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Speaker 1: Media. You.

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Speaker 2: Wow, you run a tech podcast and we're we're talking

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Speaker 2: about how everything's getting bad. Yet you rely on the

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Speaker 2: technology today, that the cloud based technology. You don't like backups,

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Speaker 2: you don't like recording on your own personal device. It's

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Speaker 2: like you don't like owning anything. It's like you don't

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Speaker 2: like owning anything.

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Speaker 3: Well, this is great. So I was trying to read

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Speaker 3: the intro to my show Better Offline, but Juniper has

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Speaker 3: decided that she will be leading us in today. Welcome

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Speaker 3: to Better Offline. That's one of my very rude guests,

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Speaker 3: Juniper from Kill the Computer, who is here with her

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Speaker 3: much less rude co host Caleb. Caleb, thank you for

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Speaker 3: joining us.

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Speaker 2: Far.

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Speaker 3: Yes, yeah, it's a competition, and of course we've got

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Speaker 3: a Refasan from Wide Left, the Football podcast plus newsletter. Well,

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Speaker 3: we've come in. I brought out one together today just

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Speaker 3: to complain, and I'm going to complain about the fact

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Speaker 3: that my microphone just fell over perfect, which is annoying.

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Speaker 3: I also want to complain I wasn't given the opportunity

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Speaker 3: to invest in something called Gary's List, which is Gary

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Speaker 3: Tan who currently runs y Combinator, says we're starting a

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Speaker 3: citizens union for radical centrism. We approved. Local politics is

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Speaker 3: wootable in San Francisco. Now we're building the community to

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Speaker 3: do everywhere news, commentary, and accountability for policies that affect

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Speaker 3: California and our society. And I'm just going to say,

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Speaker 3: go fuck yourself, Gary if you say, if you write

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Speaker 3: the words radical centrism, horrible.

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Speaker 4: Man.

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Speaker 3: I just love the status quo.

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Speaker 2: So I've actually been looking into I've been learning a

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Speaker 2: little bit about this guy recently, and like hy Combinator, I,

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Speaker 2: I primarily be like by proxy of finding out about

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Speaker 2: this place in San Francisco called Epic Church.

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Speaker 4: Are you familiar with this?

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Speaker 2: I'm immediately sold. It's it's really epic. It's it's bacon,

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Speaker 2: it's it's yeah, that's all that stuff. But no, basically

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Speaker 2: it's this this tech positive church, like basically an evangelical

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Speaker 2: church for tech minded people. Uh, and like founders in

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Speaker 2: the in the Bay Area. So like people like Trace,

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Speaker 2: uh what's the name Tray Stevens go there, Gary Tan

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Speaker 2: goes there. All these like weird fucking awful type people

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Speaker 2: like Peter Teal. I swear to God, I I am

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Speaker 2: not shinny you. Peter Teal is like one of the

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Speaker 2: thought leaders of the type of people there, like.

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Speaker 1: They like, how are we spelling thought actually.

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Speaker 4: Thought h.

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Speaker 2: Yeah, but no, it's like the worst kind of person

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Speaker 2: in the tech world getting evangelical. Like they're like, this

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Speaker 2: is actually where.

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Speaker 5: Peter Chield got the idea to like create cliviculars. I

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Speaker 5: need a new Holy.

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Speaker 2: He is there, Jesus Christ clvicular is there their Virgin's

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Speaker 2: birth Christ now.

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Speaker 1: But it's like a whole thing now, right where like

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Speaker 1: people who were like typically associated with movements that might

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Speaker 1: be humanist or atheist or whatever you want to call it,

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Speaker 1: have like because they're old white men have turned into

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Speaker 1: like embracing some version of Christianity, right, Like even Richard Dawkins,

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Speaker 1: like the Atheist Supreme or whatever the hat he wears.

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Speaker 1: He's like, well, I'm a cultural Christian and like all

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Speaker 1: of them have just been moving more.

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Speaker 3: Cultural Christians just being American.

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Speaker 1: I yes, I look, he's a piece of shit and

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Speaker 1: anyway he can kind of communicate that. I think you'll

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Speaker 1: take those opportunities. But like the Tech God Church feels

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Speaker 1: perfect to that.

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Speaker 4: I don't know, I feel like.

