:33:56,240 --> 00:33:57,840
Speaker 4: money to spend on basic things. And I think it's

669
00:33:57,880 --> 00:34:01,240
Speaker 4: easy to overlook that part. But the people and the

670
00:34:01,240 --> 00:34:04,720
Speaker 4: towns that were competing against China were worse off, right,

671
00:34:04,760 --> 00:34:07,400
Speaker 4: So overall everybody was better off, And it's important to

672
00:34:07,400 --> 00:34:09,280
Speaker 4: say that, Like, it wasn't just rich.

673
00:34:09,160 --> 00:34:10,200
Speaker 3: People who got better off.

674
00:34:10,239 --> 00:34:12,640
Speaker 4: It wasn't just the owners of capital, right, And I

675
00:34:12,680 --> 00:34:15,520
Speaker 4: think that is likely to be true here as well. Right,

676
00:34:15,520 --> 00:34:18,439
Speaker 4: it is a competitive world, and I do think that

677
00:34:18,480 --> 00:34:21,640
Speaker 4: there is a universe where overall people are better off

678
00:34:21,640 --> 00:34:24,799
Speaker 4: from AI. The really hard question is how do you

679
00:34:24,840 --> 00:34:28,800
Speaker 4: help people who are clearly losing their jobs because of AI, Right, Like, well,

680
00:34:29,000 --> 00:34:31,920
Speaker 4: give them money seems like a good part of an

681
00:34:31,920 --> 00:34:34,120
Speaker 4: answer to me, Will it happen politically? I don't know

682
00:34:34,160 --> 00:34:35,640
Speaker 4: for the reasons you said, but if I were waving

683
00:34:35,680 --> 00:34:39,279
Speaker 4: a wand give them money in a way, though it

684
00:34:39,360 --> 00:34:41,160
Speaker 4: seems like I don't want to say the easy part

685
00:34:41,200 --> 00:34:42,800
Speaker 4: because it would be great if it happened politically it

686
00:34:42,840 --> 00:34:45,840
Speaker 4: would be hard. But there is this more complicated thing

687
00:34:47,200 --> 00:34:49,640
Speaker 4: for everyone, right, and Cain's talked about it one hundred

688
00:34:49,680 --> 00:34:52,440
Speaker 4: years ago. It's like work is meaning for a lot

689
00:34:52,480 --> 00:34:59,960
Speaker 4: of people, right, Work is purpose is just an organiz

690
00:35:00,239 --> 00:35:02,440
Speaker 4: principle in life. And so if we project forward, I

691
00:35:02,440 --> 00:35:03,880
Speaker 4: don't know what's going to happen. I don't think all

692
00:35:03,920 --> 00:35:05,680
Speaker 4: jobs are going to be gone in five years. Maybe

693
00:35:05,680 --> 00:35:07,560
Speaker 4: I'll be wrong, but I doubt that. But if in

694
00:35:07,600 --> 00:35:12,040
Speaker 4: fact lots of people are losing their jobs because of AI, yes,

695
00:35:12,200 --> 00:35:14,160
Speaker 4: please give them money. Of course there will be more

696
00:35:14,160 --> 00:35:16,160
Speaker 4: money to go around. There should be money for those people.

697
00:35:16,520 --> 00:35:19,279
Speaker 4: But like, what do we do about everything else? How

698
00:35:19,280 --> 00:35:22,479
Speaker 4: do we like help them find meaning in their life?

699
00:35:22,520 --> 00:35:22,719
Speaker 2: Like that?

700
00:35:23,280 --> 00:35:26,520
Speaker 4: It feels handwavy to me, but like might be my

701
00:35:26,600 --> 00:35:30,160
Speaker 4: own problem in three years, right, Like yeah, my identity

702
00:35:30,200 --> 00:35:32,800
Speaker 4: is my work to some significant area, and like a

703
00:35:32,840 --> 00:35:34,359
Speaker 4: technology might put me out of a job. You know,

704
00:35:34,400 --> 00:35:36,120
Speaker 4: it's easier for me to talk about now that it's me.

705
00:35:36,160 --> 00:35:38,160
Speaker 4: I don't just sound like some asshole talking about other

706
00:35:38,160 --> 00:35:41,080
Speaker 4: people in a condescending way, like this is me, Like truly,

707
00:35:41,800 --> 00:35:43,120
Speaker 4: I could put me out of a job before I

708
00:35:43,120 --> 00:35:45,120
Speaker 4: want to be out of a job, and like it

709
00:35:45,160 --> 00:35:47,640
Speaker 4: will be hard in many ways if that happens. Not

710
00:35:47,800 --> 00:35:48,680
Speaker 4: just the money.

