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Speaker 1: Pushkin. There are these moments when people make huge technical

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Speaker 1: advances and it happens all of a sudden, or at

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Speaker 1: least it feels like it happens all of a sudden.

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Speaker 1: You know this is happening right now, most obviously with AI,

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Speaker 1: with artificial intelligence. It happened not too long ago with rockets,

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Speaker 1: when SpaceX dramatically lowered the cost of getting to space.

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Speaker 1: Maybe it's happening now with crypto. I'd say it's probably

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Speaker 1: too soon to say on that one. In any case,

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Speaker 1: you can look at technological breakthroughs in different fields and

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Speaker 1: at different times, and you can ask, what can we

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Speaker 1: learn from these? You can ask, can we abstract certain

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Speaker 1: you know, certain qualities, certain tendencies that seem to drive

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Speaker 1: toward these burths of technological progress. There's a recent book

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Speaker 1: called Boom that asked this question and it comes up

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Speaker 1: with an interesting answer. According to the book, one thing

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Speaker 1: that's really helpful if you want to make a wild

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Speaker 1: technological leap a bubble. I'm Jacob Goldstein, and this is

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Speaker 1: what's your problem. My guest today is Burne Hobart. He's

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Speaker 1: the author of a finance newsletter called The diff and

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Speaker 1: he's also the co author of a book called Boom,

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Speaker 1: Bubbles and the End of Stagnation. When burn talks about bubbles,

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Speaker 1: he isn't just talking about financial bubbles where investors drive

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Speaker 1: prices through the roof. When he says bubble, largely he

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Speaker 1: means social bubbles, filter bubbles, little groups of people who

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Speaker 1: share some wild belief. He really gets it what he

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Speaker 1: means in this one sentence where he and his co

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Speaker 1: author write, quote, transformative progress arises from small groups with

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Speaker 1: a unified vision, vast funding, and surprisingly poor accountability end quote,

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Speaker 1: basically living the dream. Later in the conversation, Burne and

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Speaker 1: I discussed the modern space industry and cryptocurrency and AI.

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Speaker 1: But to start, we talked about two case studies from

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Speaker 1: the US in the twentieth century. Burn writes about him

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Speaker 1: in the book, and he argues that these two moments

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Speaker 1: hold broader lessons for how technological progress works. The two

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Speaker 1: case studies we talk about are the Manhattan Project and

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Speaker 1: the Apollo Missions. So let's start with the Manhattan Project,

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Speaker 1: and maybe one place to start it is with this

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Speaker 1: famous nineteen thirty nine letter from Albert Einstein and other

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Speaker 1: scientists to FDR the President about the possibility of the

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Speaker 1: nazis building an.

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Speaker 2: Atomic bomb, right, So that letter it is it feels

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Speaker 2: like good material for maybe not a complete musical comedy,

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Speaker 2: but at least an act of a musical comedy, because

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Speaker 2: it's kind.

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Speaker 1: Of a springtime for Hitler in Germany.

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Speaker 2: There is this whole thing where you have this brilliant physicist,

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Speaker 2: but he is just kind of the stereotypical professor, you know,

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Speaker 2: crazy hair, very absent minded, always talking about these things

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Speaker 2: where no one, you know, no normal person can really

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Speaker 2: understand what he's talking about. And suddenly, instead of talking

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Speaker 2: about space time and that you know, energy and matter

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Speaker 2: in their relationship, suddenly he's saying someone could build a

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Speaker 2: really really big bomb and that person will probably be

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Speaker 2: a German, and that has some very bad implications for

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Speaker 2: the rest of the world.

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Speaker 1: So now here we are, the president decides, okay, we

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Speaker 1: need to build a bomb, we need to spend a

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Speaker 1: wild amount of money on it. And this is a

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Speaker 1: thing that you describe as a bubble, which is interesting, right,

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Speaker 1: because it's not a bubble in the sense of market prices.

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Speaker 1: It's the federal government in the military, but it has

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Speaker 1: these other bubble like characteristics in your telling, right, maybe

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Speaker 1: other meanings of bubble, the way we talk about a

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Speaker 1: social bubble or a filter bubble. Tell me about that, like,

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Speaker 1: why is the Manhattan Project a kind of bubble?

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Speaker 2: Why is it a bubble? Because there's that feedback loop

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Speaker 2: because people take the idea of actually building the bomb

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Speaker 2: more seriously as other people take it more seriously, and

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Speaker 2: the more that you have someone like more that you

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Speaker 2: have people like Oppenheimer actually dedicating time to the projects,

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Speaker 2: the more other people think the project will actually happen,

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Speaker 2: this is actually worth doing. So you have this group

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Speaker 2: of people who start taking the idea of building a

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Speaker 2: bomb more seriously. They treat it as a thing that

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Speaker 2: will actually happen rather than a thing that is hypothetically

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Speaker 2: possible if if this particular equation is right, if these

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Speaker 2: measurements are right, et cetera, and then they start actually

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Speaker 2: designing them.

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Speaker 1: The man Hen Project seems like this sort of point

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Speaker 1: that gets a bunch of really smart people to coalesce

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Speaker 1: in one place on one project at one time. Right.

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Speaker 1: It sort of solves the coordination problem the way whatever

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Speaker 1: you might say AI today is doing that, Like just

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Speaker 1: brilliant people suddenly are all in one place working on

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Speaker 1: the same thing in a way that they absolutely would

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Speaker 1: not otherwise be.

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Speaker 2: That is true. And this was both within the US

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Speaker 2: academic community and then within the global academic community, because

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Speaker 2: you had a lot of people who were in Central

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Speaker 2: Europe or Eastern Europe who realized that that is just

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Speaker 2: not a great place for them to be and tried

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Speaker 2: to get to the UK, US other Allied countries as

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Speaker 2: as quickly as possible. And so there was just this

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Speaker 2: massive intellectual dividend of a lot of the most brilliant

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Speaker 2: people in Germany and in Eastern Europe and in Hungary, etcetera.

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Speaker 2: They were all fleeing and all ended up in the

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Speaker 2: same country. So, yeah, you have just this this serendipity

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Speaker 2: machine where it was just if you were a physicist,

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Speaker 2: it was an incredible place for just overhearing really novel

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Speaker 2: ideas and putting those ideas, putting your own ideas to

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Speaker 2: the test, because you had all the all the smartest

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Speaker 2: people in the world pretty much in this one little

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Speaker 2: town in New Mexico.

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Speaker 1: Right. So the Los Alamos piece is is the famous part,

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Speaker 1: you know, it's the part one has heard of with

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Speaker 1: respect to the Manhattan Project. There's a less famous part

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Speaker 1: that's really interesting, and that also seems to hold some

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Speaker 1: broader lessons as well, right, And that is the kind

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Speaker 1: of basically the manufacturing part of the how do we

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Speaker 1: enrich enough uranium to build the bomb? If the physicists

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Speaker 1: figure out how to design it, talk about that piece

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Speaker 1: and the lessons there.

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Speaker 2: Yes, so that one, You're right, It is often under

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Speaker 2: emphasized in the history. It was more of an engineering

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Speaker 2: project than a research project, though there was a lot

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Speaker 2: of research involved. The purpose was get enough of the

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Speaker 2: get enough in rich uranium so the isotope that is

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Speaker 2: actually prone to these chain reactions, get it isolated, and

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Speaker 2: then be able to incorporate that into a bomb. They

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Speaker 2: were also working on other physom materials because there were

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Speaker 2: multiple plausible bomb designs. Some use different triggering mechanisms, some

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Speaker 2: use different materials, and there were also multiple plausible ways

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Speaker 2: to enrich enough of the physico material to actually build

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Speaker 2: a bomb. And so one version of the story is

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Speaker 2: you just go down the list and you the one

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Speaker 2: that you think is the most cost effective, most likely,

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Speaker 2: and so we choose one way to get just you

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Speaker 2: two thirty five, and we have one way to build bomb.

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Speaker 1: You two thirty five is the enriched uranium. Yes, And that,

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Speaker 1: by the way, is the way normal businesses do things

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Speaker 1: in normal times. You're like, well, we got to do

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Speaker 1: this really expensive thing. We got to build a factory,

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Speaker 1: and we don't even know if it's going to work.

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Speaker 1: Let's choose the version that's most likely to work. Like

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Speaker 1: that is the kind of standard move right, yeah, right?

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Speaker 1: And then the problem though, is that if you try

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Speaker 1: that and you just you got unlucky. You kept the

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Speaker 1: wrong bomb design and the right phissile material or right

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Speaker 1: material of wrong bomb design, you've done a lot of

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Speaker 1: work which has zero payoff, and you've lost time. Right,

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Speaker 1: Like crucially, there is there is a huge sense of

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Speaker 1: urgency present at this moment that is driving the whole

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Speaker 1: thing really right.

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Speaker 2: We could also do more than one of them in parallel,

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Speaker 2: and that is what we did. And on the manufacturing

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Speaker 2: side that was actually just murderously expensive. If you are

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Speaker 2: building a factory and you build the wrong kind of factory,

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Speaker 2: then you you've wasted a lot of a lot of

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Speaker 2: money and effort and time. So they just they did

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Speaker 2: more than one. They did several different processes for enriching

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Speaker 2: uranium and for perstonium.

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Speaker 1: All at the same time, right, And they knew they

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Speaker 1: weren't going to use all of them, they just didn't

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Speaker 1: know which one was going to work. So it's like, well,

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Speaker 1: let's try all of them at the same time and

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Speaker 1: hopefully one of them will work. Yes, Like that is

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Speaker 1: super bubbly, right. That is wild and expensive. That is

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Speaker 1: just throwing wild amounts of money at something in a

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Speaker 1: great amount of haste.

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Speaker 2: Yes. Yeah, And if you so, if you believe that

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Speaker 2: there's this pretty linear payoff, then every additional investment you make,

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Speaker 2: you know has it doesn't qualitatively change things. It just

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Speaker 2: means you're doing a little bit more of it. But

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Speaker 2: if you believe there's some kind of nonlinear payoff where

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Speaker 2: either this facility doesn't basically doesn't work at all or

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Speaker 2: it works really really well, then when you when you

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Speaker 2: diversify a little bit, you do actually get just this

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Speaker 2: this better risk adjusted return even though you're objectively taking

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Speaker 2: more risks.

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Speaker 1: Interesting, right, So in this instance, it's if the Nazis

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Speaker 1: have the bomb before we do, it's the end of

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Speaker 1: the world as we know it, Yes, and so we

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Speaker 1: better take a lot of risk. And that's actually rational.

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Speaker 1: It reminds me a little bit of aspects of operation

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Speaker 1: warp speed. I remember talking to Susan Athea, Stanford economist

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Speaker 1: early in the pandemic who was making the case to

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Speaker 1: do exactly this with vaccine manufacturing in like, you know,

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Speaker 1: early twenty twenty, we didn't know if any vaccine was

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Speaker 1: going to work, and it takes a long time to

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Speaker 1: build a factory to make a vaccine, basically a tailor

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Speaker 1: of factory. And she was like, just make a bunch

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Speaker 1: of factories to make vaccines because if one of them work,

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Speaker 1: is we want to be able to start working on

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Speaker 1: it that day. Like that seems quite similar to this

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Speaker 1: and you work.

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Speaker 2: Yeah, yeah, I think that's absolutely true that you you know,

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Speaker 2: the higher the stakes are, the more you want to

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Speaker 2: be running everything that can plausibly help in parallel. And

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Speaker 2: depending on the exact nature of what you're doing, there

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Speaker 2: can be some spillover effects. It's you know, it's possible

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Speaker 2: that you build a factory for manufacturing vaccine A and

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Speaker 2: vaccine doesn't work out, but you can retrofit that factory

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Speaker 2: and start doing vaccin B. And you know, there are

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Speaker 2: little ways to shuffle things around a bit, but you

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Speaker 2: often want to go into this basically telling yourself, if

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Speaker 2: we didn't waste money and we still got a good outcome,

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Speaker 2: it's because we got very, very lucky, and that we

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Speaker 2: only know we're being serious if we did, in fact

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Speaker 2: waste a lot of money. And yeah, I think that

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Speaker 2: kind of inverting your view of risk is often a

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Speaker 2: really good way to think about these big transformative changes.

