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Speaker 1: I feel like an old man whenever I buy a

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Speaker 1: new computer. I mean, why does my laptop need sixty

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Speaker 1: four gigabytes of memory? When I learned to program on

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Speaker 1: a PC that had twenty kilobytes? No joke. Kids these

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Speaker 1: days don't understand how hard we had it back in

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Speaker 1: the day. Right, But it's also a nice feeling. It

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Speaker 1: tells me that we're making progress and that's good. It's

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Speaker 1: creating new worlds and new ways of life. It's literally

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Speaker 1: saving lives by accelerating science. That's all great stuff, right,

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Speaker 1: But how long can it go on? What is the

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Speaker 1: engine of this exponential growth in computing power? And can

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Speaker 1: we count on it to take us to the stars,

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Speaker 1: to cure cancer and to develop self driving toothbrushes. Today

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Speaker 1: we'll dive into the physics underlying this trend and ask

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Speaker 1: whether there are fundamental limits that could block us from

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Speaker 1: achieving our dreams. And we'll talk about whether there's danger

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Speaker 1: and assuming technology will solve all of our problems. Welcome

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Speaker 1: to Daniel and Kelly's Extraordinary Universe.

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Speaker 2: Hello. I am Kelly Wiener Smith. I study parasites and space,

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Speaker 2: and I realized when we were starting to do this

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Speaker 2: episode that I wasn't one hundred percent clear on what

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Speaker 2: mores Law meant exactly.

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Speaker 1: Hi, I'm Daniel. I'm a particle physicist and I've been

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Speaker 1: programming computers for more than forty years. They get faster

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Speaker 1: and I get slower.

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Speaker 2: Oh you're not slowing down yet, Daniel, you stab.

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Speaker 1: So my question for you today, Kelly, is what was

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Speaker 1: your first computer? Let's age Kelly.

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Speaker 2: Okay, So later when we talked to Adam Becker in

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Speaker 2: our interview, he mentions that there was a while there

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Speaker 2: where folks wouldn't get a computer because you'd wait as

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Speaker 2: long as you could, because the computers kept getting so

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Speaker 2: much better so quickly that if you could wait, your

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Speaker 2: computer would be much better. Yeah, and so my family

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Speaker 2: waited way too long. We didn't get one until I

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Speaker 2: was in like high school, and I know, and I

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Speaker 2: don't even remember what it was. But in the meantime

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Speaker 2: I had to write my essays on like it was

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Speaker 2: like a brother typewriter, but it also had a little

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Speaker 2: electronic screen, and so I could very slowly and laboriously

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Speaker 2: click through my essays and then I would print it

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Speaker 2: and something would be wrong. It would take me forever

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Speaker 2: to find where the error was. It was very annoying.

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Speaker 2: But what about you, did you have like the first

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Speaker 2: Apple computer? Ever?

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Speaker 3: Oh?

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Speaker 1: Apple was way too advanced. I go way before that.

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Speaker 1: What My first computer was a Commodore VIC twenty, which

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Speaker 1: I think had twenty kilobytes of RAM, and we stored

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Speaker 1: stuff on an audio tape, you know, like you write

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Speaker 1: a little program and then you'd stored on these cassette

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Speaker 1: tapes that you could later listen to and like, ooh

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Speaker 1: what does that sound? So yeah, we were very very early.

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Speaker 1: In fact, I remember hanging out with my dad in

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Speaker 1: grad school while he was doing his research and he

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Speaker 1: was literally feeding punch cards into those punch cards machines.

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Speaker 1: So I feel like I've personally experienced a huge fraction

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Speaker 1: of the transformation of computers into the basically supercomputers we

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Speaker 1: have today. I mean, my smartphone is so much more

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Speaker 1: powerful than anything my dad ever used in his research.

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Speaker 2: That is absolutely amazing. I didn't even know that we

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Speaker 2: were storing data on like cassette tapes. Oh yeah, that's

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Speaker 2: amazing to me.

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Speaker 1: Yeah, before magnetic floppies for sure.

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Speaker 2: So when you like are using your are you a

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Speaker 2: MacBook guy?

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Speaker 1: I am? Yeah.

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Speaker 2: It's like when you're using your Mac Do you every

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Speaker 2: day think I am so lucky I'm not doing this

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Speaker 2: on punch cards or you just do you take it

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Speaker 2: for granted? Now?

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Speaker 1: I think it's awesome. It's incredible. I mean every time

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Speaker 1: I get a new MacBook, I'm like, while, this drive

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Speaker 1: is ten times bigger than anything I've ever seen, and

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Speaker 1: the memory is just shocking. And it's also then incredible

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Speaker 1: to me how rapidly our computational ambitions grow. You know,

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Speaker 1: my group does a lot of computation, and we're basically

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Speaker 1: limited by computation, and so every time we get more

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Speaker 1: powerful computers, we scale up our ambitions and solve bigger,

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Speaker 1: harder problems, and so we're always at the edge of

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Speaker 1: what the computers can do, right, Like, we have an

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Speaker 1: infinite number of questions we could ask with harder computers.

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Speaker 1: So yeah, I'm in awe of the MacBook, not just

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Speaker 1: because it's so much more powerful than anything I've used,

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Speaker 1: but it's so reliable. I mean, I spend hours and

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Speaker 1: hours a day in front of this thing. It almost

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Speaker 1: never gives me problems. So yeah, it's incredible. What engineers

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Speaker 1: have provided it is incredible.

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Speaker 2: And today we're going to talk about one way in

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Speaker 2: which that incredible ability has been expanded, which has to

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Speaker 2: do with Moore's Law, which I thought was about how

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Speaker 2: much data you can store on your computer, and I

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Speaker 2: think it's much more than that.

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Speaker 1: You're right, it's much more than that. It's also about

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Speaker 1: how things are growing over time and how long that

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Speaker 1: will continue. And there's this lore about Moore's Law which

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Speaker 1: is permeated Silicon Valley and broader culture. So in a minute,

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Speaker 1: we're gonna also talk to Adam Becker about how this

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Speaker 1: has impacted philosophy and politics and policy and how it

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Speaker 1: might affect our future. But first we wanted to know

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Speaker 1: how long people thought Moore's Law might continue to make

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Speaker 1: all of our computers faster. So I went out there

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Speaker 1: and I talked to our group of volunteers. Here's what

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Speaker 1: they had to say about the future of Moore's law.

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Speaker 1: So I'll give it six years and then I'm gonna

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Speaker 1: sell my nvidious stock. But quand computing will change the game.

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Speaker 1: We are restricted by things like housmow.

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Speaker 4: We can make stuff, so I think it's not true anymore.

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Speaker 4: My first thought was to sign no, but I know

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Speaker 4: very little bit quantuine computing. I would say until the

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Speaker 4: computer speed, which just be the light maybe ten years,

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Speaker 4: another good decade or so.

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Speaker 3: My understanding is that it's already done.

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Speaker 1: I don't think we are doubling in raw processing speed.

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Speaker 4: In my understanding, Moore's law kind of slowed down for

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Speaker 4: laptop desktop chips a number of years back, but has

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Speaker 4: continued with mobile just because they were a little behind.

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Speaker 4: But then you also have graphics and neural processing units

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Speaker 4: that power our AI platforms of today. So can we

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Speaker 4: go into the future. I think we can go for

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Speaker 4: a number more years, given the innovations in transistor stacking.

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Speaker 2: I thought Moore's law had to do with cost decreasing

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Speaker 2: as speed increased. We do seem to be close to

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Speaker 2: a tipping point with electrons being too large for the

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Speaker 2: tiny circuitry. However, it seems that optical circuitry might be

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Speaker 2: a good replacement for that.

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Speaker 1: They're currently reaching the lower limits of workability before they

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Speaker 1: start reaching quantum effects. With silicon.

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Speaker 3: We are getting closer to the particle level and that

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Speaker 3: stops us.

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Speaker 1: Honestly, I thought it had already stopped. So do you

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Speaker 1: think these are optimistic or pessimistic?

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Speaker 2: I mean I think they're realistic, which seems to be

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Speaker 2: the option that always gets left out. So I think

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Speaker 2: there were a lot of people who said, you know,

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Speaker 2: I thought we've already reached the limits. So we're getting

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Speaker 2: close to reaching the limits. And I'll admit that I

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Speaker 2: did not realize we were getting close to reaching the limits.

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Speaker 2: But it seems like a lot of our listeners are

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Speaker 2: on top of this trend.

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Speaker 1: Yeah, and so we're not giving financial advice. So I

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Speaker 1: won't tell you whether or not to buy or sell

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Speaker 1: in Vidia stock. But you know, sometimes I wonder about

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Speaker 1: these tech companies because their stocks also seem to follow

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Speaker 1: Moore's law, Like, how can Google just keep getting more valuable?

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Speaker 1: I keep missing out on buying Google.

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Speaker 2: I can tell you that if you can make a

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Speaker 2: time machine, one of the first things you should do

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Speaker 2: is go buy in VideA and Google stuff.

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Speaker 1: All right, So let's dig into it. What in the

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Speaker 1: end is Moore's law? So Moore's law was something postulated

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Speaker 1: by Gordon Moore.

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Speaker 2: He has a first name.

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Speaker 1: And he was one of the found of Intel, so

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Speaker 1: a big dude in like, you know, semiconductors and electronics.

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Speaker 1: And he suggested initially that the number of transistors you

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Speaker 1: could squeeze onto a chip would double every year. And

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Speaker 1: it's a little bit more complicated than that. He also

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Speaker 1: was talking about the power usage and the cost, but

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Speaker 1: roughly speaking, he was talking about the density of transistors

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Speaker 1: on chips getting higher every single year, which means the

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Speaker 1: speed of these computers is growing very quickly.

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Speaker 2: So transistors are about speed and not storage, or are

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Speaker 2: they about both?

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Speaker 1: They're about both. The transistor fundamentally is a tiny programmable switch.

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Speaker 1: And the reason that computers got small and got fast

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Speaker 1: is because we were able to make transistors small and

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Speaker 1: make them fast, which allows us to have lots and

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Speaker 1: lots and lots of switches in a small area, which

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Speaker 1: is what allows the computer to be complex and to

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Speaker 1: be fast. And so essentially it's saying we can make

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Speaker 1: computers denser every year, and that makes computers faster and

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Speaker 1: more powerful.

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Speaker 2: Okay, so this has to do with why we went

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Speaker 2: from computers that took up entire rooms to something you

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Speaker 2: can now stick in your bag and take with you.

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Speaker 1: Yeah, exactly, And we'll dig into that in a minute.

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Speaker 1: But the history here is that in nineteen sixty five

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Speaker 1: More predicted this and then ten years later he revised it.

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Speaker 1: He was like, well every year, maybe that's too optimistic.

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Speaker 1: Let's go for every two years. And so that was

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Speaker 1: the prediction in seventy five, and you'll see that it

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Speaker 1: mostly held up until fairly recently. It's sort of an

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Speaker 1: extraordinary prediction in that sense, though. You know, anytime there's

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Speaker 1: a prediction that holds up, you got to wonder, like, well,

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Speaker 1: what were the other predictions this person made, Like you

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Speaker 1: just spew predictions constantly, Eventually you're goind of get one Ris.

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Speaker 2: Yeah, yeah, well, especially he gave himself another decade to

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Speaker 2: like fit the trend line. That was pretty generous to himself.

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Speaker 1: But okay, exactly, So let's dick into what a transistor

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Speaker 1: is and why it allows computers to be faster, because

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Speaker 1: that's crucial to understand why More's law has worked, how

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Speaker 1: we've made it work, and whether it's going to work

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Speaker 1: in the future. Basically, a transistor is a programmable switch

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Speaker 1: like computers operate on digital logic. I have a number

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Speaker 1: in the computer, the number four. They store it in binary.

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Speaker 1: But to store things in binary you need a physical system.

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Speaker 1: But that can store a zero or a one right

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Speaker 1: the way you can like write a digit on a

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Speaker 1: piece of paper. That's like I'm representing the number four

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Speaker 1: by scratching this graphite onto this sheet of paper. I

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Speaker 1: want to store things on my computer and zeros and

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Speaker 1: ones because binary is the code for computers, and physically

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Speaker 1: that means a switch, you know, as you can imagine

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Speaker 1: either just like literally like a light switch, but here

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Speaker 1: we're doing an electronic switch.

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Speaker 2: Okay, And so just to if we switched from using

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Speaker 2: transistors to things like DNA to store data or quantum computing,

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Speaker 2: could you still apply More's law, like if we switched

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Speaker 2: to some other method or is More's law specifically about

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Speaker 2: the transistors that we're talking about now, Yeah.

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Speaker 1: Great question. You're talking about fundamental changes in how we

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Speaker 1: do computing. So currently computing operates on bits, zeros and ones,

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Speaker 1: and we're saying those are represented by transistors, which is

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Speaker 1: like a physical implementation of that bit. You switch to

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Speaker 1: quantum computing, the fundamental element of that is a cubit,

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Speaker 1: which isn't necessarily a zero one. It has the probability

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Speaker 1: to be in several different states, and so it requires

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Speaker 1: a different physical system to model that. We don't use

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Speaker 1: transistors or not even like quantum transistors. In fact, transistors

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Speaker 1: are already relying deeply on quantum mechanics, so quantum transistor

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Speaker 1: is redundant. But yeah, cubit, there's no guarantee that you

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Speaker 1: can like build cubits and then build them more densely

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Speaker 1: and more rapidly. There's certainly no More's law for quantum

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Speaker 1: computing that's a guarantee. And biological computing like DNA is

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Speaker 1: super awesome as an idea, but there you have like

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Speaker 1: four possibilities, right, DNA is basically base four, and so

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Speaker 1: it's a question of like how do you encode numbers

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Speaker 1: into DNA? Do you use all four bases? Do you

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Speaker 1: group them into two to make binary? The technology is

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Speaker 1: fundamentally different, So again you wouldn't expect necessarily further to

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Speaker 1: be more solved, but you might get some other law

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Speaker 1: which could be better. So but yeah, Mores law reflects

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Speaker 1: the details of the technology we're using to represent the

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Speaker 1: fundamental element of computing, which is a zero or one,

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Speaker 1: and then crucially the logic that operates on those zeros

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Speaker 1: and ones.