711
00:35:49,120 --> 00:35:52,000
Speaker 1: I worry that not just in the interim, but when

712
00:35:52,000 --> 00:35:54,880
Speaker 1: we're when we're dead the next you know, the next

713
00:35:55,000 --> 00:35:56,480
Speaker 1: eighty years or eighty years.

714
00:35:56,400 --> 00:35:57,040
Speaker 2: Down the line.

715
00:35:57,640 --> 00:36:01,520
Speaker 1: Yeah, if you have new new classes of people that

716
00:36:01,600 --> 00:36:05,560
Speaker 1: are so completely diametrically like they don't mix. You've got

717
00:36:05,600 --> 00:36:09,520
Speaker 1: this AI job class of folks who are working. And

718
00:36:09,560 --> 00:36:13,799
Speaker 1: then if there's even success at something like UBI, people

719
00:36:13,880 --> 00:36:17,000
Speaker 1: who have been pushed out, who are then economically locked,

720
00:36:17,600 --> 00:36:20,759
Speaker 1: whose children are economically locked in a particular scenario, does

721
00:36:20,800 --> 00:36:25,080
Speaker 1: that breed a type of class resentment that's even more

722
00:36:25,120 --> 00:36:27,759
Speaker 1: extreme than what we see today. You know, you see

723
00:36:28,320 --> 00:36:31,920
Speaker 1: scapegoating of immigrants. The fear that I have about how

724
00:36:31,960 --> 00:36:35,640
Speaker 1: that could be exacerbated without the right public policy. Once

725
00:36:35,719 --> 00:36:38,480
Speaker 1: the people who have lived it have died off is

726
00:36:38,520 --> 00:36:39,320
Speaker 1: also very scary.

727
00:36:39,360 --> 00:36:40,080
Speaker 2: I don't have to worry.

728
00:36:39,920 --> 00:36:42,280
Speaker 1: About that because I'll be dead, but it is something

729
00:36:42,320 --> 00:36:46,360
Speaker 1: that I think, you know, that worries me. My question

730
00:36:46,400 --> 00:36:49,399
Speaker 1: for you is literally the exact opposite, because I hate

731
00:36:49,440 --> 00:36:52,640
Speaker 1: wrapping up on a doomsday scenario. I think it's very

732
00:36:52,680 --> 00:36:56,239
Speaker 1: easy to do. I'm not a rage beaita. When you

733
00:36:56,280 --> 00:37:00,480
Speaker 1: imagine the next ten or twenty years, what are kind

734
00:37:00,480 --> 00:37:04,560
Speaker 1: of the most realistic ways that technology might change our

735
00:37:04,680 --> 00:37:08,239
Speaker 1: jobs that don't fit into either a doomsday scenario or

736
00:37:08,320 --> 00:37:09,480
Speaker 1: a utopian story.

737
00:37:11,280 --> 00:37:13,760
Speaker 3: Yeah, that's nice. So like it kind of the middle

738
00:37:13,800 --> 00:37:15,800
Speaker 3: of the probability distribution.

739
00:37:15,480 --> 00:37:18,120
Speaker 1: Right, Like, if I took AI to tell me to

740
00:37:18,120 --> 00:37:20,000
Speaker 1: take the emotion out of that question, that's what it

741
00:37:20,000 --> 00:37:20,719
Speaker 1: would have given me.

742
00:37:21,000 --> 00:37:21,920
Speaker 3: Yeah.

743
00:37:22,080 --> 00:37:25,720
Speaker 4: No, it's interesting to think about, right, because it's more subtle,

744
00:37:25,840 --> 00:37:28,000
Speaker 4: So people, it doesn't make so much of a story. Okay,

745
00:37:28,040 --> 00:37:31,279
Speaker 4: so let's think about that. So one thing, one thing

746
00:37:31,320 --> 00:37:36,480
Speaker 4: in that story is the capability for AI really to

747
00:37:36,600 --> 00:37:38,560
Speaker 4: actually do people's jobs.

748
00:37:39,320 --> 00:37:41,320
Speaker 3: It emerges rather slowly.