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Speaker 2: And this is actually another case where the financial metaphors

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Speaker 2: do give useful information about just real world behaviors because

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Speaker 2: at at head funds, this is actually something that risk

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Speaker 2: teams will sometimes teleportfolio managers. Is you are making money

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Speaker 2: on two higher percentage of your trades. This means that

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Speaker 2: you are not making all the trades that you could

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Speaker 2: and if you made, if you took your hit rate

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Speaker 2: from fifty five percent down to fifty three percent, we'd

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Speaker 2: be able to allocate more capital to you, even though

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Speaker 2: you'd be annoyed that you were losing money on more trades.

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Speaker 1: Interesting because overall you would likely have a more profitable

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Speaker 1: outcome by taking bigger risks and incurring a few more losses,

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Speaker 1: but winds would be bigger and make up for the losses.

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Speaker 2: Yes, and this kind of thing, you know, it's very

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Speaker 2: easy if you're the one sitting behind the desk just

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Speaker 2: talking about these relative trade offs. It's a lot harder

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Speaker 2: if you are the first person working with uranium in

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Speaker 2: the factory and we don't quite know what the risks

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Speaker 2: of that are, but it is. It is just a

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Speaker 2: it's a generally true thing about trade offs that if

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Speaker 2: you about trade offs and risk, that there is an

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Speaker 2: optimal amount of risk to take. That optimal amount is

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Speaker 2: sometimes dependent on what the what the downside risk of

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Speaker 2: inaction is. And so sometimes if you're if you're too successful,

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Speaker 2: you realize that you are actually messing something up.

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Speaker 1: Yeah, you're not taking enough risk. So we all know

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Speaker 1: how the Manhattan Project ends. It worked. I mean, it

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Speaker 1: is a little bit of a weird one to start with.

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Speaker 1: You know, the basic ideas like technological progress is good,

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Speaker 1: risks are good, and we're talking about building the atomic

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Speaker 1: bomb and dropping it on two cities and it's you know,

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Speaker 1: it's morally a much easier question if you think it's

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Speaker 1: the Nazis. Sorry, but the Nazis are absolutely the worst,

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Speaker 1: and I definitely don't want them to have a bomb first.

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Speaker 1: You know, there is the argument that more people would

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Speaker 1: have died in a conventional invasion without the bomb. I don't,

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Speaker 1: I don't know. I mean, how do you what do

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Speaker 1: you make of it? Like, obviously the book is very

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Speaker 1: pro technological progress, this show is basically pro technological progress.

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Speaker 1: But like the bomb isn't isn't a happy one to start?

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Speaker 1: I'm like, what do you make of it?

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Speaker 2: Ultimately, Yeah, it's one of those things where you do

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Speaker 2: it does make me wish that we could we could

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Speaker 2: run the same simulation, you know, a couple of million

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Speaker 2: times and see what the what the net? You know,

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Speaker 2: loss and save lives are different scenarios, but one thing

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Speaker 2: the bomb. If you I guess from like a purely

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Speaker 2: utilitarian standpoint, I suspect that there have been net lives

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Speaker 2: save because of less use of coal for electricity generation,

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Speaker 2: more use of nuclear power, And that is directly downstream

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Speaker 2: of the bomb. That you can build these, you know,

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Speaker 2: buy design uncontrollable releases of atomic energy. You can also

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Speaker 2: build more controllable ones, and then getting the funding for

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Speaker 2: that would be a lot harder.

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Speaker 1: And presumably we got nuclear power much sooner than we

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Speaker 1: otherwise would have because the incredibly rapid project progress of

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Speaker 1: the Manhattan Project. That's yes, there fair.

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Speaker 2: Which I don't I don't think you know you if

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Speaker 2: you let me push the button on what I drop

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Speaker 2: an atomic bomb on a civilian population in exchange for

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Speaker 2: fewer people dying of respiratory diseases over the next couple decades,

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Speaker 2: you know, there, I would have to give it a

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Speaker 2: lot of thought.

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Speaker 1: I'm not going to I'm not going to push that button,

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Speaker 1: but I'm never going to have a job where I

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Speaker 1: have to decide because I can't deal. Okay. Then you

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Speaker 1: mentioned in the book kind of in passing that was

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Speaker 1: really interesting and surprising to me was that nuclear power

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Speaker 1: today accounts for eighteen percent of electric power generation in

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Speaker 1: the US. Eighteen percent, Like that is so much higher

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Speaker 1: than I would have thought, given sort of how little

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Speaker 1: you hear about existing nuclear power plants, right.

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Speaker 2: Like, that is a lot. Yeah, yeah, it is. It

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Speaker 2: is a surprisingly it's a president hand number. But also

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Speaker 2: nuclear power it is it is one of the most

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Speaker 2: annoying technologies to talk about, in the sense that it

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Speaker 2: doesn't do anything really really exciting other than provide essentially

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Speaker 2: unlimited power with minimal risk.

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Speaker 1: And some amount of some amount of scary tail risk,

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Speaker 1: right ye, Like, I mean that is what is actually

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Speaker 1: interesting to talk about. Sort of unfortunately for the world,

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Speaker 1: given that it has a lot of benefits, there is

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Speaker 1: this tail risk, and once in a while something goes

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Speaker 1: horribly wrong, even though on the whole it seems to

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Speaker 1: be clearly less risky than say, a caul fired power.

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Speaker 2: Plant, right, And the industry has they they're aware of

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Speaker 2: those risks, and nobody wants to be responsible for that

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Speaker 2: kind of thing, and nobody wants to be testifying before

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Speaker 2: Congress about ever having cut any corner whatsoever in the

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Speaker 2: event that a disaster happens. So they do actually take

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Speaker 2: that incredibly seriously. So nuclear power does end up being

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Speaker 2: in practice much safer than other power sources. And then

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Speaker 2: you add in the externality of doesn't really produce emissions,

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Speaker 2: and uranium exists in some quantities just about everywhere.

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Speaker 1: No climate change, no local air pollution, has a lot

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Speaker 1: going for it. Always on. Okay, let's go to the moon.

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Speaker 1: So you write also about the Apollo missions us going

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Speaker 1: to the moon. It's the early sixties, right, was it

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Speaker 1: sixty one? Kennedy says we're gonna go to the moon

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Speaker 1: by the end of the decade. There's the Cold War context. Yes,

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Speaker 1: Kennedy announces this goal. What's the response in the US

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Speaker 1: when Kennedy says this, Yeah, so a lot of the response,

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Speaker 1: you know, at first people are somewhat hypothetically excited.

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Speaker 2: As they start realizing how much it will cost, they

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Speaker 2: go from not especially excited to actually pretty deeply opposed.

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Speaker 2: And you know this shows up in there was a

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Speaker 2: someone coined the term moondoggle.

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Speaker 1: Yeah, moondoggle. I loved moondoggle. I learned that from the book.

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Speaker 1: It was Norbert Wiener, like a famous technologist, not a

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Speaker 1: not a not a crank, right, somebody I knew what

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Speaker 1: he was talking about. Was like, this is a crazy idea.

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Speaker 2: It's a moondoggle, right, And you know, this really works

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Speaker 2: its way to popular culture. Like if you if you

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Speaker 2: go on Spotify and listen to the Tom learror song

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Speaker 2: Verner von Braun, the recording that Spotify has, it opens

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Speaker 2: with a monologue that is talking about how stupid the

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Speaker 2: idea of the Apollo program is. It's a it and

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Speaker 2: you know this is a This is again someone who

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Speaker 2: is in academia, who's a very very sharp guy and

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Speaker 2: who is just feels like he completely sees through this

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Speaker 2: political giveaway program to big defense contractors and knows that

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Speaker 2: there's no point in doing this.

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Speaker 1: You write that NASA's own analysis found a ninety percent

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Speaker 1: chance that that a failure of failing to reach the

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Speaker 1: moon by the end of the decade. Like, it wasn't

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Speaker 1: just outside people being critical, it was NASA itself didn't think.

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Speaker 2: It was good work.

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Speaker 1: There's a phrase you use in the book to talk

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Speaker 1: about talk about these sort of bubble like environments that

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Speaker 1: are of interest to you, and I found it really

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Speaker 1: interesting and I think we can talk about it in

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Speaker 1: the context of Apollo. That phrase is definite optimism. Tell

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Speaker 1: me about that phrase.

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Speaker 2: Yes, So definite optimism is the view that the future

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Speaker 2: can and will be better in some very specific way,

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Speaker 2: that there will be there is something we cannot do

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Speaker 2: now we will be able to do in the future,

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Speaker 2: and it will be good that we can do it.

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Speaker 1: And why is it important, Like it's a big deal

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Speaker 1: in your telling in an interesting way. Why is it

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Speaker 1: so important?

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Speaker 2: It's important because that is what allows you to actually

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Speaker 2: marshal those resources, whether those are the people or the

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Speaker 2: capital or the political pull, to put them all in

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Speaker 2: some specific direction and say we're going to build this thing.

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Speaker 2: So we need to actually go step by step and

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Speaker 2: figure out, Okay, what specific things have to be done,

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Speaker 2: what discoveries have have to be made, what laws have

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Speaker 2: to be passed in order for this to happen. And

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Speaker 2: that is so it's definitely optimism in the sense that

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Speaker 2: you're saying, there is a specific thing we're going to build.

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Speaker 2: It's the kind of thing that can keep you going

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Speaker 2: when you encounter temporary setbacks. And that's that's where the

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Speaker 2: optimism part comes in, because if you have a less

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Speaker 2: definitely optimistic view of about that project, you might say

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Speaker 2: the goal of the Apollo program is to figure out

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Speaker 2: if we can put a person on the moon. But

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Speaker 2: I think what that leaves you open to is the

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Speaker 2: temptation to give up at any point, because at any

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Speaker 2: point you can have you know, a botched launch or

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Speaker 2: an accident, or you are you're designing some component and

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Speaker 2: the math just doesn't pencil out. You need you know

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Speaker 2: it's going to weigh too much to actually make it

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Speaker 2: onto craft. And you could say, okay, well that's how

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Speaker 2: we figured out that we're not actually doing this. But

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Speaker 2: if you do just have this kind of delusional view

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Speaker 2: that know that if there's a mistake, it's a mistake

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Speaker 2: in my analysis, not in the ultimate plan here, and

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Speaker 2: that it is physically possible, we just have to figure

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Speaker 2: out the all the details. Then I think that does

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Speaker 2: set up a different kind of motivation, because at that

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Speaker 2: point you can view every mistake as just exhausting the

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Speaker 2: set of possibilities and letting you narrow things down to

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Speaker 2: what is the correct approach. What you sort of needed

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Speaker 2: was this sort of very localized definite optimism where someone

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Speaker 2: could you could imagine a researcher thinking to themselves, or

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Speaker 2: an engineer or someone throughout the project thinking to themselves that, Okay,

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Speaker 2: this will probably not work overall, but the specific thing

380
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Speaker 2: I'm working on, whether it is designing a spacesuit or

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Speaker 2: designing this rocket or programming the guidance computer, that one

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Speaker 2: I can tell that my part is actually going to work,

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Speaker 2: or at least I believe that I can make it work.

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Speaker 2: And two, this is my only chance to work with

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Speaker 2: these really cool toys. So if the money is going

386
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Speaker 2: to be wasted at some point, let that money be

387
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Speaker 2: wasted on me. And I think that that kind of

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Speaker 2: attitude of just you know that you have one shot

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Speaker 2: to actually do something really interesting, you will not get

390
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Speaker 2: a second chance. If everyone believes that, it does become

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Speaker 2: a coordinating mechanism where now they're working extremely hard, they

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Speaker 2: all recognize that the success of what they are doing

393
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Speaker 2: is very much up to them, and then that ends

394
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Speaker 2: up contributing to this group success.

