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Speaker 1: Al Zone Media.

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Speaker 2: Hello and welcome to this week's Better Offline. I'm your

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Speaker 2: host ed ZiT Trono. So this week is going to

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Speaker 2: be a recording of a panel held in Seattle at

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Speaker 2: the Seattle Public Library with my good man Cory Doctorow,

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Speaker 2: and we were discussing in certification, the rot economy, and

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Speaker 2: the destruction of modern tech platforms. We're moderated by the

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Speaker 2: wonderful Whitney Bell Tron and big up to Clara and West.

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Speaker 2: A wonderful group out there, nonprofit. You should definitely support

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Speaker 2: everything in the episode notes. Have fun with this, I

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Speaker 2: certainly did.

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Speaker 3: Well.

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Speaker 4: Welcome everybody again, thank you for houcking it out on

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Speaker 4: this Wednesday night.

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Speaker 5: We're really excited to have you.

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Speaker 1: All.

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Speaker 5: The energy in the room is great.

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Speaker 4: We're all here to talk about what is happening to

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Speaker 4: us and seems to be spinning wildly out of our control,

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Speaker 4: and what it is and how it's gonna affect us

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Speaker 4: and what we can possibly do about it future facing.

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Speaker 4: I have two really, really illustrious guests with me tonight,

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Speaker 4: Corey and Ed. I did my research beforehand and watched

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Speaker 4: all of their other interviews with other people. I'm not

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Speaker 4: gonna do as good of a job. So I'm really

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Speaker 4: counting on them to shine. But for those of you

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Speaker 4: who maybe just you know, showed up by accident, I

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Speaker 4: would love for first Corey and then Ed to do

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Speaker 4: a brief introduction of themselves before we launch into this

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Speaker 4: really incredible topic.

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Speaker 5: So Corey, why don't you go ahead him?

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Speaker 3: I'm Corey Doctor. I have worked with the Electronic Frontier Foundation,

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Speaker 3: a digital rights group, for a quarter of a century now.

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Speaker 3: I've been an activist, kind of journalist and a writer.

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Speaker 3: I'm covering library worker and bookseller and Clarion West instructor

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Speaker 3: every now and again. And I'm very pleased to be

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Speaker 3: back here in Seattle to talk with you folks.

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Speaker 6: All right, so yeah, let's open up this pit. I'm

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Speaker 6: at Zittron. I'm the host of a podcast called Better

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Speaker 6: Offline about technology and the decay of society. I also

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Speaker 6: write a news letical Where's You're at At that I

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Speaker 6: mostly started because I was depressed. It's got eight two

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Speaker 6: thousand subscribers now somehow. But yeah, I write about tech

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Speaker 6: the ka of society, and I had to learn a

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Speaker 6: bunch of economics to do it, which was extremely exhausting.

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Speaker 6: But I enjoy it. Thank you for being here.

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Speaker 4: Okay, so we're gonna We're just gonna have like a

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Speaker 4: little bitty five minutes on the basics before we launch

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Speaker 4: into the complexities of the situation, where I'm gonna start

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Speaker 4: with an anecdote, which is, one day I woke up

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Speaker 4: and Google sucked. I don't know what happened, but one

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Speaker 4: day I was like looking for a thing, and I'm like,

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Speaker 4: this is so much worse than I am used to

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Speaker 4: and that tripped me into a world of it sucks

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Speaker 4: on purpose.

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Speaker 5: Here's why. And I would love for Ed and Corey.

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Speaker 4: To tell me why Google sucks.

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Speaker 3: Well, do you want to start by talking about Propagar

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Speaker 3: Ragava Ragavan?

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Speaker 6: I love saying that motherfucker's name. So I don't know

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Speaker 6: if any of you have read The Man Who Google Search,

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Speaker 6: which was a piece that I wrote by accident. I

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Speaker 6: found a bunch of notes from the Department of Justice. Anyway,

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Speaker 6: long story short, Around the early part of twenty twenty,

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Speaker 6: there was a thing called the code Yellow at Google,

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Speaker 6: which just meant that was I'm going to use some

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Speaker 6: financial things. But there was query weakness in Google, which

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Speaker 6: means that not enough people were just asking Google things. Now,

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Speaker 6: you may think, well, Google ideally gets you an answer, right, No, no, no,

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Speaker 6: you pleb How dare you what do you mean you

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Speaker 6: get an answer? Fuck you? You need to look at ads,

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Speaker 6: Sundhar Pshai needs and all of his McKinsey friends need

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Speaker 6: to do their seance or whatever. Nevertheless, there was an

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Speaker 6: internal argument at Google. There was a called Ben Gomes

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Speaker 6: around search who literally said I'm worried that all Google

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Speaker 6: cares about his growth. Turns out he was right. Around

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Speaker 6: June that year, a guy called Probagar Ragavan took over.

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Speaker 6: He was formerly the head of ads, and he pushed

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Speaker 6: pushed Ben Gomes to change things to make Google Search worse,

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Speaker 6: to keep people on Google Search. So the decay of

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Speaker 6: Google Search really started then. It was actually a few

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Speaker 6: years beforehand. They were slowly making it so it was

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Speaker 6: harder to tell sponsored links and ads from regular search results,

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Speaker 6: to the point it's pretty much impossible now and most

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Speaker 6: of the results a pretty bad as well. But long

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Speaker 6: story short, Google deliberately made things worse. And then the

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Speaker 6: guy who it's called Probagar Ragavan remember that name, asked

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Speaker 6: wipe He He mandated basically that I can't prove it

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Speaker 6: but it matches up that he just reduced the quality

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Speaker 6: of results. They allowed websites that had been previously pushed

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Speaker 6: down to come back spamming results, so that you ended

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Speaker 6: up farting around on Google more, seeing more impressions and

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Speaker 6: bing bong, number go up. Probagar sadly, he sadly moved.

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Speaker 6: He's alive. He is now the chief technologist at Google.

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Speaker 6: I would say, like thirty percent of my work was

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Speaker 6: the problem. The funny thing is that propagad was riding high,

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Speaker 6: taking a massive, massive growth of Google. He was doing

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Speaker 6: so well, and then he got made to take over Gemini,

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Speaker 6: Google's AI. And then there was a thing in I

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Speaker 6: think it was early twenty twenty four where this well

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Speaker 6: maybe it was twenty twenty five where Google Gemini was

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Speaker 6: generating Chinese George Washington's and the Conservatives were going in there,

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Speaker 6: there's a black Nazi, black as a Chinese George Washington.

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Speaker 6: I can't. My tiny little brain is vibrating at one

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Speaker 6: thousand miles a minute. And for some reason propagar Ragavan

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Speaker 6: had to take responsibility. And this is not a charming man.

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Speaker 6: This isn't a man who's like a political navigate. He's

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Speaker 6: just rude. He's very good at being rude. In the end, sadly,

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Speaker 6: Propaga's work has been done. Google sucks now. And as

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Speaker 6: Corey will tell you in his excellent book and across

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Speaker 6: his wonderful literature, this is a problem basically the entirety

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Speaker 6: of the Internet.

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Speaker 3: Yeah, so Ed's got this way I talk. Don't know

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Speaker 3: there we go. I do have an honorary PhD in

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Speaker 3: computer science. So Ed has got this way of talking

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Speaker 3: about the ideology that says, let's make Google worse in

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Speaker 3: order to increase profits. And you have to understand. You know,

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Speaker 3: when Google hit this code yellow, they did a ninety

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Speaker 3: percent market share and search so of course query growth

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Speaker 3: had slowed, Right, how do you increase query growth when

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Speaker 3: you have a ninety percent market share. You can raise

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Speaker 3: a billion humans to maturity and make them Google customers.

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Speaker 3: That's a product called Google Classroom. But it doesn't work quickly,

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Speaker 3: and so they needed something else to goose growth, and

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Speaker 3: they came up with this idea, let's make Google worse

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Speaker 3: in order to make more money. And you see in

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Speaker 3: these documents that had surfaced this ferocious debate between technologists

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Speaker 3: who want to do the right thing in the form

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Speaker 3: of bend Gomes and business people who want to do

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Speaker 3: the wrong thing in the form of pregate of ourt Ragavon.

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Speaker 3: And what's interesting about this is that although for many

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Speaker 3: years you can imagine fights like this played out at Google,

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Speaker 3: where the side that wanted to make things better one

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Speaker 3: out against the side that wanted to make things worse,

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Speaker 3: in this case you see this guy losing, and he's

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Speaker 3: losing because his argument consists of if I made Google worse,

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Speaker 3: I would feel bad about my work and my life.

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Speaker 3: And pragabah Ragavan's argument is if we make Google worse,

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Speaker 3: we'll make a lot more money.

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Speaker 6: And there's one other thing as well. Jerry Dishlow, who's

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Speaker 6: now the head of ADS, one of his things was, look,

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Speaker 6: I'm not saying that revenue needs the control ADS. However,

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Speaker 6: we will have a shared reality. There's basically what he said.

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Speaker 6: It's fucking when you read these things, it's chilling and

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Speaker 6: makes you think of things you can't say in public.

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Speaker 3: So so you know you have to wonder like, what

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Speaker 3: is it that created this environment? Ed calls the mindset

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Speaker 3: that says, well, if you can, you should worsen things

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Speaker 3: to make money. The raw economy and I think it's

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Speaker 3: a very apt phrase. And the question that I try

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Speaker 3: to interrogate in in shitification is what gave rise to it? Right,

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Speaker 3: What created the in shitta scene? And my conclusion is

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Speaker 3: it wasn't because you shopped wrong. Right. It's not because, oh,

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Speaker 3: you didn't pay for the product, so you became the product.

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Speaker 3: You know, farmers who buy half million dollar tractors are

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Speaker 3: exploited by John Deere, which won't let their repairs go

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Speaker 3: live until they pay a two hundred dollars call out

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Speaker 3: fee for John Deere person to come out and type

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Speaker 3: an unlocked code into the keyboard. Now, that is not

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Speaker 3: a free ad supported tractor, right, that's a tractor they

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Speaker 3: paid you know, six large for and it won't work

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Speaker 3: until they pay ransom money. So it's not because you

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Speaker 3: shopped wrong. It's also not because these guys are the

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Speaker 3: wrong guys to be running the company. They are terrible people.

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Speaker 3: But the reality is that these zucker Muskian mediocrits that

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Speaker 3: run these companies are not smart enough to be causes.

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Speaker 3: They must be effects, right, They're responding to an eshiogenic

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Speaker 3: environment created by policy, and that policy, in the case

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Speaker 3: of Google, is the policy that oversaw for decades Google's

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Speaker 3: serial acquisition of both vertical and horizontal competitors, so that

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Speaker 3: a company that had only made one really successful consumer

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Speaker 3: facing product, which they made a millennium ago, right that

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Speaker 3: their search engine, and that had almost with that exception,

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Speaker 3: failed to launch anything internally except for things they bought

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Speaker 3: from other people in anti competitive acquisitions, to the point

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Speaker 3: where they bought all the shelf space right nowhere else's

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Speaker 3: search engine could take root. They're bribing Apple to the

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Speaker 3: tune of more than twenty billion dollars a year not

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Speaker 3: to enter the search market and have a direct competitor

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Speaker 3: that would erode their margins. So they've become too big

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Speaker 3: to care. And this is really my thesis, right that

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Speaker 3: it's as much as Ed is right to be angry

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Speaker 3: at Pepperagar Ragavon. There are people alive today and not

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Speaker 3: so recently dead, who presided over shifts in our policy

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Speaker 3: environment where they were warned at the time that the

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Speaker 3: decisions that they were contemplating would have the absolutely foreseeable

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Speaker 3: effect of rewarding firms that did bad things to us

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Speaker 3: who took those decisions anyway, and today are like hanging

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Speaker 3: around polishing their fake Nobel Prizes and economics and collecting

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Speaker 3: six figure consulting fees, working for blue chips and not

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Speaker 3: being held responsible at all, much less worrying that when

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Speaker 3: they go out abroad amongst us that someone might be

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Speaker 3: sizing them up for a pitchfork. And so that's the

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Speaker 3: thing that I want to recover in this book, and

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Speaker 3: the thing that.

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Speaker 5: I'm going to jump in here.

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Speaker 4: That's okay, So this is Seattle and collectively is seaatellites.

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Speaker 4: We tend to have certain feelings about capitalism. Would you say,

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Speaker 4: either of you, that this phenomenon right, this rat economy

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Speaker 4: is just an inescapable fact of capitalism, or is there

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Speaker 4: real policy changed? Is there some other way we could

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Speaker 4: do this, that we could ascuw this, this rat economy

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Speaker 4: and actually still have functionally capitalism in some way and

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Speaker 4: the things that we want and improved lives.

