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Speaker 1: So this episode of the Nick, Dick and Pole Show

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Speaker 1: is actually going to be called the Nick, Poul and

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Speaker 1: Tim Show.

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Speaker 2: Did I get that?

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Speaker 1: Or the Nick, Tim and Paul Show, which has a

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Speaker 1: better ring to it.

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Speaker 3: Doesn't rhyme either way, you know that's the problem.

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Speaker 1: Yeah, sorry, after this is just going to be a

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Speaker 1: one time thing.

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Speaker 2: So our guest.

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Speaker 1: We have a guest today, which we're very excited about.

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Speaker 1: It's Tim wu who I've known for fifteen years or

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Speaker 1: something like that. Now we both look like we've aged

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Speaker 1: very well.

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Speaker 4: Thank you.

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Speaker 1: You have your four hundred and sixty second book out

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Speaker 1: this week. It feels like that it's called the Age

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Speaker 1: of Extraction, How tech platforms conquered the economy and threaten

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Speaker 1: our future prosperity. So one little fun fact is when

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Speaker 1: I wrote the Twitter book in twenty thirteen, you wrote

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Speaker 1: the review in the Washington Post about it.

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Speaker 2: It's true, I did.

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Speaker 3: It was very nice about it.

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Speaker 2: It was a great book.

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Speaker 1: Thank you book, Thank you.

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Speaker 3: The movie was great too.

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Speaker 1: Yeah. Well that's a.

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Speaker 2: Whole separate story. You know.

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Speaker 5: It got me into that story, that book of yours.

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Speaker 5: And then I also reviewed some of the books written

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Speaker 5: by the Twitter founders with the knowledge of what had

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Speaker 5: actually happened. Yeah, and I think that kind of helped me,

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Speaker 5: you know, have a little more insight.

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Speaker 1: There their their version of history that was not so Yeah.

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Speaker 5: All right, So this is actually your fourth book or fifth,

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Speaker 5: fourth solo authored book.

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Speaker 1: Fourth solo author book. What what was the what what

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Speaker 1: made you want to do this one? What was the moment?

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Speaker 2: Well?

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Speaker 1: I guess should we actually before we should, let's just

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Speaker 1: back up and give everyone your a little brief resume.

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Speaker 1: So sure, you're a professor Columbia. You came up with

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Speaker 1: the term net neutrality? You were did the Biden administration?

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Speaker 2: Yeah?

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Speaker 1: And anywhere else?

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Speaker 2: Obama?

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Speaker 1: Obama too?

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Speaker 2: Yes?

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Speaker 1: Not Trump?

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Speaker 2: Not Trump.

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Speaker 1: Did you have an opportunity?

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Speaker 4: Uh?

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Speaker 5: No, Okay, I think for one of my Actually I

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Speaker 5: was in the Obama White House and I had some

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Speaker 5: kind of thing where I could have actually technically stayed

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Speaker 5: because I think I was sort of it's hard to explain,

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Speaker 5: but it didn't really seem like the thing to do.

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Speaker 1: What is it was like a technicality where they were

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Speaker 1: like they like they couldn't evict you, like squatters rights

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Speaker 1: or something.

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Speaker 2: I just wasn't fired. How's that yet? And I never

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Speaker 2: got fired for all.

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Speaker 5: I know, I still worked there, but someone said you

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Speaker 5: should stay and just like watch and.

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Speaker 1: See what's going on and write a book about it.

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Speaker 2: And I was like, I don't know, it seems weird

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Speaker 2: to just be like hanging around.

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Speaker 3: Well, just wait to see what happens.

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Speaker 1: Yeah, So what was it that got you into deciding

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Speaker 1: to do this first?

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Speaker 2: This book?

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Speaker 5: I was interested ian going back to the early days

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Speaker 5: of you know, the intern that revolution, not not the seventies,

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Speaker 5: but like the nineties and thousands, and trying to figure

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Speaker 5: out what happened to that idea that the internet was

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Speaker 5: going to make everybody rich and that it was going

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Speaker 5: to bring democracy to every country on Earth, and you know,

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Speaker 5: every creative person was going to have their out.

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Speaker 2: I mean, some of that a little bit.

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Speaker 5: Happened versus you know today, where I feel that the

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Speaker 5: sensation is that the main tech platforms have sort of

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Speaker 5: grown to dominate and extract a lot from the economy.

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Speaker 5: So I kind of wanted to tell that story. It's

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Speaker 5: also a third in a trilogy. My first book goes

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Speaker 5: through all my books, but I wrote two other books

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Speaker 5: about basically the tech industries in the United States and

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Speaker 5: the twentieth industry a twentieth century and this is kind

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Speaker 5: of the end of it coming into the twenty first

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Speaker 5: do you when you look.

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Speaker 1: Back, given that you've been there from the beginning, is

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Speaker 1: are you shocked at where we are? Or is this

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Speaker 1: kind of where you hoped we weren't and we ended up?

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Speaker 1: Or is this how do you feel about it?

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Speaker 2: I guess disappointed. I guess this is the right word.

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Speaker 5: I mean, part of when I write a book, I'm

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Speaker 5: trying to understand, you know, what my own feelings about things.

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Speaker 5: I think I was incredibly optimistic in the nineties early thousands.

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Speaker 5: You know, I kind of bought into it all.

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Speaker 1: Were you optimistic in the No.

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Speaker 2: I guess I was.

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Speaker 5: I believe what I believe, what Clay Shirky said, like

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Speaker 5: the power of love has taken over, and you know

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Speaker 5: it was I did believe in it. I thought that,

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Speaker 5: you know, we were moving into a different stage of

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Speaker 5: doing things. I mean, I wasn't totally I don't think

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Speaker 5: I was totally naive, but I feel that I had

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Speaker 5: some faith that really this was going to be great

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Speaker 5: for humanity and also the nineties in general, I was

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Speaker 5: very optimistic. You know, it felt like the Berlin Wall

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Speaker 5: had fallen down and and we've solved all the problems

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Speaker 5: of capitalism and socialism by coming up with like the

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Speaker 5: third way.

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Speaker 2: Do you remember Bill Clinton? And everybody was going to

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Speaker 2: be rich and.

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Speaker 5: Everything was going to be great, And I kind of

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Speaker 5: feel twenty first century has been a little bit rocky,

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Speaker 5: so a little bit.

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Speaker 3: Just a fit, just enough so as you'd notice.

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Speaker 5: Yeah, so my book is kind of like the explanation

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Speaker 5: of what happened in tech a little bit the whole

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Speaker 5: economy as well.

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Speaker 1: We were talking on an earlier podcast about Paul has

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Speaker 1: this whole theory about how we we regulate all these

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Speaker 1: industries not regulate, Well, how would you describe it, about

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Speaker 1: how we just let technology do whatever it is?

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Speaker 3: Oh, just the idea that many of the things that

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Speaker 3: technology executives founder CEOs take for granted are just not

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Speaker 3: true in other sectors, that there's essentially given free reign,

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Speaker 3: where if we were launching new drugs orfo we were

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Speaker 3: experimenting with new forms of chemicals and so there's a

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Speaker 3: much more complex process in terms of being able to

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Speaker 3: unleash them on the broader population, and instead we're the test, right,

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Speaker 3: the launch is on us. And my argument is that

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Speaker 3: it's a kind of path dependency thing where because it

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Speaker 3: hasn't doesn't feel like it's hurt us yet. That has

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Speaker 3: kind of become the entrenched mode by which things get

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Speaker 3: launched just because it's it's it has always worked, not

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Speaker 3: because it was a thoughtful process, but because it hasn't

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Speaker 3: hurt us yet.

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Speaker 5: I think you're right about that. And I also think

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Speaker 5: there's a path dependency in the sense that because it

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Speaker 5: was the darling of the nineties, because everyone was so

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Speaker 5: upbeat about it, because people thought the Google founders were

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Speaker 5: some really great guys.

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Speaker 1: You talked about that in the book, about like how

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Speaker 1: they had this vision for the future that was going

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Speaker 1: to be amazing, and then I mean, I.

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Speaker 5: Think they were sincere. Honestly, I think they meant well.

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Speaker 5: I think that they also felt they had to be

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Speaker 5: extraordinarily healthy too, and so when they you know, they

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Speaker 5: had all these big things, what Google was going to

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Speaker 5: do to serve the public, and so forth, when it

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Speaker 5: came time to go to to have a business model,

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Speaker 5: even though they'd written a thing about how advertising was

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Speaker 5: the death of good search, they think went with advertising,

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Speaker 5: and then they never did. They just took the standard

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Speaker 5: you know Delaware Corporation. You know, we're going to have

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Speaker 5: to do quarterly reports and have this constant pressure to

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Speaker 5: get make more money and profit and revenue.

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Speaker 2: And they I.

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Speaker 5: Described the book, how they you know, they wrote a

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Speaker 5: letter to the shareholders for the remember this letter. I

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Speaker 5: remember the letter, and it's all about how like we're

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Speaker 5: not going to be a normal company and all.

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Speaker 2: This kind of stuff.

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Speaker 5: So there's a lot of good vibe, a lot of

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Speaker 5: you know, and like people like me, I guess I

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Speaker 5: sort of believe it. And you know, one lesson I

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Speaker 5: dropped in this book is that over time, basically structure

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Speaker 5: beats out good intentions. You know, they didn't really set

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Speaker 5: it things differently.

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Speaker 3: I also think and I used touch on this a

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Speaker 3: little bit, but I mean I also think it's it

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Speaker 3: puts the old Mike Tyson is everybody has a plan

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Speaker 3: unil he get punched in the face or something like this.

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Speaker 3: I said, every good companies have a plan until they

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Speaker 3: get punched in the face by capital markets, which is

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Speaker 3: kind of to your point about structure, that you can

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Speaker 3: have all of these good intentions, but once you're on

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Speaker 3: the hamster wheel of quarterly earnings and analyst supports and

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Speaker 3: recommendations and institutional investors. That's the punch in the face.

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Speaker 3: It's very difficult as a modern twentieth twenty first century

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Speaker 3: organization to hold on to those idealist principles in the

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Speaker 3: face of that, because the system just doesn't permit it,

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Speaker 3: it actually punishes it.

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Speaker 5: I mean, there are a few. I'm I'm kind of

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Speaker 5: impressed by Wikipedia. Not a public company though, well, well

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Speaker 5: that's what I mean. I mean, there was this moment

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Speaker 5: where we think about Jimmy Whale's like, let's say two

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Speaker 5: thousand and two, he could have He basically had this

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Speaker 5: little button, yeah that said billionaire on it. They never

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Speaker 5: pressed it, didn't push it, and he must I wonder

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Speaker 5: if he woke up some days and like, maybe I'll

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Speaker 5: push this button.

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Speaker 2: But they never pushed it.

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Speaker 1: But he's one person, he had good self controls. Why

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Speaker 1: there can you name another other than Wikipedia.

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Speaker 2: It's challenging, I guess, I guess the founders of.

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Speaker 1: Hold on a second, the fact of having trouble for

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Speaker 1: those of listening at home because him is thinking right.

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Speaker 3: For the point being though, it's really unusually really, I mean,

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Speaker 3: it doesn't it's just a really unusual for people to

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Speaker 3: survive that that punch in the face.

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Speaker 5: Well yeah, well, I guess what I'm trying to say

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Speaker 5: is if they really wanted to, they could have tried

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Speaker 5: something a little different, like the meaning Googles. Yeah, Google

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Speaker 5: could have tried something different. All these guys could have

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Speaker 5: tried maybe like you know, there's other companies in Patagonia.