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Speaker 1: So it's like this, if I'm going to do this,

396
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Speaker 1: I got to do it now. Everybody's doing it. Now,

397
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Speaker 1: we got the money. Now this is our one shot.

398
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Speaker 1: We better get it right, We better do everything we

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Speaker 1: can to make it work.

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Speaker 2: Yes, fear of missing out?

401
00:20:33,036 --> 00:20:35,676
Speaker 1: Yeah, fomo right, so fomo it's funny. People talk about

402
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Speaker 1: that as like a dumb investment thesis basically right, it's

403
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Speaker 1: like a meme stock idea, but you talk about it

404
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Speaker 1: in these more interesting context basically right, more meaningful. I

405
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Speaker 1: would say, yes, yeah.

406
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Speaker 2: That it's so in the purely straightforward way. The idea

407
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Speaker 2: is there are sometimes these very time limited opportunities to

408
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Speaker 2: do something, and if you're capable of doing that thing,

409
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Speaker 2: this may be your only chance, and so missing out

410
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Speaker 2: is actually something you should be afraid of. So you know,

411
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Speaker 2: if you actually have a really clever idea for an

412
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Speaker 2: AI company, this is actually a time where you can

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Speaker 2: at least the attempt. So yeah, we do argue that

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Speaker 2: missing out is something you should absolutely fear.

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Speaker 1: So what happens with the Apollo project, just even brief

416
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Speaker 1: like talk about just how big it is and how

417
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Speaker 1: risky it is, like it's it's striking right.

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Speaker 2: Right, Yeah, so it is. It was running. The expenses

419
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Speaker 2: were running on like a low single digital percentage of

420
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Speaker 2: GDP for a while.

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Speaker 1: So a couple percent of the value of everything everybody

422
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Speaker 1: in the country does is going into the Apollo mission.

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Speaker 1: Just this one plainly unnecessary thing that the government has

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Speaker 1: decided to do.

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Speaker 2: Right, And this is one of the cases where there

426
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Speaker 2: was a very powerful spillover effects because the Apollo Guidance

427
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Speaker 2: computer needed the most lightweight and least power consuming and

428
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Speaker 2: most reliable components possible. And if you were building a

429
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Speaker 2: computer conventionally at that time and you had a budget,

430
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Speaker 2: you would probably build it out of vacum tubes. And

431
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Speaker 2: you knew that the vacuum tubes they're bulky, they consume

432
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Speaker 2: a lot of power, they throw off a lot of heat,

433
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Speaker 2: they burn out all the time, but they are fairly cheap.

434
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Speaker 2: But in this case, there there was an alternative technology.

435
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Speaker 2: It was extremely expensive, but it was it was lightweight,

436
00:22:20,436 --> 00:22:22,116
Speaker 2: didn't use a lot of power, and did not have

437
00:22:22,156 --> 00:22:26,676
Speaker 2: moving parts. And that's the integrated circuit, so transistor based computing.

438
00:22:26,356 --> 00:22:28,716
Speaker 1: The chip, well we know today as the chip.

439
00:22:29,276 --> 00:22:30,036
Speaker 2: Yes, the chip.

440
00:22:30,116 --> 00:22:34,036
Speaker 1: You read that. In nineteen sixty three, NASA bought sixty

441
00:22:34,156 --> 00:22:37,956
Speaker 1: percent of the chips made in the United States, just NASA,

442
00:22:38,076 --> 00:22:40,916
Speaker 1: not though government, just NASA sixty percent.

443
00:22:41,476 --> 00:22:44,116
Speaker 2: They actually bought more chips than they needed because they

444
00:22:44,196 --> 00:22:47,916
Speaker 2: recognized that the chip companies were run by, you know,

445
00:22:48,196 --> 00:22:51,956
Speaker 2: very very nice electrical engineering nerds who just love designing tiny,

446
00:22:51,996 --> 00:22:54,556
Speaker 2: tiny things, and that these people just don't know how

447
00:22:54,556 --> 00:22:56,996
Speaker 2: to run a business, and so they were worried that

448
00:22:57,356 --> 00:23:00,276
Speaker 2: fair Child Semiconductor would just run out of cash at

449
00:23:00,276 --> 00:23:02,996
Speaker 2: some point and then NASA would have half of a

450
00:23:03,036 --> 00:23:05,276
Speaker 2: computer and no way to build the rest of it.

451
00:23:05,356 --> 00:23:08,156
Speaker 2: So they actually over ordered the US. They used integrated

452
00:23:08,196 --> 00:23:10,796
Speaker 2: circuits for a few applications that actually were not so

453
00:23:10,956 --> 00:23:13,636
Speaker 2: dependent on the power consumption and weight and things. So

454
00:23:13,676 --> 00:23:17,196
Speaker 2: that critique of the Apollo program was directionally correct. It

455
00:23:17,316 --> 00:23:19,916
Speaker 2: was money being splashed out to defense contractors who were

456
00:23:19,916 --> 00:23:21,436
Speaker 2: favored by the government, but in this case it was

457
00:23:21,476 --> 00:23:25,676
Speaker 2: being done in a more strategic and thoughtful way and

458
00:23:25,716 --> 00:23:26,796
Speaker 2: kind of kept the industry going.

459
00:23:26,916 --> 00:23:31,156
Speaker 1: So you talk a fair bit in the book about

460
00:23:31,236 --> 00:23:35,636
Speaker 1: the sort of religious and quasi religious aspects of these

461
00:23:35,956 --> 00:23:38,516
Speaker 1: little groups of people that come together in these bubble

462
00:23:38,596 --> 00:23:42,396
Speaker 1: like moments to do these big things, and that's really

463
00:23:42,436 --> 00:23:46,196
Speaker 1: present in the Apollo section, like talk about the sort

464
00:23:46,236 --> 00:23:51,156
Speaker 1: of religious ideas associated with the Apollo mission that the

465
00:23:51,156 --> 00:23:52,676
Speaker 1: people working on the mission had.

466
00:23:52,956 --> 00:23:55,836
Speaker 2: Yeah, I mean, you name it after a Greek god

467
00:23:56,116 --> 00:24:00,076
Speaker 2: and you're already starting starting a little bit religious. So

468
00:24:00,916 --> 00:24:03,716
Speaker 2: there were people who worked on these missions who felt like,

469
00:24:03,756 --> 00:24:07,396
Speaker 2: this is part of mankind's destiny is to explore the stars,

470
00:24:07,436 --> 00:24:09,836
Speaker 2: and that there's this whole universe that is universe created

471
00:24:09,876 --> 00:24:11,956
Speaker 2: by God and it would be kind of weird. You know,

472
00:24:11,996 --> 00:24:14,156
Speaker 2: we can't second guess the divine, but it's a little

473
00:24:14,156 --> 00:24:16,716
Speaker 2: weird for God to create all of these astronomical bodies

474
00:24:16,756 --> 00:24:18,996
Speaker 2: that just kind of look good from the ground and

475
00:24:18,996 --> 00:24:20,436
Speaker 2: that you're not actually meant to go visit.

476
00:24:20,876 --> 00:24:24,156
Speaker 1: You talk about somewhat similar things in other kind of

477
00:24:24,276 --> 00:24:29,796
Speaker 1: less obviously spiritual dimensions of people coming together and having

478
00:24:29,796 --> 00:24:34,036
Speaker 1: it kind of more than rational. You use this word

479
00:24:34,316 --> 00:24:37,956
Speaker 1: thimos from the Greek meaning spirit, like what's going on there?

480
00:24:37,956 --> 00:24:42,276
Speaker 1: More broadly, why is that important more generally for technological progress,

481
00:24:43,316 --> 00:24:47,396
Speaker 1: because the so th amos is part of this tripartite

482
00:24:47,436 --> 00:24:50,956
Speaker 1: model of the soul where you have your appetites.

483
00:24:50,396 --> 00:24:53,836
Speaker 2: And your reason and then your themost like you're longing

484
00:24:53,916 --> 00:24:58,796
Speaker 2: for glory and honor and this kind of transcendent transcendent

485
00:24:58,876 --> 00:25:03,316
Speaker 2: achievement and logos reasoning it only gets you so far.

486
00:25:03,476 --> 00:25:05,636
Speaker 2: You can you can reason your way into some pretty

487
00:25:05,676 --> 00:25:08,156
Speaker 2: interesting things, but at some point you do decide that

488
00:25:08,236 --> 00:25:12,436
Speaker 2: the reason thing is probably to take it a little

489
00:25:12,476 --> 00:25:15,276
Speaker 2: bit easier and not take not take certain risks. And

490
00:25:15,836 --> 00:25:19,356
Speaker 2: it still is just this this pursuit of something, something greater,

491
00:25:19,676 --> 00:25:22,716
Speaker 2: and you know, something something beyond the ordinary, something really

492
00:25:22,716 --> 00:25:25,396
Speaker 2: beyond the logos, right, like beyond what you could get

493
00:25:25,436 --> 00:25:30,276
Speaker 2: to just by reasoning one step at a time. And

494
00:25:30,316 --> 00:25:32,196
Speaker 2: I think that that is, Yeah, that is that is

495
00:25:32,356 --> 00:25:36,276
Speaker 2: just a deeply attractive proposition to to many people. And

496
00:25:36,436 --> 00:25:38,636
Speaker 2: it's also a scary one because at that point, you know,

497
00:25:38,636 --> 00:25:40,716
Speaker 2: if you are if you're doing things that are beyond

498
00:25:40,956 --> 00:25:43,436
Speaker 2: what is the rational thing to do, then of course

499
00:25:43,436 --> 00:25:45,876
Speaker 2: you have no rational explanation for what you did wrong.

500
00:25:46,036 --> 00:25:48,876
Speaker 2: If you mess up. Yeah, and you are sort of

501
00:25:48,876 --> 00:25:50,556
Speaker 2: betting on some historical contingencies.

502
00:25:50,716 --> 00:25:54,396
Speaker 1: That's the definite optimism part, right, Betting on historical contingencies

503
00:25:54,516 --> 00:26:00,716
Speaker 1: is another way of saying definite optimism. Right. So back

504
00:26:00,756 --> 00:26:03,356
Speaker 1: to the moon. So we get to the moon. In fact,

505
00:26:03,436 --> 00:26:08,476
Speaker 1: against all odds, we make it. Uh, And there's this

506
00:26:08,556 --> 00:26:12,476
Speaker 1: moment where it's like, you know, today the moon tomorrow

507
00:26:12,716 --> 00:26:15,956
Speaker 1: the solar system, but in fact it was today the

508
00:26:15,996 --> 00:26:18,116
Speaker 1: moon tomorrow, not even the.

509
00:26:18,036 --> 00:26:22,316
Speaker 2: Moon, right, Like what happened? Well, you know, you had

510
00:26:22,356 --> 00:26:25,596
Speaker 2: asked about what these megaprojects have in common with financial bubbles,

511
00:26:25,596 --> 00:26:26,876
Speaker 2: and one of the things they have in common is

512
00:26:26,956 --> 00:26:29,876
Speaker 2: sometimes there's a bust, and sometimes that bust is actually

513
00:26:29,876 --> 00:26:34,436
Speaker 2: an overreaction in the opposite direction. And people people take

514
00:26:34,516 --> 00:26:38,156
Speaker 2: everything every true, you know, everything they believed in say

515
00:26:38,356 --> 00:26:41,836
Speaker 2: nineteen you know, nineteen sixty nine about humanity's future in

516
00:26:41,876 --> 00:26:43,276
Speaker 2: the stars, and they say, okay, this is this is

517
00:26:43,276 --> 00:26:46,196
Speaker 2: exactly the opposite of where things will actually go in

518
00:26:46,236 --> 00:26:48,076
Speaker 2: the exact opposite of what we should care about. That

519
00:26:48,236 --> 00:26:50,236
Speaker 2: we have plenty of problems here on Earth, and why

520
00:26:50,236 --> 00:26:52,316
Speaker 2: would we you know, do we really want to turn

521
00:26:52,396 --> 00:26:56,156
Speaker 2: Mars into just another planet? That also has problems of

522
00:26:56,236 --> 00:26:58,956
Speaker 2: racism and poverty and nuclear war and all that stuff.