749
00:37:41,120 --> 00:37:45,360
Speaker 4: Right, It is constrained for reasons partly technological but partly

750
00:37:45,400 --> 00:37:50,080
Speaker 4: also institutional, Like companies have all this sort of tacit information, right,

751
00:37:50,160 --> 00:37:52,600
Speaker 4: Like people at companies know how to do all these things,

752
00:37:52,600 --> 00:37:55,200
Speaker 4: but they never wrote it down right, and so AI

753
00:37:55,320 --> 00:37:57,440
Speaker 4: can't just know that like, oh you got to call

754
00:37:57,560 --> 00:38:00,719
Speaker 4: John and accounts receivable when this happens, like that kind

755
00:38:00,719 --> 00:38:04,239
Speaker 4: of thing. So it's slow, right, That's one important thing,

756
00:38:04,440 --> 00:38:06,879
Speaker 4: is like if you can see the transition coming, if

757
00:38:06,920 --> 00:38:12,360
Speaker 4: you know that it's not tomorrow, like you know, kids

758
00:38:12,400 --> 00:38:14,560
Speaker 4: don't become a journalist if you're twenty maybe, or if

759
00:38:14,560 --> 00:38:16,280
Speaker 4: you do, learn how to do it with AI, because

760
00:38:16,360 --> 00:38:17,080
Speaker 4: it's going to change.

761
00:38:17,120 --> 00:38:18,560
Speaker 3: So it's slow, right.

762
00:38:19,000 --> 00:38:25,200
Speaker 4: Two, So the extent happens, it will increase output, right, like,

763
00:38:25,280 --> 00:38:27,759
Speaker 4: it will make us more productive and more efficient, and

764
00:38:27,880 --> 00:38:30,000
Speaker 4: like at a basic level that is.

765
00:38:29,960 --> 00:38:32,200
Speaker 3: Good and I think it is underappreciated. Right.

766
00:38:32,239 --> 00:38:35,400
Speaker 4: It will increase material abundance. It will help us whatever

767
00:38:35,480 --> 00:38:38,359
Speaker 4: you want, more clean energy, certainly we can get better

768
00:38:38,360 --> 00:38:40,960
Speaker 4: at doing clean energy, get better at doing battery storage

769
00:38:41,000 --> 00:38:44,080
Speaker 4: of clean energy, better medicine, Like there are happy things

770
00:38:44,080 --> 00:38:47,239
Speaker 4: that will happen. The economic policy in the government is

771
00:38:47,280 --> 00:38:49,719
Speaker 4: a complicated one. I mean, if you want more redistribution,

772
00:38:49,960 --> 00:38:52,400
Speaker 4: like when there is more wealth, you can generally have

773
00:38:52,480 --> 00:38:56,040
Speaker 4: more redistribution right, you could the people are having crazy

774
00:38:56,120 --> 00:38:58,600
Speaker 4: capital gains windfalls, perhaps you could raise the capital gains

775
00:38:58,600 --> 00:39:00,799
Speaker 4: tax and use that to help the people who are

776
00:39:00,800 --> 00:39:02,319
Speaker 4: being put out of work, or help people go on

777
00:39:02,360 --> 00:39:04,080
Speaker 4: Medicare at fifty five instead.

778
00:39:03,760 --> 00:39:06,000
Speaker 3: Of sixty five. Right, That would be a very popular

779
00:39:06,080 --> 00:39:07,000
Speaker 3: political program.

780
00:39:07,440 --> 00:39:10,600
Speaker 4: Still, you will have people who are losing their jobs

781
00:39:10,680 --> 00:39:15,399
Speaker 4: because of AI, who, because of the political realities, may

782
00:39:15,440 --> 00:39:18,040
Speaker 4: well be worse off financially, who even if they are

783
00:39:18,120 --> 00:39:21,480
Speaker 4: better off financially, lose a sense of meaning in their life.

784
00:39:21,640 --> 00:39:21,960
Speaker 3: Probably.

785
00:39:22,000 --> 00:39:24,680
Speaker 4: You know, often jobs and job types are clustered, so

786
00:39:24,719 --> 00:39:27,080
Speaker 4: it won't be just an individual losing their job. You

787
00:39:27,080 --> 00:39:32,520
Speaker 4: will have communities impacted, you know, communities having this economic.

788
00:39:33,920 --> 00:39:36,600
Speaker 3: Disaster. Perhaps if it's bad, right, like like with the.