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Speaker 1: We've used the meme U don't create the tournament nexus

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Speaker 1: like way too often. But it just like every time

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Speaker 1: you explained something new about what's happening in the world,

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Speaker 1: like some tech billionaire has read something from a sci

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Speaker 1: fi novel and it has taken like the wrong lesson

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Speaker 1: from it, like again in again time, like oh, yeah,

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Speaker 1: we've started a church to worship technology.

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Speaker 2: Oh literally, best example of this, sorry, you can go

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Speaker 2: go on, best example of this. Just because he's been

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Speaker 2: on my mind a lot recently and I read a

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Speaker 2: lot about his antichrist like tour that he's been doing.

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Speaker 2: But Peter Tiel, he read the philosopher Rene's Gerrard and

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Speaker 2: he came away with the exact opposite interpretation. I think

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Speaker 2: of what you're supposed to take away from this philosopher,

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Speaker 2: which is that like society and powerful people use scapegoats

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Speaker 2: to uh like political scapegoats to put blame on certain

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Speaker 2: people to get whatever political end that you want. And

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Speaker 2: that's like a cautionary tale. It's like a cautionary tale

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Speaker 2: against scapegoats. Yeah, Peter Tiel, He's like, wait, but skatecoats

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Speaker 2: are kind of goaded though, like literally like we can

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Speaker 2: use them to our advantage.

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Speaker 5: Is this a bad time for me to announce that

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Speaker 5: I'm launching my new company that's called The Ring from

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Speaker 5: Lord of their.

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Speaker 2: Rings, the One Ring. And you're partnering with uh surveillance company.

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Speaker 2: You're partnering with Palantariah.

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Speaker 5: Yeah. Under under my company, all the surveillance companies will unite.

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Speaker 3: Here's the thing with this, this Gary's List thing. I've

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Speaker 3: been clicking around. He writes so often, like can he

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Speaker 3: posts like an oracle a day, So this is a

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Speaker 3: blog that he runs. I guess as well as that.

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Speaker 1: But also it's not like Emily's List for perverts, like

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Speaker 1: what is Gary's List?

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Speaker 6: It's it appears to be a blog in which he complains, okay,

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Speaker 6: we need we need more complaining, but also a degree

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Speaker 6: of it's like complaining but also saying he cares about centrism.

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Speaker 3: And then he's like a, I just thought it sim

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Speaker 3: City in four days without reading the fucking just what

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Speaker 3: makes more gun in my mouth?

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Speaker 1: Is just like he's attempting to wrangle Andrew Yang as

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Speaker 1: like a guest poster.

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Speaker 3: So I just don't know what possible, Like.

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Speaker 1: I'm still still on the list. Idea, why is it

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Speaker 1: called list? I'm actually kind of mad about this.

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Speaker 3: It's a list of his posts, like that appears to

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Speaker 3: be it. I just this guy is a billionaire. Yeah,

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Speaker 3: I assume that means he has tens of millions of

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Speaker 3: dollars liquid. You could do so much with that. You

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Speaker 3: can through pretty much anything. You could have your favorite

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Speaker 3: chef Mike and Mayo. I assume you eat those, and

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Speaker 3: you could have your favorite band playing at the same

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Speaker 3: time wherever you want it to in the world. And

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Speaker 3: you're like, no, I need to make fucking blog for centrism. Yeah,

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Speaker 3: let's like protect centrism from from something.

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Speaker 4: It looks like he's the very anti union. He is.

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Speaker 2: He's complaining about in one of these articles about how

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Speaker 2: thirty thousand kids are pawns in a union war that

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Speaker 2: won't even help the teachers there.

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Speaker 4: Yeah, no, he is. It's exactly. He's the kind of guy.

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Speaker 2: But that's the thing is, like every I feel like,

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Speaker 2: not you know, maybe not every but like the majority

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Speaker 2: of the most influential tech uh tech Silicon Valley person

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Speaker 2: these days are turning to evangelical religion. They complain about

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Speaker 2: how there's not enough centrism in the world today while

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Speaker 2: they benish it and like simultaneously they benefit and work

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Speaker 2: with the Trump administration, So like, what do you what

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Speaker 2: are they what are they even complaining about at the

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Speaker 2: end of the day, And.

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Speaker 4: What do they mean?

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Speaker 2: Yeah, what do they even really mean? I think they

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Speaker 2: just mean that they don't like. Really, what I think

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Speaker 2: it is is, I think they just don't like that

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Speaker 2: people don't like them as much as people used to people.