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Speaker 2: Let's get into that logic.

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Speaker 1: Yeah, because what you want to do is represent like

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Speaker 1: numbers in your computer. I want to put the number

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Speaker 1: four in but also I want to calculate stuff. I

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Speaker 1: don't just want to write four into my computer. I

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Speaker 1: want to be able to add four to two. I

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Speaker 1: want to be able to compare four and seven. Right,

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Speaker 1: That's what allows you to program a computer for it

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Speaker 1: to do useful computation. And if you know something about computing,

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Speaker 1: you know like the basics of computation is a Turing

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Speaker 1: machine which can like read in numbers and write numbers

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Speaker 1: onto this infinite tape. And so in order to do logic,

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Speaker 1: you need to be able to have things that respond

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Speaker 1: to different inputs. So in logic you have things like gates.

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Speaker 1: Like a not gate is something which if you give

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Speaker 1: it to zero, it responds a one. If you give

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Speaker 1: it a one, it responds to zero. It's like a

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Speaker 1: logical map from inputs to outputs, or an a gate. Right,

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Speaker 1: an and gate gives you a one if both inputs

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Speaker 1: are one and a zero otherwise. Or the converse of

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Speaker 1: that is a nand gate NA n D, which is

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Speaker 1: the combination of an N gate and a knot gate.

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Speaker 1: And the really cool thing is that if you can

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Speaker 1: build a nand gate, you can build any logical map

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Speaker 1: nandgates are like the basis function of logic. So if

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Speaker 1: you have nands, people have shown that you can build

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Speaker 1: any map from inputs to outputs and essentially any sort

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Speaker 1: of computer logic. So you can build knot gates and

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Speaker 1: and gates out of transistors. Transistors are like this digital switch,

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Speaker 1: and we'll go into the detail of the physics of

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Speaker 1: how they work, but essentially they're a programmable switch. You

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Speaker 1: can turn them on or off in response to other stuff.

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Speaker 1: So from that you can build logic, and from that

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Speaker 1: you can build nandgates, and from that you can build

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Speaker 1: literally anything like adders and comparitors and anything you need

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Speaker 1: in computers. So this is like the basically the smallest

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Speaker 1: little lego brick of computing is a switch, a programmable

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Speaker 1: switch that goes from zero to one. And that's what

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Speaker 1: a transistor is an implementation of. And it didn't have

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Speaker 1: to be a transistor. It could have been something else.

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Speaker 1: It could have been DNA, it could have been whatever.

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Speaker 1: But this is like the best, fastest, smallest thing that

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Speaker 1: we have invented, and this is what revolutionized our society.

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Speaker 2: What does the transistor look like?

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Speaker 1: Yeah, what does the transistor look like? It looks like

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Speaker 1: nothing because it's super duper tiny, right, Like the ones

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Speaker 1: that we're building these days are order nanometers right, so

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Speaker 1: like you put one on your finger, you can't see it.

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Speaker 1: The number of transistors on a typical chip is billions,

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Speaker 1: so you can't see an individual one. There used to

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Speaker 1: be able to, like when they were first building them

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Speaker 1: in the fifties, you would like make one, you know,

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Speaker 1: on a bench. You could think of it as sort

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Speaker 1: of like three wires coming together. You have a source,

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Speaker 1: a drain, and then a gait and the gate basically

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Speaker 1: decides do I connect the source and the drain Do

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Speaker 1: I open or close this switch? And so it's sort

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Speaker 1: of like a wire with a lever in it that

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Speaker 1: you know you can open or close, and then another

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Speaker 1: wire that determines what whether or not that's open or closed.

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Speaker 1: So that's not a physical description of what they look like.

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Speaker 1: We can get into like the semiconductors in a minute,

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Speaker 1: but that's sort of the logical construction. And when I

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Speaker 1: think about more's low, I think, well, what is exactly

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Speaker 1: is the connection between more transistors and speed? Like it's

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Speaker 1: cool to have things small because then you can put

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Speaker 1: a computer in your watch or whatever. But why do

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Speaker 1: smaller computers operate faster? Because that's really the crucial key.

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Speaker 1: When you sit down at your laptop, you're not like, wow,

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Speaker 1: the transistors are super dense. You're like, wow, you know

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Speaker 1: word opened in a bill a second instead of you know,

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Speaker 1: spinning my beach ball forever. Yeah, So it's the speed

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Speaker 1: that's really crucial, and that's really transformed society. Right, it's

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Speaker 1: computational power and miniaturization means faster operation for a few reasons.

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Speaker 1: Number one, things just don't have to go as far. Right,

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Speaker 1: Electronics is limited by the speed of light. It's not instantaneous.

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Speaker 1: You close a switch, the electrons don't move instantly. Right,

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Speaker 1: the current doesn't change instantly, and so we are still

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Speaker 1: limited by the speed of light. And so if the

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Speaker 1: distances between the transistors are smaller and the transistors themselves

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Speaker 1: are smaller, things just happen faster because there's a speed

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Speaker 1: limit to information in the universe.

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Speaker 2: That's awesome. I guess I hadn't imagined that as a

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Speaker 2: limiting factor. Okay, super cool, what's next.

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Speaker 1: Yeah, that's one. The other is you can have wider

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Speaker 1: data paths, like instead of just using thirty two bits

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Speaker 1: to store your numbers. You can use sixty four bits, right.

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Speaker 1: Remember bits are this essential element of binary numbers, and

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Speaker 1: so if you have like a two bit number, you

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Speaker 1: can only store between zero and four. If you have

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Speaker 1: an eight bit number, you can store many more numbers.

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Speaker 1: You have thirty two these days computing a sixty four

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Speaker 1: one hundred and twenty eight bit If you hear about

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Speaker 1: these numbers as the sort of the core the computing

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Speaker 1: of your CPU or your operating system, that's what it describes,

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Speaker 1: like what size numbers are we operating on? And this

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Speaker 1: is important because basically it's how much your computer can

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Speaker 1: do in parallel. Like if you can add two hundred

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Speaker 1: and twenty eight bit numbers, it's really one hundred and

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Speaker 1: twenty eight bit wise operations done in parallel instead of

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Speaker 1: if you're doing sixty four bit numbers, then you're only

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Speaker 1: doing sixty four operations in parallel, and so you can

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Speaker 1: do more operations in parallel, You can pass more data

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Speaker 1: at the same time, and so data flows more quickly.

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Speaker 1: Another thing that really limits the speed of computers is

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Speaker 1: how long does it take to get the data into

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Speaker 1: the actual CPU. Right, Like you have these numbers in memory,

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Speaker 1: you want to do some calculation on you got to

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Speaker 1: slurp them from memory and put them into the registers

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Speaker 1: in your CPU. They're actually doing the comparisons or the

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Speaker 1: adding or the subtracting or whatever. And so the wider

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Speaker 1: the data path, the faster the data gets loaded, and

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Speaker 1: the faster the computation happens.

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Speaker 2: And CPU probably means senorebditis pyro wetting underwater. What does

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Speaker 2: CPU mean?

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Speaker 1: CPU means central processing unit. It's a thing on your

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Speaker 1: computer that does the actual crunching, does the adding or

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Speaker 1: subtracting or comparing, or loading or unloading or writing to

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Speaker 1: memorates the closest thing we have to a digital brain. Okay,

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Speaker 1: but there's another sort of mechanical element to like why

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Speaker 1: speed means faster computers. And you know, back in the

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Speaker 1: nineteen fifties, people were doing electronics, and they're doing it

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Speaker 1: sort of the way you might do it in your garage.

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Speaker 1: You got resistors, you got capacitors, you solder them together,

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Speaker 1: you make these big sort of physical circuits. But in

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Speaker 1: the late nineteen fifties people invented what's called the integrated circuit.

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Speaker 1: Integrated circuit is just like you know, it's a big

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Speaker 1: green board and it's got the whole circuit printed onto it.

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Speaker 1: You don't like solder the components together, and this really

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Speaker 1: allows for like the embedding of these transistors and other

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Speaker 1: components inside these protective layers, which enhance their reliability. And

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Speaker 1: so that means you can make them smaller, you can

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Speaker 1: make more complex circuits that you didn't have to like

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Speaker 1: wire together yourself with dripping hot bits of solder, and

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Speaker 1: so this makes them more reliable, so you don't need

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Speaker 1: as much error correction, et cetera. And that allows things

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Speaker 1: to be smaller and to be faster. So you've got

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Speaker 1: integrated circuits, you got wider data paths, you got shorter

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Speaker 1: distances to travel, and you have faster switching. All these

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Speaker 1: things are why more transistors means faster computing.

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Speaker 2: Okay, And so when did we get our first transistor?

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Speaker 1: Yeah, so the transistor was invented in Bell Labs in

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Speaker 1: nineteen forty seven, I think it was. And there was

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Speaker 1: a lot of research in the forties different kinds of

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Speaker 1: technologies for transistors. Try this, try that, try the other thing.

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Speaker 1: But the basic concept was invented in the late forties

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Speaker 1: in Bell Labs and you know, Bell Labs is one

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Speaker 1: of these like elements of another era, an institution that

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Speaker 1: I really miss. You know, it's a privately funded research

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Speaker 1: lab that did basic research. You know, this is an

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Speaker 1: arm of the telephone company. But they just like gave

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Speaker 1: nerds money and said, hey, play around, figure stuff out,

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Speaker 1: and they came up with things like the transistor, which

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Speaker 1: is I think a trillion dollar idea would be underestimating it, right, Like,

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Speaker 1: it's literally the foundation of our entire economy. Its transformed

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Speaker 1: the way we live.

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Speaker 2: Wow.

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Speaker 1: And so I think even if every other piece of

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Speaker 1: science was a waste of money, this one brings the

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Speaker 1: average up like this one idea like means all of

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Speaker 1: science has been worthwhile just from a purely economical, cynical

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Speaker 1: point of view. And that's the way science works, right,

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Speaker 1: like a lot of fuzzes out and occasionally a huge,

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Speaker 1: huge payoff. Anyway, it was the late nineteen forties people

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Speaker 1: figured this out. And you know, we've only had quantum

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Speaker 1: mechanics for a couple of decades.

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Speaker 2: Then.

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Speaker 1: People had ideas for making transistors before then, but weren't

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Speaker 1: able to make it work. But at Bell Labs, smart

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Speaker 1: guys figured this out. One Nobel Prizes. It was really

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Speaker 1: pretty awesome.

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Speaker 2: Awesome, And when we get back, let's talk about how

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Speaker 2: we went about shrinking these transistors. All right, So in

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Speaker 2: nineteen forty seven, Bell Labs creates the transistor just in

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Speaker 2: time for us to use it to get to space,

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Speaker 2: which is the most important topic that we have to

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Speaker 2: keep getting to every episode. All right, So now we've

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Speaker 2: got the transistor, how do we go about shrinking it?

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Speaker 1: Yes, the transistors are built out of semiconductors. You know,

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Speaker 1: you hear the semiconductor industry everywhere, And what does that

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Speaker 1: really mean? Well, we understand what conductor is, right, It's

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Speaker 1: something where electricity can flow. An insulator is something where

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Speaker 1: electricity cannot flow. And to understand that, you have to

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Speaker 1: take your vision of the atom where you have like

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Speaker 1: electrons orbiting around the nucleus or being in fuzzy quantum

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Speaker 1: mechanical states, and think about what happens when you put

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Speaker 1: a lot of atoms together, Like, what is the energy

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Speaker 1: level of an electron around an iron atom? Well, it's

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Speaker 1: a bunch of levels. What happens when you have a

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Speaker 1: billion iron atoms and a lattice, what happens to those electrons? Well,

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Speaker 1: they don't really belong to any individual nucleus anymore. They

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Speaker 1: sort of like move around the iron super highway. They

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Speaker 1: can flow around from here to there. And what distinguishes

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Speaker 1: a conductor from an insulator is whether or not there's

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Speaker 1: a big gap between energy levels, like can the electrons

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Speaker 1: get up to those energy levels where they can flow

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Speaker 1: around between all the atoms or not. If they can

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Speaker 1: get up there, then it's a conductor. If there's a

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Speaker 1: really big gap so they can't get up there, then

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Speaker 1: it's an insulator. Semiconductors are things that are sort of

438
00:22:34,080 --> 00:22:36,959
Speaker 1: halfway in between. They have a medium sized gap between

439
00:22:36,960 --> 00:22:39,800
Speaker 1: the energy levels where the electrons are stuck around individual

440
00:22:39,880 --> 00:22:42,240
Speaker 1: atoms and the ones where they're just flowing across the

441
00:22:42,240 --> 00:22:45,920
Speaker 1: super highway. And so that's something you can control. If

442
00:22:45,960 --> 00:22:47,760
Speaker 1: you tweak it a little bit by like adding them

443
00:22:47,760 --> 00:22:49,960
Speaker 1: a little bit of germanium or this other kind of thing,

444
00:22:50,000 --> 00:22:52,960
Speaker 1: you can control that gap. And so what you want

445
00:22:53,000 --> 00:22:55,640
Speaker 1: when you're building circuits is you want places where things

446
00:22:55,680 --> 00:22:58,240
Speaker 1: conduct really well and then places where things conduct really

447
00:22:58,320 --> 00:23:01,720
Speaker 1: really terribly. So rather than having to have different kinds

448
00:23:01,720 --> 00:23:04,080
Speaker 1: of material, like if I build a circuit in my garage,

449
00:23:04,080 --> 00:23:06,199
Speaker 1: I use copper for the wires and then to use

450
00:23:06,280 --> 00:23:08,920
Speaker 1: rubber for the insulators. It's better if you can have

451
00:23:08,960 --> 00:23:11,080
Speaker 1: a single kind of material and sort of tweak it

452
00:23:11,280 --> 00:23:12,679
Speaker 1: and like, okay, I'm going to make this part of

453
00:23:12,720 --> 00:23:15,200
Speaker 1: it conductor and that part of it an insulator because

454
00:23:15,240 --> 00:23:18,520
Speaker 1: it allows you to print circuits onto your material.