523
00:26:58,996 --> 00:27:00,916
Speaker 2: So so maybe we should stay home and fix fix

524
00:27:00,996 --> 00:27:03,796
Speaker 2: our stuff. Then public policy, you'd actually need for there

525
00:27:03,876 --> 00:27:07,156
Speaker 2: to be some kind of resurgence in belief in space.

526
00:27:07,196 --> 00:27:09,996
Speaker 2: You need some kind of charismatic story, and perhaps to

527
00:27:09,996 --> 00:27:13,036
Speaker 2: an extent, we have that right now. Yes, maybe Elan's

528
00:27:13,076 --> 00:27:14,756
Speaker 2: not the perfect front man for all of this, but

529
00:27:14,956 --> 00:27:17,836
Speaker 2: he is certainly someone who demonstrates that space travel it

530
00:27:17,876 --> 00:27:21,676
Speaker 2: can be done, it can be improved, and that it's

531
00:27:21,716 --> 00:27:24,356
Speaker 2: just objectively cool. That it is just hard to watch

532
00:27:24,396 --> 00:27:26,916
Speaker 2: a SpaceX launch video and not feel something.

533
00:27:28,276 --> 00:27:31,636
Speaker 1: Yes, so so good. I want to talk more about

534
00:27:31,676 --> 00:27:37,196
Speaker 1: space in a minute. So it's interesting these two stories

535
00:27:37,236 --> 00:27:39,036
Speaker 1: that are kind of in the middle of your book.

536
00:27:39,076 --> 00:27:40,756
Speaker 1: They're kind of the core of the book, right, these

537
00:27:40,756 --> 00:27:44,156
Speaker 1: two interesting moments that are non financial bubbles, when you

538
00:27:44,156 --> 00:27:47,876
Speaker 1: have this incredible technological innovation and a short amount of

539
00:27:47,916 --> 00:27:52,796
Speaker 1: time that seems unrealistic, unrealistically you know, fast impressive outcome.

540
00:27:53,636 --> 00:27:58,716
Speaker 1: And they're both pure government projects. They're both you know,

541
00:27:58,836 --> 00:28:02,596
Speaker 1: command and control economy. It is not the private sector,

542
00:28:02,596 --> 00:28:05,596
Speaker 1: it does not capitalism. What do you make of that?

543
00:28:06,396 --> 00:28:09,316
Speaker 2: I would say, there's a very strong indirect link for

544
00:28:10,196 --> 00:28:15,316
Speaker 2: a couple of reasons. One is just the practical, kind

545
00:28:15,316 --> 00:28:18,236
Speaker 2: of the practically enough reason that personnelis policy, and that

546
00:28:19,236 --> 00:28:23,196
Speaker 2: the US government in the nineteen thirties, the US government

547
00:28:23,276 --> 00:28:25,916
Speaker 2: was hiring and the private sector mostly wasn't, and so

548
00:28:26,276 --> 00:28:28,676
Speaker 2: all the ambitious people, basically all the ambitious people in

549
00:28:28,716 --> 00:28:30,996
Speaker 2: the country tried to get government jobs. And that is

550
00:28:31,116 --> 00:28:34,076
Speaker 2: usually not the case, and there are there's certainly circumstances

551
00:28:34,076 --> 00:28:36,596
Speaker 2: where that's a really bad sign, but in this case

552
00:28:36,636 --> 00:28:38,436
Speaker 2: it was great. It meant that there were a lot

553
00:28:38,476 --> 00:28:40,916
Speaker 2: of New Deal projects that were staffed by the people

554
00:28:40,916 --> 00:28:43,596
Speaker 2: who would have been rising up the ranks at RCA

555
00:28:43,716 --> 00:28:46,196
Speaker 2: or General Electric or something a decade earlier. Now they're

556
00:28:46,276 --> 00:28:49,036
Speaker 2: running New Deal projects instead, and they're again rising up

557
00:28:49,036 --> 00:28:50,756
Speaker 2: their ranks really fast, having a very large real world

558
00:28:50,796 --> 00:28:53,796
Speaker 2: impact for early in their careers. And those people had

559
00:28:53,836 --> 00:28:56,796
Speaker 2: been working together for a while, and you know, they

560
00:28:56,876 --> 00:28:58,716
Speaker 2: knew each other. There was a lot of just institutional

561
00:28:58,756 --> 00:29:01,396
Speaker 2: knowledge about how to get big things done within the

562
00:29:01,476 --> 00:29:04,356
Speaker 2: US government, and a lot of that institutional knowledge could

563
00:29:04,356 --> 00:29:07,316
Speaker 2: then be redirected. So you have the New Deal and

564
00:29:07,316 --> 00:29:09,396
Speaker 2: then the war effort, and then you have the post

565
00:29:09,436 --> 00:29:11,036
Speaker 2: war economy where there's still you know, it takes a

566
00:29:11,036 --> 00:29:13,236
Speaker 2: while for the government to fully relax its control, and

567
00:29:13,276 --> 00:29:17,116
Speaker 2: then very soon we're into the Korean War. So yeah,

568
00:29:17,156 --> 00:29:20,156
Speaker 2: there was just a large increase in state capacity and

569
00:29:20,196 --> 00:29:22,436
Speaker 2: just in the quality of people making decisions within the

570
00:29:22,516 --> 00:29:24,276
Speaker 2: US government in that period.

571
00:29:28,556 --> 00:29:30,796
Speaker 1: We'll be back in a minute to talk about bubble

572
00:29:30,956 --> 00:29:43,716
Speaker 1: esque things happening right now, namely rockets, cryptocurrency, and AI. Okay,

573
00:29:44,396 --> 00:29:48,676
Speaker 1: now to space today. Burn and I talked about SpaceX

574
00:29:48,716 --> 00:29:51,996
Speaker 1: in particular, because you know, it really is the company

575
00:29:51,996 --> 00:29:55,076
Speaker 1: that launched the modern space industry, and there's this one

576
00:29:55,156 --> 00:29:58,516
Speaker 1: key trait that SpaceX shares with the other projects Burn

577
00:29:58,596 --> 00:30:01,796
Speaker 1: wrote about in the book. It brought together people who

578
00:30:01,836 --> 00:30:05,396
Speaker 1: share a wild dream. If you go to work at SpaceX,

579
00:30:05,716 --> 00:30:10,476
Speaker 1: it's probably because you believe in getting humanity to mar Yeah.

580
00:30:10,556 --> 00:30:12,356
Speaker 2: Yeah, it's not just that you believe in the dream.

581
00:30:12,396 --> 00:30:14,996
Speaker 2: But when you get the job, you're suddenly in an

582
00:30:15,036 --> 00:30:17,636
Speaker 2: environment where everyone believes in the dream. And if you're

583
00:30:17,636 --> 00:30:20,116
Speaker 2: working one of those organizations, you're probably not working nine

584
00:30:20,156 --> 00:30:22,876
Speaker 2: to five, which means you have very few hours in

585
00:30:22,916 --> 00:30:25,636
Speaker 2: your day or week where you are not completely surrounded

586
00:30:25,636 --> 00:30:29,636
Speaker 2: by people who believe that humanity will people will be

587
00:30:29,876 --> 00:30:32,436
Speaker 2: living on Mars, and that this is the organization that

588
00:30:32,476 --> 00:30:34,956
Speaker 2: will make it happen. And that just has to really

589
00:30:34,996 --> 00:30:37,916
Speaker 2: mess with your mind, Like, is what is normal to

590
00:30:38,356 --> 00:30:40,596
Speaker 2: an engineer working at SpaceX in two thousand and six

591
00:30:40,676 --> 00:30:43,996
Speaker 2: is completely abnormal to ninety nine point nine percent of

592
00:30:44,036 --> 00:30:46,476
Speaker 2: the human population. And you know most of the exceptions

593
00:30:46,476 --> 00:30:48,276
Speaker 2: are like six year old boys who just want Star

594
00:30:48,316 --> 00:30:48,996
Speaker 2: Wars for the first time.

595
00:30:49,316 --> 00:30:52,036
Speaker 1: Mars's crazy, Yeah, I mean really, as I went through

596
00:30:52,036 --> 00:30:53,996
Speaker 1: the book, I was like, oh, really, the bubble you're

597
00:30:53,996 --> 00:30:57,396
Speaker 1: talking about is a social bubble, like the meaningful bubble.

598
00:30:57,436 --> 00:30:59,716
Speaker 1: Like maybe there's a financial bubble attachment, maybe there isn't,

599
00:30:59,716 --> 00:31:02,756
Speaker 1: But what really matters is you're in this weird little

600
00:31:02,756 --> 00:31:07,836
Speaker 1: social bubble that believes some wild thing together, that believes

601
00:31:07,836 --> 00:31:09,876
Speaker 1: it is not wild, that believes it is going to happen,

602
00:31:09,956 --> 00:31:13,996
Speaker 1: like that's the thing. Yeah, and has money and has

603
00:31:14,076 --> 00:31:18,356
Speaker 1: the money to act on their wild belief yes, And

604
00:31:18,396 --> 00:31:20,916
Speaker 1: so you know, getting the money does mean interacting with

605
00:31:21,116 --> 00:31:23,756
Speaker 1: the normy sphere, interacting with people who don't.

606
00:31:23,596 --> 00:31:26,756
Speaker 2: Quite buy into all of it, but they when you

607
00:31:26,796 --> 00:31:30,716
Speaker 2: have these really ambitious plans and you're taking them seriously,

608
00:31:30,876 --> 00:31:33,116
Speaker 2: you're doing them step by step. Some of those steps

609
00:31:33,156 --> 00:31:36,236
Speaker 2: do have other practical applications, and so that is the

610
00:31:36,236 --> 00:31:38,676
Speaker 2: space story. It was not just street shot. We are

611
00:31:38,676 --> 00:31:41,076
Speaker 2: going to invest all the money Elon got from PayPal

612
00:31:41,116 --> 00:31:42,996
Speaker 2: into going to Mars and hopefully we get to Mark

613
00:31:43,036 --> 00:31:45,796
Speaker 2: before we run out. Yeah, it was you know, we're

614
00:31:46,036 --> 00:31:49,036
Speaker 2: going to build these prototypes, We're going to build reusable rockets.

615
00:31:49,036 --> 00:31:52,516
Speaker 2: We're going to use those for existing use cases, and

616
00:31:52,556 --> 00:31:54,996
Speaker 2: we will probably find new use cases. And then once

617
00:31:55,036 --> 00:31:57,356
Speaker 2: we get really really good at launching things cheaply, well,

618
00:31:57,476 --> 00:31:59,796
Speaker 2: there are a lot of satellites out there, and perhaps

619
00:31:59,836 --> 00:32:01,796
Speaker 2: we should have some of our own, and we can

620
00:32:01,796 --> 00:32:03,756
Speaker 2: do it at sufficient scale, then maybe we can just

621
00:32:03,956 --> 00:32:06,436
Speaker 2: throw a global communications network up there in the sky

622
00:32:06,556 --> 00:32:09,596
Speaker 2: and see what happens next. So yeah, that's you know,

623
00:32:09,636 --> 00:32:13,796
Speaker 2: the intermediate steps. Each one it's basically taking the themost

624
00:32:13,836 --> 00:32:16,436
Speaker 2: like the spirited. You know, here's our grand vision of

625
00:32:16,516 --> 00:32:19,076
Speaker 2: the future, and you know, here's my destiny and I

626
00:32:19,116 --> 00:32:20,836
Speaker 2: was put on earth to do this and saying okay, well,

627
00:32:21,156 --> 00:32:23,236
Speaker 2: the next step is have enough money to pay rent

628
00:32:23,236 --> 00:32:23,916
Speaker 2: next month, right.