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Speaker 6: So a lot of people talk about going back in

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Speaker 6: time and killing baby Hitler. Sure fine, I also think

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Speaker 6: Baby Reagan should be on the table. The same goes

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Speaker 6: for that fuck here. That fuckhad Milton Friedman finally an

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Speaker 6: audience with class. Because whatever however you feel about capitalism,

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Speaker 6: the whole growth focus capitalism started with Milton Friedman in

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Speaker 6: the free market nonsense. I'm not going to spend as

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Speaker 6: many angry hours as I need to to get that

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Speaker 6: out of my system. The force of capitalism are obvious,

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Speaker 6: but the real once you remove the regulation, once you

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Speaker 6: allowed Reagan and his various judges to pull away regulation

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Speaker 6: and just effectively stop doing antitrust. When Lena Kahan, who

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Speaker 6: was good, was remarkable because she tried, that was the

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Speaker 6: real Like Lena Khan is excellent, I've had her on

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Speaker 6: the show. I'm going to have her actually fantastic, but

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Speaker 6: it's like she was particularly because she was like actually

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Speaker 6: knew what she was talking about. I know, I was surprise,

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Speaker 6: and also was like, what would stop these companies from

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Speaker 6: doing things? What would be the incentives? The incentives were

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Speaker 6: created over the course of decades. Yes, capitalism is at

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Speaker 6: its route doing what capitalism will do, but by allowing

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Speaker 6: it to be unrestrained and allowing the markets to become

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Speaker 6: growth drunk, because that's all the markets. That is the

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Speaker 6: center of the rot economy. Everything is growth. Every single

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Speaker 6: thing is driven by growth. Nothing to the point that

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Speaker 6: the AI economy isn't even just like sixty billion dollars

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Speaker 6: of real revenue. It's not about even whether the product

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Speaker 6: creates money anymore. It's just number go up forever. It

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Speaker 6: sounds simple, but it is at the root of everything.

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Speaker 3: Yeah, I mean, I think it makes a really good

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Speaker 3: point here. And you know, look, I'm not someone who

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Speaker 3: believes that markets are the best or only arbiter of

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Speaker 3: how we allocate resources. I think that there are other

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Speaker 3: ways that we can do things. But even if you

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Speaker 3: are the kind of like Elon Musk adult libertarian who

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Speaker 3: can't open your copy of Atlas Shrugged anymore because the

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Speaker 3: pages are all stuck together, and you'd think that the

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Speaker 3: only thing that our government should ever do is enforce contracts,

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Speaker 3: you still want them to enforce contracts, right, and for

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Speaker 3: the referee to referee the game adequately, they have to

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Speaker 3: be more powerful than the players in the field. So

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Speaker 3: I tell my libertarian friends, Look, the smallest government you

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Speaker 3: can have is determined by the largest corporation you're willing

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Speaker 3: to tolerate. And the decision which was taken in the

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Speaker 3: late nineteen seventies, first under Carter and then accelerating under

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Speaker 3: Reagan and through the rest of it, and really coterminal

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Speaker 3: with the tech industry. So remember roll Od Reagan went

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Speaker 3: on the campaign trail the year the Apple two plus

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Speaker 3: went on sale. The tech industry grew up, and as

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Speaker 3: it got bigger, anti trust got smaller. It's really the

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Speaker 3: first post anti trust industry, which is why it looks

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Speaker 3: the way it does. And when we decided that we

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Speaker 3: would no longer enforce anti monopoly law, we really did

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Speaker 3: set in motion these very foreseeable outcomes. And the people

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Speaker 3: responsible for it, they insist that they're pro monopoly posture,

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Speaker 3: which boiled down to this, that monopolies are efficient, that

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Speaker 3: when you observe a monopoly in the wild, what you

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Speaker 3: must be seeing as a company that is very pleasing

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Speaker 3: to people. Because if a company isn't pleasing to people,

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Speaker 3: then other people will enter the market and take away

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Speaker 3: your monopoly. And so any monopoly that you find tautologically

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Speaker 3: by circular reasoning, must be a good company, right. And

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Speaker 3: so if Apple is controlling all of the app market,

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Speaker 3: if Google is controlling ninety percent of the search market,

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Speaker 3: what you are observing is a firm that is so

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Speaker 3: pleasing that everyone is voluntarily using them, and if they weren't,

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Speaker 3: then they would be out competed. And so they said, well,

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Speaker 3: Let's tolerate monopolies as efficient. Let's not engage in this

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Speaker 3: perverse labor of punishing companies for pleasing the people who've

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Speaker 3: elected us to represent them. Let us celebrate these new

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Speaker 3: efficient and you do that for forty years, and suddenly

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Speaker 3: every sector becomes a cartel. Right. We have this in

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Speaker 3: glass bottles, vitamin c eyeglasses, intermodal shipping, rail, the internet,

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Speaker 3: as Tom Eastman says, as five giant websites filled with

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Speaker 3: screenshots of texts from the other four. We have it

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Speaker 3: in semiconductors. We have it in professional wrestling. We have

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Speaker 3: it in plastic bags filled with sterile singialine. And one

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Speaker 3: company controls not just the hospital beds, but also the coffins.

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Speaker 3: Think about that for a minute. Right, So, these this

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Speaker 3: rampant monopolization did ultimately give rise to this world where

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Speaker 3: firms didn't have to worry about being disciplined by markets.

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Speaker 3: But it also meant they didn't have to worry about

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Speaker 3: being disciplined by governments. Because when you boil a sector

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Speaker 3: down to just a handful of firms, it's very easy

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Speaker 3: for them to decide what line of bullshit they're going

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Speaker 3: to feed to their regulators. And because they are so

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Speaker 3: a slash in money because they don't compete head to head.

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Speaker 3: You know, for Google, twenty billion dollars a year to

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Speaker 3: Apple not to enter the search market is a bargain

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Speaker 3: because the erosion of both of their margins if they

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Speaker 3: were competing head to head in these markets that they've

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Speaker 3: actually divided up amongst themselves like the pope dividing up

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Speaker 3: the New World, would cost them both far more than

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Speaker 3: twenty billion dollars. They'd have to hire each other's employees

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Speaker 3: and pay them more. They'd have to offer us cheaper things.

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Speaker 3: They'd have to be better to advertisers, they'd have to

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Speaker 3: be better to publishers, and that would erode their margins.

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Speaker 3: And so you end up with this moment where they

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Speaker 3: have all of this money, they find it very easy

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Speaker 3: to come to a single position, and their regulators do

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Speaker 3: as they're told, And so we end up with both

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Speaker 3: regulatory capture and market capture arising out of the same

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Speaker 3: set of policy choices, And so two of the most

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Speaker 3: important forces that we count on to punish companies that

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Speaker 3: do bad things to ensure that when pragabar Ragavon and

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Speaker 3: Les Gomes are fighting that Les Gomes can say more

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Speaker 3: than this would make me feel bad about my life's work.

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Speaker 3: You can say, and we're going to have our lunches

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Speaker 3: eaten by someone smarter than us, you, or we're going

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Speaker 3: to get I'm ACKed around by a regulator that at

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Speaker 3: that moment he's going to lose that argument. And so

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Speaker 3: this is how you end up with the devil of

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Speaker 3: your worst nature on your left shoulder, whispering your ear

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Speaker 3: and winning the argument over the angel of your better

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Speaker 3: nature and your right shoulder telling you to do the

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Speaker 3: right thing.

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Speaker 4: So on that note, our regulators are not really regulating

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Speaker 4: right now in general and for us the average person, right,

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Speaker 4: this is all really over our heads power wise, Like

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Speaker 4: we as individuals don't really have power to do anything

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Speaker 4: about getting Google to behave right, But what do we

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Speaker 4: do in our personal lives when we're affected by this? Right,

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Speaker 4: Like I work in video games and working on Cyberpunk two,

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Speaker 4: and I feel the effects of the economy initientification on

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Speaker 4: my job every day. It's very, very, very stressful. You know,

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Speaker 4: I was talking to a woman earlier, her name's Carolyn. Hi, Carolyn,

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Speaker 4: about you know a as effect on higher ed. So

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Speaker 4: for us, for the people in this room, do you

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Speaker 4: have any ideas or guidance for like, how do we

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Speaker 4: live our lives?

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Speaker 3: Now? What do we do? So?

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Speaker 6: We live in a high information, low processing environment. The

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Speaker 6: reason that regulators don't do much is not only because

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Speaker 6: they're incentivized not to, but also they don't know shit

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Speaker 6: about fuck. In fact, when you go around reading most

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Speaker 6: a ton of media, when you read a bunch of

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Speaker 6: regulatory stuff and you read what the government says about things,

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Speaker 6: they don't know what they're talking about. I don't even

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Speaker 6: mean slightly. I mean that the markets have been running

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Speaker 6: away with this idea that generative AI is the future.

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Speaker 6: The media has been reporting that generative AI is replacing jobs.

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Speaker 6: Neither of these are true. They're not true. There are

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Speaker 6: some jobs being eroded by llms, but it's jobs like

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Speaker 6: translators that were already being eroded by machine translation. I

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Speaker 6: forget the exact terms and our directors, so real jobs

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Speaker 6: put contract like and also bosses that wouldn't pay real

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Speaker 6: people anyway. The world itself is run on ignorance, not

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Speaker 6: just social ignorance, but just straight up factual ignorance about

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Speaker 6: what a I can do. And the reason I pick

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Speaker 6: AI is because This is where you can actually be disruptive.

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Speaker 6: You hear a fucking data center popping up, go to

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Speaker 6: the town hall meeting. Make your mare upset. I'm deadly serious.

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Speaker 6: You should I had. I saw one brave soul at

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Speaker 6: Wisconsin data center meeting. You just quoted my work. Love

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Speaker 6: him going to these places and actually not making a scene,

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Speaker 6: just going in and saying, Hey, there's only sixty billion

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Speaker 6: dollars worth of revenue in this industry. There's no growth

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Speaker 6: outside of chat GPT like these companies. Open Ai burned

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Speaker 6: nine point two billion dollars in the first half of

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Speaker 6: this year. That's fucking crazy. That's a completely crazy thing.

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Speaker 6: There's no path to profitability for these things on top

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Speaker 6: of them not being that much demand. I'm not just

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Speaker 6: quoting things in my newsletter at you. This is the

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Speaker 6: shit that you should be saying to your elected officials

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Speaker 6: and show up at town hall meetings. They hate it.

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Speaker 6: They do not want to see you, but they have to.

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Speaker 6: So you show up and you tell them these very

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Speaker 6: simple things, and they'll say, when I read in the newspapers,

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Speaker 6: shut the fuck up, man, I don't want to hit

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Speaker 6: I from you meds. Then I don't know a little bowser,

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Speaker 6: I guess, but nevertheless, showing up and actually just saying

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Speaker 6: the very basic things and sticking to your points will

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Speaker 6: do a lot. I know it sounds small, but really

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Speaker 6: these that's where you get things done, because these days,

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Speaker 6: sentences are probably not gang built anyway, but if you

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Speaker 6: can undermine them, then they're really not getting built.

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Speaker 3: You know, when the early reviews started to come out

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Speaker 3: for in Shitification, there was a kind of common theme

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Speaker 3: that emerged from some of these reviews. It was I

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Speaker 3: found this book very exciting and interesting. It gave me

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Speaker 3: a way to attach a handle to something that had

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Speaker 3: been kind of big and diffuse and frustrating to me

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Speaker 3: that I couldn't make sense of. And now that I

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Speaker 3: got this handle on it, I feel like I can

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Speaker 3: do something with it. And I read all of these,

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Speaker 3: you know, suggestions for how we can make policy at

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Speaker 3: the end and what that would do in order to

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Speaker 3: forestall or roll back and shitification and make a better world,

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Speaker 3: but there wasn't anything I could do personally in my

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Speaker 3: life as a consumer. And you know, they're right, They're right.

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Speaker 3: I don't think your personal consumption choices really play a

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Speaker 3: role in changing our systemic problems now by all means like,

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Speaker 3: if there's like a business you want to support, go

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Speaker 3: to Third Place Books instead of Amazon in order to

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Speaker 3: get your books because it'll make their lives better and

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Speaker 3: you'll have a bookstore on your community. But don't get

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Speaker 3: yourself that it's going to fix the structural problem of Amazon.