455
00:23:18,840 --> 00:23:21,000
Speaker 2: Okay, And so what is the material you use?

456
00:23:21,320 --> 00:23:24,960
Speaker 1: So we use silicon. Silicon is the semiconductor of choice,

457
00:23:25,080 --> 00:23:27,240
Speaker 1: and then you dope it with various things to change

458
00:23:27,280 --> 00:23:30,280
Speaker 1: its behavior to make it a conductor. And the way

459
00:23:30,320 --> 00:23:33,320
Speaker 1: that we have shrunk transistors from pretty big stuff you

460
00:23:33,320 --> 00:23:37,320
Speaker 1: could see on your garage bench to tiny stuff almost

461
00:23:37,359 --> 00:23:41,040
Speaker 1: the size of atoms is through a technique called photolithography,

462
00:23:41,119 --> 00:23:44,360
Speaker 1: which essentially prints a circuit onto a piece of silicon.

463
00:23:44,680 --> 00:23:47,240
Speaker 1: We grow these huge silicon wafers that are like ten

464
00:23:47,320 --> 00:23:50,200
Speaker 1: inches and then you want to print a circuit onto it,

465
00:23:50,400 --> 00:23:53,080
Speaker 1: and you want to print like billions and billions of transistors,

466
00:23:53,080 --> 00:23:54,720
Speaker 1: and you want them to be as small as possible.

467
00:23:54,720 --> 00:23:57,159
Speaker 1: For the reason we just point it out, So like,

468
00:23:57,280 --> 00:23:59,800
Speaker 1: how do you print this stuff onto a piece of

469
00:23:59,800 --> 00:24:03,919
Speaker 1: sar silicon? So this is what photolithography is. Essentially, you

470
00:24:03,960 --> 00:24:07,119
Speaker 1: design your circuit on the computer, and then you print

471
00:24:07,240 --> 00:24:09,919
Speaker 1: on the surface of the silicon this thing called a

472
00:24:09,960 --> 00:24:13,880
Speaker 1: photo mask, and the photomask like protects the silicon from

473
00:24:13,920 --> 00:24:15,639
Speaker 1: the next thing you're gonna do to it, which is

474
00:24:15,680 --> 00:24:19,160
Speaker 1: blast it with really high energy light. So you shoot

475
00:24:19,200 --> 00:24:22,479
Speaker 1: like super high energy light at the silicon which is

476
00:24:22,840 --> 00:24:25,400
Speaker 1: partially covered by this mask, and the parts that are

477
00:24:25,440 --> 00:24:28,640
Speaker 1: exposed get a little bit chemically changed. Then you dip

478
00:24:28,680 --> 00:24:31,159
Speaker 1: the whole thing in like acid, and the parts that

479
00:24:31,160 --> 00:24:34,040
Speaker 1: we're exposed get like eaten away, for example, and so

480
00:24:34,160 --> 00:24:36,879
Speaker 1: what you're left with is just the pattern that you wanted.

481
00:24:37,240 --> 00:24:41,240
Speaker 1: That's like a very hand wavy explanation of how photolithography works.

482
00:24:41,720 --> 00:24:44,560
Speaker 1: But the things to understand is that it's limited by

483
00:24:44,640 --> 00:24:48,719
Speaker 1: those photons. Like if you use photons that we really

484
00:24:48,800 --> 00:24:51,720
Speaker 1: wide wavelengths, then you're going to get a fuzzy picture.

485
00:24:51,760 --> 00:24:55,000
Speaker 1: If you use photons with really narrow wavelengths, which means

486
00:24:55,160 --> 00:24:58,520
Speaker 1: high energy photons, right, then you're gonna get a much

487
00:24:58,520 --> 00:25:02,360
Speaker 1: crisper picture. And so over the decades, we've been trying

488
00:25:02,400 --> 00:25:05,439
Speaker 1: to shrink these transistors to get more and more transistors

489
00:25:05,480 --> 00:25:08,160
Speaker 1: on these chips and have faster computers. And one way

490
00:25:08,160 --> 00:25:10,560
Speaker 1: to do that is to crank up the energy of

491
00:25:10,560 --> 00:25:13,639
Speaker 1: those photons, and so now we're in the like extreme

492
00:25:13,840 --> 00:25:16,560
Speaker 1: ultraviolet limit where the photons have a wavelength of like

493
00:25:16,640 --> 00:25:20,399
Speaker 1: thirteen or fourteen nanometers. Wow, And that's hard because it

494
00:25:20,440 --> 00:25:23,840
Speaker 1: requires like special optics. You can't just use normal lenses

495
00:25:23,880 --> 00:25:26,320
Speaker 1: to bend this kind of light. It's why it's very

496
00:25:26,359 --> 00:25:29,160
Speaker 1: hard to do, like X ray optics. Also, the higher

497
00:25:29,200 --> 00:25:31,080
Speaker 1: the energy light, the harder it is to bend it.

498
00:25:31,400 --> 00:25:32,520
Speaker 2: Have we maxed this out?

499
00:25:32,920 --> 00:25:35,679
Speaker 1: We probably have maxed this out, because anything beyond this

500
00:25:35,840 --> 00:25:40,080
Speaker 1: requires insane optics. Like already the optics are insane. You know,

501
00:25:40,320 --> 00:25:43,800
Speaker 1: making a single mask for these things costs like hundreds

502
00:25:43,840 --> 00:25:47,240
Speaker 1: of thousands of dollars, and there's like a few places

503
00:25:47,240 --> 00:25:49,000
Speaker 1: in the world you can do this kind of stuff.

504
00:25:49,320 --> 00:25:52,920
Speaker 1: The equipment is extremely expensive, the operating conditions are very

505
00:25:53,000 --> 00:25:56,720
Speaker 1: very particular. You have to have specialized clean rooms. Like

506
00:25:57,359 --> 00:26:01,239
Speaker 1: this is really the pinnacle of technology. It's incredible, and

507
00:26:01,280 --> 00:26:03,760
Speaker 1: that's why you know a few of these players in

508
00:26:03,800 --> 00:26:07,320
Speaker 1: this field, like the Taiwannee semiconductor industry is so important

509
00:26:07,480 --> 00:26:11,520
Speaker 1: for the worldwide computing industry. Like a single company goes

510
00:26:11,560 --> 00:26:14,560
Speaker 1: down and like we can't make computers anymore.

511
00:26:14,600 --> 00:26:18,240
Speaker 2: Wow, Oh my gosh. Right, all of the geopolitical tensions

512
00:26:18,280 --> 00:26:19,800
Speaker 2: just came into focus exactly.

513
00:26:19,880 --> 00:26:22,679
Speaker 1: That's one reason why Taiwan is so important because a

514
00:26:22,720 --> 00:26:25,280
Speaker 1: lot of this stuff is done by Taiwan, these firms.

515
00:26:25,520 --> 00:26:28,800
Speaker 2: All Right, so we figured out photo lithography and we've

516
00:26:28,920 --> 00:26:31,399
Speaker 2: kind of reached the limit. Yeah, is that the end

517
00:26:31,440 --> 00:26:32,000
Speaker 2: of the story.

518
00:26:32,160 --> 00:26:34,439
Speaker 1: It's not quite the end of the story. And you know,

519
00:26:34,600 --> 00:26:36,439
Speaker 1: I'm more to say about like how impressive it is.

520
00:26:36,480 --> 00:26:39,000
Speaker 1: Like in the mid nineties, we were doing things at

521
00:26:39,000 --> 00:26:42,280
Speaker 1: like three hundred and fifteen nanimeters scale, which sounds pretty awesome,

522
00:26:42,320 --> 00:26:45,399
Speaker 1: Like that sounds pretty tiny. And then late nineties it

523
00:26:45,440 --> 00:26:47,520
Speaker 1: was like one hundred and eighty animeters, and the two

524
00:26:47,600 --> 00:26:51,440
Speaker 1: thousands it was sub one hundred nanimeters. These days we're

525
00:26:51,440 --> 00:26:55,720
Speaker 1: getting down to like ten nanimeters single nanometers. It's amazing,

526
00:26:56,080 --> 00:26:59,440
Speaker 1: but it's getting harder and harder because we're already beyond

527
00:26:59,560 --> 00:27:02,119
Speaker 1: the wave length of the light that we're using, right,

528
00:27:02,720 --> 00:27:05,879
Speaker 1: and we're approaching the size of the atom. Right, silk

529
00:27:05,920 --> 00:27:09,679
Speaker 1: and atoms are like zero point two nanometers across, so

530
00:27:09,840 --> 00:27:12,280
Speaker 1: like you're going to be bild a transistor out of something.

531
00:27:12,760 --> 00:27:14,440
Speaker 1: It's like you know, you can't make things out of

532
00:27:14,520 --> 00:27:17,000
Speaker 1: legos if you only have a few of the bricks, right,

533
00:27:17,600 --> 00:27:21,479
Speaker 1: And so it's challenging to make transistors smaller than about

534
00:27:21,520 --> 00:27:25,440
Speaker 1: a nanometer because you're really reaching that fundamental limit of

535
00:27:25,480 --> 00:27:28,760
Speaker 1: the size of the silicon atom. And every year it

536
00:27:28,800 --> 00:27:32,160
Speaker 1: gets harder. Like it's true that we've increased the transistor

537
00:27:32,280 --> 00:27:35,919
Speaker 1: density every two years, we've doubled it, but the amount

538
00:27:35,960 --> 00:27:39,399
Speaker 1: of money spent in this research has increased by a

539
00:27:39,440 --> 00:27:41,879
Speaker 1: factor of ten or twenty, so it's not like a

540
00:27:41,960 --> 00:27:44,959
Speaker 1: constant effort every year to achieve this. We have to

541
00:27:45,080 --> 00:27:48,200
Speaker 1: ramp up the energy and the creativity. And that's great,

542
00:27:48,280 --> 00:27:50,760
Speaker 1: you know, it's like inspired all sorts of cool things

543
00:27:50,880 --> 00:27:54,520
Speaker 1: and spinoffs and whatever. But it gets really really complicated.

544
00:27:55,040 --> 00:27:57,439
Speaker 1: And the sort of cutting edge of this is to

545
00:27:57,440 --> 00:28:01,160
Speaker 1: now start stacking these transistors, so well, don't just think

546
00:28:01,160 --> 00:28:03,760
Speaker 1: of it as a plane. Let's go up in the

547
00:28:03,800 --> 00:28:07,120
Speaker 1: third dimension. Let's make the transistors more powerful by shrinking

548
00:28:07,119 --> 00:28:09,200
Speaker 1: them further and then allowing them to grow in sort

549
00:28:09,200 --> 00:28:13,240
Speaker 1: of the third dimension above this sort of plane. And

550
00:28:13,359 --> 00:28:16,520
Speaker 1: the leading edge of technology right now are these transistors

551
00:28:16,520 --> 00:28:20,119
Speaker 1: called fin fet so FET, which stands for a field

552
00:28:20,160 --> 00:28:24,120
Speaker 1: effet transistor and then a finn meaning like they literally

553
00:28:24,160 --> 00:28:28,080
Speaker 1: have this like fin over the gate that controls it

554
00:28:28,119 --> 00:28:29,840
Speaker 1: like a physical thing. It looks like a shark fin.

555
00:28:30,680 --> 00:28:34,120
Speaker 1: That makes it possible to be efficient while shrinking even further,

556
00:28:34,200 --> 00:28:36,600
Speaker 1: so you can make the sort of footprint of it

557
00:28:36,760 --> 00:28:40,400
Speaker 1: smaller while keeping its effectiveness because you have this third dimension.

558
00:28:40,400 --> 00:28:43,800
Speaker 1: And so that's what stacking is. And really people think

559
00:28:43,840 --> 00:28:46,760
Speaker 1: that we've reached the limit of what we can do technologically,

560
00:28:46,800 --> 00:28:48,920
Speaker 1: and as some of the listeners have said, we're going

561
00:28:48,920 --> 00:28:52,280
Speaker 1: in other directions, like instead of making your CPU more dense,

562
00:28:52,320 --> 00:28:55,800
Speaker 1: you just have multiple cores, or you start building other

563
00:28:55,920 --> 00:28:59,760
Speaker 1: dedicated stuff like graphics processing units that are really good

564
00:29:00,120 --> 00:29:03,080
Speaker 1: at linear algebra, which is needed for graphics and also

565
00:29:03,160 --> 00:29:06,400
Speaker 1: for machine learning. And so we're sort of like simultaneously

566
00:29:06,400 --> 00:29:08,520
Speaker 1: trying to go in many directions at once to improve

567
00:29:08,560 --> 00:29:11,360
Speaker 1: the power of computing. But it's not clear that we

568
00:29:11,400 --> 00:29:13,800
Speaker 1: can keep doing this, and a lot of people think

569
00:29:13,840 --> 00:29:16,040
Speaker 1: that we really are at the edge of what we

570
00:29:16,080 --> 00:29:17,720
Speaker 1: can do to improve computing speed.