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Speaker 5: It's not tech company. Yeah, the first king to mind

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Speaker 5: that adapted. If I say that, it actually gets back

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Speaker 5: to your point about regulation or lack thereof. You know,

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Speaker 5: ultimately these companies have become the sort of essential infrastructure

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Speaker 5: of American commerce, you know, not unlike the trains and

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Speaker 5: other generation or stock market, all those things are heavily regulated.

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Speaker 5: Because electric networks because they're in a position to just

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Speaker 5: take from everybody so easily, right, and we have sort

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Speaker 5: of let that go a very long time.

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Speaker 3: Which is kind of tied to the net neutrality argument, right.

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Speaker 3: I mean that this similar point with respect to it

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Speaker 3: being a common infrastructure act the point you made from

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Speaker 3: the beginning on this and therefore belongs it's a public

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Speaker 3: good in a sense, Yeah, that belongs to be treated

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Speaker 3: as they should be treated as such.

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Speaker 5: I mean, one of the ideas of this book and

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Speaker 5: another reason I wrote, but I've always been interested in

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Speaker 5: basically the spaces that every civilization have where things happen.

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Speaker 5: Like I had a sentence of my mind lester and

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Speaker 5: read this book, which is everything has to happen somewhere.

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Speaker 5: I know it's a good sentence, but it's like the

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Speaker 5: sentence I started ready, And I kind of believe that

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Speaker 5: places that things happen have an effect on what kind

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Speaker 5: of civilization you have. Like the ancient Greeks had this

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Speaker 5: big square in the middle of e wouldn't vote to whatever.

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Speaker 5: And so our life is online, is well, I guess

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Speaker 5: like Facebook and Instagram and Amazon like that, that's kind

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Speaker 5: of our central square. But it's a lot different. Privatize, extractive,

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Speaker 5: drives you crazy. So anyway, that's part of what I'm

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Speaker 5: interested in.

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Speaker 1: And but how does how I guess the question is

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Speaker 1: does it play out? Is this all negative, all positive,

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Speaker 1: mostly negative for society like that compared to the square?

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Speaker 1: I mean, I have my own theories, obviously, but I'm

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Speaker 1: sure poll does too. But like, what what do you

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Speaker 1: come away with?

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Speaker 5: I mean, it's a mix because you have to have

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Speaker 5: these spaces, you had to have these hosts, these platforms

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Speaker 5: that make everything happen. The question is the reason I

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Speaker 5: wrote the book. The message is these things are essential,

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Speaker 5: but should they be allowed to just take as much

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Speaker 5: money as they want from everybody else? You know what

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Speaker 5: I mean. I mean Amazon Marketplace being a good example.

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Speaker 5: You know, you it is. Amazon Marketplace is the most

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Speaker 5: successful market and I guess human history now it's like

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Speaker 5: a million sellers on it and so forth. So it is

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Speaker 5: in a way, like obviously a good thing that as

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Speaker 5: consumers were able to have it and sellers can reach people.

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Speaker 5: But you know, over the years, Amazon has just been

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Speaker 5: dialing the fees, dialing the fees. One of their fees is,

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Speaker 5: you know, the sponsored ads you search on Amazon. You

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Speaker 5: think of those, you know, little weird like they make

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Speaker 5: it hard to find out what's really the good product

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Speaker 5: or whatever. Amazon made fifty six billion dollars on those

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Speaker 5: last year, so like double the revenue of every newspaper

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Speaker 5: on Earth just for that little janky thing, weird trick.

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Speaker 5: I guess that actually makes search worse.

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Speaker 1: It makes so much worse.

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Speaker 5: So you know, part of the question is, are they

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Speaker 5: like the electric Like what if we had the electric

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Speaker 5: network and we let them charge whatever they want, which we.

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Speaker 3: Do in places obviously with consequences, parts of Texas and

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Speaker 3: what have you. But yeah, I think that's true. But

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Speaker 3: I mean, and you kind of touch on this, but

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Speaker 3: is the is the counterfact? Actual? Is the world worse

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Speaker 3: without someone controlling that platform? Is it worse for the sellers?

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Speaker 3: Did the sellers care enough about this problem or is

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Speaker 3: it a problem that we care more about in the

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Speaker 3: you know, in the abstract and the aggregate, whereas the

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Speaker 3: individuals are like, you know what that may be. But

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Speaker 3: I'm perfectly happy where.

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Speaker 2: I are, well based on what I look at.

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Speaker 5: The sellers aren't too happy about it, in the sense

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Speaker 5: that when they started a lot of them in say

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Speaker 5: twenty twelve, Amazon was taken between fifteen and twenty percent,

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Speaker 5: and now they're taking fifty sixty percent. The margins are

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Speaker 5: worse than brick and mortar, so they really care. In fact,

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Speaker 5: it's mainly Chinese, you know, linked importers. Have you've been

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Speaker 5: able to survive and if you care about like class

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Speaker 5: issues the fact, I think at twenty twelve, Amazon offered

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Speaker 5: this promise that maybe you know, people all over the

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Speaker 5: country could like start starting, but it could be like

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Speaker 5: a real class builder of a kind of wealthy small

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Speaker 5: business class. But I think that is kind of gone,

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Speaker 5: So it doesn't that's the broader consequence. It's not like

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Speaker 5: one you can see people getting electrified or run over,

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Speaker 5: but you.

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Speaker 3: Do see the sort of electrified.

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Speaker 5: But it is like you can see this hole where

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Speaker 5: there could have been this sort of nation of small

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Speaker 5: businesses and vendors and so forth.

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Speaker 3: Isn't that the story in a sense of like capital

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Speaker 3: markets in general, Like I'm thinking of examples in financial markets,

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Speaker 3: where obviously there are intermediaries, the intermediaries consolidate, they have

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Speaker 3: visibility and to order flow, and they use that visibility

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Speaker 3: and to order flow to become fantastically profitable and became

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Speaker 3: name a host of companies in the financial services industry

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Speaker 3: to do this. So in a sense, it's not particularly

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Speaker 3: surprising that that's the approach Amazon has taken, in particular

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Speaker 3: because Jeff came from that background. That's what he used

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Speaker 3: to do. And this is the point I think people

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Speaker 3: miss is that he used to be the kind of

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Speaker 3: person who was looking inside of capital markets and saying,

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Speaker 3: how do I take advantage of this? And so is

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Speaker 3: it shame on us for not realizing this was exactly

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Speaker 3: the path that was sort of inevitable given the guys

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Speaker 3: running the service.

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Speaker 5: I mean, I think it'd be shame on us if

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Speaker 5: we don't do something about it. I guess we just say, okay,

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Speaker 5: this is fine.

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Speaker 1: Okay, but but you worked in two administrations. Uh, why

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Speaker 1: has nothing been done?

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Speaker 2: That is a great question.

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Speaker 5: I mean the most basic starting point, you know, backing

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Speaker 5: even off from this utility kind of regulation stuff would

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Speaker 5: just be like passing a privacy law.

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Speaker 2: And you know they've been talking.

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Speaker 5: You ask Americans on the street, take some polls like

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Speaker 5: would you like some better privacy online? Probably huge majority

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Speaker 5: would say yes, children's privacy even stronger, and back up

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Speaker 5: what you've said. It's been twenty years and maybe twenty

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Speaker 5: five years of people saying when you to do something,

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Speaker 5: and nothing has happened. I having witnessed it firsthand, I

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Speaker 5: almost feel like I understand it less. Like we tried

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Speaker 5: when I was in the Biden White House to pass

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Speaker 5: privacy law. We had most of the Chamber of Commerce

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Speaker 5: on our side. We had the Republican Party, the Democratic Party,

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Speaker 5: we had you know, us White House who stand.

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Speaker 1: In your way? Then the janitor.

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Speaker 3: The janitor lobby.

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Speaker 1: Yeah, what stopped.

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Speaker 5: It, Actually California stopped it in part in Nancy Pelosi

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Speaker 5: because Democrats they didn't like the fact that it was

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Speaker 5: going to preemp California's law, and there was since California

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Speaker 5: might do its own thing.

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Speaker 2: So it was like a little bit of that.

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Speaker 5: There was a personal dispute between Maria Cantwell, who was

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Speaker 5: the chair of the Commerce. She didn't get along with

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Speaker 5: the other Democrats or the Republicans, so they ganged up

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Speaker 5: on her. They call each other rude names. We tried

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Speaker 5: to come have them come in the White House. They

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Speaker 5: wouldn't do it. So it was actually like a bunch

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Speaker 5: of really interesting, stupid explanations.

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Speaker 3: So it wasn't like some particularly effective lobby. It wasn't

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Speaker 3: that you know, technology industry showed up on mass or

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Speaker 3: anything else.

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Speaker 2: No.

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Speaker 5: In fact, I mean they were quietly behind it. There

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Speaker 5: were also some left wing groups that you know that

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Speaker 5: was more in the child privacy side, who wanted you know,

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Speaker 5: young LGBT youth to be able to search for whatever.

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Speaker 5: But actually that was for child protection laws. No, a

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Speaker 5: lot of it was really small things. It doesn't take

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Speaker 5: much for Congress to fail. I mean, we had so

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Speaker 5: many things lined up. It doesn't take much. They really

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Speaker 5: I felt like ultimately all they were able to do

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Speaker 5: right now is spend money or not spend money, or.

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Speaker 3: Not spend money, as the case.

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Speaker 4: We need.

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Speaker 1: You get into AI a little bit, and.

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Speaker 3: Before you go there, yeah, yeah, somewhere. I just want

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Speaker 3: to go back to the idea of extraction for a

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Speaker 3: second because it's an interesting metaphor. Obviously, capitalism is by

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Speaker 3: its nature extractive, right, I mean, back to mining. We

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Speaker 3: can go back as far as we want. It's always

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Speaker 3: been the notion of taking out, you know, taking resources,

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Speaker 3: exploiting them, using them for other purposes. So the notion

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Speaker 3: of extraction itself isn't isn't new in this context. But

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Speaker 3: I think what's interesting potentially is the idea that the

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Speaker 3: ease with which we can extract has changed, right, because

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Speaker 3: it's it's more opaque, it's more algorithmic, it's let's you know,

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Speaker 3: it's let's let's repeat myself. But it's less visible to people,

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Speaker 3: and so it's not like watching a mine in your

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Speaker 3: backyard or whatever else and saying, oh, they're taking all

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Speaker 3: that stuff and taking it somewhere else. You don't see it.

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Speaker 3: Do you buy the art? So the extraction itself isn't new.

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Speaker 3: What's new, it's a to your way of thinking. Is

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Speaker 3: the is the the opacity and algorithmic velocity, all of

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Speaker 3: these characteristics of the extraction.

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Speaker 2: I also think the emphasis on extraction as a business

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Speaker 2: model is more dominant today's abstraction is an economic term

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Speaker 2: as well as the mining term, which you may be

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Speaker 2: familiar with, which means you're able to capture value far

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Speaker 2: in excess of the right.

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Speaker 3: That's what I mean. Distraction industries are inherently extractive in

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Speaker 3: an economic sense.

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Speaker 2: That's true.

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Speaker 5: Yeah, And you know it's that feeling if you show

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Speaker 5: up at the airport and you really need to go

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Speaker 5: somewhere and I'm empty seats, which some you know, for

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Speaker 5: them there's no cost, but they're like, all right.

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Speaker 3: Till doesn't matter.

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Speaker 5: Yeah, right, Or so that I think I think there's

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Speaker 5: been a spread of those sort of business models. But

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Speaker 5: I also agree I think that, And so I say

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Speaker 5: in the book is that the platform companies are the

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Speaker 5: most sophisticated technologies of extraction ever invented or improved.

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Speaker 2: I think they have.