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Speaker 3: The structural problem of Amazon is that regulators allow them

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Speaker 3: to buy all of their competitors such that they were

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Speaker 3: able to corner markets, and then they were able to

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Speaker 3: lock people in too. Quiet is that they issue all right,

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Speaker 3: and then they were able to lock people into their

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Speaker 3: platform through getting them to pay in advance by a

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Speaker 3: year for their shipping with Prime, such that merchants found

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Speaker 3: that they couldn't sell anywhere else, and then they were

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Speaker 3: able to lard onto those merchants forty five to fifty

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Speaker 3: one percent junk fees on every dollar they brought in,

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Speaker 3: and then they were able to hit them with this

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Speaker 3: Most Favored Nation deal that says that if you raise

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Speaker 3: your prices on Amazon to recover some of those forty

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Speaker 3: five to fifty one percent junk fees, you have to

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Speaker 3: raise your prices everywhere else at Target and Walmart and

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Speaker 3: mom and pop stores, and your own warehouse store. So

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Speaker 3: that Amazon started to impose a worldwide tax on all consumption, right,

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Speaker 3: And that's not a thing you solve by changing your

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Speaker 3: own consumption habits. You know, by all means, if you

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Speaker 3: feel that Twitter is bad for your mental health, then

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Speaker 3: it probably is, and you want to go to Blue Sky,

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Speaker 3: you want to go to mask it on sure, But

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Speaker 3: just don't fetishize that as the thing that's going to

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Speaker 3: make a systemic difference. Right. The thing that makes a

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Speaker 3: systemic difference is intervening not as an individual but as

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Speaker 3: a polity. So, as I've mentioned before, I work at

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Speaker 3: the Electronic Frontier Foundation. We have a national network of

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Speaker 3: grassroots groups, including several here in Seattle, called the Electronic

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Speaker 3: Frontier Alliance. If you go to e FA dot EFF

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Speaker 3: dot org you can find some of these local chapters

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Speaker 3: and they work on things like limiting police use of

433
00:22:55,680 --> 00:23:02,440
Speaker 3: facial facial recognition, buying surveillance technology, privacy for abortion seekers,

434
00:23:02,720 --> 00:23:05,840
Speaker 3: limitation on the use of digital infrastructure to track down people,

435
00:23:05,840 --> 00:23:09,880
Speaker 3: that ICE is chasing right to repair laws. Washington's got

436
00:23:09,880 --> 00:23:12,560
Speaker 3: a really good one. All of those things start at

437
00:23:12,600 --> 00:23:15,080
Speaker 3: these grassroots levels, and it's getting involved as a quality

438
00:23:15,080 --> 00:23:17,520
Speaker 3: that makes a difference. I know it's easy to despare

439
00:23:17,560 --> 00:23:19,959
Speaker 3: if you think you can't solve things individually through your

440
00:23:19,960 --> 00:23:22,880
Speaker 3: own consumption choices. We've been told this for forty years

441
00:23:22,880 --> 00:23:25,000
Speaker 3: that you have to vote with your wallet. The reason

442
00:23:25,080 --> 00:23:26,800
Speaker 3: rich people want to vote want you to vote with

443
00:23:27,040 --> 00:23:30,040
Speaker 3: your wallet is they have thicker wallets than you. Right,

444
00:23:30,080 --> 00:23:33,119
Speaker 3: you are always going to lose that election, right, So

445
00:23:33,640 --> 00:23:36,880
Speaker 3: you know, don't vote with your wallet. Be a citizen.

446
00:23:37,720 --> 00:23:41,680
Speaker 3: Someone in the reception before this asked me about what

447
00:23:41,960 --> 00:23:44,760
Speaker 3: advice I would give to tech workers, and statistically a

448
00:23:44,800 --> 00:23:48,520
Speaker 3: bunch of you are probably tech workers. So you know,

449
00:23:49,160 --> 00:23:54,280
Speaker 3: the tech workers did have this chance to consolidate their

450
00:23:54,320 --> 00:23:56,280
Speaker 3: power and they missed it. So for a long time,

451
00:23:56,320 --> 00:24:00,240
Speaker 3: one of the forces that constrain in shitification was workers themselves,

452
00:24:00,400 --> 00:24:04,119
Speaker 3: because tech workers are this uniquely constituted workforce. Historically, it

453
00:24:04,200 --> 00:24:06,560
Speaker 3: has the tech sector has always had very low union

454
00:24:06,600 --> 00:24:09,680
Speaker 3: density but an enormous amount of worker power, and that's

455
00:24:09,680 --> 00:24:13,720
Speaker 3: because tech workers were both very scarce and very very valuable.

456
00:24:13,960 --> 00:24:16,760
Speaker 3: The National Beer of Economic Research estimates that the average

457
00:24:16,760 --> 00:24:19,639
Speaker 3: tech worker in Silicon Valley in Seattle was adding a

458
00:24:19,720 --> 00:24:22,439
Speaker 3: million dollars a year to their employer's bottom line. Right,

459
00:24:22,520 --> 00:24:25,640
Speaker 3: That's why they gave you freekimbucha and massages, and why

460
00:24:25,680 --> 00:24:27,879
Speaker 3: they'd hire a surgeon to freeze your eggs so you

461
00:24:27,920 --> 00:24:30,480
Speaker 3: could work through your fertile years. It wasn't because they

462
00:24:30,520 --> 00:24:32,639
Speaker 3: loved you, right, it was because they were afraid of

463
00:24:32,640 --> 00:24:35,359
Speaker 3: you getting a job across the street. Now we know

464
00:24:35,400 --> 00:24:39,440
Speaker 3: how tech bosses treat the workers. They're not afraid of losing. Right.

465
00:24:39,640 --> 00:24:42,959
Speaker 3: That's the Amazon driver who's got the AI camera that

466
00:24:43,080 --> 00:24:46,400
Speaker 3: takes points off if they look away from their from

467
00:24:46,480 --> 00:24:49,840
Speaker 3: the road to check something to one side or the

468
00:24:49,840 --> 00:24:51,920
Speaker 3: other and it decides that their eyeballs were in the

469
00:24:51,960 --> 00:24:55,320
Speaker 3: wrong orientation. It's the warehouse workers that are injured at

470
00:24:55,320 --> 00:24:57,840
Speaker 3: three times the rate of other warehouse workers in the sector.

471
00:24:57,960 --> 00:25:00,159
Speaker 3: It's everyone who peas in a bottle. It's pe lo

472
00:25:00,240 --> 00:25:04,040
Speaker 3: assemble iPhones in China and have suicide nets around the factory. Right.

473
00:25:04,160 --> 00:25:06,199
Speaker 3: That's how they treat the workers. They're not afraid of

474
00:25:06,640 --> 00:25:09,240
Speaker 3: So there was this opportunity at one point for tech

475
00:25:09,280 --> 00:25:12,439
Speaker 3: workers to use that power and consolidate it through a union,

476
00:25:13,000 --> 00:25:15,480
Speaker 3: and they missed it, right, because tech workers thought that

477
00:25:15,560 --> 00:25:18,040
Speaker 3: they were temporarily embarrassed founders. They didn't think that they

478
00:25:18,040 --> 00:25:22,520
Speaker 3: were workers. And they thought that because their bosses would

479
00:25:22,520 --> 00:25:24,920
Speaker 3: meet them in monthly town hall meetings where they could

480
00:25:24,920 --> 00:25:28,200
Speaker 3: ask impertinent questions about corporate strategy, that their bosses thought

481
00:25:28,200 --> 00:25:30,639
Speaker 3: that they were peers. But your boss didn't think you

482
00:25:30,640 --> 00:25:32,280
Speaker 3: were a peer. Your boss thought you were a problem

483
00:25:32,320 --> 00:25:35,399
Speaker 3: to solve. And after half a million layoffs in the

484
00:25:35,400 --> 00:25:38,040
Speaker 3: tech sector, they're not afraid of you anymore. There's other

485
00:25:38,080 --> 00:25:40,800
Speaker 3: workers who will take your job. You can no longer

486
00:25:40,840 --> 00:25:42,840
Speaker 3: say I refuse to and shittify the thing. I missed

487
00:25:42,840 --> 00:25:45,360
Speaker 3: my mother's funeral to ship. And you can't hire someone

488
00:25:45,400 --> 00:25:47,560
Speaker 3: else to replace me, because they'll just fire you and

489
00:25:47,600 --> 00:25:49,960
Speaker 3: hire someone else to replace you. And so now is

490
00:25:50,000 --> 00:25:51,760
Speaker 3: the time to unionize and it can.

491
00:25:52,920 --> 00:25:57,480
Speaker 6: Yes, Also, Elma, if you have anything pertaining to the

492
00:25:57,600 --> 00:26:01,240
Speaker 6: revenues or spend of anthropic or open AIE, you could

493
00:26:01,280 --> 00:26:07,120
Speaker 6: email me that information and I could ad shit pis

494
00:26:07,119 --> 00:26:10,919
Speaker 6: and fuck between the numbers. They love it in the podcast.

495
00:26:12,560 --> 00:26:14,160
Speaker 3: And so you know, it might feel like a bad

496
00:26:14,200 --> 00:26:16,720
Speaker 3: time to be unionizing because we no longer have the

497
00:26:17,560 --> 00:26:20,720
Speaker 3: National Labor Relations Board as it was constituted under Biden.

498
00:26:20,720 --> 00:26:24,040
Speaker 3: In fact, it's been so illegally denuded of commissioners that

499
00:26:24,080 --> 00:26:26,879
Speaker 3: it can no longer form a quorum and investigate unfair

500
00:26:26,960 --> 00:26:28,879
Speaker 3: labor practices. So it can feel like this is a

501
00:26:28,920 --> 00:26:31,720
Speaker 3: bad time. But here's the category error Trump is making.

502
00:26:32,040 --> 00:26:34,200
Speaker 3: Trump thinks that the reason we have unions is because

503
00:26:34,240 --> 00:26:38,440
Speaker 3: we have the National Labor Relations Act. It's backwards, right,

504
00:26:38,760 --> 00:26:42,199
Speaker 3: Long before unions were legal, we had unions, right. And

505
00:26:42,280 --> 00:26:45,480
Speaker 3: the union piece represented by the National Labor Relations Act

506
00:26:45,760 --> 00:26:49,440
Speaker 3: was brought about because bosses were scared of what their

507
00:26:49,480 --> 00:26:52,560
Speaker 3: workers were doing to them at that point, because militancy

508
00:26:52,600 --> 00:26:56,080
Speaker 3: had gotten so intense that they sued for peace, and

509
00:26:56,119 --> 00:26:59,080
Speaker 3: that piece came in two parts. Part of the National

510
00:26:59,160 --> 00:27:02,280
Speaker 3: Labor Relations Act describes what your boss can't do to you,

511
00:27:02,800 --> 00:27:04,960
Speaker 3: but a lot of the National Labor Relations Act is

512
00:27:04,960 --> 00:27:07,560
Speaker 3: about what you can't do to your boss. And one

513
00:27:07,600 --> 00:27:09,959
Speaker 3: guess which half of the National Labor Relations Act has

514
00:27:09,960 --> 00:27:13,000
Speaker 3: been most vigorously enforced. So Trump thinks that we fired

515
00:27:13,040 --> 00:27:14,840
Speaker 3: the referee, and so that means all the players have

516
00:27:14,880 --> 00:27:18,240
Speaker 3: to leave the field. He's wrong. When you fire the referee,

517
00:27:18,280 --> 00:27:20,879
Speaker 3: it means there's no more rules. Right. And there's a

518
00:27:20,920 --> 00:27:24,960
Speaker 3: reason that fascist attack unions first is because the opposite

519
00:27:24,960 --> 00:27:27,879
Speaker 3: of fascism is solidarity.

520
00:27:35,640 --> 00:27:38,879
Speaker 4: Yes, excellent answers all around. And just to summarize what

521
00:27:38,960 --> 00:27:42,280
Speaker 4: I heard was don't vote with your wallet, unionize. So

522
00:27:43,080 --> 00:27:47,040
Speaker 4: take that out the door and dot org plug it.

523
00:27:47,640 --> 00:27:48,000
Speaker 5: Okay.

524
00:27:48,080 --> 00:27:51,280
Speaker 4: My next question for you is one that a lot

525
00:27:51,280 --> 00:27:53,959
Speaker 4: of people have been thinking about, which is the bubble?

526
00:27:55,160 --> 00:27:57,320
Speaker 4: The bubble is coming. We've all heard about the bubble?

527
00:27:57,440 --> 00:27:59,960
Speaker 4: Is the bubble real? What is gonna happen?

528
00:28:00,080 --> 00:28:00,199
Speaker 6: Then?