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Speaker 2: These people are mad that no one's really following following

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Speaker 2: for the the like AI grift of like, oh, it's

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Speaker 2: going to simultaneously AI is going to replace all of

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Speaker 2: our jobs but simultaneously make the world a better place. Like,

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Speaker 2: no one's really following for that, And I think they're

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Speaker 2: mad about that. I think these people are genuinely upset.

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Speaker 1: AI is in its gentleman science era.

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Speaker 3: What the fuck does that? What do you mean? What

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Speaker 3: do you mean science?

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Speaker 1: It's a four minute read from Gary Tan who come

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Speaker 1: on who? Okay, So it's beautiful because all of the

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Speaker 1: all of the images in this article are obviously AI generated,

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Speaker 1: as for all of these articles, right, because if you're

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Speaker 1: gonna have an ethos being a cheap piece of shit,

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Speaker 1: you might as well embrace it. But in it it

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Speaker 1: has a jar labeled maple syrup. Yeah, what is and

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Speaker 1: what is in the no idea?

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Speaker 4: What is in the jar? But it is not maple yrup.

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Speaker 3: Will include a link to this because it is just

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Speaker 3: it's wonderful because it's just a bucket with maple syrup

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Speaker 3: in it.

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Speaker 1: I assume rings or gaskets or something.

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Speaker 4: This is what it looks like. That's what it looks like.

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Speaker 4: It has like some carrots in it. I don't know.

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Speaker 3: Dr of this article is LMS have reset the reset

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Speaker 3: the research game. The biggest breakthrough is the simple ideas

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Speaker 3: anyone can try, and the easy questions haven't been answered yet,

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Speaker 3: to which I say, what the fuck are you talking about?

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Speaker 4: What do you mean?

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Speaker 3: The biggest breakthrough the simple ideas, but the easy questions

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Speaker 3: haven't been answered yet?

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Speaker 4: Come on, what dot mean?

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Speaker 3: I don't know. I just this guy's a billionaire. He

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Speaker 3: could do anything, like you can hire a good writer, like,

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Speaker 3: for example.

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Speaker 1: I can't get over the idea that the gentleman's issue

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Speaker 1: is that science is too hard.

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Speaker 3: Like, well, I mean that is kind of the issue

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Speaker 3: of science in general.

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Speaker 1: Well that's a good thing, though, It's like we've gotten

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Speaker 1: past the part of science that is easy, which means

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Speaker 1: we have made advancements, and you have to study science

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Speaker 1: to be good.

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Speaker 4: I don't this feels what the fuck are we doing?

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Speaker 2: He wants every dope to be able to advance the field,

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Speaker 2: which like, I guess.

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Speaker 5: What science in the hands of adult like me, I'm

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Speaker 5: going to start the one ring company.

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Speaker 2: If you do, you'll you'll accidentally create a black hole

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Speaker 2: with a Hadron collider, like you will be saying, dude.

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Speaker 5: Jilan Musk is the richest man on the planet and

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Speaker 5: he is so fucking stupid, But I could be worse.

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Speaker 2: Actually speaking of him, He's another person that's embraced this

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Speaker 2: like soft religion aesthetic. He was in an interview with

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Speaker 2: Katie Miller. Of course you need more demons around to

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Speaker 2: talk to people like you, my Musk, and he's like, yeah,

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Speaker 2: you know, I believe in God. You know, I do

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Speaker 2: believe in a creator. And it's like all of this.

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Speaker 2: I guess the reason I've been focusing so much on

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Speaker 2: that in particular recently with all of these people like

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Speaker 2: Gary Tan, all of these people, is it's all happening

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Speaker 2: at the same time. They're all sort of doing this

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Speaker 2: weird embrace of evangelical religion all sort of it started before. Yeah,

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Speaker 2: but it's I don't know if it's like a cope

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Speaker 2: thing I've been.

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Speaker 1: I think some of it might be, And this might

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Speaker 1: be just doing too much like armchair pop psychology, but like,

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Speaker 1: I think some of it is that in order to

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Speaker 1: kind of proselytize for AI, in order to evangelize their

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Speaker 1: own kind of element or this thing, they have to

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Speaker 1: put themselves in a frame of mind that allows them

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Speaker 1: to believe in the kind of powerful myth making that

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Speaker 1: is analogous to religion, especially because some of these Singularity people,

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Speaker 1: which is not necessarily the same as some of these

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Speaker 1: AI people, but there's a lot of overlap. I do

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Speaker 1: believe that AI will become God, right like a backwards

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Speaker 1: looking you know.