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Speaker 5: You know, the first ten years was getting the stuff

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Speaker 5: like the plumbing in place, the stuff that were you

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Speaker 5: know it, know, the stuff that appeals to us. Oh

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Speaker 5: you know what I mean, Like the first ten years

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Speaker 5: was to be appealing to consumers. The next ten years

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Speaker 5: was kind of trying to figure out subtle, less subtle ways,

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Speaker 5: you know, turning the dials to maximize extraction. And you know,

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Speaker 5: I agree with everything you say. It's like you realize, obviously, data,

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Speaker 5: you know, rails capitalism. I guess is is is extracted

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Speaker 5: just subtle sort of fees, weird tricks like that Amazon

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Speaker 5: sponsored link thing.

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Speaker 2: I mean it, I would have thought of that.

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Speaker 5: But you get sellers bidding against each other, against their

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Speaker 5: own margins, right right, They're destroying their own margins. I

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Speaker 5: really fully realizing it because they desperately have to try

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Speaker 5: to get a sale. And you know, and it's not

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Speaker 5: even from so consum it's not from consumers. It's from sellers.

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Speaker 5: Other consumers ultimately pay, right.

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Speaker 2: Yeah. So anyway, but I think I think I like that.

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Speaker 3: I mean, so I take it further though, and say,

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Speaker 3: the misallocation isn't just the notion of the extraction from

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Speaker 3: the users of these resorts, the users of this resource

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Speaker 3: in an economic sense. It's also extractive in terms of

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Speaker 3: employment in the sense that there's a famous line I

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Speaker 3: forget who was Jeff Hasselbot or whatever it was his

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Speaker 3: name is years ago where he talked about how the

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Speaker 3: greatest minds of his generation.

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Speaker 2: Oh yeah, yeah, we're you know.

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Speaker 3: Working to you know, maximize extraction or extraction maximis advertising

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Speaker 3: revenues from advertising these companies, and they.

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Speaker 5: Say something like the grace minds my generation are trying

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Speaker 5: to find ways to have people click on things they

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Speaker 5: don't want.

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Speaker 3: To, or yeah about the ad optimization for people. The

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Speaker 3: point being though, so that that was in service of

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Speaker 3: making the algorithmic, the algorithm more successful in terms of

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Speaker 3: the things you're talking about. But there's a secondary effect,

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Speaker 3: which was it was so successful that it created this

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Speaker 3: great sucking sound which pulled engineering some of the most

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Speaker 3: talented people, and so that becomes a snowballing so they

447
00:21:21,520 --> 00:21:24,280
Speaker 3: become more effective at extraction. And these people who might

448
00:21:24,320 --> 00:21:26,399
Speaker 3: be doing more productive and then you talk about this

449
00:21:26,440 --> 00:21:29,280
Speaker 3: in the book, but more creative things instead are doing this.

450
00:21:29,520 --> 00:21:31,320
Speaker 3: So there's these secondary spirals.

451
00:21:31,400 --> 00:21:33,400
Speaker 5: Right, No, I agree with you, and I think it's

452
00:21:34,560 --> 00:21:37,760
Speaker 5: it starts to become about how this nation's resources or

453
00:21:37,760 --> 00:21:41,119
Speaker 5: a productivity resources allocated. Yeah, you know, where you have

454
00:21:41,840 --> 00:21:44,480
Speaker 5: in some ways innovations which are not innovations of the

455
00:21:44,560 --> 00:21:46,760
Speaker 5: kind we used to think of like a better product

456
00:21:47,359 --> 00:21:50,600
Speaker 5: or like a faster airplane or something, but more about

457
00:21:50,880 --> 00:21:54,320
Speaker 5: weird methods of pricing that you know, managed to get

458
00:21:54,359 --> 00:21:56,840
Speaker 5: money out. You can't it's even hard to describe them, but.

459
00:21:56,800 --> 00:21:58,639
Speaker 3: It's like it, but it's for me. The analogy is

460
00:21:58,680 --> 00:22:01,639
Speaker 3: to capital markets again, because where they've become very effective

461
00:22:01,760 --> 00:22:04,920
Speaker 3: at extracting maximum rents from the users of the markets

462
00:22:04,920 --> 00:22:08,159
Speaker 3: in ways that are relatively opaque and difficult to detect

463
00:22:08,240 --> 00:22:10,439
Speaker 3: unless you really inside the system. And so it's this

464
00:22:10,520 --> 00:22:14,240
Speaker 3: financialization of the platform economy that's I think that's the

465
00:22:14,320 --> 00:22:17,320
Speaker 3: piece that people miss, is that's that has been imported

466
00:22:17,359 --> 00:22:20,800
Speaker 3: into that world and then wedded to technology to do

467
00:22:20,840 --> 00:22:22,360
Speaker 3: some of the same abstractive effects.

468
00:22:22,480 --> 00:22:22,720
Speaker 2: Yeah.

469
00:22:22,760 --> 00:22:24,679
Speaker 5: I guess that's why I call it the Age of

470
00:22:24,680 --> 00:22:27,440
Speaker 5: extraction because I write mainly about tech platforms because they're

471
00:22:27,440 --> 00:22:29,520
Speaker 5: obvious in front of us. But I do think we've

472
00:22:29,640 --> 00:22:33,080
Speaker 5: moved to that kind of economy. I also teach business school,

473
00:22:33,119 --> 00:22:35,800
Speaker 5: and you know, the strategy classes are all basically about

474
00:22:35,800 --> 00:22:39,200
Speaker 5: finding opportunities, you know, to get somebody and.

475
00:22:39,200 --> 00:22:41,200
Speaker 3: Right, build them out and keep them there.

476
00:22:41,359 --> 00:22:44,000
Speaker 1: Yeah, when just go back to real quick to talking

477
00:22:44,000 --> 00:22:48,639
Speaker 1: about the government for a moment and how difficult it

478
00:22:48,680 --> 00:22:52,119
Speaker 1: is to get things past you. The ultimate extraction right now,

479
00:22:52,160 --> 00:22:56,040
Speaker 1: of course, is artificial intelligence, right? And why is that

480
00:22:56,040 --> 00:22:59,600
Speaker 1: the ultimate extraction? Because it's taking everything that we've done, oh,

481
00:22:59,640 --> 00:23:05,840
Speaker 1: I see and using it to create new things. And

482
00:23:09,320 --> 00:23:12,720
Speaker 1: how do you think it all plays out? And how

483
00:23:12,760 --> 00:23:15,840
Speaker 1: does how in a scenario where let's just say it's

484
00:23:15,840 --> 00:23:17,719
Speaker 1: two years or five years or ten years, and all

485
00:23:17,760 --> 00:23:19,840
Speaker 1: this job loss happens, and we've read all the same

486
00:23:19,880 --> 00:23:21,800
Speaker 1: reports from McKinsey and this, that and the other, and

487
00:23:22,160 --> 00:23:24,600
Speaker 1: two hundred million or five hundred million, whatever the number is,

488
00:23:25,880 --> 00:23:32,000
Speaker 1: how do we end up saving society from total collapse,

489
00:23:33,040 --> 00:23:36,480
Speaker 1: whether it's a I don't think it's an economic collapse.

490
00:23:36,520 --> 00:23:40,399
Speaker 1: I mean it's an economic collapse for certain people in

491
00:23:40,480 --> 00:23:45,760
Speaker 1: a certain segment of society. But when we can't even

492
00:23:45,800 --> 00:23:48,320
Speaker 1: pass a privacy law, how do we pass a UBI?

493
00:23:49,040 --> 00:23:51,360
Speaker 2: Yes, well that's a very upbeat question.

494
00:23:53,040 --> 00:23:54,320
Speaker 3: In a dystopian comes out.

495
00:23:54,440 --> 00:23:56,320
Speaker 2: Yeah, yeah, Well I think there's a lot.

496
00:23:56,840 --> 00:24:00,920
Speaker 5: There's a lot going on with artificial intelligence. I mean, look,

497
00:24:00,960 --> 00:24:03,840
Speaker 5: obviously I'm going to agree with your premise that a

498
00:24:04,080 --> 00:24:08,359
Speaker 5: government that is disabled from doing anything, you know, and

499
00:24:09,080 --> 00:24:13,640
Speaker 5: if we end up with wealth and balances that are

500
00:24:13,720 --> 00:24:16,600
Speaker 5: so extreme and so much resentment.

501
00:24:16,920 --> 00:24:17,800
Speaker 2: I mean, we haven't got.

502
00:24:17,640 --> 00:24:20,440
Speaker 1: There trillion dollar pay package for Elon.

503
00:24:21,880 --> 00:24:27,080
Speaker 5: As we move further on to a increasingly unfair me

504
00:24:27,080 --> 00:24:31,679
Speaker 5: he hasn't earned that money yet and could come crashing down,

505
00:24:31,880 --> 00:24:34,400
Speaker 5: although people have predicted that, and he has some ability

506
00:24:34,440 --> 00:24:38,000
Speaker 5: to survive. You know, I think that if you I mean,

507
00:24:38,040 --> 00:24:40,679
Speaker 5: this was frankly democratic fit. What you're talking about is

508
00:24:41,840 --> 00:24:45,439
Speaker 5: we talk about market failure, sometimes is political failure where

509
00:24:46,160 --> 00:24:50,240
Speaker 5: you know, you have extraordinary inequality or unfairness in an

510
00:24:50,240 --> 00:24:55,199
Speaker 5: economy and people see it, they get mad, and I

511
00:24:55,240 --> 00:24:58,640
Speaker 5: think democracy has to be able to address it somehow,

512
00:24:59,119 --> 00:25:04,240
Speaker 5: you know, either rye redistributing money maybe, or by as

513
00:25:05,600 --> 00:25:08,560
Speaker 5: I've said, trying to limit the take of an essential service.

514
00:25:08,880 --> 00:25:11,879
Speaker 5: I mean, the train is a good metaphor. In the

515
00:25:12,040 --> 00:25:17,080
Speaker 5: nineteenth century, the train was new, powering a different kind

516
00:25:17,080 --> 00:25:20,560
Speaker 5: of economy, but they had almost unlimited power over farmers

517
00:25:20,600 --> 00:25:23,439
Speaker 5: and small business and other people. There was almost a

518
00:25:23,480 --> 00:25:27,159
Speaker 5: revolution there, this Granger movement in the nineteenth century of

519
00:25:27,240 --> 00:25:30,199
Speaker 5: farmers who were like, you know, this is insane. We

520
00:25:30,240 --> 00:25:32,640
Speaker 5: can't pay this. And you know, they could do whatever

521
00:25:32,640 --> 00:25:35,240
Speaker 5: they want. They could charge whatever rates, they could discriminate

522
00:25:35,280 --> 00:25:39,240
Speaker 5: between them and and other companies, or just play them

523
00:25:39,240 --> 00:25:43,800
Speaker 5: off against each other. So ultimately Congress passed laws to

524
00:25:44,680 --> 00:25:48,040
Speaker 5: limit discrimination and also force everyone to pay the same

525
00:25:48,040 --> 00:25:52,359
Speaker 5: price and prevent price gouging. So you know, Congress was

526
00:25:52,440 --> 00:25:56,240
Speaker 5: and the government was able to do stuff. Then I'm

527
00:25:56,280 --> 00:25:58,639
Speaker 5: basically I'm agreeing Nick. If we can't do something now,

528
00:25:59,160 --> 00:26:02,760
Speaker 5: if we end up in a absolute dictatorship, will have

529
00:26:02,960 --> 00:26:03,960
Speaker 5: we will have earned it.

530
00:26:04,320 --> 00:26:07,480
Speaker 3: I guess a can of make a I don't know,

531
00:26:07,480 --> 00:26:08,879
Speaker 3: a contrarian argument sort of.

532
00:26:08,960 --> 00:26:11,119
Speaker 1: I love a good Poul contrarian argument.