529
00:28:00,920 --> 00:28:03,399
Speaker 4: If we are tiny investors that have our four to

530
00:28:03,400 --> 00:28:05,359
Speaker 4: oh one K and index funds, what the fuck do

531
00:28:05,440 --> 00:28:05,800
Speaker 4: we do?

532
00:28:05,960 --> 00:28:09,680
Speaker 3: Buy long polls to dig through rubble with can goods?

533
00:28:10,720 --> 00:28:11,879
Speaker 5: Let's try again, ed.

534
00:28:14,160 --> 00:28:18,320
Speaker 6: Go back in time. But if you can't know, the

535
00:28:18,600 --> 00:28:22,640
Speaker 6: gravity exists right now, the market is going absolutely badshit insane.

536
00:28:22,760 --> 00:28:25,479
Speaker 6: I'm not a stock analyst. I cannot give you financial advice,

537
00:28:25,480 --> 00:28:28,440
Speaker 6: but right now number going up, so maybe you could.

538
00:28:28,480 --> 00:28:30,959
Speaker 6: I don't own stocks. I'm a psychopath. I live in

539
00:28:31,000 --> 00:28:34,800
Speaker 6: cash invest in words. The problem we have right now

540
00:28:34,840 --> 00:28:36,960
Speaker 6: is that in Video is the largest stock on the

541
00:28:37,000 --> 00:28:40,000
Speaker 6: stock market. There has never been a more problematic fact

542
00:28:40,040 --> 00:28:42,600
Speaker 6: than that eighty eight percent or more of their revenue

543
00:28:42,640 --> 00:28:45,240
Speaker 6: is selling these fucking GPUs. You want to know what

544
00:28:45,280 --> 00:28:48,000
Speaker 6: happens when you plug those GPUs and they start losing

545
00:28:48,000 --> 00:28:52,959
Speaker 6: your money. Nobody, not a single company other than Nvidia,

546
00:28:53,080 --> 00:28:55,360
Speaker 6: is making any kind of profit on ai. In fact,

547
00:28:55,360 --> 00:28:57,480
Speaker 6: they're burning billions. The reason I tell you this is

548
00:28:58,920 --> 00:29:01,520
Speaker 6: you can't really navigate away from the bubble right now

549
00:29:01,600 --> 00:29:04,520
Speaker 6: other than just selling I imagine, So know what you're

550
00:29:04,560 --> 00:29:08,400
Speaker 6: going into. Everything you're reading Oracle say right now about

551
00:29:08,440 --> 00:29:13,400
Speaker 6: their relationship with open Ai is bullshit. And to get specific,

552
00:29:13,520 --> 00:29:16,000
Speaker 6: is Oracle it has a five year long, three hundred

553
00:29:16,040 --> 00:29:20,000
Speaker 6: billion dollar contract with open Ai. Sounds amazing, right, They

554
00:29:20,080 --> 00:29:22,880
Speaker 6: just need four and a half gigawatts of data center capacity.

555
00:29:23,440 --> 00:29:27,680
Speaker 6: Any guesses how much they have two hundred megawats now,

556
00:29:27,840 --> 00:29:29,560
Speaker 6: don't that's less than four hundred giggles.

557
00:29:29,840 --> 00:29:30,600
Speaker 3: That's a lot less.

558
00:29:31,040 --> 00:29:33,080
Speaker 6: That's a lot less. And I must be clear, I

559
00:29:33,120 --> 00:29:36,240
Speaker 6: actually mean power it load. They've got about a buck thirty.

560
00:29:36,880 --> 00:29:39,440
Speaker 6: So not to worry though, because open Ai also doesn't

561
00:29:39,440 --> 00:29:42,240
Speaker 6: have the money. Nevertheless, the stock is run. AMD now

562
00:29:42,240 --> 00:29:44,720
Speaker 6: has a deal where they're gonna sell chips to open Ai.

563
00:29:44,960 --> 00:29:47,120
Speaker 6: Who the fuck knows who's paying the video same deal.

564
00:29:47,680 --> 00:29:51,280
Speaker 6: Whenever you see open Ai involved, just don't just don't

565
00:29:51,280 --> 00:29:54,480
Speaker 6: do it. There is no avoiding a bubble popping now,

566
00:29:54,560 --> 00:29:56,840
Speaker 6: there just isn't. So the smartest thing you can do

567
00:29:57,000 --> 00:30:00,080
Speaker 6: is stick to the fundamentals. We're going to get in

568
00:30:00,120 --> 00:30:02,160
Speaker 6: to the more hysterical phase. Now we're going to see

569
00:30:02,160 --> 00:30:05,120
Speaker 6: some crazy shit. I had someone suggest the other day

570
00:30:05,280 --> 00:30:08,080
Speaker 6: that open Ai may do a spac ipo. I think

571
00:30:08,120 --> 00:30:11,320
Speaker 6: it's insane, which is why it's possible. Do not let

572
00:30:11,400 --> 00:30:14,720
Speaker 6: your money touch open Ai under any circumstance. That company

573
00:30:14,760 --> 00:30:17,640
Speaker 6: is cancer. I'd say the same thing about Anthropic, But

574
00:30:17,840 --> 00:30:20,280
Speaker 6: just be aware that right now the stock market and

575
00:30:20,360 --> 00:30:23,320
Speaker 6: all of the associated media is going to tell you, oh,

576
00:30:23,320 --> 00:30:26,400
Speaker 6: there's a bubble. I've read many places, Oh there's bubbles

577
00:30:26,400 --> 00:30:29,280
Speaker 6: can be good. They can never be good. That's we

578
00:30:29,320 --> 00:30:31,720
Speaker 6: don't call them bubble. I'd see it in multiple headlines.

579
00:30:31,720 --> 00:30:35,600
Speaker 6: We're like, yeah, but this is a good bubble. Yeah,

580
00:30:35,640 --> 00:30:39,240
Speaker 6: you know, I've got the good kind of cancer. I've

581
00:30:39,240 --> 00:30:43,120
Speaker 6: got the good kind of diarrhea. Anyway, the point is

582
00:30:44,640 --> 00:30:46,400
Speaker 6: knowledge is power here, and you're going to look at

583
00:30:46,440 --> 00:30:47,840
Speaker 6: the media, and the media is going to say AI

584
00:30:47,960 --> 00:30:51,080
Speaker 6: number go up. Everything great. Broadcom's gonna ship ten billion

585
00:30:51,120 --> 00:30:53,320
Speaker 6: dollars of chip, ten gigabats of chips to open AI

586
00:30:53,600 --> 00:30:56,880
Speaker 6: by the end of twenty twenty nine cost fifty billion

587
00:30:56,960 --> 00:30:58,320
Speaker 6: dollars and two and a half years to make a

588
00:30:58,320 --> 00:31:00,800
Speaker 6: giga what a dates set in a capacity everyone is

589
00:31:00,840 --> 00:31:03,400
Speaker 6: going to feel like they're saying that this is inevitable.

590
00:31:03,760 --> 00:31:06,280
Speaker 6: Know that it's not. I realize I can't give you

591
00:31:06,360 --> 00:31:09,440
Speaker 6: much better advice than that, but just know that gravity exists.

592
00:31:09,880 --> 00:31:15,120
Speaker 6: This cannot succeed. On top of the fact that everyone's unprofitable.

593
00:31:15,640 --> 00:31:19,400
Speaker 6: It's not actually that popular either. Chat GPT is very

594
00:31:19,440 --> 00:31:22,479
Speaker 6: popular because a lot of people love being driven, insane

595
00:31:22,560 --> 00:31:30,320
Speaker 6: or trying to fuck it, I think, and terrible for

596
00:31:30,400 --> 00:31:34,400
Speaker 6: me that's what my friends. No sorry Kelen, but it's

597
00:31:35,520 --> 00:31:37,840
Speaker 6: it's frustrating as well, because I'm sure all of you

598
00:31:37,840 --> 00:31:41,080
Speaker 6: have felt the poison of generative AI within your workplace,

599
00:31:41,120 --> 00:31:43,280
Speaker 6: and these people will tell you that you must learn

600
00:31:43,760 --> 00:31:46,400
Speaker 6: AI's coming, you must learn to use AI. The reason

601
00:31:46,440 --> 00:31:48,520
Speaker 6: it's not able to do your jobs is it shit

602
00:31:49,600 --> 00:31:52,240
Speaker 6: Like I realized that all of this sounds may be

603
00:31:52,360 --> 00:31:54,920
Speaker 6: very elementary to you, sounds like most of you get it.

604
00:31:55,840 --> 00:31:57,680
Speaker 6: You're going to keep reading that it's not the case

605
00:31:57,760 --> 00:32:01,560
Speaker 6: that it's replacing coders. It isn't that is a fucking lie.

606
00:32:01,760 --> 00:32:04,000
Speaker 6: Everyone's saying it sooned up to show such in the

607
00:32:04,040 --> 00:32:08,760
Speaker 6: Della Andy fucking Jesse. These chunda fucks love to say

608
00:32:08,760 --> 00:32:11,960
Speaker 6: this stuff. It's not true. So really, the advice I

609
00:32:11,960 --> 00:32:14,720
Speaker 6: can give you is this is going to pop. It's

610
00:32:14,760 --> 00:32:17,160
Speaker 6: going to happen and acts accordingly.

611
00:32:18,320 --> 00:32:21,240
Speaker 3: So let me advance a theory of the less bad

612
00:32:21,320 --> 00:32:24,400
Speaker 3: and more bad bubble. If not a good bubble, that

613
00:32:24,400 --> 00:32:27,640
Speaker 3: would be nice. So some bubbles have productive residues and

614
00:32:27,720 --> 00:32:32,000
Speaker 3: some don't. Right, So Enron left nothing behind right now, WorldCom,

615
00:32:32,120 --> 00:32:35,200
Speaker 3: which was a grotesque fraud. It some of you will remember, right,

616
00:32:35,440 --> 00:32:38,240
Speaker 3: They raised billions of dollars claiming that they had orders

617
00:32:38,280 --> 00:32:40,440
Speaker 3: for fiber. They dug up the streets all over the world.

618
00:32:40,520 --> 00:32:42,280
Speaker 3: They put fiber in the ground. They didn't have the

619
00:32:42,360 --> 00:32:45,440
Speaker 3: orders for the fiber. They stole billions of dollars from

620
00:32:45,440 --> 00:32:48,280
Speaker 3: everyday investors, people who just wanted to go through their

621
00:32:48,320 --> 00:32:51,080
Speaker 3: old age without starving to death or not having a

622
00:32:51,160 --> 00:32:54,640
Speaker 3: roof over their head. The CEO died in prison and

623
00:32:54,680 --> 00:32:57,040
Speaker 3: it was good riddance. But there was still all that

624
00:32:57,120 --> 00:33:00,000
Speaker 3: fiber in the ground. Right, So I've got two gigabits

625
00:33:00,000 --> 00:33:02,520
Speaker 3: symmetrical fiber at home in Burbank because AT and T

626
00:33:02,640 --> 00:33:05,440
Speaker 3: bought some old dark fiber from world coom. Because fiber

627
00:33:05,520 --> 00:33:09,200
Speaker 3: lasts forever. Right, It's just it's glass, So once it's there,

628
00:33:09,360 --> 00:33:13,080
Speaker 3: it is a productive residue. Right, So what kind of

629
00:33:13,120 --> 00:33:15,760
Speaker 3: bubbles are we living through? Well, Crypto is not going

630
00:33:15,800 --> 00:33:17,800
Speaker 3: to leave behind anything. Crypto is going to leave behind

631
00:33:17,800 --> 00:33:24,400
Speaker 3: shitty Austrian economics and worst jpigs. Right. Ai AI is

632
00:33:24,440 --> 00:33:26,240
Speaker 3: actually going to leave behind some stuff. So if you

633
00:33:26,240 --> 00:33:29,120
Speaker 3: want to think about like a post AI bubble world,

634
00:33:29,200 --> 00:33:33,520
Speaker 3: and I'm just I just got edits from my editor.

635
00:33:33,560 --> 00:33:36,600
Speaker 3: I wrote a book over the summer called The Reverse

636
00:33:36,760 --> 00:33:41,720
Speaker 3: Centaur's Guide to Life After AI. And if you want

637
00:33:41,760 --> 00:33:45,320
Speaker 3: to think about a post AI world, imagine what you

638
00:33:45,320 --> 00:33:48,320
Speaker 3: would do if GPUs were ten cents on the dollar,

639
00:33:49,160 --> 00:33:52,440
Speaker 3: if there were a lot of skilled applied statisticians looking

640
00:33:52,480 --> 00:33:54,760
Speaker 3: for work, and if you had a bunch of open

641
00:33:54,760 --> 00:33:57,240
Speaker 3: source models that had barely been optimized and had a

642
00:33:57,240 --> 00:33:58,280
Speaker 3: lot of room at the bottom.