571
00:29:17,880 --> 00:29:20,080
Speaker 2: Wow, and so stacking isn't going to be the magic

572
00:29:20,120 --> 00:29:22,280
Speaker 2: solution because there's like limits on stacking.

573
00:29:22,560 --> 00:29:24,760
Speaker 1: Yeah, exactly, like stack can get you a little further.

574
00:29:24,800 --> 00:29:27,080
Speaker 1: But if we're going to keep doubling, then it's hard.

575
00:29:27,640 --> 00:29:29,000
Speaker 1: And you know, I think there's something to be said

576
00:29:29,000 --> 00:29:33,600
Speaker 1: about the sociological impact of this doubling. You know, Moore's

577
00:29:33,680 --> 00:29:36,160
Speaker 1: law is not something that comes out of like the

578
00:29:36,160 --> 00:29:39,959
Speaker 1: fundamental laws of physics. It's something that was predicted and

579
00:29:40,000 --> 00:29:43,640
Speaker 1: that we maintained really over decades, which is really incredible.

580
00:29:43,720 --> 00:29:46,920
Speaker 1: Like one of Intel's earliest processors, the four thousand and four,

581
00:29:47,360 --> 00:29:50,400
Speaker 1: had twenty three hundred transistors in it, right, whereas like

582
00:29:50,640 --> 00:29:52,959
Speaker 1: the eighty three eighty six, which I've spent a lot

583
00:29:52,960 --> 00:29:56,280
Speaker 1: of time programming on as a teenager, had like hundreds

584
00:29:56,320 --> 00:29:58,800
Speaker 1: of thousands of transistors in this MacBook I'm sitting in

585
00:29:58,840 --> 00:30:01,880
Speaker 1: front of has billions. It's incredible, but it's sort of

586
00:30:01,960 --> 00:30:04,760
Speaker 1: guided the field. I think people because they thought this

587
00:30:04,920 --> 00:30:08,960
Speaker 1: was possible and maybe even inevitable, they worked for it.

588
00:30:08,600 --> 00:30:11,280
Speaker 1: It's a target, you know, And so if you think

589
00:30:11,320 --> 00:30:14,000
Speaker 1: something as possible, then like you stay late and you

590
00:30:14,040 --> 00:30:16,040
Speaker 1: push hard and you come up with new ideas, and

591
00:30:16,080 --> 00:30:19,040
Speaker 1: so in some sense it's a self fulfilling prophecy.

592
00:30:19,240 --> 00:30:21,160
Speaker 2: Okay, so first of all, have we hit the limit

593
00:30:21,200 --> 00:30:23,520
Speaker 2: to More's law? Already, or you just think we're going

594
00:30:23,600 --> 00:30:26,240
Speaker 2: to hit it soon, Like when is the first year

595
00:30:26,280 --> 00:30:28,560
Speaker 2: you think where we'll be like Moore's law gone.

596
00:30:30,480 --> 00:30:32,520
Speaker 1: I think we're right at that inflection point. You know,

597
00:30:32,560 --> 00:30:36,360
Speaker 1: we're still seeing improvements in speed, we're still seeing big

598
00:30:36,400 --> 00:30:40,160
Speaker 1: boosts and productivity, but we're sort of running out of

599
00:30:40,200 --> 00:30:43,400
Speaker 1: avenues and so I still see that, Like my MacBook

600
00:30:43,480 --> 00:30:45,600
Speaker 1: is faster than the one I gave my son, which

601
00:30:45,640 --> 00:30:48,280
Speaker 1: is my two year old MacBook, But I don't know

602
00:30:48,360 --> 00:30:50,000
Speaker 1: that the one I'm going to get in two years

603
00:30:50,200 --> 00:30:53,120
Speaker 1: is going to be as much faster. So I think

604
00:30:53,120 --> 00:30:54,520
Speaker 1: we're right at that inflection point.

605
00:30:54,880 --> 00:30:57,240
Speaker 2: So that feels a little scary to me. So, like,

606
00:30:57,360 --> 00:30:59,960
Speaker 2: you know, over time, we've gotten computers that are better

607
00:31:00,200 --> 00:31:02,720
Speaker 2: and so at least, you know, in my field, almost

608
00:31:02,800 --> 00:31:05,920
Speaker 2: every five years you expect, you know, the statistical models

609
00:31:05,920 --> 00:31:08,680
Speaker 2: of the systems that we study to get more complicated

610
00:31:08,760 --> 00:31:10,680
Speaker 2: so that we can get a better understanding out of

611
00:31:10,680 --> 00:31:13,640
Speaker 2: each one of our data sets. Are we not going

612
00:31:13,720 --> 00:31:15,360
Speaker 2: to be able to do that anymore? Or do you

613
00:31:15,360 --> 00:31:17,280
Speaker 2: think in twenty years our computers are just going to

614
00:31:17,280 --> 00:31:19,640
Speaker 2: start getting bigger again until they fill up a room,

615
00:31:19,960 --> 00:31:22,680
Speaker 2: Because we're going to want to keep getting more complicated

616
00:31:23,120 --> 00:31:24,240
Speaker 2: in our analyses.

617
00:31:24,640 --> 00:31:28,000
Speaker 1: Yeah, well, I think we're already seeing our computing getting bigger.

618
00:31:28,080 --> 00:31:30,200
Speaker 1: I mean, think about like the data centers that are

619
00:31:30,240 --> 00:31:33,160
Speaker 1: being built by Meta and Microsoft is like trying to

620
00:31:33,200 --> 00:31:36,160
Speaker 1: turn back on nuclear reactors because they need the power

621
00:31:36,160 --> 00:31:39,040
Speaker 1: for their AI data centers. These things are vast and

622
00:31:39,040 --> 00:31:42,320
Speaker 1: they're consuming huge amounts of our resources. So I think, yeah,

623
00:31:42,360 --> 00:31:45,040
Speaker 1: our appetite for computing is just growing, and even if

624
00:31:45,040 --> 00:31:46,920
Speaker 1: our computers don't get faster, we're just going to keep

625
00:31:46,920 --> 00:31:49,760
Speaker 1: building them bigger and bigger. But I also think that

626
00:31:49,800 --> 00:31:51,239
Speaker 1: for those of us who do things that are not

627
00:31:51,400 --> 00:31:55,760
Speaker 1: directly just computing, that there are other ways to increase speed.

628
00:31:56,000 --> 00:31:57,840
Speaker 1: I was talking to Katrina about this, and she was

629
00:31:57,880 --> 00:32:01,800
Speaker 1: saying that Moore's law also kind of applies to genomics.

630
00:32:02,280 --> 00:32:05,040
Speaker 1: You know, like the first study of a human genome

631
00:32:05,480 --> 00:32:09,040
Speaker 1: costs like how many millions for one genome, And then

632
00:32:09,040 --> 00:32:11,320
Speaker 1: the NIA had a target like it should cost less

633
00:32:11,360 --> 00:32:14,320
Speaker 1: than one thousand dollars to sequence a human genome, and

634
00:32:14,360 --> 00:32:16,480
Speaker 1: they hit that target and now it's cheaper than one

635
00:32:16,520 --> 00:32:19,200
Speaker 1: thousand dollars. And where does this come from? Part of

636
00:32:19,240 --> 00:32:21,640
Speaker 1: it come from computing, but also part of it comes

637
00:32:21,680 --> 00:32:25,240
Speaker 1: from like the miniaturization of biology. And I've seen this

638
00:32:25,440 --> 00:32:28,320
Speaker 1: just like observing her field. Something that used to be

639
00:32:28,480 --> 00:32:31,640
Speaker 1: like a PhD level of work then in a few

640
00:32:31,720 --> 00:32:34,280
Speaker 1: years becomes a little box on the lab bench. You

641
00:32:34,320 --> 00:32:37,120
Speaker 1: press a button and it's done while you're at lunch, right, Yeah,

642
00:32:37,120 --> 00:32:39,680
Speaker 1: and that allows you to now do things that were

643
00:32:39,760 --> 00:32:43,400
Speaker 1: impossible ten years earlier. And that kind of transformation of

644
00:32:43,440 --> 00:32:46,800
Speaker 1: the scope of the capacity of the field enables broader,

645
00:32:46,960 --> 00:32:49,880
Speaker 1: deeper thinking. And that's not just computing, right, that's the

646
00:32:49,880 --> 00:32:53,720
Speaker 1: menigtization of like the actual biology, like micro little bits.

647
00:32:53,960 --> 00:32:56,920
Speaker 1: It's essentially like what therapnose was tapping into this feeling like, oh,

648
00:32:56,960 --> 00:32:59,800
Speaker 1: eventually we should be able to diagnose diseases with tiny

649
00:32:59,800 --> 00:33:02,480
Speaker 1: life jobs of blood in this kind of sense. So

650
00:33:02,480 --> 00:33:05,760
Speaker 1: I think that there's lots of dimensions that we can

651
00:33:05,960 --> 00:33:10,480
Speaker 1: follow for improving our scientific and technological industrial capacity. Is

652
00:33:10,560 --> 00:33:12,640
Speaker 1: not just is my computer faster.

653
00:33:13,120 --> 00:33:16,720
Speaker 2: So when someone says, like you just did such and

654
00:33:16,800 --> 00:33:21,880
Speaker 2: such follows Moore's law, do they essentially mean we do

655
00:33:21,920 --> 00:33:25,200
Speaker 2: it better with smaller stuff, and like we do it

656
00:33:25,240 --> 00:33:27,480
Speaker 2: exponentially better in particular.

657
00:33:27,360 --> 00:33:31,120
Speaker 1: Yeah, I think it's about exponential growth that's a crucial

658
00:33:31,120 --> 00:33:34,640
Speaker 1: thing because you know, exponential growth builds on itself. You know,

659
00:33:34,680 --> 00:33:37,720
Speaker 1: it's like putting a dollar in the bank. Every year

660
00:33:37,760 --> 00:33:40,200
Speaker 1: you have more dollars, and those dollars earn more dollars,

661
00:33:40,200 --> 00:33:43,200
Speaker 1: and eventually you have all the dollars. Whereas like if

662
00:33:43,240 --> 00:33:46,000
Speaker 1: you're just selling lemonade and you're making a dollar every day,

663
00:33:46,160 --> 00:33:48,840
Speaker 1: you're making the same amount of dollars every day. It's

664
00:33:48,840 --> 00:33:51,760
Speaker 1: not increasing. So it's all about that exponential growth. And

665
00:33:51,800 --> 00:33:54,520
Speaker 1: I think that that's what people mean when they refer

666
00:33:54,640 --> 00:33:57,280
Speaker 1: to Moore's law sort of more colloquially than just like

667
00:33:57,440 --> 00:33:59,480
Speaker 1: the density of transistors on a chip.

668
00:33:59,600 --> 00:34:02,480
Speaker 2: That's kind of interesting because like Moore's law isn't really

669
00:34:02,480 --> 00:34:05,040
Speaker 2: a law, like it's an observation. And so it seems

670
00:34:05,040 --> 00:34:07,560
Speaker 2: like now anytime we see exponential growth, we say the

671
00:34:07,600 --> 00:34:11,880
Speaker 2: words Moore's law instead of just saying exponential growth or

672
00:34:11,920 --> 00:34:13,200
Speaker 2: am I being negative?

673
00:34:14,040 --> 00:34:16,360
Speaker 1: No? I think you're right, And I think it says

674
00:34:16,400 --> 00:34:20,200
Speaker 1: something about our aspirations. You know, we live in a

675
00:34:20,280 --> 00:34:23,520
Speaker 1: time when we expect our children's lives to be very

676
00:34:23,560 --> 00:34:26,600
Speaker 1: different from our lives and our grandparent lives, and that's

677
00:34:26,960 --> 00:34:30,080
Speaker 1: really unusual, Like most of human history. You could tell

678
00:34:30,120 --> 00:34:31,520
Speaker 1: your kids what their life was going to be like,

679
00:34:31,520 --> 00:34:33,440
Speaker 1: because it's going to be basically the same as yours

680
00:34:33,480 --> 00:34:37,120
Speaker 1: and your grandparents for like the last ten thousand years, right,

681
00:34:37,160 --> 00:34:41,520
Speaker 1: because like change was inconceivable because nobody had ever experienced it.

682
00:34:42,120 --> 00:34:43,960
Speaker 1: But now we live in a time when, like we

683
00:34:44,080 --> 00:34:47,000
Speaker 1: know that's not true, and so I think it leaves

684
00:34:47,080 --> 00:34:49,880
Speaker 1: us with this like gap in our wisdom. And then

685
00:34:49,920 --> 00:34:52,480
Speaker 1: we project forward, and some of us are optimistic and

686
00:34:52,520 --> 00:34:55,480
Speaker 1: we're like, Yay, this is going to change our lives

687
00:34:55,480 --> 00:34:57,840
Speaker 1: in a way that solves all of our problems. And

688
00:34:57,840 --> 00:35:00,640
Speaker 1: as you'll hear from Adam, some of us are less optimistic,

689
00:35:01,200 --> 00:35:03,719
Speaker 1: you know, about what this means and whether it's the

690
00:35:03,800 --> 00:35:05,520
Speaker 1: right way to place our bets.