533
00:26:11,320 --> 00:26:14,920
Speaker 3: Well, I just think so it's about AI and so,

534
00:26:15,200 --> 00:26:17,800
Speaker 3: and I'm you know, deeply in the Nick camp with

535
00:26:17,880 --> 00:26:21,000
Speaker 3: respected dystopian AI outcomes. But nevertheless, I just to argue

536
00:26:21,040 --> 00:26:24,520
Speaker 3: that Wikipedia is one of the largest training sources of

537
00:26:24,560 --> 00:26:29,160
Speaker 3: training data in all the AI corpuses. So you might

538
00:26:29,320 --> 00:26:32,639
Speaker 3: argue that it's ironic in a sense, obviously given what's happened.

539
00:26:32,680 --> 00:26:34,960
Speaker 3: But one of the reasons why it is is because

540
00:26:35,440 --> 00:26:39,040
Speaker 3: it wasn't a public company and wasn't take it was

541
00:26:39,119 --> 00:26:41,399
Speaker 3: mispriced content in a sense like you don't see the

542
00:26:41,480 --> 00:26:44,879
Speaker 3: mining Amazon that wasn't possible to do, but mining Wikipedia

543
00:26:44,960 --> 00:26:46,800
Speaker 3: was very straightforward to do. If Wikipedia had been a

544
00:26:46,800 --> 00:26:52,600
Speaker 3: public company that assiduously protected its data, the seating of

545
00:26:52,720 --> 00:26:55,320
Speaker 3: large language models would have been much more difficult. So

546
00:26:55,359 --> 00:26:58,840
Speaker 3: in a perverse sort of way, Wikipedia, by staying in

547
00:26:59,119 --> 00:27:01,560
Speaker 3: that structure that it had actually made life so much

548
00:27:01,600 --> 00:27:04,760
Speaker 3: easier for AI that you could almost take the reverse

549
00:27:04,840 --> 00:27:06,879
Speaker 3: argument that that's part of the problem if only they

550
00:27:06,880 --> 00:27:09,800
Speaker 3: had gone pubbling early in their history and priced their

551
00:27:09,840 --> 00:27:10,520
Speaker 3: data properly.

552
00:27:10,920 --> 00:27:13,760
Speaker 1: So you're saying the road to the end of humanity

553
00:27:13,880 --> 00:27:16,879
Speaker 1: was paved on the way with Wikipedia.

554
00:27:17,119 --> 00:27:18,960
Speaker 5: On the failure to hit the billionaire button, on the

555
00:27:18,960 --> 00:27:22,360
Speaker 5: failure to hit the I think, well, taking more positive

556
00:27:22,400 --> 00:27:24,880
Speaker 5: I mean this taking more slightly more positive view. If

557
00:27:24,880 --> 00:27:27,720
Speaker 5: you take AI as an advance, which it doesn't seem

558
00:27:27,720 --> 00:27:30,520
Speaker 5: like that's our conversation here, but if it is, it

559
00:27:30,640 --> 00:27:33,359
Speaker 5: is interesting to me that every AI advance is linked

560
00:27:33,800 --> 00:27:36,720
Speaker 5: to a public data set. I mean going back to

561
00:27:36,800 --> 00:27:39,120
Speaker 5: image net and you know the training.

562
00:27:38,760 --> 00:27:40,920
Speaker 3: That was a big that was a huge image data.

563
00:27:40,840 --> 00:27:42,280
Speaker 2: Huge im public image data set.

564
00:27:42,320 --> 00:27:46,399
Speaker 5: I guess out of Stanford. I was a Princeton er

565
00:27:46,440 --> 00:27:50,040
Speaker 5: Stanford and then oh no, Toronto was where it was

566
00:27:50,080 --> 00:27:52,879
Speaker 5: actually first Hinton. I mean, and obviously a lot of

567
00:27:52,880 --> 00:27:55,480
Speaker 5: the protein sort of efforts to do better protein. There's

568
00:27:55,480 --> 00:27:57,600
Speaker 5: a public database, so I mean, if you think about

569
00:27:57,640 --> 00:28:00,399
Speaker 5: different as innovation policy, you need to have these public

570
00:28:00,400 --> 00:28:02,919
Speaker 5: if you if you think AI is good. So I mean,

571
00:28:02,960 --> 00:28:03,960
Speaker 5: I guess, I guess.

572
00:28:03,800 --> 00:28:06,920
Speaker 3: My point is some different, which is that my objection

573
00:28:07,000 --> 00:28:08,960
Speaker 3: to some of this stuff is the free rider problem

574
00:28:09,080 --> 00:28:11,879
Speaker 3: of having all of having these companies play fast and

575
00:28:11,880 --> 00:28:14,320
Speaker 3: loose with the idea of whose content it is and

576
00:28:14,359 --> 00:28:17,000
Speaker 3: I'll deal with it after the fact and pay the fine. Yes,

577
00:28:17,640 --> 00:28:21,159
Speaker 3: and that's with perspective proprietary content, but it applies equally

578
00:28:21,200 --> 00:28:24,359
Speaker 3: to Wikipedia if you imagine an alternative scenario where that

579
00:28:24,480 --> 00:28:26,840
Speaker 3: data hadn't just been floating around freely for these people

580
00:28:26,840 --> 00:28:29,720
Speaker 3: to ride freely on top of. And you know, you

581
00:28:29,720 --> 00:28:32,159
Speaker 3: could argue that that that the original sin here was

582
00:28:32,160 --> 00:28:34,960
Speaker 3: that Wikipedia data was mispriced, and AI follows.

583
00:28:35,040 --> 00:28:37,399
Speaker 1: But that's like, that's kind of making the argument that

584
00:28:37,480 --> 00:28:40,560
Speaker 1: it's I remember when I'm not sure I believe that,

585
00:28:41,080 --> 00:28:45,320
Speaker 1: but I remember when all these flux capacitors. One of

586
00:28:45,320 --> 00:28:48,120
Speaker 1: the things in the in the in the back of

587
00:28:48,120 --> 00:28:53,200
Speaker 1: the fors no, the sorry in the in the the catalog.

588
00:28:54,560 --> 00:28:56,400
Speaker 1: So they're still especially in l A. You get these

589
00:28:56,400 --> 00:28:58,560
Speaker 1: people to drive around and they and they find all

590
00:28:58,560 --> 00:29:00,800
Speaker 1: these hybrid cars and they steal the catlet converters. And

591
00:29:00,840 --> 00:29:06,040
Speaker 1: then there was a congresswoman here who argued that it

592
00:29:06,120 --> 00:29:08,400
Speaker 1: was the it was not the fault of the criminals.

593
00:29:08,440 --> 00:29:12,440
Speaker 1: It was the fault of Toyota for like making so

594
00:29:12,520 --> 00:29:12,920
Speaker 1: he steal.

595
00:29:12,960 --> 00:29:15,560
Speaker 3: It's the fault of the fucking criminals. Like I think

596
00:29:15,920 --> 00:29:16,800
Speaker 3: you think I'm blaming the victim.

597
00:29:16,840 --> 00:29:18,520
Speaker 2: Yeah, I think you're blaming the victim. I think.

598
00:29:18,560 --> 00:29:21,520
Speaker 5: And also a lot of the data was honestly, not

599
00:29:21,600 --> 00:29:23,959
Speaker 5: just from Wikipedia, but from every book ever written, every

600
00:29:23,960 --> 00:29:25,360
Speaker 5: book that's like.

601
00:29:25,720 --> 00:29:28,040
Speaker 3: Yeah, I'm being somewhat disingenuous, but my point is it

602
00:29:28,080 --> 00:29:30,080
Speaker 3: is just it is interesting that one of the larger

603
00:29:30,160 --> 00:29:32,880
Speaker 3: data sets out there was one that was from an

604
00:29:32,920 --> 00:29:35,600
Speaker 3: economic standpoint, was mispriced and then became a seed, a

605
00:29:35,640 --> 00:29:37,680
Speaker 3: seed chunk of the AI training set.

606
00:29:37,760 --> 00:29:41,520
Speaker 1: Do you are you a pro AI person? Where are you?

607
00:29:41,960 --> 00:29:45,520
Speaker 5: I kind of that's a good question. I kind of

608
00:29:45,560 --> 00:29:48,600
Speaker 5: want to see what it could become if that If

609
00:29:48,600 --> 00:29:53,360
Speaker 5: that's fair enough. In other words I've What I don't

610
00:29:53,480 --> 00:29:58,560
Speaker 5: like about AI, sort of philosophically is I've always like

611
00:29:58,720 --> 00:30:03,760
Speaker 5: human augmentation technology better than sort of independent technolo. I've

612
00:30:03,760 --> 00:30:08,280
Speaker 5: always I think we're tool using creatures, so obviously, and

613
00:30:08,840 --> 00:30:14,200
Speaker 5: stuff that makes us smarter or more capable or better

614
00:30:14,200 --> 00:30:17,280
Speaker 5: at hitting a baseball over a fence is like things

615
00:30:17,360 --> 00:30:19,040
Speaker 5: I think are awesome and technology.

616
00:30:19,400 --> 00:30:20,960
Speaker 2: One of my problems with platforms is.

617
00:30:20,880 --> 00:30:23,800
Speaker 5: They've always kind of done what you say, but then

618
00:30:23,800 --> 00:30:26,959
Speaker 5: they sort of have their own agenda as well. And

619
00:30:27,040 --> 00:30:31,120
Speaker 5: so I like AI if it is kind of purely augmentative,

620
00:30:31,160 --> 00:30:33,360
Speaker 5: like you have an agent that does exactly what you want.

621
00:30:33,760 --> 00:30:37,720
Speaker 5: What I don't like is if AI is something that

622
00:30:37,800 --> 00:30:39,960
Speaker 5: kind of is out of our control, or you know,

623
00:30:40,080 --> 00:30:41,240
Speaker 5: replaces me as a professor.

624
00:30:41,320 --> 00:30:42,560
Speaker 2: Let's say that kind of thing.

625
00:30:42,560 --> 00:30:44,400
Speaker 5: And I guess I think it could go either way

626
00:30:44,480 --> 00:30:47,080
Speaker 5: right now, I think we're not totally clear. I'm not

627
00:30:47,120 --> 00:30:49,440
Speaker 5: as dystopian, I think as you guys on this or

628
00:30:50,400 --> 00:30:50,760
Speaker 5: having fun.

629
00:30:50,920 --> 00:30:53,840
Speaker 3: You see Jensen Wang's comments of GtC. This is so

630
00:30:53,920 --> 00:30:56,320
Speaker 3: he very explicitly makes the case that this is all

631
00:30:56,320 --> 00:31:00,880
Speaker 3: fairly pointless unless we're trying to replace people, and talks

632
00:31:00,880 --> 00:31:03,960
Speaker 3: explicitly about the idea of taking a meaningful chunk out

633
00:31:04,000 --> 00:31:08,560
Speaker 3: of global labors totally tam total available market. And so

634
00:31:09,000 --> 00:31:11,320
Speaker 3: having people say these things out loud, even if they're

635
00:31:11,360 --> 00:31:14,040
Speaker 3: non you know, nonsensical or unlikely at least for me,

636
00:31:14,200 --> 00:31:17,000
Speaker 3: is directional with respect to the because intent matters, right.

637
00:31:17,040 --> 00:31:19,480
Speaker 3: Technology is not some sort of neutral entity. It matters

638
00:31:19,520 --> 00:31:21,920
Speaker 3: what people are, hopes and dreams are with respect to

639
00:31:21,960 --> 00:31:23,840
Speaker 3: the things they're creating, and so I think that's important

640
00:31:23,880 --> 00:31:24,560
Speaker 3: to know. Well.