643
00:33:58,360 --> 00:34:03,200
Speaker 6: Right, got to push back on this, Okay, These aigpus

644
00:34:03,240 --> 00:34:07,120
Speaker 6: are mostly owned by private equity firms and big tech.

645
00:34:07,160 --> 00:34:09,640
Speaker 6: The majority of the GPUs are not owned by people

646
00:34:09,640 --> 00:34:11,839
Speaker 6: who will let them enter the market, and they are

647
00:34:11,920 --> 00:34:14,680
Speaker 6: really not a ton of Like if the idea is

648
00:34:14,719 --> 00:34:18,799
Speaker 6: that GPUs, someone will work something out, sure, But you

649
00:34:18,880 --> 00:34:20,920
Speaker 6: don't think all the king's sources and all the king's

650
00:34:20,920 --> 00:34:23,600
Speaker 6: men were to come up with something else. Because that's

651
00:34:23,600 --> 00:34:25,440
Speaker 6: the thing. The thing that I worry about the thing

652
00:34:25,480 --> 00:34:27,719
Speaker 6: that terrifies me about this bubble is this is not

653
00:34:27,880 --> 00:34:30,600
Speaker 6: useful infrastructure at all. Everyone loves say, oh, it's just

654
00:34:30,640 --> 00:34:32,719
Speaker 6: like the dot com bubble. It's nothing like that. It's

655
00:34:32,920 --> 00:34:34,960
Speaker 6: absolutely it's it's not even like the fire. At least

656
00:34:35,000 --> 00:34:37,960
Speaker 6: the fiber was somewhat useful. These GPUs the amount of

657
00:34:38,000 --> 00:34:40,719
Speaker 6: power alone, sure, And one of the reasons that open

658
00:34:40,760 --> 00:34:43,040
Speaker 6: AI and Anthropic have been able to have their monopolies

659
00:34:43,560 --> 00:34:47,080
Speaker 6: is because because they have the capital, and no one's

660
00:34:47,120 --> 00:34:49,759
Speaker 6: going to have the capitol to run these things. And

661
00:34:49,920 --> 00:34:52,359
Speaker 6: I mean they'll be'll be selling eight one hundreds, they'll

662
00:34:52,400 --> 00:34:56,160
Speaker 6: be paying you fifty cents an hour. It's just what

663
00:34:56,239 --> 00:34:59,839
Speaker 6: terrifies me is the I'm not saying you're doing this.

664
00:35:00,080 --> 00:35:03,600
Speaker 6: There are people already trying to rationalize this. They say, well,

665
00:35:03,880 --> 00:35:05,240
Speaker 6: and you were the one that actually told the stories

666
00:35:05,239 --> 00:35:07,600
Speaker 6: of adult comb where they're a useful service space BECASEUS

667
00:35:07,640 --> 00:35:10,520
Speaker 6: before AWS. Right, I don't see that usefulness here.

668
00:35:10,920 --> 00:35:13,759
Speaker 3: So I take your point. I think that there's going

669
00:35:13,840 --> 00:35:15,960
Speaker 3: to be a lot of firms in receivership, right, I

670
00:35:16,000 --> 00:35:18,680
Speaker 3: don't think. I don't think that that private equity bosses

671
00:35:19,040 --> 00:35:21,360
Speaker 3: preferences are going to enter into it. I think that

672
00:35:21,360 --> 00:35:23,160
Speaker 3: there's going to be a lot of firms and receivership.

673
00:35:23,160 --> 00:35:27,440
Speaker 3: And I do think that when you contemplate the intersection

674
00:35:27,560 --> 00:35:30,520
Speaker 3: of optimization of existing open source models, you know, think

675
00:35:30,520 --> 00:35:33,080
Speaker 3: about what happened when when deep sick entered the market. Right,

676
00:35:33,120 --> 00:35:36,560
Speaker 3: You've got Chinese firms that are prohibited from using the

677
00:35:36,560 --> 00:35:39,600
Speaker 3: more advanced GPUs. So rather than doing this sort of

678
00:35:39,640 --> 00:35:43,200
Speaker 3: display of how serious they are about AI by spending

679
00:35:43,200 --> 00:35:45,560
Speaker 3: as much money as they can, they went they said, okay,

680
00:35:45,560 --> 00:35:48,200
Speaker 3: well what can we juice out of you know, uh,

681
00:35:48,520 --> 00:35:51,279
Speaker 3: previous generations of GPUs And then they got some pretty

682
00:35:51,320 --> 00:35:52,120
Speaker 3: impressive results.

683
00:35:52,200 --> 00:35:55,600
Speaker 6: Yeah, but what results? Like, I'm so they made it cheaper, sure,

684
00:35:55,640 --> 00:35:57,879
Speaker 6: but what actually happened is the result of deep Sea

685
00:35:57,960 --> 00:36:01,000
Speaker 6: other than everyone all of silicon value when didn't happen?

686
00:36:01,560 --> 00:36:03,600
Speaker 3: Sure, we get they can't through it cheap, but the

687
00:36:03,800 --> 00:36:06,000
Speaker 3: Chinese So my point, my point is not about the

688
00:36:06,400 --> 00:36:08,719
Speaker 3: market reality is the material reality. So I'm talking about

689
00:36:08,760 --> 00:36:11,880
Speaker 3: what happens after the market pops, right. So you know

690
00:36:12,480 --> 00:36:15,040
Speaker 3: I've seen people do interesting and useful things with AI.

691
00:36:15,160 --> 00:36:17,200
Speaker 3: I think you've probably seen people do some useful and

692
00:36:17,239 --> 00:36:19,440
Speaker 3: interesting I'll give you an example. I was writing an

693
00:36:19,520 --> 00:36:23,520
Speaker 3: essay and I couldn't remember where i'd heard a quote

694
00:36:23,560 --> 00:36:25,600
Speaker 3: i'd heard in a podcast. I couldn't remember which quote

695
00:36:25,600 --> 00:36:27,359
Speaker 3: it was, so I downloaded Whisper, which is an open

696
00:36:27,400 --> 00:36:30,680
Speaker 3: source model from open AI, to my laptop, which doesn't

697
00:36:30,680 --> 00:36:34,560
Speaker 3: have a GPU, right, little commodity laptop. I through thirty

698
00:36:34,600 --> 00:36:37,000
Speaker 3: hours of podcasts that i'd recently listened to. At it,

699
00:36:37,000 --> 00:36:38,879
Speaker 3: I got a full transcription an how where my fan

700
00:36:38,960 --> 00:36:39,880
Speaker 3: didn't even turn on.

701
00:36:40,520 --> 00:36:41,160
Speaker 6: That's awesome.

702
00:36:41,320 --> 00:36:43,200
Speaker 3: Yeah, so I know tons of people who use this.

703
00:36:43,280 --> 00:36:46,000
Speaker 3: In the title of the book, reverse Centaur refers to

704
00:36:46,040 --> 00:36:49,399
Speaker 3: this idea from automation theory, where a centaur is someone

705
00:36:49,440 --> 00:36:52,080
Speaker 3: who gets to use machines to assist them. A human

706
00:36:52,160 --> 00:36:54,640
Speaker 3: head on a machine body, right, and so you know

707
00:36:54,680 --> 00:36:56,080
Speaker 3: you're riding a bicycle, you use it.

708
00:36:56,120 --> 00:36:59,040
Speaker 6: Thought it was with human legs.

709
00:36:58,480 --> 00:37:02,680
Speaker 3: Yes, and a reverse center is that's right? A machine

710
00:37:02,680 --> 00:37:05,279
Speaker 3: head on a human body. Right, it's someone who's been

711
00:37:05,320 --> 00:37:08,880
Speaker 3: conscripted to be a peripheral for a machine. Right. And

712
00:37:09,239 --> 00:37:11,959
Speaker 3: when I you know, I should say, you talked about

713
00:37:11,960 --> 00:37:14,280
Speaker 3: good cancer. I have the least bad kind of cancer.

714
00:37:14,320 --> 00:37:16,200
Speaker 3: I've got a very treatable form of cancer. But I'm

715
00:37:16,239 --> 00:37:19,319
Speaker 3: paying a lot of attention to stories about cancer. And

716
00:37:20,680 --> 00:37:23,640
Speaker 3: you know open source models or AI models that can

717
00:37:23,920 --> 00:37:27,839
Speaker 3: sometimes see solid mass tumors that radiologists miss. And if

718
00:37:27,840 --> 00:37:31,800
Speaker 3: what we said was we at the Kaiser Oncology department

719
00:37:32,200 --> 00:37:34,919
Speaker 3: are going to invest in a service that is going

720
00:37:34,920 --> 00:37:37,880
Speaker 3: to sometimes ask our radiologists to take a second look

721
00:37:38,160 --> 00:37:40,799
Speaker 3: to see if they miss something such that instead of

722
00:37:40,840 --> 00:37:42,719
Speaker 3: doing one hundred X rays a day, they're going to

723
00:37:42,719 --> 00:37:45,640
Speaker 3: do ninety eight, Right, then I would say, as someone

724
00:37:45,640 --> 00:37:48,919
Speaker 3: with cancer, that sounds interesting to me. I don't think

725
00:37:48,960 --> 00:37:53,960
Speaker 3: anyone is pitching any oncology ward in the world on that.

726
00:37:54,160 --> 00:37:57,560
Speaker 3: I think the pitches fire. Ninety percent of your oncologists

727
00:37:58,040 --> 00:38:01,880
Speaker 3: find ninety percent of your radiologists have the remainder babysit

728
00:38:01,960 --> 00:38:05,280
Speaker 3: AI have them be the accountability sinks and moral crumple

729
00:38:05,360 --> 00:38:08,600
Speaker 3: zones for a machine that is processing this stuff at

730
00:38:08,600 --> 00:38:11,160
Speaker 3: a speed that no human could possibly account for have

731
00:38:11,280 --> 00:38:13,120
Speaker 3: them put their name at the bottom of it, and

732
00:38:13,200 --> 00:38:15,920
Speaker 3: have them absorb the blame for your cost cutting measures.

733
00:38:16,120 --> 00:38:18,960
Speaker 3: And so you know, when I hear people talk about AI, right,

734
00:38:19,000 --> 00:38:21,520
Speaker 3: I hear programmers talk about AI doing things that are useful.

735
00:38:21,560 --> 00:38:24,000
Speaker 3: Like one that I've heard many programmers say is I

736
00:38:24,040 --> 00:38:27,319
Speaker 3: had one data file in a weird format that for

737
00:38:27,440 --> 00:38:29,359
Speaker 3: one as a one off, I needed to get into

738
00:38:29,440 --> 00:38:31,399
Speaker 3: a different format, and I had ways that I could

739
00:38:31,440 --> 00:38:33,680
Speaker 3: check it and tell what was going on, and so

740
00:38:33,880 --> 00:38:35,839
Speaker 3: I asked, I once shot at it with an AI.

741
00:38:36,239 --> 00:38:37,960
Speaker 3: I did some check sums and it was great, and

742
00:38:38,000 --> 00:38:40,520
Speaker 3: it saved me an hour. That's a centaur, right, Sure,

743
00:38:40,680 --> 00:38:41,520
Speaker 3: But here's the thing.

744
00:38:41,880 --> 00:38:44,960
Speaker 6: How many of those those actually exist in any given time,

745
00:38:45,000 --> 00:38:47,719
Speaker 6: and how many of them are I the DGX books,

746
00:38:47,760 --> 00:38:50,120
Speaker 6: the Nvidia's launched like I could see a future with

747
00:38:50,280 --> 00:38:53,600
Speaker 6: lage language models are run locally. Sure, well, so I

748
00:38:53,800 --> 00:38:56,360
Speaker 6: questioned how much of the cancer related stuff would actually

749
00:38:56,400 --> 00:38:59,160
Speaker 6: be generative. But that's a separate discussion because one of

750
00:38:59,200 --> 00:39:02,560
Speaker 6: the things these people, as they conflate, aire been around

751
00:39:02,600 --> 00:39:05,880
Speaker 6: for a while with generative and my concern is that

752
00:39:07,160 --> 00:39:09,480
Speaker 6: I don't know a lot of the like anecdotal one

753
00:39:09,520 --> 00:39:12,279
Speaker 6: shot staff and the Carle Brown Internet of Bugs. If

754
00:39:12,280 --> 00:39:15,200
Speaker 6: any of you have ever seen fantastic YouTube channel should

755
00:39:15,200 --> 00:39:17,399
Speaker 6: look him up. He's fucking brilliant, he said. It makes

756
00:39:17,400 --> 00:39:20,080
Speaker 6: the easy things easier and the harder things harder. Sure,

757
00:39:20,560 --> 00:39:23,400
Speaker 6: if we could have that as client side, awesome, But

758
00:39:23,560 --> 00:39:28,520
Speaker 6: I don't really think any of this GPU infrastructure, even

759
00:39:28,520 --> 00:39:31,120
Speaker 6: in receivership, is gonna like you can already get discount

760
00:39:31,120 --> 00:39:33,640
Speaker 6: a one hundreds h one hundreds h two hundreds, can't

761
00:39:33,640 --> 00:39:35,600
Speaker 6: wait for the discount Blackwells pieces of shit.