789
00:39:36,600 --> 00:39:39,040
Speaker 4: China shock, and so, you know, you might see this

790
00:39:39,120 --> 00:39:41,359
Speaker 4: kind of heterogeneous outcome. And I don't want to make

791
00:39:41,400 --> 00:39:43,120
Speaker 4: it as simple as rich and poor. I don't want

792
00:39:43,160 --> 00:39:45,279
Speaker 4: to make it as simple as like the trillionaires get

793
00:39:45,320 --> 00:39:48,680
Speaker 4: more trillions than everybody else is screwed. I think, in fact,

794
00:39:48,719 --> 00:39:52,160
Speaker 4: a likely outcome is more subtle than that and more varied.

795
00:39:52,239 --> 00:39:55,160
Speaker 4: And there are some people who are working class, middle

796
00:39:55,160 --> 00:39:57,239
Speaker 4: class who learn how to use AI who get more

797
00:39:57,239 --> 00:39:59,480
Speaker 4: money because they're being more productive.

798
00:40:00,120 --> 00:40:01,560
Speaker 3: Others who are totally screwed.

799
00:40:02,280 --> 00:40:05,600
Speaker 1: Then final question for you, when, because we opened with this,

800
00:40:06,000 --> 00:40:08,319
Speaker 1: when you look at this long history of people who

801
00:40:08,680 --> 00:40:11,279
Speaker 1: have been afraid that machines would take their jobs, and

802
00:40:11,760 --> 00:40:14,759
Speaker 1: many cases where they did, what still worries you when

803
00:40:14,800 --> 00:40:17,360
Speaker 1: you look ahead? And what gives you the most hope?

804
00:40:17,840 --> 00:40:20,319
Speaker 4: So I guess what still worries me when I look

805
00:40:20,360 --> 00:40:24,000
Speaker 4: ahead is the rate of change of AI because when

806
00:40:24,040 --> 00:40:27,480
Speaker 4: you look at these instances and the alternatives, right, so,

807
00:40:27,640 --> 00:40:30,400
Speaker 4: like the Ludites are this very dramatic story where these

808
00:40:30,400 --> 00:40:32,480
Speaker 4: people who had a decent life lost their jobs and

809
00:40:32,520 --> 00:40:36,240
Speaker 4: machines and their lives got worse. On a much vaster

810
00:40:36,400 --> 00:40:40,480
Speaker 4: scale was the automation of farm work, right, that was

811
00:40:40,560 --> 00:40:43,760
Speaker 4: like almost everybody worked on a farm, and then almost

812
00:40:43,760 --> 00:40:46,440
Speaker 4: nobody works on a farm now. But there wasn't really

813
00:40:46,840 --> 00:40:49,479
Speaker 4: I mean, you know, farmers organized politically in various ways,

814
00:40:49,480 --> 00:40:51,360
Speaker 4: and they wanted different policies. But you never had a

815
00:40:51,440 --> 00:40:53,880
Speaker 4: kind of lud Eite moment for farmers, in part because

816
00:40:53,880 --> 00:40:57,560
Speaker 4: it was gradual, in part because you had industrialization alongside it,

817
00:40:57,600 --> 00:40:59,840
Speaker 4: so people could leave the farms and go work in

818
00:40:59,840 --> 00:41:04,360
Speaker 4: a factory. So it's if it's really sudden, if a

819
00:41:04,440 --> 00:41:06,799
Speaker 4: huge amount of people lose their jobs really fast, like

820
00:41:07,320 --> 00:41:12,359
Speaker 4: that just feels super politically dangerous and unstable, right, Like

821
00:41:12,640 --> 00:41:15,279
Speaker 4: it could be violent, it could be bad. I mean,

822
00:41:15,400 --> 00:41:17,960
Speaker 4: the hopeful thing fundamentally to me is that people have

823
00:41:18,000 --> 00:41:20,520
Speaker 4: been worried about technological unemployment for two hundred years, and

824
00:41:20,600 --> 00:41:22,880
Speaker 4: indeed pockets of people have really suffered from it for

825
00:41:22,880 --> 00:41:27,560
Speaker 4: a long periods of time. But like today, the employment

826
00:41:27,560 --> 00:41:30,440
Speaker 4: to population ratio, the share of working age people.

827
00:41:30,440 --> 00:41:32,240
Speaker 3: With jobs is like as high.