641
00:31:24,560 --> 00:31:28,000
Speaker 1: Also, I think on top of that, the one of

642
00:31:28,000 --> 00:31:33,959
Speaker 1: the founders of Deep Seek, the Chinese, said recently that

643
00:31:33,960 --> 00:31:38,400
Speaker 1: that he believes that AI will drastically change everything about

644
00:31:38,400 --> 00:31:40,840
Speaker 1: society within the next ten to twenty years, and not

645
00:31:40,880 --> 00:31:41,360
Speaker 1: for the better.

646
00:31:42,760 --> 00:31:47,520
Speaker 5: That's okay, But people, you know, as a historian of technologies,

647
00:31:47,560 --> 00:31:49,960
Speaker 5: I have read a million of these things about the radio,

648
00:31:50,640 --> 00:31:52,520
Speaker 5: about this, but it's this.

649
00:31:52,720 --> 00:31:55,080
Speaker 3: I mean, radio did change things change sort of and

650
00:31:55,120 --> 00:31:55,840
Speaker 3: sorted railroad.

651
00:31:56,200 --> 00:31:58,440
Speaker 1: Yeah, that's true, and it's funny. I remember my grandfather

652
00:31:58,480 --> 00:32:00,920
Speaker 1: lived in England, in the North of England, in London, sorry,

653
00:32:00,960 --> 00:32:06,400
Speaker 1: and the nineteen hundreds, and he he said he remembered

654
00:32:06,400 --> 00:32:08,160
Speaker 1: they never had a lock on their door. I always

655
00:32:08,160 --> 00:32:09,920
Speaker 1: remember him telling me the story they didn't have a

656
00:32:09,920 --> 00:32:12,120
Speaker 1: lock on their door, walking them out, and then one

657
00:32:12,200 --> 00:32:14,480
Speaker 1: day as dad came home with the radio and they

658
00:32:14,480 --> 00:32:16,479
Speaker 1: put on the radio and there's the news and everything.

659
00:32:16,520 --> 00:32:18,000
Speaker 1: A week later they had a lock on their door.

660
00:32:18,960 --> 00:32:22,800
Speaker 1: And for him it was this big moment of like, oh,

661
00:32:22,920 --> 00:32:25,800
Speaker 1: you hear these things and they scare you and you

662
00:32:25,840 --> 00:32:27,960
Speaker 1: feel unsafe, and then you have to have a lock

663
00:32:27,960 --> 00:32:31,600
Speaker 1: on your door. And I think, you know, society. The

664
00:32:31,640 --> 00:32:33,960
Speaker 1: thing that I've learned as someone who's written about tech

665
00:32:34,000 --> 00:32:38,040
Speaker 1: for twenty five years is that every technology has a

666
00:32:38,040 --> 00:32:40,280
Speaker 1: good side and a bad size, right right. There's nothing.

667
00:32:40,360 --> 00:32:42,320
Speaker 1: There's no technology that I can think of that has

668
00:32:42,520 --> 00:32:44,640
Speaker 1: that's just good. Can you think of one?

669
00:32:44,720 --> 00:32:46,479
Speaker 2: I can't nail and hammer?

670
00:32:47,080 --> 00:32:48,680
Speaker 1: People get killed with hammers all the time.

671
00:32:48,800 --> 00:32:50,160
Speaker 3: Well that's good, that's true.

672
00:32:50,320 --> 00:32:53,840
Speaker 1: I actually thought I thought dishwashers turns out people climb inside.

673
00:32:54,000 --> 00:32:55,800
Speaker 3: Yeah, that's actually a serious problem.

674
00:32:56,160 --> 00:32:59,360
Speaker 1: Hair dryer now they called fires or they throw them

675
00:32:59,400 --> 00:33:02,880
Speaker 1: in the bathroom. Yeah, stuff Like there's literally there's nothing.

676
00:33:03,000 --> 00:33:03,560
Speaker 1: There's nothing.

677
00:33:03,600 --> 00:33:05,960
Speaker 2: What about shoes, shoe bomb?

678
00:33:08,800 --> 00:33:11,760
Speaker 5: Think about the guy exactly, That damn guy made me

679
00:33:11,880 --> 00:33:14,560
Speaker 5: sit in like, you know, same lines forever.

680
00:33:15,800 --> 00:33:17,080
Speaker 3: Think of the costs he's put out.

681
00:33:17,200 --> 00:33:22,880
Speaker 1: All thees shoes are pretty good, but yes, anyway, but

682
00:33:23,560 --> 00:33:25,280
Speaker 1: the guy throw the shoe at George Bush.

683
00:33:25,440 --> 00:33:27,440
Speaker 2: I mean it was I forgot about that. Yeah, and

684
00:33:27,480 --> 00:33:29,320
Speaker 2: then krush Chef with his shoe.

685
00:33:29,120 --> 00:33:31,760
Speaker 1: And weeks from now, I'm going to get a call

686
00:33:31,760 --> 00:33:34,880
Speaker 1: from Tim. He's gonna be like it's microphones.

687
00:33:36,200 --> 00:33:38,640
Speaker 5: After that, I'll think of all the companies that didn't

688
00:33:38,680 --> 00:33:41,520
Speaker 5: push the billion dollar button exactly. But I do think

689
00:33:41,520 --> 00:33:43,880
Speaker 5: in this conversation and in a lot of you know,

690
00:33:44,000 --> 00:33:48,240
Speaker 5: talk about AI, there is a kind of error that

691
00:33:48,280 --> 00:33:51,920
Speaker 5: a lot of people possibly are making, which is there's

692
00:33:51,960 --> 00:33:55,120
Speaker 5: a lot of linear projection based on the last three

693
00:33:55,200 --> 00:33:57,880
Speaker 5: years that suggests a I just keeps on this kind

694
00:33:57,920 --> 00:34:02,440
Speaker 5: of linear you know, upwards arctory when most technologies, and

695
00:34:02,480 --> 00:34:03,760
Speaker 5: we don't know when that is, they do.

696
00:34:03,720 --> 00:34:04,360
Speaker 2: Start to bend.

697
00:34:04,840 --> 00:34:05,920
Speaker 3: This one's already bending.

698
00:34:06,160 --> 00:34:07,400
Speaker 2: Yeah, I mean I think it is bending.

699
00:34:07,400 --> 00:34:09,200
Speaker 3: But yeah, the data is pretty clear that it already is.

700
00:34:09,200 --> 00:34:12,120
Speaker 3: We were talking about it earlier today about how you know,

701
00:34:12,160 --> 00:34:14,960
Speaker 3: cycle training times are increasing, costs are increasing in the

702
00:34:14,960 --> 00:34:18,160
Speaker 3: delta per model on a benchmark basis is much smaller

703
00:34:18,200 --> 00:34:20,440
Speaker 3: than it was in any prior iteration. So in a

704
00:34:20,480 --> 00:34:21,680
Speaker 3: sense that's already happening.

705
00:34:22,320 --> 00:34:24,799
Speaker 5: Yeah, And you know, I read you read some of

706
00:34:24,840 --> 00:34:28,160
Speaker 5: the just in every Love media and tech history, and

707
00:34:28,920 --> 00:34:30,520
Speaker 5: you know, like with the airplane, people are like, well,

708
00:34:30,560 --> 00:34:31,440
Speaker 5: what are we going to do when we go to

709
00:34:31,480 --> 00:34:33,440
Speaker 5: the moon by airplane? You know, whether that going to

710
00:34:33,480 --> 00:34:34,959
Speaker 5: be like a lot of people talk about that kind stuff,

711
00:34:35,080 --> 00:34:37,160
Speaker 5: you know, because they're just thinking, what's getting faster faster fast?

712
00:34:37,200 --> 00:34:38,239
Speaker 5: The rocket ships to the moon.

713
00:34:38,719 --> 00:34:39,839
Speaker 2: And you know, I.

714
00:34:39,760 --> 00:34:43,040
Speaker 5: Guess who knows what the limits? I guess they're ultimately physics.

715
00:34:43,840 --> 00:34:46,960
Speaker 5: I guess like physics is kind of a bitch. Itvasually

716
00:34:47,200 --> 00:34:51,319
Speaker 5: harshness stops, stops like the Maybe I shouldn't have said that,

717
00:34:51,360 --> 00:34:53,399
Speaker 5: but anyway, it gets in the way of the really

718
00:34:53,400 --> 00:34:55,840
Speaker 5: good times that you project from a linear projection, which.

719
00:34:56,239 --> 00:35:21,000
Speaker 1: Yeah, but do you have you have little kids nine

720
00:35:21,000 --> 00:35:23,200
Speaker 1: to twelve? Do you worry about the future for them

721
00:35:23,280 --> 00:35:24,279
Speaker 1: or do you think it's gonna be great?

722
00:35:25,440 --> 00:35:30,520
Speaker 5: I would say I worry maybe a little less about

723
00:35:30,760 --> 00:35:33,680
Speaker 5: technology than the state of government.

724
00:35:33,880 --> 00:35:36,640
Speaker 1: But what they are one in part part in a way,

725
00:35:36,800 --> 00:35:37,319
Speaker 1: because I.

726
00:35:37,280 --> 00:35:39,239
Speaker 5: Think that we got a little wiser we were just

727
00:35:39,239 --> 00:35:42,560
Speaker 5: speaking about children. I think we wizened up a little

728
00:35:42,600 --> 00:35:45,839
Speaker 5: bit to the like the dangers of social media and

729
00:35:45,920 --> 00:35:49,120
Speaker 5: like totally unrestrained. I want to talk about a crazy time.

730
00:35:49,160 --> 00:35:51,680
Speaker 5: It's only twenty tens where people are like, oh, get

731
00:35:51,680 --> 00:35:53,440
Speaker 5: your kid a phone, that that's the answer to all

732
00:35:53,520 --> 00:35:58,160
Speaker 5: the problems. So I feel like, look, I'm still a

733
00:35:58,200 --> 00:36:02,040
Speaker 5: little scared when I meet their friends, teenagers who cannot

734
00:36:02,080 --> 00:36:07,719
Speaker 5: seem to make eye contact and have a regular meal

735
00:36:07,760 --> 00:36:09,680
Speaker 5: with adults and like a lot of them.

736
00:36:10,480 --> 00:36:12,400
Speaker 1: To you talk about this in the book the Laziness

737
00:36:12,480 --> 00:36:16,279
Speaker 1: and the meat space and like, but you think that

738
00:36:16,280 --> 00:36:17,280
Speaker 1: that's going to change?

739
00:36:17,440 --> 00:36:21,040
Speaker 5: Or maybe I just have a very deep love for

740
00:36:21,160 --> 00:36:24,720
Speaker 5: my children, and I have trouble, Like I have trouble thinking.

741
00:36:24,920 --> 00:36:27,480
Speaker 5: I don't know, maybe I'm not able to envision like

742
00:36:27,560 --> 00:36:31,239
Speaker 5: them being harmed by things. But yeah, I like, maybe

743
00:36:31,239 --> 00:36:35,359
Speaker 5: I gave it. Yeah, I just I feel like we're

744
00:36:35,360 --> 00:36:37,080
Speaker 5: a little wiser to children's dangers.

745
00:36:37,080 --> 00:36:37,759
Speaker 2: I hope. I guess.

746
00:36:37,760 --> 00:36:40,680
Speaker 5: Maybe I'm being optimistic. But am I am I hopeful

747
00:36:40,680 --> 00:36:41,880
Speaker 5: about their economic future?

748
00:36:41,960 --> 00:36:42,040
Speaker 2: No?

749
00:36:42,120 --> 00:36:46,440
Speaker 1: Not really, because you think that AI will end up well.

750
00:36:46,480 --> 00:36:48,520
Speaker 2: Actually, I think my children the problem is.