691
00:35:05,920 --> 00:35:08,080
Speaker 2: I do feel like that was a slightly simplified view

692
00:35:08,080 --> 00:35:11,400
Speaker 2: of history, But this isn't Daniel and Kelly's historical universe.

693
00:35:11,440 --> 00:35:12,239
Speaker 2: So we're moving on.

694
00:35:12,600 --> 00:35:14,360
Speaker 1: Hey, I have to fit it into about one minute,

695
00:35:14,360 --> 00:35:16,040
Speaker 1: so I'm not going to do a deep dive. But yeah,

696
00:35:16,160 --> 00:35:18,520
Speaker 1: I mean, do you disagree with me about the broader

697
00:35:18,520 --> 00:35:21,960
Speaker 1: assessment of the way that human experience has changed.

698
00:35:22,360 --> 00:35:24,839
Speaker 2: I do think human experience was similar for a really

699
00:35:24,880 --> 00:35:27,319
Speaker 2: long time. You know, like when our ancestors moved out

700
00:35:27,360 --> 00:35:30,000
Speaker 2: of Africa, there was probably a lot that changed in

701
00:35:30,040 --> 00:35:34,040
Speaker 2: a couple generations, and the Industrial Revolution and climate. Yeah, yeah,

702
00:35:34,239 --> 00:35:36,759
Speaker 2: I think there's probably been a lot of moments where

703
00:35:36,800 --> 00:35:39,359
Speaker 2: things were like, oh crud, but usually probably they were

704
00:35:39,400 --> 00:35:42,160
Speaker 2: getting worse, whereas now we're hoping that it's getting better.

705
00:35:42,280 --> 00:35:47,200
Speaker 2: But anyway, so when I was reading Adam Becker's new

706
00:35:47,239 --> 00:35:51,640
Speaker 2: book More Everything Forever, there was a discussion on Moore's

707
00:35:51,680 --> 00:35:54,759
Speaker 2: Law where I realized, like, oh my gosh, I fundamentally

708
00:35:54,800 --> 00:35:58,239
Speaker 2: didn't understand Moore's Law very well or what like underpinned

709
00:35:58,280 --> 00:36:00,839
Speaker 2: Moore's Law, and I didn't realize that we were, you know,

710
00:36:00,960 --> 00:36:04,200
Speaker 2: perhaps reaching the end of Moore's Law. And so we

711
00:36:04,400 --> 00:36:07,120
Speaker 2: reached out to Adam Becker and asked if he would

712
00:36:07,120 --> 00:36:10,480
Speaker 2: talk to us about sort of the implication of, you know,

713
00:36:10,520 --> 00:36:13,359
Speaker 2: the death of Moore's Law. I'll be super dramatic about it,

714
00:36:13,480 --> 00:36:17,759
Speaker 2: but how this expectation of exponential growth impacts our view

715
00:36:17,800 --> 00:36:22,000
Speaker 2: of the future in ways that are not always necessarily realistic.

716
00:36:22,080 --> 00:36:44,480
Speaker 1: Let's say, all right, so then we're very happy to

717
00:36:44,520 --> 00:36:48,799
Speaker 1: welcome to the podcast. Adam Becker, who is an astrophysicist

718
00:36:48,920 --> 00:36:53,200
Speaker 1: turned author. He wrote the widely acclaimed book What Is Real,

719
00:36:53,320 --> 00:36:56,000
Speaker 1: one of my favorite books about quantum chinnings. If you

720
00:36:56,120 --> 00:36:58,400
Speaker 1: write me to ask for a book about quantum mechanics

721
00:36:58,400 --> 00:37:01,600
Speaker 1: that explain stuff in an accessible way, often recommended. And

722
00:37:01,680 --> 00:37:05,800
Speaker 1: he has a new book out called More Everything Forever

723
00:37:06,080 --> 00:37:09,920
Speaker 1: about the rise of techno utopiasts and how we can

724
00:37:09,920 --> 00:37:13,760
Speaker 1: project our future and the future of technology. Adam, Welcome

725
00:37:13,800 --> 00:37:16,520
Speaker 1: back to the podcast. Thanks, it's great to be here.

726
00:37:16,800 --> 00:37:20,279
Speaker 1: So let's start just by talking about Moore's law. It's

727
00:37:20,320 --> 00:37:23,720
Speaker 1: the foundation of so much of the techno utopian movement.

728
00:37:24,320 --> 00:37:27,360
Speaker 1: Why do you think that it has inspired sort of

729
00:37:27,400 --> 00:37:31,960
Speaker 1: this broader fanaticism, especially when it's just like an empirical observation,

730
00:37:32,160 --> 00:37:34,360
Speaker 1: not like a deep law of the universe.

731
00:37:34,760 --> 00:37:35,920
Speaker 3: Yeah, that's a good question.

732
00:37:37,200 --> 00:37:42,000
Speaker 5: I mean Moore's law. Yeah, it is an empirical observation.

733
00:37:42,920 --> 00:37:48,360
Speaker 5: But it's so regular, it's so comforting, and it has

734
00:37:48,440 --> 00:37:52,160
Speaker 5: you know, the fact that Moore's law held more or

735
00:37:52,280 --> 00:37:57,560
Speaker 5: less accurately for what about fifty years. It did change

736
00:37:58,000 --> 00:38:00,760
Speaker 5: a lot of things about the world, and it took

737
00:38:01,520 --> 00:38:08,960
Speaker 5: computers from being these large, slow, you know, refrigerator sized

738
00:38:09,040 --> 00:38:13,600
Speaker 5: things that live in mainframe rooms at corporations to you know,

739
00:38:13,760 --> 00:38:15,480
Speaker 5: tiny little things that live in our pockets, are on

740
00:38:15,560 --> 00:38:20,560
Speaker 5: our wrists and have much more power than all of

741
00:38:20,840 --> 00:38:23,440
Speaker 5: the main frames that existed, you know in the nineteen

742
00:38:23,480 --> 00:38:29,400
Speaker 5: seventies combined, right, caused all sorts of changes in our society,

743
00:38:29,760 --> 00:38:32,400
Speaker 5: some for the better, so much for the worse. But

744
00:38:32,520 --> 00:38:37,799
Speaker 5: you know, living through that, it seemed like clockwork, right,

745
00:38:37,960 --> 00:38:40,000
Speaker 5: you know. I mean I only lived through like the

746
00:38:40,080 --> 00:38:42,960
Speaker 5: last part of it, But I remember when I was

747
00:38:42,960 --> 00:38:44,920
Speaker 5: a kid, it seemed like, you know, computers were just

748
00:38:45,040 --> 00:38:48,600
Speaker 5: always getting smaller and faster and better every single year,

749
00:38:48,640 --> 00:38:52,239
Speaker 5: and you could just get you know, the advice was

750
00:38:52,360 --> 00:38:54,880
Speaker 5: wait as long as you can to get a new computer,

751
00:38:55,000 --> 00:38:58,040
Speaker 5: because the longer you wait, the better it'll be. Right,

752
00:38:58,600 --> 00:39:01,200
Speaker 5: And it was this amazing thing, and it made a

753
00:39:01,200 --> 00:39:03,759
Speaker 5: lot of people a lot of money, and a few

754
00:39:03,760 --> 00:39:07,120
Speaker 5: people truly enormous amounts of money. And so you put

755
00:39:07,160 --> 00:39:09,239
Speaker 5: all of that together and it's it kind of makes

756
00:39:09,239 --> 00:39:12,920
Speaker 5: some sense that some people would take it extremely seriously

757
00:39:12,960 --> 00:39:17,279
Speaker 5: as this general thing, because it seemed to be, you know,

758
00:39:17,400 --> 00:39:21,960
Speaker 5: if you lived a comfortable middle or upper class life,

759
00:39:22,320 --> 00:39:24,520
Speaker 5: it seemed like the most important thing in the world.

760
00:39:24,520 --> 00:39:28,520
Speaker 5: In the late twentieth century, right, and it wasn't really

761
00:39:28,560 --> 00:39:32,279
Speaker 5: like anything that you'd seen before. It was easy to think, oh,

762
00:39:32,360 --> 00:39:37,000
Speaker 5: this is just going to continue. So Ray Kurzweil is

763
00:39:37,040 --> 00:39:40,759
Speaker 5: this inventor and futurist who you know. He made like

764
00:39:40,920 --> 00:39:46,600
Speaker 5: real serious contributions to text to speech technology and like

765
00:39:46,640 --> 00:39:50,280
Speaker 5: assistive devices for the visually impaired, and I think hearing

766
00:39:50,280 --> 00:39:53,400
Speaker 5: impaired as well. You know, he made serious contributions to

767
00:39:53,520 --> 00:39:58,000
Speaker 5: the field of electronic instruments, like you know, musical instruments.

768
00:39:58,640 --> 00:40:00,480
Speaker 3: But he is best known as a futurist.

769
00:40:00,560 --> 00:40:02,200
Speaker 5: He is best known as somebody who you know, makes

770
00:40:02,239 --> 00:40:04,640
Speaker 5: these forecasts about what the future is going to be like.

771
00:40:04,800 --> 00:40:08,120
Speaker 1: So he's a retired electrical engineer, you're saying, essentially, yeah,

772
00:40:08,160 --> 00:40:09,640
Speaker 1: I have a lot of those in my inbox.

773
00:40:10,280 --> 00:40:12,080
Speaker 3: Yeah, me too, man.

774
00:40:14,120 --> 00:40:16,839
Speaker 5: I'm pretty sure that if you put anywhere on the

775
00:40:16,840 --> 00:40:19,640
Speaker 5: Internet that you have a PhD in physics, you get

776
00:40:19,640 --> 00:40:21,840
Speaker 5: a lot of retired electrical engineers in your inbox.

777
00:40:22,800 --> 00:40:25,680
Speaker 2: Guys, my inbox has pictures of feces from people who

778
00:40:25,719 --> 00:40:28,600
Speaker 2: want to note the parasite infections. I'm feeling pretty low

779
00:40:28,719 --> 00:40:30,160
Speaker 2: on sympathy right now.

780
00:40:30,400 --> 00:40:34,080
Speaker 5: Oh, but I once got so sorry for you guys.

781
00:40:34,200 --> 00:40:35,279
Speaker 3: Yeah, no, we should.

782
00:40:35,360 --> 00:40:37,600
Speaker 5: We should have a separate episode just talking about what's

783
00:40:37,600 --> 00:40:42,120
Speaker 5: in our inboxes, because I have some crazy stuff in

784
00:40:42,239 --> 00:40:43,120
Speaker 5: any event.

785
00:40:42,920 --> 00:40:45,440
Speaker 1: All right, So you're telling us how Ray Kurzweild was

786
00:40:45,480 --> 00:40:49,880
Speaker 1: thinking about how Moore's law is transforming technology and that's

787
00:40:50,000 --> 00:40:54,040
Speaker 1: the engine of transformation of society and predicting the future

788
00:40:54,080 --> 00:40:55,840
Speaker 1: of society more broadly exactly.

789
00:40:55,960 --> 00:41:01,320
Speaker 5: Yeah, And like Kurzweil extends Moore's Law in his Forecasts

790
00:41:01,360 --> 00:41:04,239
Speaker 5: of the Future and says, oh, this is part of

791
00:41:04,239 --> 00:41:08,160
Speaker 5: a more general trend in the history of technology and

792
00:41:08,360 --> 00:41:12,320
Speaker 5: the history of you know, even life in the universe.

793
00:41:12,600 --> 00:41:15,239
Speaker 5: And he calls it the law of accelerating returns, where

794
00:41:15,239 --> 00:41:18,239
Speaker 5: he says, you know, once you have better technology, it's

795
00:41:18,280 --> 00:41:20,279
Speaker 5: going to allow you to make the technology that you've

796
00:41:20,280 --> 00:41:22,960
Speaker 5: already got even better, and then that'll just be a

797
00:41:23,000 --> 00:41:26,520
Speaker 5: self reinforcing cycle that leads to this exponential trend. And

798
00:41:26,640 --> 00:41:30,000
Speaker 5: More's law is just one manifestation of that trend, and

799
00:41:30,040 --> 00:41:33,080
Speaker 5: it's going to you know, he says, it's something that

800
00:41:33,160 --> 00:41:35,440
Speaker 5: you can see if you look back through the entire

801
00:41:35,560 --> 00:41:39,560
Speaker 5: history not just of human technology, but evolution of life

802
00:41:39,560 --> 00:41:41,839
Speaker 5: on Earth, because you see the same thing with biological

803
00:41:42,160 --> 00:41:45,480
Speaker 5: quote unquote technology, and he says, you know, this is

804
00:41:45,520 --> 00:41:48,399
Speaker 5: going to continue, and in short order we are going

805
00:41:48,440 --> 00:41:50,959
Speaker 5: to reach this point that he calls the singularity, which

806
00:41:51,000 --> 00:41:54,440
Speaker 5: is where you've got, you know, technology that has developed

807
00:41:54,480 --> 00:41:56,799
Speaker 5: to such an advanced degree that it gives us, you know,

808
00:41:56,920 --> 00:42:00,560
Speaker 5: godlike powers of creation and destruction and transform and just

809
00:42:00,680 --> 00:42:04,799
Speaker 5: changes the fundamental nature of life on Earth and in

810
00:42:04,840 --> 00:42:05,400
Speaker 5: the universe.