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Speaker 3: I don't think these people believe in anything, sure, I

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Speaker 3: just like that's kind of like but I guess that

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Speaker 3: it's just a.

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Speaker 4: Convenient label to right, Yeah, hear.

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Speaker 1: The word believe just serves as like an analog for

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Speaker 1: telling us the things that they think we should believe, right, Like,

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Speaker 1: I don't really care if they actually believe this shit.

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Speaker 1: I care that they're telling people this ship.

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Speaker 5: There's a really good profile on Peter Teel. I think

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Speaker 5: it wasn't wired about how he uses like Christian apocalyptic

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Speaker 5: end Time's imagery and a lot of his speeches, and

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Speaker 5: I was really interested in all that, which we talked

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Speaker 5: about on our Showju.

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Speaker 2: Yeah, that's the piece about Rene Gerard the philosopher that

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Speaker 2: he like sort of go on, sorry.

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Speaker 5: Yeah, that's it, just he does that and then Gerard

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Speaker 5: and then you know he's very big on Carl Schmidt.

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Speaker 5: I'll let you if you know who that is.

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Speaker 7: God.

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Speaker 3: It comes back to a very simple thing for me,

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Speaker 3: which is, these are not things you do if you

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Speaker 3: have anything you enjoy. If I'm sitting alone, it's been

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Speaker 3: a long day I have. I can name five movies

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Speaker 3: and six video games I could. I've been weary watching

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Speaker 3: Personal Interest excellent show makes me so happy. Wat you

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Speaker 3: go over and it's awesome. But I drink a diet

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Speaker 3: coke with that. Wow, I feel awesome. These people like

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Speaker 3: I need to take on a new religion. I need

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Speaker 3: to create a pro centrism blog, and only then will

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Speaker 3: I possibly be happy. It's just, it's just. I would

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Speaker 3: say it was sad if I cared for these people

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Speaker 3: in any way, shape or for It's just.

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Speaker 4: I hear, Oh, they're into religion.

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Speaker 3: No, they're not. They just they They sit around like

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Speaker 3: board motherfuckers, tugging on the winkies, going.

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Speaker 4: Oh, what's going on in the world.

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Speaker 3: What Jesus was real God was to gpu. We've got

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Speaker 3: ndre GPUs. He put the GPS together, make the a

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Speaker 3: g A. I don't know what that really means. But anyway,

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Speaker 3: who's in your email in box, Peter Jeffrey Epstein. Yeah,

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Speaker 3: by the way, here's here's the thing. I'll say, jmail.

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Speaker 3: I think the people who made jmail should get a

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Speaker 3: Pulitzer for sure. The people that made jmail, which is

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Speaker 3: the searchable Epstein database, really really like they deserve a

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Speaker 3: Pulitzer here. And also so does the guy who the

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Speaker 3: final email that Jeffrey Epstein received on j ee project

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Speaker 3: at yahoo dot com was just like called cod Cody

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Speaker 3: Rudland and he said lol, good riddance and the subject was.

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Speaker 4: Fucking buzz.

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Speaker 1: Buzzerbe Jeffrey Epstein.

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Speaker 3: Also, jeff got invited to a Michael Clayton screening. Anyway,

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Speaker 3: I don't want to get into Epstein because I know

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Speaker 3: that all.

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Speaker 4: Three is a whole different thing.

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Speaker 3: It's just we're in such a weird spot of the

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Speaker 3: moment with all this AI shit as well, because there

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Speaker 3: was that something big is happening piece.

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Speaker 4: Oh yeah, I wanted to kill myself with that.

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Speaker 1: Oh I haven't heard about this peace. I also want

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Speaker 1: to be in a state where I'm going to kill myself.

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Speaker 4: Tell me.

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Speaker 3: Yeah, well, it's a piece by this guy who's like

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Speaker 3: a scammer, guy called Matt Schumer, who back in twenty

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Speaker 3: twenty four claimed his reflection seventy b.

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Speaker 1: Wait is this is this when the AI like tried

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Speaker 1: to post something to like get up and it got rigid.

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Speaker 3: No, no, that's a different thing entirely.

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Speaker 4: That was just the scam of the week before. Yeah,

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Speaker 4: that was no.