751
00:36:48,560 --> 00:36:53,520
Speaker 5: I think like they'll either be plumbers really really wealthy

752
00:36:53,680 --> 00:36:55,640
Speaker 5: or they'll be really really poor. And I guess what

753
00:36:55,680 --> 00:36:58,279
Speaker 5: I'm worried about is like, it doesn't feel like you

754
00:36:58,360 --> 00:37:01,400
Speaker 5: have you can feel confident they're going to somewhere end

755
00:37:01,440 --> 00:37:02,720
Speaker 5: up in some good middle place.

756
00:37:02,880 --> 00:37:04,880
Speaker 1: So can you talk about that a little more about

757
00:37:04,960 --> 00:37:08,920
Speaker 1: about this idea of there just being two different economic

758
00:37:09,480 --> 00:37:13,320
Speaker 1: placeholders in society and what that looks like in your opinions.

759
00:37:13,400 --> 00:37:15,640
Speaker 2: Yeah, sure, you know this is.

760
00:37:17,480 --> 00:37:20,080
Speaker 1: A little dark, but I do think we love dark

761
00:37:20,160 --> 00:37:22,120
Speaker 1: hair on the Tim Paul Show.

762
00:37:23,040 --> 00:37:23,799
Speaker 3: Dark as our thing.

763
00:37:23,960 --> 00:37:26,680
Speaker 5: Well in the same way that frankly, democracy is like

764
00:37:26,760 --> 00:37:32,239
Speaker 5: a blip and a rarity among human civilizations over time. Similarly,

765
00:37:32,320 --> 00:37:36,319
Speaker 5: I think feudalism has been like the main system for

766
00:37:36,440 --> 00:37:39,880
Speaker 5: most of human history, which is a small number of

767
00:37:39,920 --> 00:37:43,520
Speaker 5: people controlling the means of product, like the land or

768
00:37:43,120 --> 00:37:48,040
Speaker 5: the most valuable assets, and then everybody else, and I

769
00:37:48,160 --> 00:37:54,000
Speaker 5: think their conditions are have been created, and in some

770
00:37:54,080 --> 00:37:57,800
Speaker 5: ways maybe we're going there to have in a sense,

771
00:37:58,640 --> 00:38:04,480
Speaker 5: a two class society, one full of one tech and

772
00:38:04,520 --> 00:38:08,600
Speaker 5: other entities that have managed to either master the extraction

773
00:38:08,680 --> 00:38:11,400
Speaker 5: technologies or work very closely with them, and then all

774
00:38:11,400 --> 00:38:13,480
Speaker 5: the rest of the industries and businesses in the United

775
00:38:13,480 --> 00:38:17,280
Speaker 5: States or the employees who are serving in that second class,

776
00:38:17,600 --> 00:38:19,759
Speaker 5: and I think it's very possible imagine just a two

777
00:38:19,800 --> 00:38:21,360
Speaker 5: class society, and.

778
00:38:22,000 --> 00:38:25,319
Speaker 3: The history tells us that's profoundly destabilizing. Also. I mean

779
00:38:25,320 --> 00:38:27,279
Speaker 3: you think back to the twenties, you can think back

780
00:38:27,320 --> 00:38:30,520
Speaker 3: to Germany in the earlier in the nineteenth century. These

781
00:38:30,520 --> 00:38:34,040
Speaker 3: are these very destabilizing moments in companies or country's history

782
00:38:34,440 --> 00:38:35,560
Speaker 3: for obvious reasons. Yeah.

783
00:38:35,560 --> 00:38:37,160
Speaker 5: I mean in the book I called the Real Road

784
00:38:37,160 --> 00:38:40,480
Speaker 5: to Serve Them, where I say, like, you can't rebalance

785
00:38:40,560 --> 00:38:44,400
Speaker 5: economic power, I feel really start to get mad. You know,

786
00:38:44,440 --> 00:38:48,920
Speaker 5: that tends to set the stage for the autocratic dictator

787
00:38:49,000 --> 00:38:51,719
Speaker 5: strong man who's like, no, I'm going to really help

788
00:38:51,760 --> 00:38:52,040
Speaker 5: you out.

789
00:38:52,760 --> 00:38:54,319
Speaker 2: We have a little bit that's going on right now.

790
00:38:55,120 --> 00:38:57,760
Speaker 3: And the US is uniquely susceptible to that particular virus

791
00:38:57,760 --> 00:38:59,799
Speaker 3: because there's this built in ethos of that, you know,

792
00:39:00,080 --> 00:39:02,200
Speaker 3: everyone for themselves and I can pull myselves up. And

793
00:39:02,200 --> 00:39:05,200
Speaker 3: I mean the example of the new mayor in New

794
00:39:05,280 --> 00:39:06,520
Speaker 3: York that I was reading a piece I think was

795
00:39:06,560 --> 00:39:08,560
Speaker 3: an economist talking about how they were a little bit

796
00:39:08,600 --> 00:39:10,920
Speaker 3: baffled why he was causing such an uproar in New

797
00:39:11,000 --> 00:39:15,000
Speaker 3: York because he from a European mayoral standpoint, he was

798
00:39:15,080 --> 00:39:17,440
Speaker 3: very middle of the road. He was very typical of

799
00:39:17,480 --> 00:39:19,080
Speaker 3: what you might have. So the idea that he was

800
00:39:19,120 --> 00:39:21,960
Speaker 3: somehow an extremist, leaving aside some of his other views,

801
00:39:22,000 --> 00:39:24,520
Speaker 3: but specifically with respect to social policies and it's sort

802
00:39:24,560 --> 00:39:28,879
Speaker 3: of the economic views, was really striking because it didn't

803
00:39:28,880 --> 00:39:32,160
Speaker 3: seem that. But that's a uniquely American phenomenon, this idea

804
00:39:32,320 --> 00:39:35,719
Speaker 3: that you know, individualism and this Anyone who's sort of

805
00:39:36,360 --> 00:39:38,719
Speaker 3: going against that is going against the grain of what

806
00:39:38,800 --> 00:39:41,600
Speaker 3: makes Americans. You know, this shining city and this unique

807
00:39:41,640 --> 00:39:43,120
Speaker 3: society is the at least the argument.

808
00:39:43,200 --> 00:39:45,160
Speaker 2: Am I allowed to inject a note of optimism.

809
00:39:45,440 --> 00:39:47,520
Speaker 1: We would love a note of optimism. That's why we

810
00:39:47,560 --> 00:39:50,040
Speaker 1: had you on. Tim. Yeah, well, because you're always pretty.

811
00:39:50,040 --> 00:39:52,400
Speaker 1: You always end with an optimistic.

812
00:39:53,560 --> 00:39:55,920
Speaker 5: On the positive. You know, the United you said, the

813
00:39:56,000 --> 00:39:57,440
Speaker 5: United States uniquely susceptible.

814
00:39:57,480 --> 00:39:59,560
Speaker 1: What Tim turns around and says, only seven and a

815
00:39:59,600 --> 00:40:01,200
Speaker 1: half bill million people are going to die?

816
00:40:02,520 --> 00:40:04,160
Speaker 2: Actually, you want me to something dark. I read a

817
00:40:04,160 --> 00:40:05,759
Speaker 2: stat today.

818
00:40:06,080 --> 00:40:06,960
Speaker 1: I want to hear the dark thing.

819
00:40:07,080 --> 00:40:07,160
Speaker 4: Go.

820
00:40:07,560 --> 00:40:10,120
Speaker 5: I read a stat today that right now only twelve

821
00:40:10,160 --> 00:40:15,800
Speaker 5: percent of the world population is living on democracies. Liberal democracies. Really, yes,

822
00:40:15,840 --> 00:40:21,319
Speaker 5: it's this German study, and over five billion people are

823
00:40:21,360 --> 00:40:24,920
Speaker 5: living in one or another kind of pure autocracy or

824
00:40:24,960 --> 00:40:27,880
Speaker 5: some kind of you know, obviously depends how you count India,

825
00:40:28,400 --> 00:40:29,160
Speaker 5: China's obviously.

826
00:40:29,320 --> 00:40:29,480
Speaker 3: Yeah.

827
00:40:29,520 --> 00:40:32,960
Speaker 5: Anyway, the note of optimism I have, and this is

828
00:40:33,000 --> 00:40:36,319
Speaker 5: about America, is the United States has actually survived these

829
00:40:36,320 --> 00:40:41,360
Speaker 5: shocks before. You know, we have had extraordinary wealth and

830
00:40:41,480 --> 00:40:43,960
Speaker 5: balance at other periods and everyone's like it's coming to

831
00:40:44,000 --> 00:40:47,600
Speaker 5: an end, and you know, beginnings of revolutions. A lot

832
00:40:47,600 --> 00:40:49,880
Speaker 5: of our countries have, you know that had the revolution

833
00:40:50,600 --> 00:40:55,400
Speaker 5: in Germany. You mentioned, you know, saarist Russia. United States has,

834
00:40:56,320 --> 00:41:00,239
Speaker 5: maybe because it's revolutionary history, has an anti monopoly kind

835
00:41:00,239 --> 00:41:02,919
Speaker 5: of character to it. But you know, I talked about

836
00:41:02,920 --> 00:41:06,440
Speaker 5: the railroads, Progressive era, the New Deal. There have been

837
00:41:06,480 --> 00:41:11,560
Speaker 5: times where someone representing basically the people successfully within the

838
00:41:11,600 --> 00:41:14,840
Speaker 5: structure we have today managed reverse things. So that's my

839
00:41:14,960 --> 00:41:16,040
Speaker 5: optimistic note.

840
00:41:15,960 --> 00:41:17,600
Speaker 3: And I think it's an important note because in the

841
00:41:17,600 --> 00:41:19,279
Speaker 3: twenties that was exactly it was, that same kind of

842
00:41:19,280 --> 00:41:21,840
Speaker 3: inequality had really taken hold, and granted it led to

843
00:41:21,880 --> 00:41:23,680
Speaker 3: a recession and the Great Depression. But on the other

844
00:41:23,760 --> 00:41:26,640
Speaker 3: side of that was the New Deal and was a

845
00:41:26,680 --> 00:41:30,319
Speaker 3: new relationship between you know, the people wealth and real

846
00:41:30,400 --> 00:41:32,839
Speaker 3: flattening in terms of the inequality in the country.

847
00:41:32,680 --> 00:41:34,360
Speaker 2: And then a war. So all we need is a

848
00:41:34,360 --> 00:41:36,520
Speaker 2: great depression in a world war and then we're golden.

849
00:41:36,600 --> 00:41:38,560
Speaker 1: I think we're pretty on our way to the depression, right,

850
00:41:38,640 --> 00:41:39,759
Speaker 1: Are we getting close to that?

851
00:41:40,120 --> 00:41:40,560
Speaker 3: You think?

852
00:41:41,120 --> 00:41:43,439
Speaker 5: I wonder if we're going to look back this period, like, boy,

853
00:41:43,520 --> 00:41:46,280
Speaker 5: this was we were just thought what things were great?

854
00:41:46,320 --> 00:41:49,759
Speaker 5: And then you know, it seemed like we were, you know,

855
00:41:50,280 --> 00:41:51,960
Speaker 5: walking on water for a little while, and then all

856
00:41:52,000 --> 00:41:52,680
Speaker 5: of a sudden we sank.

857
00:41:52,680 --> 00:41:54,279
Speaker 2: Because there's a lot of weird stuff going on.