811
00:42:05,880 --> 00:42:08,160
Speaker 1: Well, a lot of what you said sounds reasonable. R

812
00:42:08,400 --> 00:42:11,520
Speaker 1: There is evolution, and there is transformation, and things are

813
00:42:11,640 --> 00:42:14,560
Speaker 1: changing more rapidly. But from reading your book and from

814
00:42:14,600 --> 00:42:17,800
Speaker 1: your tone, I'm guessing you don't agree with Cursewil about

815
00:42:18,000 --> 00:42:20,440
Speaker 1: singularity and how we're all going to be techno gods

816
00:42:20,440 --> 00:42:22,960
Speaker 1: in the future. Why not? Why will Daniel not be

817
00:42:23,000 --> 00:42:23,759
Speaker 1: a techno god?

818
00:42:24,160 --> 00:42:24,560
Speaker 3: Yeah?

819
00:42:24,600 --> 00:42:27,200
Speaker 2: I mean, look Daniel in particular, Yes.

820
00:42:26,960 --> 00:42:28,960
Speaker 1: Yeah, I have a personal stake in this question.

821
00:42:29,040 --> 00:42:32,719
Speaker 5: Now, yes, Daniel in particular, Yeah, you are not going

822
00:42:32,760 --> 00:42:36,759
Speaker 5: to be a techno god, Daniel, because you know, by

823
00:42:36,840 --> 00:42:39,520
Speaker 5: having me on this podcast, Ray Kurzwild is going to

824
00:42:39,600 --> 00:42:41,440
Speaker 5: put you on his list, and then you know he

825
00:42:41,520 --> 00:42:43,960
Speaker 5: won't allow you to ascend to God.

826
00:42:44,920 --> 00:42:46,080
Speaker 2: I knew this was a mistake.

827
00:42:46,200 --> 00:42:51,640
Speaker 1: Yeah, exactly, worse than trying to fight a land born Asia. Huh, yes, exactly.

828
00:42:51,760 --> 00:42:52,839
Speaker 3: Yeah, No, that's number two.

829
00:42:52,840 --> 00:42:56,080
Speaker 5: Now number one is inviting Adam Becker onto your podcast.

830
00:42:57,440 --> 00:43:01,040
Speaker 5: But I mean, look, Curzwild is taking this exponential trend

831
00:43:01,040 --> 00:43:03,200
Speaker 5: and just extending it out into the future and saying

832
00:43:03,239 --> 00:43:06,600
Speaker 5: it's going to last forever. And the one thing that's

833
00:43:06,719 --> 00:43:10,000
Speaker 5: always true about exponential trends is that they end.

834
00:43:10,800 --> 00:43:10,960
Speaker 1: Right.

835
00:43:11,239 --> 00:43:14,560
Speaker 5: If you see any sort of exponential trend in nature

836
00:43:14,920 --> 00:43:18,520
Speaker 5: or in you know, technology or whatever, your first thought

837
00:43:18,560 --> 00:43:23,520
Speaker 5: should be, oh, that can't last, because it just doesn't.

838
00:43:23,560 --> 00:43:27,640
Speaker 5: There are not enough resources, there's not enough space, there's

839
00:43:27,719 --> 00:43:31,840
Speaker 5: not enough anything to allow exponential trends in general to

840
00:43:31,960 --> 00:43:36,239
Speaker 5: continue forever. One of the examples that Churzwild gives in

841
00:43:36,280 --> 00:43:38,600
Speaker 5: his book The Singularity Is Near which is probably his

842
00:43:38,640 --> 00:43:41,280
Speaker 5: most famous book from about two thousand and five.

843
00:43:41,520 --> 00:43:43,799
Speaker 1: Doesn't he have a few books like The Singularity is Near,

844
00:43:43,960 --> 00:43:46,759
Speaker 1: The Singularity is near Er, the Singularity is near Ish?

845
00:43:46,920 --> 00:43:47,600
Speaker 3: Yeah, yeah, yeah.

846
00:43:47,600 --> 00:43:50,760
Speaker 5: The Singularity is Near Er came out last year, and

847
00:43:50,800 --> 00:43:52,920
Speaker 5: when I tell people that that's the title, they usually

848
00:43:52,960 --> 00:43:53,640
Speaker 5: don't believe me.

849
00:43:53,719 --> 00:43:55,920
Speaker 3: But that is actually the title. He wrote a book

850
00:43:55,960 --> 00:43:58,359
Speaker 3: called the singularity is nearer.

851
00:43:58,400 --> 00:44:00,600
Speaker 1: Next year, the singularity is near your ear.

852
00:44:01,160 --> 00:44:03,200
Speaker 3: Yeah, neariest.

853
00:44:04,360 --> 00:44:11,719
Speaker 5: But the classic example in biology of exponential growth is

854
00:44:11,760 --> 00:44:15,279
Speaker 5: something like bacterial growth in a petri dish and yeah,

855
00:44:15,320 --> 00:44:18,279
Speaker 5: if you chart the number of bacteria in this you know,

856
00:44:18,400 --> 00:44:23,160
Speaker 5: nutrient rich medium over time, Yeah, it grows exponentially until

857
00:44:23,200 --> 00:44:25,720
Speaker 5: they fill the dish and eat all of the agar

858
00:44:26,440 --> 00:44:28,319
Speaker 5: and then they die.

859
00:44:29,200 --> 00:44:31,120
Speaker 2: To try to play Devil's advocates, so when I was

860
00:44:31,120 --> 00:44:33,880
Speaker 2: talking to space settlement folks, they would say something like,

861
00:44:33,960 --> 00:44:37,080
Speaker 2: you know, the reason we need to go into space

862
00:44:37,239 --> 00:44:40,080
Speaker 2: is because exponential growth does end at some point. But

863
00:44:40,200 --> 00:44:43,040
Speaker 2: our species is so amazing that we can see when

864
00:44:43,080 --> 00:44:46,400
Speaker 2: we're getting close to the like asymptope and the exponential curve,

865
00:44:46,840 --> 00:44:48,680
Speaker 2: and so we can go out to space and get

866
00:44:48,719 --> 00:44:51,480
Speaker 2: resources and we can be more proactive about it. What

867
00:44:52,000 --> 00:44:53,240
Speaker 2: is wrong about that argument?

868
00:44:53,719 --> 00:44:55,600
Speaker 3: Yeah?

869
00:44:55,640 --> 00:44:57,040
Speaker 2: I mean where to start?

870
00:44:56,440 --> 00:44:56,520
Speaker 3: Ye?

871
00:44:57,320 --> 00:44:58,200
Speaker 2: What you got to pick somewhere?

872
00:44:58,280 --> 00:45:01,080
Speaker 5: Okay, I'm going to pick on you know, I'm going

873
00:45:01,120 --> 00:45:02,840
Speaker 5: to do what we should all strive to do, or

874
00:45:02,880 --> 00:45:04,719
Speaker 5: what I strive to do, and punch up right, I'm

875
00:45:04,719 --> 00:45:06,080
Speaker 5: going to pick on somebody bigger than me.

876
00:45:06,480 --> 00:45:09,040
Speaker 3: Jeff Bezos makes the same argument, right, Yeah.

877
00:45:09,040 --> 00:45:12,640
Speaker 5: Jeff Bezos says that we need to go out into

878
00:45:12,680 --> 00:45:16,560
Speaker 5: space because of exactly this. He says, you know, we

879
00:45:16,800 --> 00:45:20,839
Speaker 5: are using exponentially more energy as time goes on, and

880
00:45:20,960 --> 00:45:23,600
Speaker 5: if that trend continues as it has for decades, if

881
00:45:23,640 --> 00:45:27,520
Speaker 5: not centuries, then in about two three hundred years, we're

882
00:45:27,560 --> 00:45:29,920
Speaker 5: going to be using all of the energy on Earth

883
00:45:30,000 --> 00:45:32,160
Speaker 5: that we get from the Sun and will have used

884
00:45:32,239 --> 00:45:35,359
Speaker 5: up all of the non renewable resources. And so at

885
00:45:35,360 --> 00:45:37,160
Speaker 5: that point we need to go out into space, if

886
00:45:37,160 --> 00:45:39,200
Speaker 5: not before, then otherwise we're going to have what he

887
00:45:39,239 --> 00:45:42,719
Speaker 5: calls a civilization of stasis and rationing. And you know,

888
00:45:42,800 --> 00:45:46,160
Speaker 5: he's not wrong about the first part. If somehow we

889
00:45:46,280 --> 00:45:49,359
Speaker 5: continue that exponential trend in energy usage, then yeah, and

890
00:45:49,400 --> 00:45:52,399
Speaker 5: I think it's in about three four hundred years, we'd

891
00:45:52,400 --> 00:45:54,880
Speaker 5: be using all of the energy available to us on Earth.

892
00:45:55,080 --> 00:45:58,120
Speaker 5: And also we'd be using so much energy that like

893
00:45:58,239 --> 00:46:02,080
Speaker 5: the waste heat from our energy usage would like boil

894
00:46:02,120 --> 00:46:02,880
Speaker 5: off the oceans.

895
00:46:03,719 --> 00:46:07,080
Speaker 3: We can't we can't do that, right, it's not possible.

896
00:46:07,600 --> 00:46:10,520
Speaker 5: I mean, putting aside that, you know, it's it's implausible

897
00:46:10,560 --> 00:46:13,319
Speaker 5: that that trend will continue. The problem is with the

898
00:46:13,320 --> 00:46:16,799
Speaker 5: second half, because yeah, Okay, we get like three to

899
00:46:16,800 --> 00:46:19,000
Speaker 5: four hundred more years here on Earth if you continue

900
00:46:19,000 --> 00:46:21,759
Speaker 5: that trend. So Bezos says, we have to go out

901
00:46:21,800 --> 00:46:23,920
Speaker 5: into space, and you know what he doesn't say is

902
00:46:24,280 --> 00:46:27,440
Speaker 5: where where resources are unlimited, but you know he implies it.

903
00:46:28,120 --> 00:46:31,640
Speaker 5: The problem is that if you really want exponential growth

904
00:46:31,719 --> 00:46:34,120
Speaker 5: to continue, going out into space, it doesn't actually help

905
00:46:34,160 --> 00:46:36,560
Speaker 5: you that much if you're looking on a timescale of centuries,

906
00:46:36,960 --> 00:46:41,800
Speaker 5: because if you do that, like about I think it's

907
00:46:41,840 --> 00:46:45,520
Speaker 5: like one thousand years after we hit that point of

908
00:46:45,640 --> 00:46:48,320
Speaker 5: using all of the sunlight that hits Earth, we get

909
00:46:48,600 --> 00:46:50,680
Speaker 5: to a point where we're just using the entire energy

910
00:46:50,680 --> 00:46:56,120
Speaker 5: output of the Sun. And then if we spot Bezos

911
00:46:56,160 --> 00:46:59,560
Speaker 5: and company a warp drive so they can go faster

912
00:46:59,640 --> 00:47:02,839
Speaker 5: than the line to try to amass even more resources

913
00:47:02,920 --> 00:47:05,960
Speaker 5: very very quickly outside of the Solar system, which we

914
00:47:06,000 --> 00:47:09,000
Speaker 5: shouldn't spot them a warp drive. There's no reason to

915
00:47:09,040 --> 00:47:10,560
Speaker 5: think that you can build a warp drive, and a

916
00:47:10,600 --> 00:47:12,680
Speaker 5: lot of reason to think that you can't. But if

917
00:47:12,719 --> 00:47:15,640
Speaker 5: we do spot them a warp drive, that only gets

918
00:47:15,680 --> 00:47:18,480
Speaker 5: you like about another two thousand years before you're using

919
00:47:18,640 --> 00:47:23,400
Speaker 5: all of the energy in the observable universe. Wow, so

920
00:47:23,960 --> 00:47:28,520
Speaker 5: you know there are limits, growth ends, and the fact

921
00:47:28,640 --> 00:47:31,520
Speaker 5: is that you know, all of that is wildly implausible.

922
00:47:31,560 --> 00:47:34,160
Speaker 5: It's not like the lesson that I want people to

923
00:47:34,160 --> 00:47:36,960
Speaker 5: take away from all of this is, oh, well, we

924
00:47:37,080 --> 00:47:39,640
Speaker 5: better keep in mind that growth has to end at

925
00:47:39,719 --> 00:47:42,280
Speaker 5: some point to the next like three thousand days years.

926
00:47:42,600 --> 00:47:44,719
Speaker 5: The answer is, oh, no, growth has to end a

927
00:47:44,760 --> 00:47:49,000
Speaker 5: lot sooner than that, because you know, going out into

928
00:47:49,000 --> 00:47:51,839
Speaker 5: space has lots of problems, even putting aside the lack

929
00:47:51,880 --> 00:47:54,799
Speaker 5: of warp drive, just living in the Solar System is

930
00:47:55,000 --> 00:47:59,400
Speaker 5: an extraordinarily difficult and dubious proposition. To give Bezos a

931
00:47:59,400 --> 00:48:01,959
Speaker 5: little bit of cris after ragging on him just now,

932
00:48:02,200 --> 00:48:04,319
Speaker 5: one of the things I like that Jeff Bezos has

933
00:48:04,360 --> 00:48:07,320
Speaker 5: said is he makes fun of Elon Musk for wanting

934
00:48:07,360 --> 00:48:09,200
Speaker 5: to go to Mars because Mars sucks.

935
00:48:09,680 --> 00:48:11,759
Speaker 3: But Bezos's solution is.

936
00:48:11,800 --> 00:48:14,640
Speaker 5: You know, for going out into space, it's not considerably better,

937
00:48:15,040 --> 00:48:18,640
Speaker 5: which is to build like hundreds of thousands or millions

938
00:48:18,680 --> 00:48:21,920
Speaker 5: of enormous city size space stations and then have everybody

939
00:48:22,000 --> 00:48:22,719
Speaker 5: live inside of them.