629
00:32:24,796 --> 00:32:29,476
Speaker 1: Tomorrow to get to Mars. So is there a space

630
00:32:29,996 --> 00:32:33,796
Speaker 1: bubble right now. I think so.

631
00:32:34,116 --> 00:32:36,876
Speaker 2: I think there is I think there are people who

632
00:32:37,236 --> 00:32:40,956
Speaker 2: look at SpaceX and say this is achievable, and that

633
00:32:41,036 --> 00:32:43,196
Speaker 2: more is achievable. They also look at SpaceX and say

634
00:32:43,396 --> 00:32:46,716
Speaker 2: this is a kind of infrastructure, that there are things

635
00:32:46,796 --> 00:32:50,636
Speaker 2: like doing manufacturing in orbit or doing manufacturing on the Moon,

636
00:32:50,796 --> 00:32:53,636
Speaker 2: where in some cases that is actually the best place

637
00:32:53,636 --> 00:32:54,596
Speaker 2: to build something.

638
00:32:54,916 --> 00:32:58,236
Speaker 1: Basically because SpaceX has driven down the cost so much

639
00:32:58,276 --> 00:33:02,116
Speaker 1: of getting stuff into orbit, new ideas that would have

640
00:33:02,116 --> 00:33:06,596
Speaker 1: been economically absurd twenty years ago, like manufacturing and space

641
00:33:06,636 --> 00:33:08,516
Speaker 1: are now plausible. And so this is the sort of

642
00:33:08,516 --> 00:33:11,236
Speaker 1: bubble building on itself, and like, why is it not

643
00:33:11,356 --> 00:33:13,156
Speaker 1: just an industry now? Why is it a bubble?

644
00:33:13,196 --> 00:33:17,836
Speaker 2: In your telling, it is the feedback loop where what

645
00:33:17,916 --> 00:33:20,756
Speaker 2: SpaceX does makes more sense if they believe that there

646
00:33:20,756 --> 00:33:24,076
Speaker 2: will be a lot of demand to move physical things

647
00:33:24,156 --> 00:33:27,516
Speaker 2: off of Earth and into orbit, and perhaps further out

648
00:33:27,836 --> 00:33:30,356
Speaker 2: that if they believe that there's more demand for that,

649
00:33:30,396 --> 00:33:32,556
Speaker 2: they should be investing more on R and D. They

650
00:33:32,556 --> 00:33:35,076
Speaker 2: should be building better and bigger and better rockets, and

651
00:33:35,276 --> 00:33:38,436
Speaker 2: they should be doing the you know, big fixed cost

652
00:33:38,436 --> 00:33:41,356
Speaker 2: investment that incrementally reduces the cost of launches and only

653
00:33:41,396 --> 00:33:42,956
Speaker 2: pays for itself, you do a lot of them. And

654
00:33:42,996 --> 00:33:46,156
Speaker 2: then if they're doing that and you have your dream

655
00:33:46,396 --> 00:33:49,276
Speaker 2: of we're going to manufacture drugs in space and they

656
00:33:49,276 --> 00:33:51,756
Speaker 2: will be you know, like the marginal cost is low

657
00:33:51,836 --> 00:33:53,916
Speaker 2: once you get stuff up there, well that dream is

658
00:33:53,916 --> 00:33:56,276
Speaker 2: a little bit more plausible if you can actually plot

659
00:33:56,356 --> 00:33:57,996
Speaker 2: that curve of how much does it cost to get

660
00:33:57,996 --> 00:34:00,836
Speaker 2: a kilogram into space and say this, you know, there

661
00:34:00,956 --> 00:34:03,876
Speaker 2: is a specific ear at which point we would actually

662
00:34:03,916 --> 00:34:06,716
Speaker 2: have the cost advantage versus terrestrial manufacturing.

663
00:34:06,756 --> 00:34:09,556
Speaker 1: So it's this sort of coordinating mechan and that like

664
00:34:09,596 --> 00:34:12,316
Speaker 1: you also write about with Microsoft and Intel in the

665
00:34:12,676 --> 00:34:15,156
Speaker 1: eighties and nineties, where it's like, oh, they're building better chips,

666
00:34:15,196 --> 00:34:18,196
Speaker 1: so we will build better software, and then because they're

667
00:34:18,196 --> 00:34:21,516
Speaker 1: building better software, will build better chips. So this is

668
00:34:21,556 --> 00:34:24,796
Speaker 1: like a more exciting version of that, right because it's

669
00:34:24,836 --> 00:34:28,036
Speaker 1: going to get even cheaper to send stuff to space.

670
00:34:28,076 --> 00:34:31,596
Speaker 1: We can build this crazy factory to exist in space,

671
00:34:31,636 --> 00:34:33,996
Speaker 1: and then that tells SpaceX, oh, we can in fact

672
00:34:34,956 --> 00:34:37,316
Speaker 1: keep building, keep innovating, keep spending money.

673
00:34:37,916 --> 00:34:41,436
Speaker 2: Yes, And so someone has to do just half of that,

674
00:34:41,516 --> 00:34:43,796
Speaker 2: like the half of that that makes no sense whatsoever.

675
00:34:43,916 --> 00:34:46,716
Speaker 1: That was SpaceX at the beginning, right, that was like, yes,

676
00:34:46,876 --> 00:34:50,116
Speaker 1: just a guy with a lot of money in a crazy.

677
00:34:49,636 --> 00:34:51,956
Speaker 2: Yeah, it just really helps to have someone who's eccentric

678
00:34:52,036 --> 00:34:54,156
Speaker 2: and it has a lot of money and is willing

679
00:34:54,156 --> 00:34:55,676
Speaker 2: to throw it at a lot of different things. Like Musk.

680
00:34:56,476 --> 00:34:59,916
Speaker 2: He spent some substantial fraction of his network right after

681
00:34:59,956 --> 00:35:03,236
Speaker 2: this PayPal sale on a really nice sports car and

682
00:35:03,276 --> 00:35:05,996
Speaker 2: then immediately took it for a drive and wrecked. It

683
00:35:06,196 --> 00:35:08,396
Speaker 2: had no insurance and was not wearing a seat belt.

684
00:35:08,436 --> 00:35:10,756
Speaker 2: So the Elon mus story could have just been this

685
00:35:10,756 --> 00:35:14,916
Speaker 2: this proverb about dot com access and what happened when

686
00:35:14,956 --> 00:35:17,596
Speaker 2: you finally gave these people money as they immediately bought

687
00:35:17,636 --> 00:35:20,916
Speaker 2: sportscrossing Rerexiam. Instead, it's it's a story about a different

688
00:35:20,956 --> 00:35:23,756
Speaker 2: kind of success, but it's still I guess know what

689
00:35:23,836 --> 00:35:24,916
Speaker 2: that illustrates really risky.

690
00:35:25,476 --> 00:35:25,956
Speaker 1: Yeah.

691
00:35:26,036 --> 00:35:30,836
Speaker 2: Yeah, there's there's a risk level where you you are

692
00:35:31,796 --> 00:35:33,476
Speaker 2: going on going for a joy ride in your two

693
00:35:33,476 --> 00:35:35,516
Speaker 2: million dollar car and you haven't bothered to fill out

694
00:35:35,516 --> 00:35:37,876
Speaker 2: all the paperwork or by the insurance, and that is

695
00:35:37,916 --> 00:35:40,756
Speaker 2: the risk tolerance of someone who starts a company like SpaceX.

696
00:35:41,396 --> 00:35:46,716
Speaker 1: Okay, enough about space let's talk about Crypto, formerly known

697
00:35:46,756 --> 00:35:51,476
Speaker 1: as cryptocurrency. Let's talk about bitcoin, and let's talk about

698
00:35:51,516 --> 00:35:55,276
Speaker 1: bitcoin especially at the beginning, right before it was number

699
00:35:55,356 --> 00:35:58,996
Speaker 1: go up, when it was it really was true believers, right,

700
00:35:59,116 --> 00:36:01,796
Speaker 1: it was people who had a crazy worldview like you're

701
00:36:01,836 --> 00:36:03,956
Speaker 1: talking about in these in these other contexts.

702
00:36:04,996 --> 00:36:08,676
Speaker 2: Yes, so we we still don't know for sure who

703
00:36:08,756 --> 00:36:10,956
Speaker 2: said to A Nakamoto was, and I think everyone in

704
00:36:10,996 --> 00:36:14,116
Speaker 2: crypto has at least one guest, sometimes many guesses. But

705
00:36:14,396 --> 00:36:16,796
Speaker 2: whoever Satoshi was, whoever they were.

706
00:36:16,796 --> 00:36:18,956
Speaker 1: This is the creator of bitcoin for the one person

707
00:36:18,996 --> 00:36:19,476
Speaker 1: who doesn't know.

708
00:36:19,596 --> 00:36:24,516
Speaker 2: Yeah, they had this view that one of the fundamental

709
00:36:24,596 --> 00:36:28,236
Speaker 2: problems in the world today is that if you are

710
00:36:28,316 --> 00:36:30,956
Speaker 2: going to transfer value from one party to another, you

711
00:36:31,036 --> 00:36:32,716
Speaker 2: need some trusted intermediary.

712
00:36:32,996 --> 00:36:37,316
Speaker 1: You need a trusted intermediary like a government and a bank. Right.

713
00:36:37,396 --> 00:36:40,076
Speaker 1: Typically in money you need both governments and banks the

714
00:36:40,156 --> 00:36:42,596
Speaker 1: way it works in the world today, right, Yes, And

715
00:36:42,716 --> 00:36:45,396
Speaker 1: Satoshi happened to publish the Bitcoin White Paper in October

716
00:36:45,436 --> 00:36:47,916
Speaker 1: two thousand and eight, which was a great moment to

717
00:36:47,956 --> 00:36:50,716
Speaker 1: find people who really didn't want to have to deal

718
00:36:50,796 --> 00:36:53,276
Speaker 1: with governments and banks when they were dealing with money

719
00:36:53,676 --> 00:36:55,716
Speaker 1: at the financial crisis, right right in the teeth of

720
00:36:55,756 --> 00:36:56,596
Speaker 1: the financial crisis.

721
00:36:57,316 --> 00:36:59,716
Speaker 2: Yes, so hes So it is in one sense just

722
00:36:59,796 --> 00:37:03,356
Speaker 2: this this technically clever thing, and then in another sense

723
00:37:03,396 --> 00:37:06,676
Speaker 2: it's this very ideological project where he doesn't like central banks,

724
00:37:06,876 --> 00:37:09,596
Speaker 2: he doesn't like regular banks. He feels like all of

725
00:37:09,636 --> 00:37:12,476
Speaker 2: these institutions are corrupt, and you know, your money is

726
00:37:12,596 --> 00:37:15,476
Speaker 2: just an entry in somebody's database, and they can update

727
00:37:15,516 --> 00:37:18,356
Speaker 2: that database tomorrow and either change how much you have

728
00:37:18,636 --> 00:37:20,356
Speaker 2: or change what it's worth, and we need to just

729
00:37:20,756 --> 00:37:23,476
Speaker 2: build something new from a clean slate. And there's also

730
00:37:23,676 --> 00:37:25,476
Speaker 2: I think there's this tendency among a lot of tech

731
00:37:25,556 --> 00:37:29,676
Speaker 2: people too. When you look at any kind of communications

732
00:37:29,716 --> 00:37:32,436
Speaker 2: technology and money broadly defined as a communication technology, you're

733
00:37:32,436 --> 00:37:34,756
Speaker 2: always looking at something that has evolved from something simple,

734
00:37:34,996 --> 00:37:37,796
Speaker 2: and it has just been patched and altered and edited

735
00:37:37,956 --> 00:37:40,636
Speaker 2: and tweaked and so on until it works the way

736
00:37:40,796 --> 00:37:43,476
Speaker 2: that it works. But that always means that you can

737
00:37:43,596 --> 00:37:46,116
Speaker 2: easily come up with some first principles view that's a

738
00:37:46,156 --> 00:37:49,636
Speaker 2: whole lot cleaner, easier to reason about amidst some mistakes,

739
00:37:49,676 --> 00:37:52,036
Speaker 2: and then you often find that, Okay, you emitted all

740
00:37:52,076 --> 00:37:53,916
Speaker 2: the mistakes that are really really salient about FIAT, but

741
00:37:53,996 --> 00:37:56,716
Speaker 2: then you added some brand new mistakes or added mistakes

742
00:37:56,716 --> 00:37:59,036
Speaker 2: that we haven't made in hundreds of years. So there

743
00:37:59,716 --> 00:38:00,436
Speaker 2: it's full of trade off.