858
00:41:54,360 --> 00:41:56,080
Speaker 1: There's a lot of weird stuff. I think the weirdest

859
00:41:56,080 --> 00:41:59,480
Speaker 1: thing that's happened is that as a result of technology,

860
00:41:59,560 --> 00:42:05,040
Speaker 1: of course, is that the you used to have TV personalities,

861
00:42:05,080 --> 00:42:07,400
Speaker 1: and you had journalists and you had politicians. They were

862
00:42:07,400 --> 00:42:11,640
Speaker 1: all different, you know, and it's everything's it's funny. It's

863
00:42:11,640 --> 00:42:13,400
Speaker 1: like when I came out to Hollywood. I remember I

864
00:42:13,400 --> 00:42:16,200
Speaker 1: would meet people like eleven years ago or something like that,

865
00:42:16,200 --> 00:42:18,319
Speaker 1: and I'd be like, what do you do. I'd be like, oh,

866
00:42:18,360 --> 00:42:21,800
Speaker 1: I'm a director, writer, producer, and like they've never done anything.

867
00:42:21,800 --> 00:42:23,239
Speaker 1: But you can you can say that out here, you

868
00:42:23,320 --> 00:42:26,320
Speaker 1: literally can just decide, like you, Paul can just decide

869
00:42:26,320 --> 00:42:27,400
Speaker 1: tonight that you are.

870
00:42:27,360 --> 00:42:29,040
Speaker 2: A writer, director, producer.

871
00:42:29,080 --> 00:42:31,160
Speaker 1: I don't want to be, but you could be a documentary.

872
00:42:31,200 --> 00:42:34,000
Speaker 1: You don't have to have made anything you have done.

873
00:42:34,000 --> 00:42:35,360
Speaker 3: As a matter of fact, you'd be in the majority

874
00:42:35,360 --> 00:42:35,960
Speaker 3: if you have made me.

875
00:42:36,040 --> 00:42:39,600
Speaker 1: Yes, exactly. And so it feels almost like now because

876
00:42:39,600 --> 00:42:42,400
Speaker 1: of social media and attack and everything and these devices

877
00:42:42,440 --> 00:42:47,560
Speaker 1: in our hands that we that politicians are you know,

878
00:42:47,600 --> 00:42:50,000
Speaker 1: it's Trump and but everyone is like that now, and

879
00:42:50,040 --> 00:42:52,480
Speaker 1: I think it's like AOC. I think why. I think

880
00:42:52,560 --> 00:42:56,960
Speaker 1: two reasons, Mandamie. One, in my opinion is he's excellent

881
00:42:57,000 --> 00:43:02,080
Speaker 1: on social media. He's really entertaining, and he is promising

882
00:43:02,160 --> 00:43:06,640
Speaker 1: something that he can't deliver on. But he's promising this

883
00:43:07,200 --> 00:43:09,680
Speaker 1: essentially what you just both said about this, that he

884
00:43:09,680 --> 00:43:14,439
Speaker 1: could that he can reverse things. And so my optimism

885
00:43:14,880 --> 00:43:16,440
Speaker 1: if we make it the next.

886
00:43:16,280 --> 00:43:21,359
Speaker 3: Five years, it's a huge provisor we do, is.

887
00:43:21,320 --> 00:43:26,840
Speaker 1: That Ai starts to take jobs. You know, maybe we

888
00:43:26,920 --> 00:43:30,280
Speaker 1: end up in war with Venezuela or China or whoever

889
00:43:30,480 --> 00:43:35,880
Speaker 1: will figure that out, but Canada, but then we will

890
00:43:36,080 --> 00:43:39,440
Speaker 1: I think you'll see society vote for someone who will

891
00:43:39,480 --> 00:43:42,480
Speaker 1: then reverse things. And but I think it's a big

892
00:43:42,560 --> 00:43:45,919
Speaker 1: question of as we've discussed, if we get there in time.

893
00:43:46,400 --> 00:43:49,880
Speaker 2: My most optimistic take on AI and now not talking politics,

894
00:43:49,880 --> 00:43:51,919
Speaker 2: but goes back to other things that it turns out

895
00:43:52,440 --> 00:43:55,200
Speaker 2: not really to be good enough to fully take people's jobs,

896
00:43:55,920 --> 00:43:58,440
Speaker 2: and so it makes people better at their jobs, are

897
00:43:58,440 --> 00:43:59,680
Speaker 2: more productive.

898
00:43:59,320 --> 00:44:00,920
Speaker 3: Right, which is kind of what happens to It's not

899
00:44:01,000 --> 00:44:03,279
Speaker 3: because people didn't want it to take jobs. They wanted

900
00:44:03,280 --> 00:44:04,360
Speaker 3: it to, but it wasn't capable.

901
00:44:04,400 --> 00:44:05,280
Speaker 1: Do you think that's true?

902
00:44:05,360 --> 00:44:07,200
Speaker 3: I do, Actually, I think that this is the point

903
00:44:07,200 --> 00:44:09,239
Speaker 3: earlier with respect to so you.

904
00:44:09,200 --> 00:44:10,880
Speaker 1: Don't think it'll get good enough to be able to

905
00:44:10,880 --> 00:44:11,919
Speaker 1: take our jobs.

906
00:44:11,560 --> 00:44:13,840
Speaker 3: And it's not in the large language model form.

907
00:44:14,000 --> 00:44:15,000
Speaker 1: It has to be something there.

908
00:44:15,040 --> 00:44:17,799
Speaker 3: It has to be something different because the architectural limitations

909
00:44:17,800 --> 00:44:20,440
Speaker 3: of large language models are so dramatic in terms of

910
00:44:20,480 --> 00:44:23,680
Speaker 3: training cycles, in terms of data sizes, data models required

911
00:44:23,719 --> 00:44:26,640
Speaker 3: to generate you know, incremental improvements. All of these reasons

912
00:44:27,000 --> 00:44:29,319
Speaker 3: mean that it's what's yam Mukun's well he just left.

913
00:44:29,320 --> 00:44:32,120
Speaker 3: Did you see that, by the way, that just left meta?

914
00:44:32,320 --> 00:44:33,319
Speaker 1: Oh yah yeah, yeah, yea.

915
00:44:33,239 --> 00:44:34,920
Speaker 3: Yeah, which the funniest thing because he's like the chief

916
00:44:34,960 --> 00:44:36,919
Speaker 3: AI scientist and it's like, yeah, well, you know, we're gone.

917
00:44:37,040 --> 00:44:39,160
Speaker 3: So but his point has always been that this is

918
00:44:39,239 --> 00:44:41,440
Speaker 3: kind of an architectural dead end. It's a great trick,

919
00:44:41,520 --> 00:44:44,600
Speaker 3: but there's inherently limitation. So I do think that that,

920
00:44:44,680 --> 00:44:48,240
Speaker 3: in a weird way, not through you know, want of effort,

921
00:44:48,400 --> 00:44:51,160
Speaker 3: we may actually find out that the current incarnation of

922
00:44:51,200 --> 00:44:53,240
Speaker 3: AI isn't enough to cause these problems.

923
00:44:52,880 --> 00:44:56,320
Speaker 5: And just stick on that. I also think that computational

924
00:44:56,400 --> 00:45:00,520
Speaker 5: challenges of physical space are strong, and we've managed to cars,

925
00:45:00,640 --> 00:45:03,919
Speaker 5: which is very limited or you know, I mean, there's

926
00:45:04,000 --> 00:45:06,719
Speaker 5: kind of amazing, but that you know, there's so many

927
00:45:06,760 --> 00:45:09,000
Speaker 5: other things we do in physical space and sometimes we

928
00:45:09,080 --> 00:45:11,120
Speaker 5: jump over that problem.

929
00:45:11,600 --> 00:45:14,759
Speaker 3: In this context all the time, right exactly. Yeah, and

930
00:45:14,800 --> 00:45:17,919
Speaker 3: it's atoms are fairly resistant to those kinds of leaves. Yeah.

931
00:45:17,960 --> 00:45:21,279
Speaker 5: And so if we're you know, to remain employed, it's

932
00:45:21,320 --> 00:45:25,880
Speaker 5: because maybe because we have like good ability to negotiating

933
00:45:25,920 --> 00:45:28,920
Speaker 5: physical space. That sounds crazy, that's the only thing that

934
00:45:29,120 --> 00:45:29,880
Speaker 5: keeping us here.

935
00:45:30,560 --> 00:45:34,360
Speaker 1: But you talk about how because of the way technology

936
00:45:34,400 --> 00:45:36,320
Speaker 1: has made it easy not to be in physical space,

937
00:45:36,400 --> 00:45:36,719
Speaker 1: we're not.

938
00:45:38,400 --> 00:45:44,239
Speaker 5: Well, that's so that's true. I guess I'm saying I'm

939
00:45:44,239 --> 00:45:47,319
Speaker 5: talking about two different things. One is AI and it's

940
00:45:47,320 --> 00:45:50,879
Speaker 5: full replacement of humanity and the jobs. And I'm saying

941
00:45:50,880 --> 00:45:54,120
Speaker 5: our mastery of physical space actually might keep us in

942
00:45:54,200 --> 00:45:58,080
Speaker 5: some jobs. Plumbing is a good example where you have

943
00:45:58,120 --> 00:45:59,839
Speaker 5: to be a little bit more flexible or they need

944
00:45:59,880 --> 00:46:00,680
Speaker 5: some to help him.

945
00:46:00,800 --> 00:46:03,680
Speaker 3: Although my plumber the other day was using live streaming

946
00:46:04,920 --> 00:46:07,840
Speaker 3: video as he did things, which I found really interesting.

947
00:46:07,960 --> 00:46:10,319
Speaker 1: Imagine if he just became a plumber like this week

948
00:46:10,360 --> 00:46:13,040
Speaker 1: and he's still figuring it all just he used to

949
00:46:13,040 --> 00:46:16,040
Speaker 1: be like a hedge fund investor or a journalist.

950
00:46:16,360 --> 00:46:18,279
Speaker 3: That's right, he's just making it up as he goes.

951
00:46:18,560 --> 00:46:21,680
Speaker 5: My politically optimistic thing is that is what I said earlier,

952
00:46:21,680 --> 00:46:24,600
Speaker 5: is I think this country has almost a DNA of

953
00:46:24,640 --> 00:46:26,760
Speaker 5: fighting off these things.

954
00:46:26,800 --> 00:46:28,160
Speaker 2: You know, we have to recapture it.

955
00:46:28,200 --> 00:46:33,200
Speaker 5: We could fail, We could have the next election suspended

956
00:46:33,920 --> 00:46:38,320
Speaker 5: and actually turn into a dictatorship so that that's all possible.

957
00:46:38,760 --> 00:46:42,319
Speaker 5: Or we could have like people who legitimately promise to

958
00:46:42,320 --> 00:46:44,640
Speaker 5: rebalance things a little bit. And I do think ultimately

959
00:46:44,719 --> 00:46:49,200
Speaker 5: this country has, in its wealthier periods, a more distributed

960
00:46:49,200 --> 00:46:52,800
Speaker 5: economy than just a few, you know, big guys deciding everything.

961
00:46:53,400 --> 00:46:55,680
Speaker 1: And that you hope will be what the future looks like.

962
00:46:55,960 --> 00:46:57,560
Speaker 2: That is my most optimistic take.

963
00:46:57,640 --> 00:46:59,560
Speaker 1: Yeah, that's not that, that's good.

964
00:46:59,600 --> 00:47:02,080
Speaker 2: I mean that future political philosophy.

965
00:47:02,200 --> 00:47:05,520
Speaker 5: I mean, I'm not anti capitalism, but I'm into distributed capitalism.