762
00:39:49,320 --> 00:39:52,160
Speaker 4: But I'll give you an I'm going to my fingers

763
00:39:52,160 --> 00:39:53,040
Speaker 4: between the tigers here.

764
00:39:54,960 --> 00:39:57,239
Speaker 3: I really want to squeeze out one more example here.

765
00:39:57,480 --> 00:39:59,719
Speaker 3: It's a really good one because it's about a nonprofit

766
00:39:59,719 --> 00:40:02,160
Speaker 3: people should be supporting as well. So it's a nonprofit

767
00:40:02,200 --> 00:40:05,040
Speaker 3: called the Human Rights Data Analysis Group HRDAG dot org.

768
00:40:05,280 --> 00:40:09,760
Speaker 3: It's run by some really brilliant mathematician statisticians. They started

769
00:40:09,760 --> 00:40:13,800
Speaker 3: off doing statistical excrapolations of war crimes for human rights tribunals,

770
00:40:13,880 --> 00:40:16,759
Speaker 3: mostly in the Hague, and talking about the aspects of

771
00:40:17,480 --> 00:40:20,080
Speaker 3: war crimes that were not visible but could be statistically

772
00:40:20,080 --> 00:40:24,160
Speaker 3: inferred from adjacent data. They did a project with Innocence

773
00:40:24,200 --> 00:40:27,120
Speaker 3: Project New Orleans, where they used LLMS to identify the

774
00:40:27,239 --> 00:40:31,440
Speaker 3: linguistic correlates of arrest reports that produced exonerations, and they

775
00:40:31,520 --> 00:40:34,120
Speaker 3: use that to analyze a lot more arrest reports than

776
00:40:34,120 --> 00:40:36,600
Speaker 3: they could otherwise. And they put that at the top

777
00:40:36,640 --> 00:40:39,400
Speaker 3: of a funnel where lawyers and paralegals were able to

778
00:40:39,440 --> 00:40:42,640
Speaker 3: accelerate their exoneration work. That's a new thing on this earth, right,

779
00:40:42,880 --> 00:40:46,040
Speaker 3: It's very cool, And like I'm like, Okay, well, if

780
00:40:46,080 --> 00:40:50,279
Speaker 3: these guys can accelerate that work with cheap hardware that

781
00:40:50,320 --> 00:40:52,920
Speaker 3: today is out of reach, if they can figure out

782
00:40:52,920 --> 00:40:54,799
Speaker 3: how to use open source models but make them more

783
00:40:54,840 --> 00:40:57,640
Speaker 3: efficient because you've got all these skilled applied statisticians who

784
00:40:57,640 --> 00:41:00,120
Speaker 3: are no longer caught up in the bubble, then I

785
00:41:00,120 --> 00:41:02,640
Speaker 3: think we could see some useful things after the bubble.

786
00:41:02,640 --> 00:41:04,600
Speaker 3: That's that's my argument for this is fiber in the

787
00:41:04,600 --> 00:41:06,759
Speaker 3: ground and not shitty monkey jpigs.

788
00:41:08,000 --> 00:41:11,120
Speaker 5: Thank you both, we got there.

789
00:41:11,160 --> 00:41:15,040
Speaker 4: So what I think I heard was we feel like

790
00:41:15,239 --> 00:41:17,480
Speaker 4: the GPU infrastructure is just kind of fucked that's not

791
00:41:17,520 --> 00:41:22,000
Speaker 4: going to be useful afterwards. But also the actual technology

792
00:41:22,040 --> 00:41:24,719
Speaker 4: itself could possibly be useful in a number of use

793
00:41:24,760 --> 00:41:27,120
Speaker 4: cases that could socially.

794
00:41:27,280 --> 00:41:29,920
Speaker 3: Just if workers get to decide how they use their tools,

795
00:41:30,200 --> 00:41:32,640
Speaker 3: they generally will be able to make some good decisions

796
00:41:32,640 --> 00:41:35,360
Speaker 3: about it. And new tools for workers who are skilled

797
00:41:35,360 --> 00:41:37,040
Speaker 3: and get to decide how they use them, that's great.

798
00:41:37,239 --> 00:41:40,600
Speaker 3: You know, look, if you're I've met a video editor

799
00:41:40,880 --> 00:41:43,360
Speaker 3: who changed the eye lines of two hundred extras in

800
00:41:43,480 --> 00:41:48,560
Speaker 3: a crowd scene, right, using a deep fake, And he

801
00:41:48,640 --> 00:41:50,480
Speaker 3: was like, yeah, I was, you know, I was sitting

802
00:41:50,480 --> 00:41:52,960
Speaker 3: over the director. We thought, wouldn't this scene be really

803
00:41:53,000 --> 00:41:54,920
Speaker 3: interesting if they were all looking that way instead of

804
00:41:54,960 --> 00:41:57,160
Speaker 3: this way? And they were able to do it, And

805
00:41:57,200 --> 00:41:58,799
Speaker 3: I'm like, Okay, that's a new tool on this earth.

806
00:41:58,800 --> 00:42:01,279
Speaker 3: It's like generative. That was that looks Yeah, it's a

807
00:42:01,320 --> 00:42:03,200
Speaker 3: chief fake, right, they just moved the eyeballs.

808
00:42:02,880 --> 00:42:05,560
Speaker 6: Yeah, the deeping is that generative? AI? Because this is

809
00:42:05,560 --> 00:42:07,600
Speaker 6: a really this is really appoint point.

810
00:42:07,600 --> 00:42:10,480
Speaker 3: It's just because well, they made new pixels where pixels

811
00:42:10,520 --> 00:42:13,200
Speaker 3: didn't exist before by making an inference, right.

812
00:42:13,120 --> 00:42:15,239
Speaker 6: Right, But that doesn't mean it's a drunk swoman. Of

813
00:42:15,600 --> 00:42:17,319
Speaker 6: the reason I say this is not because you're wrong

814
00:42:17,360 --> 00:42:19,840
Speaker 6: about that. That example is great. It's just these pigs

815
00:42:19,840 --> 00:42:24,600
Speaker 6: have got rich conflating the useful with the useless. Okay, okay,

816
00:42:24,680 --> 00:42:29,760
Speaker 6: all right is also right, But thank you sirs.

817
00:42:30,160 --> 00:42:33,640
Speaker 4: I do have one, one small anecdote ad myself. Yes,

818
00:42:33,680 --> 00:42:35,359
Speaker 4: I am of value.

819
00:42:36,000 --> 00:42:37,560
Speaker 5: I work in the video games industry.

820
00:42:37,640 --> 00:42:38,759
Speaker 6: Of course you work.

821
00:42:38,840 --> 00:42:41,279
Speaker 4: I work in the narrative department, which means I write

822
00:42:41,320 --> 00:42:44,960
Speaker 4: a lot of dialogue and in my pipelines, we've actually,

823
00:42:44,960 --> 00:42:47,479
Speaker 4: we have found a use for generative AI that doesn't

824
00:42:47,520 --> 00:42:51,240
Speaker 4: steal anybody's work, uh and makes us work about fifteen

825
00:42:51,280 --> 00:42:54,840
Speaker 4: percent faster. So what we've done is we got permission

826
00:42:55,000 --> 00:42:59,640
Speaker 4: from our SAG actors to train our personal closed generative

827
00:42:59,680 --> 00:43:03,120
Speaker 4: model on their voices, plug that into what we call

828
00:43:03,239 --> 00:43:05,640
Speaker 4: robo voice, so our lines that do speech to text

829
00:43:05,719 --> 00:43:09,040
Speaker 4: instead of the horrible Amazon poly monstrosity that's like hello,

830
00:43:09,080 --> 00:43:12,200
Speaker 4: my name is robot. And what we're able to do

831
00:43:12,320 --> 00:43:15,640
Speaker 4: is hear the lines and iterate on them faster so

832
00:43:15,680 --> 00:43:17,880
Speaker 4: that we're done by the time we actually get to

833
00:43:17,920 --> 00:43:20,960
Speaker 4: record with the SAG actors at their sack fees, and

834
00:43:21,000 --> 00:43:23,840
Speaker 4: nobody has lost any work. So I think my point

835
00:43:23,880 --> 00:43:27,680
Speaker 4: is it's cool, right, Like, generative AI absolutely does have

836
00:43:27,719 --> 00:43:29,880
Speaker 4: some uses. Is it worth burning down the planet? I

837
00:43:29,920 --> 00:43:32,759
Speaker 4: can't answer that for you. It's a Wednesday, right, But

838
00:43:32,880 --> 00:43:36,319
Speaker 4: there are like there's baby and there's bathwater, And I

839
00:43:36,360 --> 00:43:38,680
Speaker 4: think it it behooves us all to continue to have

840
00:43:38,760 --> 00:43:41,839
Speaker 4: the discussion, right to not one hundred percent close the door.

841
00:43:42,239 --> 00:43:44,880
Speaker 4: And on that note, we are going to switch to

842
00:43:45,040 --> 00:43:48,600
Speaker 4: answering questions from the audience, and I have one picked

843
00:43:48,640 --> 00:43:51,480
Speaker 4: out already, since we're all adults here, I thought I

844
00:43:51,520 --> 00:43:55,880
Speaker 4: would start with a spicy one, which is woodn't slowing

845
00:43:55,960 --> 00:44:00,160
Speaker 4: AI development in the United States, just allow China to

846
00:44:00,239 --> 00:44:01,359
Speaker 4: dominate all of us?

847
00:44:01,400 --> 00:44:04,439
Speaker 3: Oh good, Oh good?

848
00:44:05,320 --> 00:44:09,600
Speaker 6: So I love hearing a shit that was said about

849
00:44:09,640 --> 00:44:12,680
Speaker 6: the Soviet Union said again, Oh no, what if we

850
00:44:12,719 --> 00:44:16,000
Speaker 6: don't have chat GPT like China? Oh no, what will

851
00:44:16,040 --> 00:44:17,880
Speaker 6: we ever do if we don't have a chat? But

852
00:44:18,000 --> 00:44:21,520
Speaker 6: we can fuck like cha? It's nonsense. The reason that

853
00:44:21,680 --> 00:44:24,239
Speaker 6: China is pulling ahead of America in any way is

854
00:44:24,280 --> 00:44:27,320
Speaker 6: because they have a massive renewables initiative and also terrible

855
00:44:27,360 --> 00:44:31,399
Speaker 6: working conditions. But putting all that aside, that whole thing

856
00:44:31,480 --> 00:44:35,520
Speaker 6: is built on an inherently cymophobic idea and also just nonsense,

857
00:44:35,560 --> 00:44:39,160
Speaker 6: which is that America does not have the resources to

858
00:44:39,239 --> 00:44:44,000
Speaker 6: get this. Nowhere ever has anywhere near this much money

859
00:44:44,120 --> 00:44:47,319
Speaker 6: been shoved into one thing forever for years, not the

860
00:44:47,360 --> 00:44:50,439
Speaker 6: dot com boom, not their Post Telecommunications Act, which Corey

861
00:44:50,480 --> 00:44:52,440
Speaker 6: probably knows way better than I do. But there were

862
00:44:52,440 --> 00:44:55,040
Speaker 6: think it's four hundred billion dollars put into that nonsense.