744
00:38:00,436 --> 00:38:02,676
Speaker 1: It gets complicated, but at the beginning, right, so the

745
00:38:02,756 --> 00:38:06,796
Speaker 1: white paper comes out and you know, I covered I

746
00:38:06,876 --> 00:38:08,876
Speaker 1: did a story about Quinn in twenty eleven, which was

747
00:38:08,876 --> 00:38:12,796
Speaker 1: still quite early. You know, we'd shocked that it had

748
00:38:12,836 --> 00:38:15,476
Speaker 1: gone from ten dollars a bitcoin to twenty dollars a bitcoin.

749
00:38:15,516 --> 00:38:17,756
Speaker 1: Thought we were reading it wrong. And at that time,

750
00:38:17,836 --> 00:38:20,196
Speaker 1: like I took to Gavin Drieson, who was very early

751
00:38:21,156 --> 00:38:23,596
Speaker 1: in the bitcoin universe, like he was not in it

752
00:38:23,716 --> 00:38:26,356
Speaker 1: to get rich, right, Like he really believed, he really

753
00:38:26,396 --> 00:38:29,756
Speaker 1: believed in it, and that was the vibe then, and

754
00:38:29,956 --> 00:38:32,996
Speaker 1: like he thought it was gonna be money, right. The

755
00:38:33,116 --> 00:38:36,556
Speaker 1: dream was people will use this to buy stuff. And

756
00:38:37,396 --> 00:38:40,276
Speaker 1: one thing that is interesting to me is, yeah, some

757
00:38:40,436 --> 00:38:43,996
Speaker 1: people sort of use it to buy stuff, but basically

758
00:38:44,476 --> 00:38:48,356
Speaker 1: not right like that it would go from twenty dollars

759
00:38:48,396 --> 00:38:50,916
Speaker 1: a bitcoin to one hundred thousand dollars a bitcoin without

760
00:38:51,036 --> 00:38:54,396
Speaker 1: some crazy killer app, without becoming the web, without becoming

761
00:38:54,436 --> 00:38:57,556
Speaker 1: something that everybody uses whether they care about it or not.

762
00:38:57,996 --> 00:39:00,436
Speaker 1: That I would not have guessed. And it seems weird

763
00:39:01,076 --> 00:39:03,356
Speaker 1: and plainly now crypto is full of some people who

764
00:39:03,396 --> 00:39:04,796
Speaker 1: are true believers and a lot of people who just

765
00:39:04,996 --> 00:39:07,676
Speaker 1: want to get rich, and some of them are pretty scammy.

766
00:39:08,356 --> 00:39:11,396
Speaker 2: Yeah. Yeah, there's like the grifter coefficient always goes up

767
00:39:11,436 --> 00:39:14,236
Speaker 2: with the price, and then you know, the true believers

768
00:39:14,236 --> 00:39:16,756
Speaker 2: are still there during the next eighty percent draw down.

769
00:39:16,796 --> 00:39:18,636
Speaker 2: And I'm sure there will be a drawdown something like

770
00:39:18,716 --> 00:39:20,396
Speaker 2: that at some point in the future. It's just that

771
00:39:20,596 --> 00:39:22,916
Speaker 2: that's kind of the nature of these kinds of assets.

772
00:39:24,196 --> 00:39:27,596
Speaker 2: The bitcoin, it was originally conceived as more of a currency,

773
00:39:27,876 --> 00:39:30,956
Speaker 2: and so so she talked about some hypothetical products you

774
00:39:30,996 --> 00:39:35,036
Speaker 2: could buy with it, and then one like the first

775
00:39:35,076 --> 00:39:37,316
Speaker 2: bitcoin killer app to be fair was e commerce. It

776
00:39:37,436 --> 00:39:40,636
Speaker 2: was specifically drugs. Yes, it is.

777
00:39:40,756 --> 00:39:41,636
Speaker 1: It is a very very.

778
00:39:41,556 --> 00:39:44,036
Speaker 2: Libertarian product in that way. So so it doesn't it

779
00:39:44,076 --> 00:39:46,756
Speaker 2: doesn't work very well as a dollar substitute for many reasons,

780
00:39:46,956 --> 00:39:49,836
Speaker 2: you know, most of the obvious reasons. But it is

781
00:39:50,076 --> 00:39:52,716
Speaker 2: interesting as a gold substitute, where part of the point

782
00:39:52,756 --> 00:39:55,676
Speaker 2: of gold is that it is very divisible and your

783
00:39:55,756 --> 00:39:58,636
Speaker 2: gold is the same as my gold, and we've all

784
00:39:58,956 --> 00:40:01,916
Speaker 2: kind of collectively agreed that gold is worth more than

785
00:40:02,316 --> 00:40:05,196
Speaker 2: the value than its value as just an industrial product.

786
00:40:05,636 --> 00:40:09,476
Speaker 2: And then and the neat thing about gold is it's

787
00:40:09,516 --> 00:40:13,356
Speaker 2: really hard to dig up anymore. Gold supply is extremely anelastic.

788
00:40:12,916 --> 00:40:16,596
Speaker 1: And so and bitcoin is designed to have a finite supply, right, Yeah,

789
00:40:16,636 --> 00:40:21,316
Speaker 1: important analogy, Yeah, yes, more generally, like it's what, it's

790
00:40:21,356 --> 00:40:25,196
Speaker 1: a long time out now, it's you know, seventeen years

791
00:40:25,316 --> 00:40:30,036
Speaker 1: or something since the white paper. What do you make

792
00:40:30,116 --> 00:40:34,476
Speaker 1: of the sort of costs and benefits of cryptocurrency so far?

793
00:40:34,756 --> 00:40:36,956
Speaker 1: The costs are more obvious to me, Like, there's a

794
00:40:36,996 --> 00:40:41,436
Speaker 1: lot of grift. It's you know, by design, very energy intensive,

795
00:40:41,676 --> 00:40:45,236
Speaker 1: Like I'm open to like better payment systems. There's lots

796
00:40:45,236 --> 00:40:48,276
Speaker 1: of just like boring efficiency gains you would think we

797
00:40:48,356 --> 00:40:51,596
Speaker 1: could get that we haven't gotten, right, Yeah, what do

798
00:40:51,636 --> 00:40:53,636
Speaker 1: you think about the cost versus the benefits so far?

799
00:40:54,316 --> 00:40:56,996
Speaker 2: I think I think in terms of the present value

800
00:40:57,116 --> 00:40:59,716
Speaker 2: of future gains, probably better off. I think in terms

801
00:40:59,756 --> 00:41:02,796
Speaker 2: of yeah, realize gains so far, worse off? Huh so

802
00:41:03,116 --> 00:41:05,276
Speaker 2: basically worse off so far, but in the long run

803
00:41:05,396 --> 00:41:07,596
Speaker 2: will be better off. We just haven't got the payss yet.

804
00:41:08,196 --> 00:41:10,876
Speaker 2: This is actually something that general purpose technologies. It is

805
00:41:10,916 --> 00:41:13,556
Speaker 2: a feature of general purpose technologies that there's often a

806
00:41:13,636 --> 00:41:15,516
Speaker 2: point early in their history where the net benefit has

807
00:41:15,596 --> 00:41:16,236
Speaker 2: been negative.

808
00:41:16,756 --> 00:41:20,476
Speaker 1: What what what wouldn't make it clearly positive? Like what's

809
00:41:20,516 --> 00:41:23,916
Speaker 1: the killer return you're hoping to see from cryptocurrency?

810
00:41:24,276 --> 00:41:26,036
Speaker 2: Yeah, so I think I think the killer return would

811
00:41:26,036 --> 00:41:29,436
Speaker 2: be if there is a a financial system that is

812
00:41:29,636 --> 00:41:32,836
Speaker 2: open in the sense that starting starting a financial institution,

813
00:41:32,876 --> 00:41:34,676
Speaker 2: starting a bank or an insurance company or something is

814
00:41:34,756 --> 00:41:38,116
Speaker 2: basically you write some code and you click the deploy

815
00:41:38,236 --> 00:41:41,276
Speaker 2: button and your code is running. You have capitalized your

816
00:41:41,356 --> 00:41:44,556
Speaker 2: little entity, and now you can provide whatever it is.

817
00:41:44,716 --> 00:41:47,436
Speaker 2: Like mean, tweet insurance. You're selling people for a dollar

818
00:41:47,436 --> 00:41:48,876
Speaker 2: a day. You will pay them one hundred dollars if

819
00:41:48,876 --> 00:41:51,556
Speaker 2: there's a tweet tweet that makes them cry, you know

820
00:41:53,076 --> 00:41:56,116
Speaker 2: in your insurance business, Yes, you know, you get to

821
00:41:56,156 --> 00:41:58,156
Speaker 2: speed run all kinds of financial history. I'm sure you

822
00:41:58,276 --> 00:42:00,716
Speaker 2: learn all about adverse selection. But like a financial system

823
00:42:00,716 --> 00:42:03,876
Speaker 2: where anything, anything can be plugged into something else and

824
00:42:04,436 --> 00:42:07,116
Speaker 2: basically everything is an API call away is just a

825
00:42:07,196 --> 00:42:10,676
Speaker 2: really interesting concept. And there's the field system is moving

826
00:42:10,716 --> 00:42:12,276
Speaker 2: in that direction, but slowly, and just.

827
00:42:12,316 --> 00:42:15,436
Speaker 1: To be clear, like why is it? Why is that

828
00:42:16,476 --> 00:42:18,796
Speaker 1: better on balance? So for it to for it to

829
00:42:18,876 --> 00:42:21,276
Speaker 1: be net positive that has to be not only interesting,

830
00:42:21,356 --> 00:42:24,436
Speaker 1: but that has to like lead to more human flourishing

831
00:42:24,476 --> 00:42:26,636
Speaker 1: and less suffering than we would have in its absence.

832
00:42:26,756 --> 00:42:32,396
Speaker 2: Right, Yeah, markets provide large positive externalities. There's a lot

833
00:42:32,436 --> 00:42:35,556
Speaker 2: of effort in those markets that feels wasted. But it

834
00:42:35,756 --> 00:42:39,916
Speaker 2: is like markets transmit information better than basically anything else

835
00:42:39,956 --> 00:42:42,436
Speaker 2: because what they're always transmitting is the information you actually

836
00:42:42,516 --> 00:42:46,316
Speaker 2: care about. So like oil prices, you don't have to

837
00:42:46,516 --> 00:42:50,036
Speaker 2: know that oil prices are up because there was a

838
00:42:50,116 --> 00:42:52,796
Speaker 2: terrorist attack or because someone drilled a dry hole or whatever.

839
00:42:53,436 --> 00:42:56,516
Speaker 2: You what you respond to is just gas is more expensive,

840
00:42:56,716 --> 00:42:59,916
Speaker 2: and therefore I will drive less, or you know, energy

841
00:42:59,996 --> 00:43:01,716
Speaker 2: is cheaper or more expensive, and so I need to

842
00:43:01,876 --> 00:43:04,636
Speaker 2: change my behavior. So it's always transmitted the actually useful

843
00:43:04,676 --> 00:43:06,516
Speaker 2: information to the people who would want to use it.