966
00:47:05,560 --> 00:47:09,040
Speaker 5: I guess I'm kind of into, you know, totally a

967
00:47:09,080 --> 00:47:11,480
Speaker 5: lot of people being rich and whatever is it. I

968
00:47:11,480 --> 00:47:14,160
Speaker 5: mean even just like an AI, like we kind of

969
00:47:14,200 --> 00:47:16,799
Speaker 5: have this weird world where there's like a small number

970
00:47:16,800 --> 00:47:21,040
Speaker 5: of companies who are kind of like setting industrial policy

971
00:47:21,080 --> 00:47:23,000
Speaker 5: for a whole country by deciding, all right, we're going

972
00:47:23,080 --> 00:47:26,040
Speaker 5: to invest all these billions in this exactly and like

973
00:47:26,080 --> 00:47:28,400
Speaker 5: it could be a big mistake, like a big waste.

974
00:47:28,960 --> 00:47:29,200
Speaker 4: You know.

975
00:47:29,560 --> 00:47:31,719
Speaker 5: It's I'm kind of more into more people trying to

976
00:47:31,760 --> 00:47:34,279
Speaker 5: make decisions and try things, and like a.

977
00:47:34,280 --> 00:47:36,080
Speaker 3: Few companies making massive decisions.

978
00:47:36,239 --> 00:47:36,560
Speaker 2: Yeah.

979
00:47:36,719 --> 00:47:39,840
Speaker 5: Right, it does sort of feel like General Motors the fifties,

980
00:47:39,920 --> 00:47:42,080
Speaker 5: I don't know, deciding what we need is the bigger car,

981
00:47:42,200 --> 00:47:43,000
Speaker 5: even bigger car.

982
00:47:43,080 --> 00:47:45,120
Speaker 3: Yeah, what's good for open AI is good for the country.

983
00:47:45,320 --> 00:47:45,880
Speaker 2: Yeah.

984
00:47:45,960 --> 00:47:49,120
Speaker 1: I have a friend who I felt this way about

985
00:47:49,200 --> 00:47:51,080
Speaker 1: about the LM's reaching their limit. And then I have

986
00:47:51,080 --> 00:47:54,279
Speaker 1: a friend who was he's a Google and he was

987
00:47:54,320 --> 00:48:00,400
Speaker 1: telling me about them working on their quantum computers. The

988
00:48:00,400 --> 00:48:02,759
Speaker 1: AI is helping the quantum computer get better, and the

989
00:48:02,800 --> 00:48:04,920
Speaker 1: quantum computer is helping the AI get better. And then

990
00:48:04,960 --> 00:48:06,120
Speaker 1: I got freaked out again.

991
00:48:06,520 --> 00:48:09,879
Speaker 5: The intelligence explosion where it starts like having a kind

992
00:48:09,880 --> 00:48:11,640
Speaker 5: of intelligence yea, and then it just takes over the world.

993
00:48:11,800 --> 00:48:13,400
Speaker 3: I feel like I've been your therapy session.

994
00:48:13,480 --> 00:48:16,319
Speaker 1: I've really it's really been a it's been a few

995
00:48:16,360 --> 00:48:18,800
Speaker 1: episodes of this. All right, we have a couple of

996
00:48:18,840 --> 00:48:21,160
Speaker 1: minutes left. Anything you want to get to before I

997
00:48:21,200 --> 00:48:23,279
Speaker 1: have a last question for you, but anything.

998
00:48:23,040 --> 00:48:25,440
Speaker 2: Give me the last question ready for I'm just curious.

999
00:48:25,560 --> 00:48:29,719
Speaker 1: You've you've you've researched and written all these books about

1000
00:48:30,840 --> 00:48:34,320
Speaker 1: these technologies over time and everything.

1001
00:48:34,880 --> 00:48:35,319
Speaker 2: Do you.

1002
00:48:37,280 --> 00:48:39,440
Speaker 1: What do you think has been the most positive and

1003
00:48:39,520 --> 00:48:41,080
Speaker 1: what has been the most negative?

1004
00:48:41,440 --> 00:48:44,000
Speaker 2: Oh, individually of moments.

1005
00:48:43,600 --> 00:48:48,799
Speaker 1: Or moments, technologies, advancements in society, like what you know

1006
00:48:48,800 --> 00:48:50,719
Speaker 1: when you look back at them all you can go

1007
00:48:50,760 --> 00:48:52,720
Speaker 1: back to the nineteen hundreds whatever.

1008
00:48:53,440 --> 00:48:57,879
Speaker 5: Well, I know it's the worst cotton gin that is.

1009
00:48:58,000 --> 00:49:01,120
Speaker 5: And actually it bears on our AI conversation a little bit,

1010
00:49:01,760 --> 00:49:04,160
Speaker 5: because I mean, I think I think you said that

1011
00:49:04,239 --> 00:49:08,200
Speaker 5: every technology maybe has good er Dad sides. I also

1012
00:49:08,360 --> 00:49:11,480
Speaker 5: think what I've tried to understand in all my books.

1013
00:49:12,640 --> 00:49:16,560
Speaker 5: One is like the long cycles of political and economic

1014
00:49:16,680 --> 00:49:19,680
Speaker 5: power related to technology. This is a big, big theme.

1015
00:49:21,160 --> 00:49:26,240
Speaker 5: But another is the sense that a new technology interacts

1016
00:49:26,920 --> 00:49:31,120
Speaker 5: with the existing economic structure and that can have a

1017
00:49:31,160 --> 00:49:35,560
Speaker 5: profound impact on how it affects us. And let me

1018
00:49:35,600 --> 00:49:38,680
Speaker 5: explain that a little better. In the book, I talk

1019
00:49:38,760 --> 00:49:43,040
Speaker 5: about two different technologies. One is the plow, the farming plow.

1020
00:49:44,040 --> 00:49:46,080
Speaker 5: You know, that was invented many places at once, but

1021
00:49:46,120 --> 00:49:49,200
Speaker 5: it was an invention and it made a lot of

1022
00:49:49,239 --> 00:49:53,560
Speaker 5: farmers rich richer than they were. I'm not saying like billionaires,

1023
00:49:53,600 --> 00:49:55,839
Speaker 5: but it made them able to plow much more effectively,

1024
00:49:56,680 --> 00:49:58,759
Speaker 5: you know, and it appeared in where it appeared. It

1025
00:49:58,800 --> 00:50:02,360
Speaker 5: had a real powerful effect, but a very distributed effect.

1026
00:50:02,840 --> 00:50:05,600
Speaker 5: The opposite is the cotton gin, which invented in the

1027
00:50:05,719 --> 00:50:09,920
Speaker 5: early nineteenth century, which fortified the economics of plantation slavery

1028
00:50:10,440 --> 00:50:13,120
Speaker 5: and made it so that you could make a fortune,

1029
00:50:13,239 --> 00:50:15,040
Speaker 5: or the you know, the plantation order can make a

1030
00:50:15,080 --> 00:50:20,160
Speaker 5: fortune with a huge plantation, and so I guess you know,

1031
00:50:20,200 --> 00:50:22,719
Speaker 5: those sort of just happened. When it comes to technology

1032
00:50:22,760 --> 00:50:26,040
Speaker 5: like AI or our platforms. I am believer we want

1033
00:50:26,080 --> 00:50:28,920
Speaker 5: to push things in a certain direction, basically loading the

1034
00:50:28,960 --> 00:50:31,360
Speaker 5: deck by saying away from slavery, away from trying to

1035
00:50:31,400 --> 00:50:35,080
Speaker 5: reinforce these structures. But I guess that that's what I'd

1036
00:50:35,080 --> 00:50:36,720
Speaker 5: call the worst. Maybe that doesn't fully answer.

1037
00:50:36,880 --> 00:50:43,160
Speaker 1: No, it's such a great spread answer and the best

1038
00:50:43,560 --> 00:50:44,680
Speaker 1: I had the plou.

1039
00:50:44,520 --> 00:50:46,880
Speaker 2: That's like all of human history. It's a little unfair.

1040
00:50:46,920 --> 00:50:52,160
Speaker 5: Maybe in my lifetime, my first thought went to my

1041
00:50:52,280 --> 00:50:54,040
Speaker 5: like Atari twenty six D when I was a kid,

1042
00:50:55,920 --> 00:50:58,440
Speaker 5: I think personal computer. I think I think personally I

1043
00:50:58,680 --> 00:51:02,719
Speaker 5: have I think personal computer. Kind of amazing, very sort

1044
00:51:02,760 --> 00:51:05,400
Speaker 5: of seventies coded kind of thinking. It was all about

1045
00:51:05,920 --> 00:51:08,560
Speaker 5: everyone's going to have their own computer. It was a

1046
00:51:08,640 --> 00:51:12,520
Speaker 5: kind of you know, as opposed to the computer, is

1047
00:51:12,520 --> 00:51:15,800
Speaker 5: this thing that is inside a giant building that IBM

1048
00:51:15,960 --> 00:51:18,920
Speaker 5: or like the federal government controls. You're going to have it,

1049
00:51:18,960 --> 00:51:23,200
Speaker 5: like Steve Wisenach kind of spirit early on, you could

1050
00:51:23,280 --> 00:51:26,680
Speaker 5: mess with it and let people do things. Yeah, I

1051
00:51:27,120 --> 00:51:29,600
Speaker 5: kind of like the personal computer. And then you own

1052
00:51:29,640 --> 00:51:31,920
Speaker 5: your data, I mean the thing we've given up a

1053
00:51:31,960 --> 00:51:34,600
Speaker 5: lot since then, you know, you own all your stuff,

1054
00:51:34,600 --> 00:51:36,200
Speaker 5: will be on your do remember these things called like

1055
00:51:36,200 --> 00:51:37,200
Speaker 5: hard drives and had all.

1056
00:51:37,080 --> 00:51:39,040
Speaker 2: Your stuff, you know, not the cloud.

1057
00:51:39,080 --> 00:51:40,960
Speaker 5: I don't know some of our listeners might not know

1058
00:51:41,000 --> 00:51:43,000
Speaker 5: about this, but used to have your stuff on your

1059
00:51:43,000 --> 00:51:45,640
Speaker 5: actual computer. Used to own thing we also used to own,

1060
00:51:45,680 --> 00:51:48,440
Speaker 5: like music collections. This was collections.

1061
00:51:50,400 --> 00:51:53,560
Speaker 1: We used to own things we did, used to own things.

1062
00:51:54,480 --> 00:51:56,719
Speaker 1: People are always whenever I'm doing a zoom in here

1063
00:51:56,719 --> 00:51:58,920
Speaker 1: with all the books people like? Is that an AI backup?

1064
00:52:00,080 --> 00:52:00,160
Speaker 4: Oh?

1065
00:52:00,239 --> 00:52:01,360
Speaker 1: They are real those.

1066
00:52:01,200 --> 00:52:04,200
Speaker 3: They are real books, some of which you've read, all

1067
00:52:04,239 --> 00:52:04,960
Speaker 3: of which he's read.

1068
00:52:05,640 --> 00:52:07,040
Speaker 2: There's one of Japanese there.

1069
00:52:06,920 --> 00:52:10,880
Speaker 1: So that's mine in Japanese that I wrote.

1070
00:52:11,080 --> 00:52:12,520
Speaker 2: There you go Japanese.

1071
00:52:13,320 --> 00:52:15,160
Speaker 1: Well, thank you guys. Thank you so much, Tim for

1072
00:52:15,200 --> 00:52:18,560
Speaker 1: coming on. That really amazing to have you. And the

1073
00:52:18,560 --> 00:52:21,560
Speaker 1: book is the Age of extraction, How tech platforms conquered

1074
00:52:21,600 --> 00:52:25,600
Speaker 1: the economy and threaten our future prosperity. Uh. Tim, you

1075
00:52:25,640 --> 00:52:27,680
Speaker 1: are always an amazing guest and you always write an

1076
00:52:27,719 --> 00:52:28,279
Speaker 1: amazing book.

1077
00:52:28,440 --> 00:52:29,359
Speaker 2: It's great to see you again.