863
00:44:55,440 --> 00:44:58,560
Speaker 6: Nobody has ever had more chances and more money to

864
00:44:58,640 --> 00:45:02,960
Speaker 6: do anything. Using China as a convenient excuse to spunk

865
00:45:03,080 --> 00:45:06,120
Speaker 6: money every month is a fucking stupid idea. But also

866
00:45:06,440 --> 00:45:10,600
Speaker 6: to develop what. Look at the last releases from open

867
00:45:10,640 --> 00:45:14,360
Speaker 6: ai Atlas. Oh work, Wow, a fucking web browser. Great,

868
00:45:14,440 --> 00:45:16,000
Speaker 6: I've never used one of us. Oh it can read

869
00:45:16,000 --> 00:45:17,919
Speaker 6: the web page and then fuck up and not buy

870
00:45:17,960 --> 00:45:22,000
Speaker 6: a thick Wow. I can just take five edibles if

871
00:45:22,040 --> 00:45:26,440
Speaker 6: I want to not use Amazon properly. But in all seriousness,

872
00:45:26,760 --> 00:45:28,680
Speaker 6: there is no development to be made that is not

873
00:45:28,680 --> 00:45:32,480
Speaker 6: being made in America. Shit, tons of talent, tons of

874
00:45:32,520 --> 00:45:36,040
Speaker 6: bit Chinese by the way, tons of Chinese engineers, as

875
00:45:36,080 --> 00:45:38,200
Speaker 6: you all know, like it's a very common thing. There

876
00:45:38,280 --> 00:45:41,279
Speaker 6: is no development that's being missed. There's what are we gonna?

877
00:45:41,320 --> 00:45:44,680
Speaker 6: Oh No, we can't cand anthropic five billion dollars a

878
00:45:44,800 --> 00:45:48,160
Speaker 6: year to destroy it for no real reason. There's no

879
00:45:48,960 --> 00:45:52,920
Speaker 6: it's not a cogent argument. Because look at what China

880
00:45:53,040 --> 00:45:55,800
Speaker 6: seems to be doing about the same thing as America

881
00:45:55,960 --> 00:45:58,360
Speaker 6: is with large language models is way less that just

882
00:45:58,840 --> 00:46:01,080
Speaker 6: that says more about large whiche models than anything.

883
00:46:01,520 --> 00:46:04,720
Speaker 3: So there's a long history of saying that we should

884
00:46:05,080 --> 00:46:10,680
Speaker 3: subsidize and protect large American firms to defend America against

885
00:46:10,719 --> 00:46:15,160
Speaker 3: foreign firms. So for many, many years, the argument against

886
00:46:15,200 --> 00:46:17,879
Speaker 3: breaking up AT and T was that they were our

887
00:46:17,960 --> 00:46:21,680
Speaker 3: national champion and that they were keeping us safe from

888
00:46:21,760 --> 00:46:24,400
Speaker 3: foreign aggressors. In the mid nineteen fifties, AT and T

889
00:46:24,600 --> 00:46:27,960
Speaker 3: was almost broken up. The DoD intervened to say that

890
00:46:28,000 --> 00:46:30,200
Speaker 3: if we lost AT and T, if it wasn't intact,

891
00:46:30,239 --> 00:46:35,279
Speaker 3: America might lose the Korean War. So AT and T

892
00:46:35,360 --> 00:46:38,040
Speaker 3: won the Korean War because they got another thirty years. Right,

893
00:46:39,080 --> 00:46:41,799
Speaker 3: the only people arguably who won the Korean War were

894
00:46:41,840 --> 00:46:45,359
Speaker 3: AT and T, including both sides in the Korean War.

895
00:46:45,800 --> 00:46:50,400
Speaker 3: So when the eighties rolled around, right, there was this

896
00:46:50,480 --> 00:46:52,279
Speaker 3: law in the seventies. In the eighties rolled around and

897
00:46:52,320 --> 00:46:54,400
Speaker 3: we were once again thinking about breaking up AT and T.

898
00:46:54,480 --> 00:46:56,960
Speaker 3: There was this long argument about how there was this

899
00:46:57,120 --> 00:47:01,799
Speaker 3: belligerent foreign power in the Pacific rim that they weren't original,

900
00:47:01,880 --> 00:47:05,000
Speaker 3: they stole our ip and cloned our technology, and they

901
00:47:05,000 --> 00:47:08,040
Speaker 3: would destroy our high tech industries if we did not

902
00:47:08,239 --> 00:47:11,080
Speaker 3: have a giant company to defend us against them. That

903
00:47:11,200 --> 00:47:14,880
Speaker 3: country was Japan. Right now, it turns out that the

904
00:47:15,040 --> 00:47:18,680
Speaker 3: major project of AT and T was not depending America

905
00:47:18,800 --> 00:47:22,200
Speaker 3: defending America from Japan. It was preventing Americans from getting

906
00:47:22,280 --> 00:47:26,279
Speaker 3: modems because AT and T really did not want you

907
00:47:26,560 --> 00:47:29,080
Speaker 3: and anyone else in America to be able to provide

908
00:47:29,080 --> 00:47:31,360
Speaker 3: a service to one another without them being able to

909
00:47:31,480 --> 00:47:33,960
Speaker 3: veto it or charge rent on it. Right, if you

910
00:47:34,000 --> 00:47:36,959
Speaker 3: think about like the rollout of caller ID six dollars

911
00:47:37,120 --> 00:47:39,520
Speaker 3: ninety nine per month when it rolled out, right, this

912
00:47:39,640 --> 00:47:41,319
Speaker 3: was what it cost you to find out who was

913
00:47:41,360 --> 00:47:44,680
Speaker 3: calling you before you picked up the phone. You can't

914
00:47:44,719 --> 00:47:46,920
Speaker 3: do that once people have modems. There is no caller

915
00:47:46,960 --> 00:47:50,600
Speaker 3: ID for email. Right if your email provider says until

916
00:47:50,640 --> 00:47:54,160
Speaker 3: you click the message in the list pain you don't

917
00:47:54,200 --> 00:47:56,480
Speaker 3: get to find out who the from address is, you

918
00:47:56,520 --> 00:48:00,920
Speaker 3: would just change email providers. So by controlling the network,

919
00:48:00,960 --> 00:48:04,320
Speaker 3: by centralizing the network, they were able to just basically

920
00:48:04,640 --> 00:48:07,560
Speaker 3: like kneel on the throat of the American tech industry.

921
00:48:08,040 --> 00:48:10,759
Speaker 3: And so it is always the case that when we

922
00:48:10,880 --> 00:48:15,840
Speaker 3: defend monopolists in order to prevent foreign firms from destroying

923
00:48:15,880 --> 00:48:18,319
Speaker 3: our high tech sectors, what we're actually doing is we

924
00:48:18,360 --> 00:48:21,799
Speaker 3: are defending the firms that are structuring and controlling the

925
00:48:21,840 --> 00:48:25,239
Speaker 3: market domestically for our own innovation. So these very large

926
00:48:25,280 --> 00:48:27,840
Speaker 3: firms that are doing AI. Now, AI is only like

927
00:48:27,880 --> 00:48:29,720
Speaker 3: one of the things they do. Obviously we have anthropic

928
00:48:29,800 --> 00:48:33,680
Speaker 3: and open AI, but you know, Microsoft, Google, Apple, Oracle.

929
00:48:33,880 --> 00:48:36,799
Speaker 3: These are firms that are effectively market structures. They get

930
00:48:36,840 --> 00:48:39,560
Speaker 3: to decide what products exist and what products don't exist,

931
00:48:39,760 --> 00:48:41,600
Speaker 3: how much they're going to cost, and who can see

932
00:48:41,600 --> 00:48:43,719
Speaker 3: them and who can use them. Right, And when we

933
00:48:43,800 --> 00:48:46,360
Speaker 3: defend them in the name of preventing China from pulling

934
00:48:46,360 --> 00:48:49,000
Speaker 3: ahead in AI, what we're effectively doing is we're saying

935
00:48:49,000 --> 00:48:52,279
Speaker 3: that we should maintain this shadow FTC that has more

936
00:48:52,360 --> 00:48:55,120
Speaker 3: power than the FTC ever managed to exercise, but that

937
00:48:55,400 --> 00:48:57,840
Speaker 3: never exercise it in the interest of the American public,

938
00:48:58,040 --> 00:48:59,839
Speaker 3: but only in the interests of their shareholders.

939
00:49:02,239 --> 00:49:08,799
Speaker 4: Thank you, all right, We have about five minutes left,

940
00:49:08,800 --> 00:49:10,600
Speaker 4: and I run a tight ship. So this is the

941
00:49:10,719 --> 00:49:14,200
Speaker 4: last question. So the question is I have a high

942
00:49:14,200 --> 00:49:17,719
Speaker 4: school senior who is technically minded. Ten years ago I

943
00:49:17,719 --> 00:49:19,640
Speaker 4: would have told her to get a CS degree and

944
00:49:19,680 --> 00:49:23,400
Speaker 4: go into tech. What should you tell someone to study now?

945
00:49:25,440 --> 00:49:29,560
Speaker 6: Finance? It's interesting, will I want to tell you a

946
00:49:29,560 --> 00:49:32,920
Speaker 6: little little story. So I taught myself economics in this

947
00:49:32,920 --> 00:49:35,000
Speaker 6: past year and a half. I'm a fucking idiot. I

948
00:49:35,080 --> 00:49:37,320
Speaker 6: just want to be really clear. I don't consider myself

949
00:49:37,360 --> 00:49:40,240
Speaker 6: particularly smart, however you feels up to you. The point

950
00:49:40,360 --> 00:49:45,640
Speaker 6: is the world right now. The media, tons of analysts,

951
00:49:45,960 --> 00:49:48,920
Speaker 6: tons of tech founders, don't know shit from fuck. Those

952
00:49:48,920 --> 00:49:52,719
Speaker 6: are technical terms. In all seriousness, finance is not as

953
00:49:52,840 --> 00:49:55,399
Speaker 6: difficult to complex as it sounds. And indeed the world

954
00:49:55,480 --> 00:49:58,880
Speaker 6: runs on money. But also these corporate structures are run

955
00:49:59,400 --> 00:50:02,239
Speaker 6: fairly same, but they dress them up in these confusing

956
00:50:02,360 --> 00:50:05,120
Speaker 6: and annoying terms that actually, when you break them down,

957
00:50:05,160 --> 00:50:08,080
Speaker 6: are pretty simple. There are people to blame, they've got houses,

958
00:50:08,200 --> 00:50:15,040
Speaker 6: they're very flammable. But in all seriousness, these people they're

959
00:50:15,040 --> 00:50:19,160
Speaker 6: empowered through ignorance. They're not powered through ability, intelligence or

960
00:50:19,200 --> 00:50:22,600
Speaker 6: even making good products. They are powerful because we have

961
00:50:22,760 --> 00:50:25,000
Speaker 6: and I'm not trying to sound paranoid, this is just

962
00:50:25,040 --> 00:50:28,800
Speaker 6: the truth. Media that in many cases does not understand

963
00:50:28,840 --> 00:50:31,279
Speaker 6: the things they are writing about or indeed have the

964
00:50:31,320 --> 00:50:35,240
Speaker 6: capability of reading an earning statement. This stuff seems very scary.

965
00:50:35,239 --> 00:50:38,120
Speaker 6: You may say I'm not a numbers person. I couldn't

966
00:50:38,160 --> 00:50:40,480
Speaker 6: possibly you are a numbers person. It's not that. It

967
00:50:40,560 --> 00:50:43,200
Speaker 6: really is if I can do it. I failed mathematics

968
00:50:43,480 --> 00:50:46,839
Speaker 6: years and years in a row. It really is fairly straightforward.

969
00:50:47,080 --> 00:50:50,319
Speaker 6: They literally format things to make them boring, They worth

970
00:50:50,360 --> 00:50:53,680
Speaker 6: them to make them seem confusing. You can pick upart

971
00:50:53,719 --> 00:50:56,920
Speaker 6: corporate structure. Any one of you can do it. That

972
00:50:57,080 --> 00:50:59,360
Speaker 6: is where anyone can have an effect. I think a

973
00:50:59,400 --> 00:51:02,120
Speaker 6: high school could do it. You want to change the world.

974
00:51:02,160 --> 00:51:04,640
Speaker 6: You pull away their power, You take away their ability

975
00:51:04,640 --> 00:51:08,120
Speaker 6: to obfuscate their wealth and the way they accumulate power

976
00:51:08,280 --> 00:51:10,520
Speaker 6: by learning how these things work. I've written a lot

977
00:51:10,520 --> 00:51:12,440
Speaker 6: about it. I had to do everything you read. It's

978
00:51:12,480 --> 00:51:14,359
Speaker 6: so long because I'm learning as I go. It's ten

979
00:51:14,400 --> 00:51:16,480
Speaker 6: thousand words because I'm teaching most like, what the fuck's this?