844
00:43:06,916 --> 00:43:10,836
Speaker 2: And the more complete markets are and the more things

845
00:43:10,876 --> 00:43:13,556
Speaker 2: there are where that information can be instantaneously transmitted to

846
00:43:13,556 --> 00:43:15,596
Speaker 2: the people who want to respond to it, the more

847
00:43:15,836 --> 00:43:19,596
Speaker 2: everyone's real world behavior actually reflects whatever the underlying material

848
00:43:19,636 --> 00:43:21,556
Speaker 2: constraints are on doing what we want to do.

849
00:43:21,836 --> 00:43:25,916
Speaker 1: The sort of crypto dream there is just more more

850
00:43:26,076 --> 00:43:32,836
Speaker 1: finance markets, more feedback, more market feedback, better financial services,

851
00:43:32,916 --> 00:43:35,716
Speaker 1: as a result. That's the that's the basic view you're

852
00:43:35,996 --> 00:43:37,196
Speaker 1: arguing for it, and it's just.

853
00:43:37,396 --> 00:43:41,156
Speaker 2: A really interesting way to build up new financial products

854
00:43:41,196 --> 00:43:43,676
Speaker 2: from first principles. And sometimes you learn whether those first

855
00:43:43,716 --> 00:43:46,516
Speaker 2: principles are wrong, but that itself is valuable, Like there

856
00:43:46,676 --> 00:43:50,636
Speaker 2: there is actual value in understanding something that is a

857
00:43:50,716 --> 00:43:53,356
Speaker 2: tradition or a norm and understanding why it works and

858
00:43:53,756 --> 00:43:56,196
Speaker 2: therefore deciding that that norm is actually a good norm.

859
00:43:56,636 --> 00:44:00,396
Speaker 1: Good last one? All right, you know what it's going

860
00:44:00,436 --> 00:44:02,036
Speaker 1: to be. You tell me what the last one is?

861
00:44:03,196 --> 00:44:04,556
Speaker 2: Is AI a bubble?

862
00:44:05,916 --> 00:44:08,596
Speaker 1: Yes? But you sound so sad about it? Of course

863
00:44:08,676 --> 00:44:10,756
Speaker 1: we've got to talk about AI, right, are you sad?

864
00:44:11,036 --> 00:44:14,876
Speaker 1: Talk about AI? Like it's exactly like what what you're

865
00:44:14,916 --> 00:44:19,916
Speaker 1: writing about. Yeah. When you hear Sam Altman talk about

866
00:44:20,556 --> 00:44:26,676
Speaker 1: creating open AI, starting open AI, it's like we basically said,

867
00:44:27,276 --> 00:44:31,716
Speaker 1: you know, we're going to make a GI artificial general intelligence.

868
00:44:32,356 --> 00:44:34,436
Speaker 1: Come work with us. And when he talks about it's

869
00:44:34,476 --> 00:44:36,596
Speaker 1: like there was a universe of people who were like

870
00:44:36,916 --> 00:44:40,276
Speaker 1: the smartest people who really believed, oh that's what they

871
00:44:40,356 --> 00:44:41,956
Speaker 1: wanted to do, so they came and worked with us,

872
00:44:42,116 --> 00:44:44,916
Speaker 1: which seems like exactly your story.

873
00:44:45,796 --> 00:44:47,876
Speaker 2: Yes, it turns out that a lot of people have

874
00:44:47,996 --> 00:44:50,156
Speaker 2: had that dream and for a lot of people, and

875
00:44:50,236 --> 00:44:53,036
Speaker 2: maybe it wasn't what they were studying in grad school,

876
00:44:53,076 --> 00:44:55,156
Speaker 2: but it was why they ended up being the kind

877
00:44:55,156 --> 00:44:56,796
Speaker 2: of person with major computer science and then try to

878
00:44:56,836 --> 00:44:58,716
Speaker 2: get a PhD in it and you know, would go

879
00:44:59,316 --> 00:45:01,716
Speaker 2: go into a more researchy end of the software world.

880
00:45:02,116 --> 00:45:05,116
Speaker 2: So yeah, there were there were people for whom this was.

881
00:45:05,276 --> 00:45:07,396
Speaker 2: It was incredibly refreshing to hear that someone actually wants

882
00:45:07,436 --> 00:45:08,076
Speaker 2: to build a thing.

883
00:45:08,236 --> 00:45:11,396
Speaker 1: So you have that kind of shared belief. I mean,

884
00:45:11,436 --> 00:45:13,876
Speaker 1: at this point, you have these other elements of what

885
00:45:13,996 --> 00:45:20,236
Speaker 1: you're talking about, right, like a sense of urgency, an

886
00:45:20,316 --> 00:45:27,916
Speaker 1: incredible amount of money, elements of spiritual or quasi spiritual belief.

887
00:45:29,116 --> 00:45:32,436
Speaker 2: Yes, there are pseudonymous open Aye employees on Twitter who

888
00:45:32,436 --> 00:45:35,916
Speaker 2: will tweet about things like building God. So yeah, they're

889
00:45:35,956 --> 00:45:39,276
Speaker 2: they're taking it in a weird spiritual direction. But there,

890
00:45:39,396 --> 00:45:42,236
Speaker 2: I think there there is something you know, it is

891
00:45:42,436 --> 00:45:44,716
Speaker 2: it is interesting that a feature of the natural world

892
00:45:45,076 --> 00:45:48,436
Speaker 2: is that you can actually if you put enough of

893
00:45:48,716 --> 00:45:51,876
Speaker 2: a you know, you you arrange refined sand and a

894
00:45:51,916 --> 00:45:54,556
Speaker 2: couple of metals in exactly the right way and type

895
00:45:54,596 --> 00:45:57,276
Speaker 2: in the right incantations and add a lot of power

896
00:45:57,596 --> 00:46:00,356
Speaker 2: that you get something that appears to think and that

897
00:46:00,636 --> 00:46:02,836
Speaker 2: can trick someone into thinking that it's a real human being.

898
00:46:03,556 --> 00:46:05,836
Speaker 1: The is it good or is it bad? Question is

899
00:46:05,916 --> 00:46:10,076
Speaker 1: quite interesting here, obviously too soon to tell well, but

900
00:46:11,356 --> 00:46:13,956
Speaker 1: striking to me in the case of AI that the

901
00:46:13,956 --> 00:46:16,436
Speaker 1: people who seem most worried about it are the people

902
00:46:16,476 --> 00:46:19,316
Speaker 1: who know the most about it, which is not often

903
00:46:19,396 --> 00:46:22,236
Speaker 1: the case, right. Usually the people doing the work building

904
00:46:22,316 --> 00:46:24,396
Speaker 1: the thing just love it and think it's great. In

905
00:46:24,476 --> 00:46:26,196
Speaker 1: this case, it's kind of the opposite.

906
00:46:26,596 --> 00:46:30,356
Speaker 2: Yeah, I think the times when I am calmst about

907
00:46:30,396 --> 00:46:33,436
Speaker 2: AI and least worried about it taking my job are

908
00:46:33,716 --> 00:46:39,116
Speaker 2: times when I'm using AI products to slightly improve how

909
00:46:39,156 --> 00:46:41,916
Speaker 2: I do my job that is better natural language search,

910
00:46:42,116 --> 00:46:46,036
Speaker 2: or actually most of it is processing natural language. When

911
00:46:46,196 --> 00:46:48,396
Speaker 2: there are a lot of pages I need to read

912
00:46:48,556 --> 00:46:50,796
Speaker 2: which contain you know, if it's like a thousand pages

913
00:46:50,916 --> 00:46:53,036
Speaker 2: of which five sentences matter to me, that is a

914
00:46:53,156 --> 00:46:55,036
Speaker 2: job for the API and not a job for me.

915
00:46:55,316 --> 00:46:57,156
Speaker 2: But it is now a job that the API and

916
00:46:57,236 --> 00:47:00,076
Speaker 2: I can actually get done. And my function is to

917
00:47:00,276 --> 00:47:03,356
Speaker 2: figure out what those five sentences are and figure out

918
00:47:03,356 --> 00:47:04,916
Speaker 2: a clever way to find them and then the AI's

919
00:47:04,996 --> 00:47:07,236
Speaker 2: job is to do the grant work of actually reading

920
00:47:07,276 --> 00:47:07,596
Speaker 2: through them.

921
00:47:07,836 --> 00:47:10,676
Speaker 1: That's AI as you full tool, right, that's the happy

922
00:47:10,796 --> 00:47:11,476
Speaker 1: AI story.

923
00:47:11,596 --> 00:47:14,956
Speaker 2: Yeah, And I actually think that preserving preserving your own

924
00:47:14,996 --> 00:47:17,516
Speaker 2: agency is a pretty big deal in this context. So

925
00:47:17,676 --> 00:47:19,876
Speaker 2: I think that if you are, if you're making a decision,

926
00:47:20,036 --> 00:47:23,556
Speaker 2: it needs to be something where you have actually formalized

927
00:47:23,596 --> 00:47:25,036
Speaker 2: it to the extent that you can formalize it, and

928
00:47:25,396 --> 00:47:28,596
Speaker 2: then you have made the call. But for for a

929
00:47:28,636 --> 00:47:31,236
Speaker 2: lot of the gruntwork, AI is just it's a way

930
00:47:31,276 --> 00:47:34,276
Speaker 2: to massively parallelize having an intern.

931
00:47:34,516 --> 00:47:37,196
Speaker 1: Plainly, it's powerful, and you're talking about what it can

932
00:47:37,236 --> 00:47:41,596
Speaker 1: do right now. I mean, the smartest people are like, yes,

933
00:47:41,676 --> 00:47:43,876
Speaker 1: but we're gonna have AGI in two years, which I

934
00:47:43,956 --> 00:47:45,476
Speaker 1: don't know if that's right or not. I don't know

935
00:47:45,516 --> 00:47:48,196
Speaker 1: how to evaluate that claim. But it's a wild claim.

936
00:47:48,836 --> 00:47:53,156
Speaker 1: It's plainly not obviously wrong on its face, right, it's possible.

937
00:47:54,316 --> 00:47:56,676
Speaker 1: Can you even start to parse that. You're giving sort

938
00:47:56,676 --> 00:47:59,236
Speaker 1: of little things today about oh, here's a useful tool,

939
00:47:59,276 --> 00:48:00,436
Speaker 1: and here's the thing I don't use it for. But

940
00:48:00,476 --> 00:48:02,916
Speaker 1: there's a much bigger set of questions that seem imminent.

941
00:48:03,276 --> 00:48:05,236
Speaker 2: You know, there's certain kinds of radical insert day there.

942
00:48:05,756 --> 00:48:10,196
Speaker 2: You know, I think it increases wealth inequality, but also

943
00:48:10,716 --> 00:48:14,356
Speaker 2: means that intelligence is just more abundant and is available

944
00:48:14,396 --> 00:48:17,796
Speaker 2: on demand and is baked into more things. I think

945
00:48:17,996 --> 00:48:20,356
Speaker 2: that it's you know, you can definitely sketch out really

946
00:48:20,396 --> 00:48:23,316
Speaker 2: really negative scenarios. You could sketch out, you know, not

947
00:48:23,596 --> 00:48:26,276
Speaker 2: end of the world, but maybe might as well be

948
00:48:26,396 --> 00:48:29,516
Speaker 2: for the average person. Scenarios where every white collar job

949
00:48:29,556 --> 00:48:32,076
Speaker 2: gets eliminated and then a tiny handful of people have

950
00:48:32,156 --> 00:48:35,116
Speaker 2: just unimaginable wealth and you know, rearrange the system to

951
00:48:35,156 --> 00:48:37,796
Speaker 2: make sure that doesn't change. But I think there are

952
00:48:37,796 --> 00:48:41,396
Speaker 2: a lot of intermediate stories that are closer to just

953
00:48:41,516 --> 00:48:44,276
Speaker 2: the story of say, accountants after the rise of Excel,

954
00:48:44,476 --> 00:48:47,716
Speaker 2: where there were parts of their job that got much

955
00:48:47,796 --> 00:48:49,636
Speaker 2: much easier and then the scope of what they could

956
00:48:49,636 --> 00:48:50,876
Speaker 2: do expanded. It was the.