940
00:48:22,800 --> 00:48:26,080
Speaker 3: This is also not a great idea for many many reasons.

941
00:48:26,440 --> 00:48:29,160
Speaker 1: All right, So it's reasonable, I think to make these

942
00:48:29,239 --> 00:48:33,080
Speaker 1: arguments against like the strongest version of those claims. You know,

943
00:48:33,400 --> 00:48:36,680
Speaker 1: exponential growth will last forever. Sure, and you're right, that's

944
00:48:36,719 --> 00:48:39,840
Speaker 1: obviously practical because the universe is finite, or the observable

945
00:48:39,840 --> 00:48:41,160
Speaker 1: part of it is finite at least.

946
00:48:41,239 --> 00:48:41,399
Speaker 3: Yeah.

947
00:48:41,480 --> 00:48:43,560
Speaker 1: Yeah, But what if we just like water down those

948
00:48:43,560 --> 00:48:45,880
Speaker 1: claims a little bit and we just say, you know,

949
00:48:46,080 --> 00:48:50,560
Speaker 1: technology is transforming society very rapidly, and even the future

950
00:48:50,640 --> 00:48:54,760
Speaker 1: you describe as refuting exponential growth. That sounds pretty awesome.

951
00:48:54,840 --> 00:48:57,480
Speaker 1: Like if in two thousand years we're tapping into all

952
00:48:57,480 --> 00:49:00,239
Speaker 1: the energy from the Sun and nearby stars and have

953
00:49:00,280 --> 00:49:03,759
Speaker 1: an incredible, you know, star spanning civilization a lot of

954
00:49:03,760 --> 00:49:06,279
Speaker 1: people out there, and be like, that sounds great. What's

955
00:49:06,320 --> 00:49:06,879
Speaker 1: wrong with that?

956
00:49:07,239 --> 00:49:10,720
Speaker 5: The prospect of large numbers of people living and working

957
00:49:10,760 --> 00:49:16,680
Speaker 5: in space has an enormous number of technological and social

958
00:49:16,719 --> 00:49:20,239
Speaker 5: and political questions tied to it that are very very

959
00:49:20,320 --> 00:49:24,680
Speaker 5: difficult to solve and may not be solvable. And some

960
00:49:24,719 --> 00:49:28,359
Speaker 5: of those problems are sort of irreducibly time consuming. You

961
00:49:28,440 --> 00:49:33,319
Speaker 5: can't solve them without doing like lengthy experiments involving things

962
00:49:33,360 --> 00:49:36,240
Speaker 5: like radiation exposure and low gravity exposure.

963
00:49:35,920 --> 00:49:37,760
Speaker 3: And things like that. And I see Kelly nodding.

964
00:49:38,200 --> 00:49:40,560
Speaker 5: And you know, Kelly may know more about this than

965
00:49:40,600 --> 00:49:42,640
Speaker 5: I do, because you know, this is one of the

966
00:49:42,640 --> 00:49:45,920
Speaker 5: subjects in my book. Kelly and Zach wrote an entire

967
00:49:45,960 --> 00:49:49,600
Speaker 5: book about this, an excellent book that I really like.

968
00:49:49,920 --> 00:49:51,759
Speaker 2: I do always find a way to pull the conversation

969
00:49:51,840 --> 00:49:54,560
Speaker 2: back to space settlement. So sorry for derailing us, but

970
00:49:54,719 --> 00:49:56,600
Speaker 2: you do a great chapter on it in your book.

971
00:49:56,719 --> 00:49:59,040
Speaker 5: Yeah, thank you, and you have nothing to apologize for

972
00:49:59,160 --> 00:50:00,120
Speaker 5: it's in my book.

973
00:50:00,239 --> 00:50:02,800
Speaker 1: But let me maybe highlight a difference between the takes

974
00:50:02,800 --> 00:50:05,880
Speaker 1: you guys have in your books. Kelly and Zach say

975
00:50:05,960 --> 00:50:09,120
Speaker 1: that you know, we're maybe not ready to settle space,

976
00:50:09,200 --> 00:50:12,240
Speaker 1: that we haven't done the necessary legwork, and we shouldn't

977
00:50:12,280 --> 00:50:15,520
Speaker 1: get over excited and jump too fast and send people

978
00:50:15,560 --> 00:50:17,239
Speaker 1: to Mars now, because there's a lot of stuff we

979
00:50:17,239 --> 00:50:19,759
Speaker 1: need to figure out, but that it's possible and if

980
00:50:19,800 --> 00:50:22,359
Speaker 1: we do it right, maybe you could figure this out.

981
00:50:22,360 --> 00:50:24,600
Speaker 1: We just aren't there yet. But I feel like your

982
00:50:24,600 --> 00:50:27,600
Speaker 1: book goes a step further and suggests that you know

983
00:50:27,640 --> 00:50:30,600
Speaker 1: it's dangerous to make these projections. You know, somebody out

984
00:50:30,600 --> 00:50:33,239
Speaker 1: there listening might say, all right, Adam, maybe we won't

985
00:50:33,280 --> 00:50:36,480
Speaker 1: get there, you know, to as far as these guys project,

986
00:50:36,800 --> 00:50:39,319
Speaker 1: but however far we get will be great. What do

987
00:50:39,360 --> 00:50:41,319
Speaker 1: you say to that person? Is there a danger in

988
00:50:41,400 --> 00:50:42,360
Speaker 1: this kind of thinking?

989
00:50:42,760 --> 00:50:43,240
Speaker 3: Yeah?

990
00:50:43,280 --> 00:50:45,600
Speaker 5: I mean this This gets back sort of to the

991
00:50:45,640 --> 00:50:48,680
Speaker 5: last question that you asked me as well, because we

992
00:50:48,920 --> 00:50:52,960
Speaker 5: don't know that it's possible to have large numbers of

993
00:50:53,040 --> 00:50:57,040
Speaker 5: humans living off of Earth. Because it's very possible that

994
00:50:57,040 --> 00:51:00,640
Speaker 5: that's not, you know, something that we can do. We

995
00:51:00,719 --> 00:51:04,480
Speaker 5: need to find a way to live safely and healthily

996
00:51:04,560 --> 00:51:09,360
Speaker 5: within the limits imposed by Earth. We can't just assume

997
00:51:09,440 --> 00:51:12,000
Speaker 5: that we're going to be able to leave. The danger

998
00:51:12,239 --> 00:51:15,759
Speaker 5: is that this rhetoric of oh, it's always going to

999
00:51:15,800 --> 00:51:18,400
Speaker 5: be possible to expand out into space and grow forever

1000
00:51:18,880 --> 00:51:22,120
Speaker 5: can be used, and in fact it's not hypothetical. It

1001
00:51:22,239 --> 00:51:27,560
Speaker 5: is being used to justify this sort of logic of

1002
00:51:27,719 --> 00:51:31,720
Speaker 5: rapacious consumption that is not sustainable here on Earth.

1003
00:51:32,280 --> 00:51:34,520
Speaker 3: And because there's a very.

1004
00:51:34,360 --> 00:51:38,720
Speaker 5: Good chance that we cannot in any meaningful way leave Earth,

1005
00:51:39,680 --> 00:51:41,719
Speaker 5: we need to stop doing that and find a way

1006
00:51:41,719 --> 00:51:42,279
Speaker 5: to live here.

1007
00:51:42,840 --> 00:51:45,160
Speaker 3: That's not to say that we shouldn't explore space. I

1008
00:51:45,160 --> 00:51:47,200
Speaker 3: think robots in space are amazing.

1009
00:51:47,640 --> 00:51:51,399
Speaker 5: Like the voyager probes make me cry, you know, I'm

1010
00:51:51,440 --> 00:51:54,840
Speaker 5: a cosmologist by training. I think getting data from space

1011
00:51:54,960 --> 00:51:57,680
Speaker 5: is really important and interesting. I'm not even saying that

1012
00:51:57,680 --> 00:52:01,360
Speaker 5: we shouldn't send people into space to you know, the

1013
00:52:01,400 --> 00:52:05,200
Speaker 5: Apollo missions were amazing and really interesting. They were, of course,

1014
00:52:05,239 --> 00:52:08,200
Speaker 5: you know, not primarily missions of scientific discovery. It was

1015
00:52:08,239 --> 00:52:10,759
Speaker 5: about the Cold War. But still like the fact that

1016
00:52:10,800 --> 00:52:14,279
Speaker 5: we did like a crude sample return mission to the

1017
00:52:14,320 --> 00:52:19,600
Speaker 5: Moon several times and nobody died is amazing. But the

1018
00:52:19,719 --> 00:52:21,640
Speaker 5: visions that we have of the future are used to

1019
00:52:21,880 --> 00:52:24,840
Speaker 5: justify all sorts of things right here and now, and

1020
00:52:24,880 --> 00:52:28,879
Speaker 5: so we need to be careful about what we think

1021
00:52:28,960 --> 00:52:30,960
Speaker 5: the future is going to look like and whether that's

1022
00:52:31,040 --> 00:52:34,799
Speaker 5: remotely plausible. And I really think that the things that

1023
00:52:34,960 --> 00:52:38,000
Speaker 5: Musk and Bezos and these other tech billionaires are talking

1024
00:52:38,080 --> 00:52:41,680
Speaker 5: about are sort of like saying, you know, yeah, well

1025
00:52:41,680 --> 00:52:43,640
Speaker 5: it's okay that we're doing what we're doing right now,

1026
00:52:43,680 --> 00:52:46,040
Speaker 5: because in the future we're all going to live in

1027
00:52:46,080 --> 00:52:49,440
Speaker 5: like Hogwarts and have broomsticks and magic wands and like,

1028
00:52:49,719 --> 00:52:54,200
Speaker 5: it's roughly the same level of plausibility.

1029
00:52:54,480 --> 00:52:57,279
Speaker 2: And so to try to get us connecting More's law

1030
00:52:57,320 --> 00:52:59,480
Speaker 2: back with where we are in the conversation. To me,

1031
00:52:59,560 --> 00:53:02,520
Speaker 2: I see the connection being that you've got this thinking

1032
00:53:02,560 --> 00:53:05,480
Speaker 2: that we're going to have exponential growth and our ability

1033
00:53:05,520 --> 00:53:07,279
Speaker 2: to do everything. So like when I was talking to

1034
00:53:07,320 --> 00:53:09,799
Speaker 2: space settlement people, they'd be like, I'd talk about a

1035
00:53:09,800 --> 00:53:12,080
Speaker 2: problem and they'd say, well, AI is going to solve

1036
00:53:12,120 --> 00:53:15,600
Speaker 2: that everything is expanding. Our ability to do anything related

1037
00:53:15,640 --> 00:53:19,239
Speaker 2: to technology keeps expanding exponentially, and so you know, we've

1038
00:53:19,239 --> 00:53:22,160
Speaker 2: talked about how we have limits and so you can't

1039
00:53:22,200 --> 00:53:25,560
Speaker 2: expect exponential trends to go on forever. Do you connect

1040
00:53:25,640 --> 00:53:29,879
Speaker 2: then this kind of Moore's law thinking with techno optimism

1041
00:53:30,120 --> 00:53:33,520
Speaker 2: and this these sort of views of the future. Or

1042
00:53:33,640 --> 00:53:35,320
Speaker 2: have we just gotten off on a different topic.

1043
00:53:35,760 --> 00:53:37,880
Speaker 5: No, No, No, I think these things are connected, right, Like,

1044
00:53:37,920 --> 00:53:39,759
Speaker 5: there's a reason why all of these different things are.

1045
00:53:39,800 --> 00:53:42,319
Speaker 5: In my book, one of the things that I like

1046
00:53:42,840 --> 00:53:45,480
Speaker 5: to remind people about when we're talking about Moore's law

1047
00:53:45,560 --> 00:53:48,560
Speaker 5: is that More's law. It's not just that it's an

1048
00:53:48,600 --> 00:53:52,839
Speaker 5: empirical observation rather than a law of nature. More's law

1049
00:53:52,920 --> 00:53:57,240
Speaker 5: was a decision. More'slaw is a choice that the leaders

1050
00:53:57,280 --> 00:54:01,280
Speaker 5: of the semiconductor industry made, and then they continued making

1051
00:54:01,320 --> 00:54:03,919
Speaker 5: it for decades, you know, and there was a road

1052
00:54:04,000 --> 00:54:07,200
Speaker 5: map and lots and lots of different, you know, plans

1053
00:54:07,400 --> 00:54:10,879
Speaker 5: made in order to ensure the continuation of Moore's law

1054
00:54:11,160 --> 00:54:15,360
Speaker 5: for as long as possible. There are massive, massive amounts

1055
00:54:15,360 --> 00:54:18,680
Speaker 5: of money and corporate resources poured into this, and in fact,

1056
00:54:18,960 --> 00:54:22,440
Speaker 5: Moore's law is not even an example of accelerating returns,

1057
00:54:22,239 --> 00:54:24,480
Speaker 5: as as Kurzweil would have, but in a sense it's

1058
00:54:24,480 --> 00:54:27,200
Speaker 5: an example of diminishing returns because they got, you know,

1059
00:54:27,280 --> 00:54:31,520
Speaker 5: the semiconductor industry got less bang for their buck over time.