980
00:51:17,480 --> 00:51:19,359
Speaker 6: Anyone can do this. You want to teach a young person,

981
00:51:19,400 --> 00:51:22,960
Speaker 6: anythink honestly teaching them who the fuck Milton Friedman was

982
00:51:23,040 --> 00:51:25,160
Speaker 6: teaching them how the world got the way it got,

983
00:51:25,200 --> 00:51:29,440
Speaker 6: But also learn about how companies are structured. It is

984
00:51:29,520 --> 00:51:32,640
Speaker 6: not that complex. And the reason that you get knocked

985
00:51:32,680 --> 00:51:36,200
Speaker 6: off course is because the powerful people go, no, it's

986
00:51:36,280 --> 00:51:39,520
Speaker 6: not that simple. It's actually very mystical. Sam Altman, he

987
00:51:39,600 --> 00:51:42,480
Speaker 6: is not just a con artist. He's not just a

988
00:51:42,480 --> 00:51:44,719
Speaker 6: guy that's really good at convincing rich people to give

989
00:51:44,760 --> 00:51:48,200
Speaker 6: him money. He has secret brilliance. No, he doesn't. They speak.

990
00:51:48,280 --> 00:51:51,160
Speaker 6: None of these people do. The McKinsey egg plants like

991
00:51:51,320 --> 00:51:55,480
Speaker 6: these fucking people. Long story short, learn finance seriously, and

992
00:51:55,480 --> 00:51:56,600
Speaker 6: you don't have to go to school.

993
00:51:56,680 --> 00:51:59,440
Speaker 3: It's good advice. You know. There's this idea from the

994
00:51:59,440 --> 00:52:03,680
Speaker 3: finance sect, this acronym ego. My eyes glaze over. It's

995
00:52:03,680 --> 00:52:06,600
Speaker 3: when you large so much complexity and a prospectus that

996
00:52:06,640 --> 00:52:08,320
Speaker 3: no one can get through it, and they assume that

997
00:52:08,360 --> 00:52:09,799
Speaker 3: a pile of shit that big has to have a

998
00:52:09,840 --> 00:52:15,960
Speaker 3: pony underneath it. I have different advice, though. So my

999
00:52:16,120 --> 00:52:19,000
Speaker 3: daughter started college this year and she wasn't sure what

1000
00:52:19,040 --> 00:52:20,279
Speaker 3: to do, and we had a lot of talks about

1001
00:52:20,280 --> 00:52:21,759
Speaker 3: what to do. She didn't take my advice, but I'll

1002
00:52:21,760 --> 00:52:24,879
Speaker 3: tell you what I told her, which is that if

1003
00:52:24,880 --> 00:52:27,400
Speaker 3: you don't know what you want to do at university,

1004
00:52:27,960 --> 00:52:30,759
Speaker 3: don't go to university. Go to college and become an electrician.

1005
00:52:31,880 --> 00:52:32,640
Speaker 6: Absolucking Lou.

1006
00:52:32,960 --> 00:52:36,160
Speaker 3: There is so much work for electricians, and we are

1007
00:52:36,200 --> 00:52:39,239
Speaker 3: going to be solarizing for the next forty years. There's

1008
00:52:39,280 --> 00:52:42,440
Speaker 3: going to be infinity work for electricians. It's like being

1009
00:52:42,480 --> 00:52:44,799
Speaker 3: a plumber, but you don't have to touch pooh. You

1010
00:52:44,840 --> 00:52:47,520
Speaker 3: can be you can be an electrician who just does

1011
00:52:47,560 --> 00:52:50,160
Speaker 3: emergency call outs when money's getting low and you charge

1012
00:52:50,160 --> 00:52:52,360
Speaker 3: five hundred bucks an hour. You can be an electrician

1013
00:52:52,400 --> 00:52:54,360
Speaker 3: on a cruise ship. You can be a theater kid.

1014
00:52:54,480 --> 00:52:56,320
Speaker 3: You can be an electrician on a job site. You

1015
00:52:56,360 --> 00:52:58,000
Speaker 3: can be an electrician for the government. You can be

1016
00:52:58,040 --> 00:53:01,200
Speaker 3: an electrician on a battleship. You can go abroad and

1017
00:53:01,239 --> 00:53:07,280
Speaker 3: be an electrician because electricity is the same. Right, there's

1018
00:53:07,320 --> 00:53:10,040
Speaker 3: so and it's and it's interesting work and you get

1019
00:53:10,080 --> 00:53:12,680
Speaker 3: paid on the job. Right, you get paid for your apprenticeship,

1020
00:53:14,040 --> 00:53:17,560
Speaker 3: and you can be in a union. And if you

1021
00:53:17,680 --> 00:53:21,080
Speaker 3: decide later you want to learn more and you like it,

1022
00:53:21,160 --> 00:53:23,600
Speaker 3: you can become an eying and if you don't, you

1023
00:53:23,600 --> 00:53:26,440
Speaker 3: can put yourself through college by being an electrician and

1024
00:53:26,520 --> 00:53:27,400
Speaker 3: learn finance.

1025
00:53:27,600 --> 00:53:31,800
Speaker 6: And one of the thing, seriously, I've talked to basically

1026
00:53:31,880 --> 00:53:34,440
Speaker 6: every power analyst that's at this point or read their work,

1027
00:53:34,480 --> 00:53:36,919
Speaker 6: and I will say, there's always space for someone who

1028
00:53:36,920 --> 00:53:39,040
Speaker 6: knows electricity.

1029
00:53:38,400 --> 00:53:39,120
Speaker 3: And one hundred.

1030
00:53:39,600 --> 00:53:41,799
Speaker 6: You will make so much I'm not kidding the people

1031
00:53:41,800 --> 00:53:43,319
Speaker 6: who pay you fifteen hundred dollars an hour.

1032
00:53:43,560 --> 00:53:47,640
Speaker 3: Yeah, so it's crazy or HVAC HVAC electricity. Oh hell,

1033
00:53:47,719 --> 00:53:50,280
Speaker 3: yes that is. That is a killer combo.

1034
00:53:50,760 --> 00:53:52,640
Speaker 6: No, really, you will make so much money. I'm not

1035
00:53:52,680 --> 00:53:55,080
Speaker 6: even kidding. And it's so like the electricity stuff, that's hard.

1036
00:53:55,120 --> 00:53:57,239
Speaker 6: I can do that. The numbers easy piece, like just

1037
00:53:57,280 --> 00:53:58,680
Speaker 6: going yeah.

1038
00:53:58,600 --> 00:54:01,280
Speaker 3: Yeah, if you get your numbers wrong, you don't electrocute yourself.

1039
00:54:01,320 --> 00:54:01,920
Speaker 1: So there.

1040
00:54:03,239 --> 00:54:05,200
Speaker 3: No one ever fell off a roof doing math.

1041
00:54:06,400 --> 00:54:10,719
Speaker 4: That's true. Well Ed, Corey, we have time for one

1042
00:54:10,719 --> 00:54:14,239
Speaker 4: more question. Okay, speaking that in there and we were

1043
00:54:14,280 --> 00:54:17,080
Speaker 4: at time. Okay, Oh no, now you're making me panic.

1044
00:54:17,360 --> 00:54:21,920
Speaker 5: Okay, Oh no, I got a certification at work.

1045
00:54:22,239 --> 00:54:23,160
Speaker 6: Anyone used to compute?

1046
00:54:23,400 --> 00:54:32,080
Speaker 5: No, we're going, We're going hang on, hang on.

1047
00:54:34,160 --> 00:54:36,640
Speaker 6: I texted Samuel one the other day and you didn't

1048
00:54:36,680 --> 00:54:41,040
Speaker 6: take back to me. Just fact to share with the audience,

1049
00:54:42,080 --> 00:54:43,840
Speaker 6: Dario pr anthropics.

1050
00:54:44,560 --> 00:54:48,480
Speaker 4: This is this is a short answer lightning round, which is.

1051
00:54:48,520 --> 00:54:51,120
Speaker 5: If you had to guess what is the.

1052
00:54:50,960 --> 00:54:53,800
Speaker 4: Timeline we were looking at for the AI bubble tupop

1053
00:54:54,360 --> 00:54:55,600
Speaker 4: make your bets people.

1054
00:54:56,960 --> 00:55:00,000
Speaker 6: No light thing Q two, twenty twenty six. Oh.

1055
00:55:00,400 --> 00:55:04,239
Speaker 3: So I am a firm believer that the market can

1056
00:55:04,280 --> 00:55:07,120
Speaker 3: remain irrational longer than you can remain solvent. So I'm

1057
00:55:07,120 --> 00:55:10,240
Speaker 3: not going to try and gas. But I think it's coming,

1058
00:55:10,280 --> 00:55:13,000
Speaker 3: and I also think the number. So to your point ed,

1059
00:55:13,120 --> 00:55:15,400
Speaker 3: I would say that the number of foundation models that

1060
00:55:15,480 --> 00:55:20,319
Speaker 3: will be around after the crash very likely could be zero.

1061
00:55:20,480 --> 00:55:22,120
Speaker 3: I'm not saying that it must be zero, but I

1062
00:55:22,160 --> 00:55:22,880
Speaker 3: think it very.

1063
00:55:22,719 --> 00:55:24,440
Speaker 6: Likely the open ones will be around.

1064
00:55:24,440 --> 00:55:26,879
Speaker 3: But yeah, the open models. You can't kill an open

1065
00:55:26,920 --> 00:55:28,640
Speaker 3: source model if people like it in contry.

1066
00:55:28,680 --> 00:55:30,280
Speaker 6: A fringe mode, or they'll kill clued.

1067
00:55:31,120 --> 00:55:32,560
Speaker 3: Yeah.

1068
00:55:32,640 --> 00:55:38,640
Speaker 4: Yeah, all right, so we have immediately and later then

1069
00:55:38,719 --> 00:55:39,600
Speaker 4: you can stay solvent.

1070
00:55:39,680 --> 00:55:40,680
Speaker 5: So don't try to guess.

1071
00:55:40,920 --> 00:55:43,000
Speaker 3: Yeah, save your sacks, don't try and short that market.

1072
00:55:43,200 --> 00:55:47,880
Speaker 3: Bipolls the practice digging for can goods and rubble use

1073
00:55:47,920 --> 00:55:48,440
Speaker 3: that time.

1074
00:55:48,239 --> 00:55:49,440
Speaker 6: Effect on my electrician.

1075
00:55:50,520 --> 00:55:52,960
Speaker 3: I had both my hips replaced and my cataracts done

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00:55:53,120 --> 00:55:56,200
Speaker 3: just in case civilization was about to fail. I saved

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00:55:56,239 --> 00:55:59,960
Speaker 3: one of my femurs. I had a cane topper made

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00:56:00,120 --> 00:56:03,040
Speaker 3: cast in brass. I had it scanned at three d

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00:56:03,080 --> 00:56:05,480
Speaker 3: If you got to interarchive dot org, slash doctor or

1080
00:56:05,520 --> 00:56:08,400
Speaker 3: dash femur, you can get a twelve hundred dpi stlphile

1081
00:56:08,400 --> 00:56:10,520
Speaker 3: that you can print my disease feamer. I wanted to

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00:56:10,520 --> 00:56:12,400
Speaker 3: make soup stock, but my wife said no.

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00:56:16,080 --> 00:56:18,359
Speaker 5: And on that note, everything's gonna be fine. It would

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00:56:18,400 --> 00:56:20,800
Speaker 5: be fine, all right, have a wonderful night, everybody.

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Speaker 1: Thank you, Thank you for listening to Better Offline.

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00:56:31,120 --> 00:56:33,560
Speaker 6: The editor and composer of the Better Offline theme song

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00:56:33,600 --> 00:56:36,280
Speaker 6: is Metasowski. You can check out more of his music

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00:56:36,280 --> 00:56:39,919
Speaker 6: and audio projects at Matasowski dot com, M A T

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00:56:39,920 --> 00:56:44,400
Speaker 6: T O S O W s ki dot com. You

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00:56:44,440 --> 00:56:46,919
Speaker 6: can email me at easy at Better Offline dot com,

1091
00:56:47,040 --> 00:56:49,320
Speaker 6: or visit Better Offline dot com to find more podcast

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00:56:49,400 --> 00:56:52,760
Speaker 6: links and of course my newsletter. I also really recommend

1093
00:56:52,760 --> 00:56:54,640
Speaker 6: you go to chat dot Where's your ed dot at

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00:56:54,640 --> 00:56:57,120
Speaker 6: to visit the discord, and go to our slash.

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Speaker 1: Better Offline to check out I'll Reddit. Thank you so

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00:57:00,160 --> 00:57:03,520
Speaker 1: much for listening. Better Offline is a production of cool

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Speaker 1: Zone Media.

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Speaker 4: For more from cool Zone Media, visit our website Coolzonemedia

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Speaker 4: dot com or check us out on the iHeartRadio, app,

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Speaker 4: Apple Podcasts, or wherever you get your podcasts.