957
00:48:50,916 --> 00:48:53,636
Speaker 1: Bookkeepers who took it on the chin. It turns out, yeah,

958
00:48:53,716 --> 00:48:56,636
Speaker 1: like Excel actually did drive bookkeepers out of work and

959
00:48:56,716 --> 00:48:58,956
Speaker 1: it made accountants more powerful.

960
00:49:00,756 --> 00:49:02,996
Speaker 2: Yeah, So, you you know, within I think within a

961
00:49:03,756 --> 00:49:06,196
Speaker 2: kind of company function, you'll have specific job functions that

962
00:49:06,276 --> 00:49:08,116
Speaker 2: do mostly go away, and then a lot of them

963
00:49:08,636 --> 00:49:11,876
Speaker 2: will evolve, and so the way that AI seems to

964
00:49:11,916 --> 00:49:15,916
Speaker 2: be rolling out in big companies in practice is they

965
00:49:16,036 --> 00:49:18,956
Speaker 2: generally don't lay off a ton of people. They will

966
00:49:19,036 --> 00:49:22,796
Speaker 2: sometimes end outsource contracts, but in a lot of the case,

967
00:49:22,916 --> 00:49:25,156
Speaker 2: a lot of cases, they don't lay people off. They

968
00:49:25,756 --> 00:49:29,476
Speaker 2: change people's responsibilities. They ask them to do less of

969
00:49:29,556 --> 00:49:31,236
Speaker 2: one thing and a whole lot more of something else.

970
00:49:31,556 --> 00:49:33,636
Speaker 2: And then in some cases that means they don't have

971
00:49:33,676 --> 00:49:34,956
Speaker 2: to do much hiring right now, but they think that

972
00:49:34,996 --> 00:49:36,956
Speaker 2: a layoff would be pretty demoralizing, so they sort of

973
00:49:37,396 --> 00:49:40,516
Speaker 2: grow into the new cost structure that they can support.

974
00:49:41,276 --> 00:49:43,916
Speaker 2: And then in other cases there are companies where they realize, wait,

975
00:49:44,236 --> 00:49:46,836
Speaker 2: we can ship features twice as fast now, and so

976
00:49:46,956 --> 00:49:48,596
Speaker 2: our revenue is going up faster, So we actually need

977
00:49:48,636 --> 00:49:50,796
Speaker 2: more developers because our developers are so much more productive.

978
00:49:55,476 --> 00:50:05,796
Speaker 1: We'll be back in a minute with the lightning round. Okay,

979
00:50:05,876 --> 00:50:10,196
Speaker 1: let's finish with the lightning round. The most interesting thing

980
00:50:10,236 --> 00:50:12,916
Speaker 1: you learned from an earnings call transcript in the last.

981
00:50:12,796 --> 00:50:17,276
Speaker 2: Year, most interesting thing from a transcript in the last year,

982
00:50:17,756 --> 00:50:21,676
Speaker 2: I would say there was a point, this might have

983
00:50:21,716 --> 00:50:23,476
Speaker 2: been a little over yeargo. There was a point at

984
00:50:23,516 --> 00:50:28,076
Speaker 2: which Satia Nadella was talking about Microsoft's AI spending, and

985
00:50:28,276 --> 00:50:30,796
Speaker 2: he said we are still at the point, and I

986
00:50:30,836 --> 00:50:32,596
Speaker 2: think he and Zuckerberg both said something to the same

987
00:50:32,596 --> 00:50:35,196
Speaker 2: effect in the same quarter, which is very exciting for

988
00:50:35,356 --> 00:50:37,676
Speaker 2: Nvidia people. But it was like, we're at the point

989
00:50:37,676 --> 00:50:39,996
Speaker 2: where we see a lot more risk to underspending than

990
00:50:40,036 --> 00:50:42,716
Speaker 2: to overspending on AI specifically.

991
00:50:43,516 --> 00:50:46,316
Speaker 1: That really speaks to your book, right, that really is

992
00:50:46,476 --> 00:50:49,276
Speaker 1: like bubbly as hell in the context of your book,

993
00:50:49,396 --> 00:50:53,956
Speaker 1: like overspending, like the Apollo missions, like the Manhattan Project,

994
00:50:54,036 --> 00:50:56,036
Speaker 1: like the big risk is that we don't spend enough.

995
00:50:56,876 --> 00:50:59,116
Speaker 2: And also they know that their competitors are listening to

996
00:50:59,156 --> 00:51:02,836
Speaker 2: these calls too, so they were also seeing that this

997
00:51:02,996 --> 00:51:05,636
Speaker 2: is kind of a winnable fight that they do think

998
00:51:05,676 --> 00:51:07,876
Speaker 2: that there is a level of capital spending at which

999
00:51:08,276 --> 00:51:10,796
Speaker 2: Microsoft can win simply because they took it more seriously

1000
00:51:10,916 --> 00:51:11,676
Speaker 2: than everybody else.

1001
00:51:11,916 --> 00:51:15,436
Speaker 1: So he's like, yes, We're going to spend billions and

1002
00:51:15,516 --> 00:51:18,916
Speaker 1: billions of dollars on AI because we think we can win.

1003
00:51:20,316 --> 00:51:30,556
Speaker 1: Zuckerberg implicitly, what's one innovation in history that you wish

1004
00:51:30,636 --> 00:51:31,196
Speaker 1: didn't happen?

1005
00:51:36,436 --> 00:51:40,476
Speaker 2: I wish there were some reason that it was infeasible

1006
00:51:40,636 --> 00:51:45,396
Speaker 2: to have really, really tight feedback loops for consumer facing apps,

1007
00:51:45,436 --> 00:51:46,356
Speaker 2: particularly games.

1008
00:51:48,636 --> 00:51:50,316
Speaker 1: Is that a way of saying you wish games were

1009
00:51:50,436 --> 00:51:51,116
Speaker 1: less addictive?

1010
00:51:51,796 --> 00:51:53,436
Speaker 2: Yeah, I wish games were less addictive, or that they

1011
00:51:53,476 --> 00:51:57,116
Speaker 2: didn't get as they weren't as good at getting more addictive.

1012
00:51:57,116 --> 00:51:58,836
Speaker 2: So I wrote a piece in the newsletter about this recently,

1013
00:51:59,036 --> 00:52:01,596
Speaker 2: because there was that wonderful article in the Loneliness Economy

1014
00:52:01,756 --> 00:52:03,956
Speaker 2: in the Atlantic a couple of weeks back that was

1015
00:52:04,036 --> 00:52:07,116
Speaker 2: talking about it. We just spend One of the pandemic

1016
00:52:07,196 --> 00:52:09,076
Speaker 2: trends that has mean reverted the least is how much

1017
00:52:09,156 --> 00:52:11,156
Speaker 2: time people spend alone. And I think one of the

1018
00:52:11,156 --> 00:52:13,956
Speaker 2: reasons for that is that all the things you do alone,

1019
00:52:14,316 --> 00:52:17,276
Speaker 2: they are things that produce data for the company that

1020
00:52:17,636 --> 00:52:20,556
Speaker 2: monetizes the time that you spend alone. And so the

1021
00:52:20,676 --> 00:52:23,236
Speaker 2: fact that we all washed a whole lot of Netflix

1022
00:52:23,276 --> 00:52:25,316
Speaker 2: in the spring of twenty twenty means that Netflix has

1023
00:52:25,316 --> 00:52:27,076
Speaker 2: a lot more data on what our preferences are.

1024
00:52:27,756 --> 00:52:30,796
Speaker 1: So they got better at making us want to watch Netflix,

1025
00:52:30,876 --> 00:52:33,596
Speaker 1: and all the video games we played on our phones

1026
00:52:34,076 --> 00:52:37,036
Speaker 1: got better at making us addicted to keep playing video

1027
00:52:37,076 --> 00:52:40,516
Speaker 1: games on our phones. Yeah, that's a bummer. It's a bummer.

1028
00:52:42,676 --> 00:52:44,716
Speaker 1: What was the best thing about dropping out of college

1029
00:52:44,756 --> 00:52:46,596
Speaker 1: and moving to New York City at age eighteen.

1030
00:52:48,116 --> 00:52:53,036
Speaker 2: So I would say that it was it really meant

1031
00:52:53,076 --> 00:52:56,836
Speaker 2: that I could could and had to just take full

1032
00:52:56,876 --> 00:53:01,316
Speaker 2: responsibility for outcomes, and that I get to get to

1033
00:53:01,356 --> 00:53:03,996
Speaker 2: take a lot more credit for what I've done since then,

1034
00:53:04,316 --> 00:53:07,796
Speaker 2: but also get a lot more blame. Where there isn't

1035
00:53:07,996 --> 00:53:10,516
Speaker 2: really a brand named fall back on and so if

1036
00:53:10,556 --> 00:53:12,756
Speaker 2: someone hires me, they can't say this person got a

1037
00:53:12,876 --> 00:53:15,636
Speaker 2: degree from institution X. You know I didn't eve. I

1038
00:53:15,756 --> 00:53:18,396
Speaker 2: dropped out of a really bad school too, So there's

1039
00:53:18,516 --> 00:53:21,276
Speaker 2: there's not even like the not even the the extra

1040
00:53:21,436 --> 00:53:24,116
Speaker 2: upside of you know, I startup was so great, I

1041
00:53:24,236 --> 00:53:26,996
Speaker 2: just had to leave Stanford after only a couple of semesters. No,

1042
00:53:27,196 --> 00:53:29,756
Speaker 2: it was it was Arizona State and I didn't even party,

1043
00:53:29,876 --> 00:53:34,836
Speaker 2: so they yeah, but yeah, it's it's that it's just

1044
00:53:35,396 --> 00:53:37,916
Speaker 2: being being a little more in control of the narrative

1045
00:53:37,996 --> 00:53:41,116
Speaker 2: and also just knowing that it's it's a lot more

1046
00:53:41,196 --> 00:53:41,436
Speaker 2: up to me.

1047
00:53:42,156 --> 00:53:44,196
Speaker 1: What was the worst thing about dropping out of college

1048
00:53:44,236 --> 00:53:45,596
Speaker 1: and moving to New York at eighteen?

1049
00:53:46,956 --> 00:53:48,796
Speaker 2: So one time I went through a really really long

1050
00:53:48,916 --> 00:53:51,156
Speaker 2: interview process for a job that I really wanted and

1051
00:53:52,436 --> 00:53:55,036
Speaker 2: at the end of many, many rounds of interviews and

1052
00:53:55,196 --> 00:53:58,876
Speaker 2: you know, a work session and lots of stuff. The

1053
00:53:59,396 --> 00:54:01,556
Speaker 2: hiring committee rejected because I didn't have a degree and

1054
00:54:01,916 --> 00:54:04,556
Speaker 2: that was on my resume, so that was kind of inconvenient.

1055
00:54:04,916 --> 00:54:08,796
Speaker 2: I guess another another downside, like it might have been

1056
00:54:09,636 --> 00:54:14,196
Speaker 2: to spend more time with fewer obligations and access to

1057
00:54:14,236 --> 00:54:15,076
Speaker 2: a really good library.

1058
00:54:22,036 --> 00:54:25,196
Speaker 1: Burn Hobart is the co author of Boom Bubbles and

1059
00:54:25,276 --> 00:54:28,956
Speaker 1: the End of Stagnation. Today's show was produced by Gabriel

1060
00:54:29,036 --> 00:54:32,196
Speaker 1: Hunter Cheng. It was edited by Lydia Jeane Kott and

1061
00:54:32,476 --> 00:54:36,076
Speaker 1: engineered by Sarah Brugier. You can email us at problem

1062
00:54:36,236 --> 00:54:39,636
Speaker 1: at Pushkin dot FM. I'm Jacob Goldstein and we'll be

1063
00:54:39,716 --> 00:54:42,276
Speaker 1: back next week with another episode of What's Your Problem.