1060
00:54:31,600 --> 00:54:34,360
Speaker 5: They had to spend more and more money, even adjusting

1061
00:54:34,360 --> 00:54:37,759
Speaker 5: for inflation, just to get the same doubling of the

1062
00:54:37,840 --> 00:54:42,120
Speaker 5: number of processors crammed into the same space. The techno

1063
00:54:42,239 --> 00:54:47,239
Speaker 5: utopian sort of ideas that Kurzweil pushes using Moore's law,

1064
00:54:47,440 --> 00:54:50,799
Speaker 5: as you know, sort of the justification and this you

1065
00:54:50,840 --> 00:54:53,839
Speaker 5: know eternal expansion into space stuff that we've just been

1066
00:54:53,840 --> 00:54:57,760
Speaker 5: talking about, they all sort of traffic in the idea

1067
00:54:57,800 --> 00:55:01,799
Speaker 5: that the future of technology is not just eternal exponential

1068
00:55:01,840 --> 00:55:06,120
Speaker 5: growth and expansion, but that it's inevitably that not that

1069
00:55:06,120 --> 00:55:08,160
Speaker 5: that's you know, something that we could do, but that

1070
00:55:08,280 --> 00:55:10,759
Speaker 5: it's it's what we have to do. It's what is

1071
00:55:10,840 --> 00:55:14,040
Speaker 5: going to happen, and the only alternative, if there is one,

1072
00:55:14,360 --> 00:55:17,720
Speaker 5: is the extinction of the species. And you know, again

1073
00:55:17,920 --> 00:55:22,120
Speaker 5: Musk is extremely clear about this. Musk has said the

1074
00:55:22,200 --> 00:55:26,000
Speaker 5: only choice we have is eternal expansion out into cosmos

1075
00:55:26,360 --> 00:55:29,880
Speaker 5: or extinction. And when he's pushed on this, he, you know,

1076
00:55:29,920 --> 00:55:31,800
Speaker 5: he brings up the fact that you know, in about

1077
00:55:31,920 --> 00:55:34,400
Speaker 5: half a billion or a billion years, it's going to

1078
00:55:34,440 --> 00:55:36,360
Speaker 5: get so hot on Earth because of you know, the

1079
00:55:36,400 --> 00:55:41,319
Speaker 5: sun getting hotter, that the oceans will boil off. And yeah,

1080
00:55:41,600 --> 00:55:45,560
Speaker 5: that's not wrong, but you know, a lot's gonna happen

1081
00:55:45,640 --> 00:55:48,279
Speaker 5: between now and then. Not only is it not a

1082
00:55:48,280 --> 00:55:52,400
Speaker 5: particularly pressing problem, but it may not even end up

1083
00:55:52,400 --> 00:55:55,319
Speaker 5: being our problem at all, because there are many other

1084
00:55:55,480 --> 00:55:59,640
Speaker 5: things that could cause humanity to go extinct between now

1085
00:55:59,640 --> 00:56:03,240
Speaker 5: and then, like say, civilizational collapse due to global warming,

1086
00:56:03,920 --> 00:56:08,239
Speaker 5: for example, a problem that tech oligarchs and other billionaires

1087
00:56:08,280 --> 00:56:10,120
Speaker 5: have done a lot of work to try to prevent

1088
00:56:10,239 --> 00:56:13,760
Speaker 5: humanity from solving. But instead Musk says that the solution

1089
00:56:13,840 --> 00:56:15,680
Speaker 5: is to leave Earth. And this is the sort of

1090
00:56:15,760 --> 00:56:17,760
Speaker 5: rhetoric that I was talking about, you know, in terms

1091
00:56:17,760 --> 00:56:20,759
Speaker 5: of like, this is what this eternal expansion idea gets you.

1092
00:56:21,239 --> 00:56:23,960
Speaker 5: But it's also I think part of the connection with

1093
00:56:24,920 --> 00:56:28,840
Speaker 5: the logic of taking Moore's law as this law of

1094
00:56:28,960 --> 00:56:33,040
Speaker 5: nature that we can always count on these exponential trends,

1095
00:56:33,080 --> 00:56:36,560
Speaker 5: and we can always count on human ingenuity and technical

1096
00:56:36,600 --> 00:56:39,640
Speaker 5: knowledge and know how to get us out of any problem.

1097
00:56:39,800 --> 00:56:40,919
Speaker 3: If you believe that.

1098
00:56:41,560 --> 00:56:43,880
Speaker 5: Account for all of the problems in the world today, Like,

1099
00:56:43,920 --> 00:56:46,880
Speaker 5: there's so many problems that we have that are not

1100
00:56:46,960 --> 00:56:50,000
Speaker 5: amenable to technological solutions that people have tried to solve

1101
00:56:50,000 --> 00:56:53,680
Speaker 5: for a long time, that are fundamentally social in nature,

1102
00:56:54,239 --> 00:56:57,040
Speaker 5: or you know, had a technological component but also have

1103
00:56:57,080 --> 00:57:00,840
Speaker 5: a social component, like climate change. Right, we have a lot,

1104
00:57:01,120 --> 00:57:03,200
Speaker 5: if not all, of the technology that we need to

1105
00:57:03,280 --> 00:57:06,760
Speaker 5: address climate change, but we haven't yet as a species,

1106
00:57:06,800 --> 00:57:09,560
Speaker 5: and that's primarily a social and political issue, not an

1107
00:57:09,560 --> 00:57:10,640
Speaker 5: issue of technology.

1108
00:57:11,040 --> 00:57:14,399
Speaker 1: So, to paraphrase your argument, I think you're saying, it's

1109
00:57:14,440 --> 00:57:17,600
Speaker 1: not that computers won't get faster and the technology can't

1110
00:57:17,640 --> 00:57:20,280
Speaker 1: help us in the future. It's just that we can't

1111
00:57:20,560 --> 00:57:23,160
Speaker 1: rely on it always doing so to magically solve all

1112
00:57:23,200 --> 00:57:26,440
Speaker 1: of our problems, and doing so distract ourselves from the

1113
00:57:26,480 --> 00:57:28,600
Speaker 1: real problems we face in the more immediate future.

1114
00:57:28,800 --> 00:57:32,240
Speaker 5: Yeah, yeah, I mean, also, more's laws over. I mean,

1115
00:57:32,360 --> 00:57:36,840
Speaker 5: come on, we have the transistors down about as small

1116
00:57:36,840 --> 00:57:41,000
Speaker 5: as we can get them. You know, you can't make

1117
00:57:41,040 --> 00:57:44,000
Speaker 5: a silicon transistor smaller than an atom of silicon.

1118
00:57:44,120 --> 00:57:46,320
Speaker 1: But you do see a role for technology in shaping

1119
00:57:46,320 --> 00:57:48,479
Speaker 1: our future. I mean, it's not that you don't want

1120
00:57:48,720 --> 00:57:50,080
Speaker 1: chet gpt to cure cancer.

1121
00:57:50,720 --> 00:57:52,720
Speaker 5: I definitely hear that there's a role for technology in

1122
00:57:52,760 --> 00:57:53,520
Speaker 5: shaping our future.

1123
00:57:53,560 --> 00:57:55,920
Speaker 3: Technology is a big part of how we shape our future.

1124
00:57:56,360 --> 00:57:58,120
Speaker 5: I'm going to just pretend that you didn't say the

1125
00:57:58,160 --> 00:58:01,840
Speaker 5: thing about chatch ept curing cancer. Though. God, there's this tweet,

1126
00:58:02,600 --> 00:58:05,720
Speaker 5: like one of my favorite tweet and responses, effor is

1127
00:58:05,760 --> 00:58:10,439
Speaker 5: where Sam Altman said something like be me, build chatch

1128
00:58:10,520 --> 00:58:13,160
Speaker 5: ept to cure cancer or whatever, and then people start

1129
00:58:13,160 --> 00:58:15,240
Speaker 5: criticizing you, and then he like goes on and on

1130
00:58:15,360 --> 00:58:17,400
Speaker 5: and like has a pity party for himself. And then

1131
00:58:17,440 --> 00:58:22,720
Speaker 5: somebody just responded with did you cure cancer or whatever?

1132
00:58:23,000 --> 00:58:26,760
Speaker 5: But you know, there has been actually great progress made

1133
00:58:26,760 --> 00:58:29,280
Speaker 5: and treating cancer just in the last few years, right,

1134
00:58:29,360 --> 00:58:32,360
Speaker 5: you know, like these I don't remember the names of

1135
00:58:32,360 --> 00:58:34,120
Speaker 5: the drugs because like I'm not a cancer guy, but

1136
00:58:34,240 --> 00:58:39,080
Speaker 5: like these approaches of like getting cancer patient's own immune

1137
00:58:39,120 --> 00:58:41,640
Speaker 5: systems to properly recognize and attack the cancers in their

1138
00:58:41,640 --> 00:58:46,160
Speaker 5: own bodies has been like incredibly successful and is really

1139
00:58:46,160 --> 00:58:49,720
Speaker 5: promising for further developments. It's really amazing, and like there

1140
00:58:49,760 --> 00:58:52,200
Speaker 5: have been all sorts of really amazing biomedical advances that

1141
00:58:52,200 --> 00:58:55,000
Speaker 5: are currently being destroyed by RFK Junior and Trump. Like

1142
00:58:55,160 --> 00:58:57,480
Speaker 5: mRNA vaccines are one of the great success stories of

1143
00:58:57,560 --> 00:59:00,960
Speaker 5: you know, biomedical science in the last twenty years. And

1144
00:59:01,000 --> 00:59:04,080
Speaker 5: I think that's important, and I think in general, vaccines

1145
00:59:04,120 --> 00:59:07,080
Speaker 5: are great. You know, there's all sorts of really wonderful

1146
00:59:07,160 --> 00:59:10,920
Speaker 5: technology that we've created that has made the world generally

1147
00:59:11,040 --> 00:59:13,960
Speaker 5: a better place, or has at least enabled.

1148
00:59:13,560 --> 00:59:15,080
Speaker 3: People to make the world a better place.

1149
00:59:15,160 --> 00:59:15,320
Speaker 1: Right.

1150
00:59:15,360 --> 00:59:17,840
Speaker 5: In general, technology is a tool, and there are questions

1151
00:59:17,880 --> 00:59:20,600
Speaker 5: about how you use it, right. You know, nuclear power

1152
00:59:20,640 --> 00:59:22,520
Speaker 5: can be used to build nuclear power plants, but it

1153
00:59:22,520 --> 00:59:25,200
Speaker 5: can also be used to make bombs. YadA, YadA, YadA.

1154
00:59:25,280 --> 00:59:27,480
Speaker 5: I think I just YadA YadA, nuclear apocalypse.

1155
00:59:27,560 --> 00:59:31,040
Speaker 3: But yeah, you did, Yeah whatever. I'm a physicist. Of

1156
00:59:31,080 --> 00:59:32,080
Speaker 3: course that's what I'm gonna do.

1157
00:59:32,160 --> 00:59:34,520
Speaker 5: But the point is, yeah, of course there's a role

1158
00:59:34,600 --> 00:59:36,760
Speaker 5: for technology to play in shaping our future.

1159
00:59:36,760 --> 00:59:38,640
Speaker 3: It's just not two things.

1160
00:59:39,080 --> 00:59:41,280
Speaker 5: Technology is not the only thing that shapes our future,

1161
00:59:41,640 --> 00:59:46,440
Speaker 5: and the development and future direction of technology is not inevitable.

1162
00:59:47,000 --> 00:59:51,680
Speaker 5: Technology is something that humans make, and the future development

1163
00:59:51,720 --> 00:59:55,880
Speaker 5: of technology is filled with contingency and human choice. It

1164
00:59:56,000 --> 00:59:58,840
Speaker 5: is not like we build every single technology that it

1165
00:59:58,880 --> 01:00:02,439
Speaker 5: is physically possible to build. It's not on rails. It's

1166
01:00:02,480 --> 01:00:04,760
Speaker 5: not like it's you know, the analogy I make in

1167
01:00:04,800 --> 01:00:07,840
Speaker 5: the book, It's not like a tech tree in civilization, right,

1168
01:00:08,320 --> 01:00:10,360
Speaker 5: where like the future of technology is just sort of

1169
01:00:10,400 --> 01:00:12,320
Speaker 5: revealed to us and we have we just make a

1170
01:00:12,400 --> 01:00:14,680
Speaker 5: choice about which branch we're going to pursue first.

1171
01:00:15,160 --> 01:00:17,439
Speaker 3: That's not how anything works.

1172
01:00:17,440 --> 01:00:20,160
Speaker 1: All right. Well, thanks Adam for coming on, and let's

1173
01:00:20,160 --> 01:00:22,680
Speaker 1: hope that chat GBT desk your cancer before any of

1174
01:00:22,760 --> 01:00:23,200
Speaker 1: us get it.

1175
01:00:23,400 --> 01:00:32,920
Speaker 2: Thanks for being on the show, Adam absolutely. Daniel and

1176
01:00:33,000 --> 01:00:36,919
Speaker 2: Kelly's Extraordinary Universe is produced by iHeartRadio. We would love

1177
01:00:37,000 --> 01:00:38,920
Speaker 2: to hear from you, We really would.

1178
01:00:39,080 --> 01:00:41,840
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1179
01:00:42,040 --> 01:00:43,680
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1180
01:00:43,760 --> 01:00:46,720
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1181
01:00:46,720 --> 01:00:49,720
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1182
01:00:49,760 --> 01:00:50,160
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1183
01:00:50,440 --> 01:00:51,440
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1184
01:00:51,560 --> 01:00:56,400
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1185
01:00:56,280 --> 01:00:58,360
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1186
01:00:58,440 --> 01:01:02,240
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1187
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1188
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1189
01:01:06,440 --> 01:01:08,000
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