828
00:41:32,040 --> 00:41:34,880
Speaker 4: As it has ever been, right, much higher than it

829
00:41:34,960 --> 00:41:39,120
Speaker 4: was fifty years ago, when far fewer women were working,

830
00:41:39,640 --> 00:41:44,680
Speaker 4: despite incredible amounts of innovation, technological change, you know, technological

831
00:41:44,760 --> 00:41:47,800
Speaker 4: job laws. So like, I think we really do underrate

832
00:41:47,880 --> 00:41:49,759
Speaker 4: the extent to which we are good at coming up

833
00:41:49,760 --> 00:41:52,839
Speaker 4: with new jobs. Jobs we cannot imagine today, And like

834
00:41:53,000 --> 00:41:55,120
Speaker 4: I actually think that'll happen in the long run. It'll

835
00:41:55,120 --> 00:41:57,719
Speaker 4: be maybe embodied things, you know, like maybe I'll go,

836
00:41:58,600 --> 00:42:00,120
Speaker 4: I don't know, be a meditation.

837
00:41:59,840 --> 00:42:01,920
Speaker 3: In instructor or something. And yes, you could have aib

838
00:42:01,960 --> 00:42:02,960
Speaker 3: a meditation instructor.

839
00:42:02,960 --> 00:42:05,680
Speaker 4: But there are some things I think people who will

840
00:42:05,719 --> 00:42:08,560
Speaker 4: be richer overall will pay for a human being to do.

841
00:42:09,080 --> 00:42:10,560
Speaker 3: I just don't know what those things will.

842
00:42:10,440 --> 00:42:13,520
Speaker 1: Be, So forget podcasting. We will have other jobs that

843
00:42:13,600 --> 00:42:15,440
Speaker 1: we just can't even think of right now.

844
00:42:16,520 --> 00:42:19,279
Speaker 4: I believe that we you and I I don't know,

845
00:42:19,360 --> 00:42:22,120
Speaker 4: but people will have jobs, our grandkids.

846
00:42:22,680 --> 00:42:26,120
Speaker 3: Yeah, I mean it could happen to me. Yeah, it

847
00:42:26,160 --> 00:42:27,520
Speaker 3: will be super interesting.

848
00:42:27,920 --> 00:42:30,040
Speaker 2: Yeah, for sure. What a time to be alive to

849
00:42:30,040 --> 00:42:30,560
Speaker 2: witness it.

850
00:42:30,600 --> 00:42:33,000
Speaker 3: You know, what a fucking time to be alive.

851
00:42:34,719 --> 00:42:37,839
Speaker 1: I was journalist Jacob Goldstein to hear more of what's

852
00:42:37,840 --> 00:42:41,280
Speaker 1: in Jacob's brain, Listen to his podcast What's Your Problem

853
00:42:41,480 --> 00:42:45,319
Speaker 1: and Business History, and read his book Money, The True

854
00:42:45,320 --> 00:42:50,839
Speaker 1: Story of a Made Up Thing. Here we go again

855
00:42:50,880 --> 00:42:54,120
Speaker 1: as a production of iHeart Podcasts and Snafu Media in

856
00:42:54,200 --> 00:42:58,040
Speaker 1: association with New Metric Media. Our executive producers are me

857
00:42:58,320 --> 00:43:02,840
Speaker 1: kalpen at Helm's, Mike fa Alissa Martino, Andy Kim, Pat Kelly,

858
00:43:02,960 --> 00:43:06,480
Speaker 1: Chris Kelly, and Dylan Fagan. Meghan tan is our producer

859
00:43:06,480 --> 00:43:09,359
Speaker 1: and writer. Dave Shumka is our producer and editor. Our

860
00:43:09,400 --> 00:43:14,080
Speaker 1: consulting producer is Romin Borsolino. Tory Smith is our associate producer.

861
00:43:14,400 --> 00:43:18,320
Speaker 1: Theme music by Chris Kelly logo by Matt Gosson. Legal

862
00:43:18,360 --> 00:43:22,480
Speaker 1: review from Daniel Welsh, Caroline Johnson, and Meghan Halson. Special

863
00:43:22,480 --> 00:43:26,400
Speaker 1: thanks to Glenn Bassner, Isaac Dunham, Adam Horn, Lane Klein,

864
00:43:26,560 --> 00:43:30,640
Speaker 1: and everyone at iHeart Podcasts, but especially Will Pearson, Carrie

865
00:43:30,640 --> 00:43:36,160
Speaker 1: Lieberman and Nikki Etour. Thanks for listening. Everybody, tell your

866
00:43:36,160 --> 00:43:38,800
Speaker 1: friends write a review. All of this helps. I appreciate

867
00:43:38,800 --> 00:43:41,320
Speaker 1: you listening, and until we go again, I'm Kel Penn