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Speaker 3: No, that one I'll get to in a second.

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Speaker 4: Though.

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Speaker 3: This was the one where it was like a four thousand,

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Speaker 3: seven hundred word blog. That's my fault.

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Speaker 4: I read it. No, I did read this.

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Speaker 5: Yeah I didn't.

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Speaker 1: I remember four thousand, seven hundred because I put it

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Speaker 1: into a word counter yep, because I was like, what

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Speaker 1: the fuck is this?

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Speaker 4: Yeah?

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Speaker 1: No, it sucks, but yeah, but we have listeners they

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Speaker 1: should also know what this is.

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Speaker 3: So I mean I heard about it on the monologue

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Speaker 3: last week. So there and then the most people said

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Speaker 3: don't don't worry about being angry, and one person was like,

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Speaker 3: you need to be less angry. It's just I think

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Speaker 3: we're entering like a hysteriozone. And I actually my favorite

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Speaker 3: thing about that blog by far was watching two or

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Speaker 3: three other people try and do similar blogs and just

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Speaker 3: nothing happened. People be actually, yeah, it's actually, it's actually crazier.

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Speaker 3: It's actually even bigger and crazier.

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Speaker 2: I think the thing that I thought was even weirder

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Speaker 2: about that piece is there was a lot of people

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Speaker 2: I do genuinely respect that like shared that piece and

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Speaker 2: they were like, oh my god, like everyone needs to

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Speaker 2: read this, like typically you know, very skeptical people that

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Speaker 2: have very good takes.

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Speaker 4: Yeah, yeah, like all these people.

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Speaker 5: Yeah, because I said some nuts and nice things about

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Speaker 5: my friend Chris Tredulu, So you're a dollar.

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Speaker 1: The thing about this article that like really gets me

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Speaker 1: article blog post unvetted piece of AI because it is

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Speaker 1: clearly written by AI, by the way, like which should

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Speaker 1: not be shocking given that it's an AI evangelism piece.

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Speaker 1: Of course it's written by AI, which, by the way,

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Speaker 1: Cleveland dot Com uh released it like an editor's thing

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Speaker 1: today oh god, yeah, we'll get to where. Second, they

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Speaker 1: turned a journalism candidate had turned down a job with them,

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Speaker 1: because the Cleveland dot com is like the newspaper of Cleveland, right,

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Speaker 1: and a student who is trying to get a job

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Speaker 1: with them turned down the job because they don't have

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Speaker 1: reporters writing articles. They have an AI rewrite specialist who

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Speaker 1: turns their material into drafts, so they just have the

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Speaker 1: reporters do like sourcing and quote gathering and stuff like that. Yeah,

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Speaker 1: and the editorial explaining this and explaining that journalism schools

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Speaker 1: are failing students by making them think they should write.

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Speaker 1: He's also obviously written by AI. But back to this

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Speaker 1: article by Schumer, so it is obviously written by AI.

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Speaker 1: But it like keeps on coming up with these examples

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Speaker 1: that like are contextually actually very poor examples of the

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Speaker 1: thing that they're trying to But there's like a million

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Speaker 1: of them, right. It's like a gift gallop of like

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Speaker 1: AI benchmarks that are meant to create this sense that

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Speaker 1: AI is like the like smarter than PhDs are past

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Speaker 1: the bar exam, and it's like, well, when you.

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Speaker 3: Sorry, also PhDs, listen to this, and I will say this.

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Speaker 1: Not very smart people. I know, if you you've not met.

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Speaker 3: Enough PhDs, if you're like PhD level in time, not

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Speaker 3: to say they're every PhD is stupid, but not to

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Speaker 3: say every PhD is smart either.

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Speaker 4: Well, okay, I will I'll say this.

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Speaker 1: Ben Carson is a gifted neurosurgeon, right, Like they think

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Speaker 1: about that?

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Speaker 4: Well was it was?

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Speaker 1: But like think about that people, right, yeah, and and

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Speaker 1: and PhDs are incredibly talented within but like even with

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Speaker 1: analogous specialists, like we've seen PhDs in like chemistry go

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Speaker 1: come out and write books about how like AIDS isn't real, right,

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Speaker 1: Like chemistry is pretty close to biology, and you still

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Speaker 1: got that fucking wrong, right, But regardless of like that,

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Speaker 1: like stuff like past the bar Exam is like in

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Speaker 1: the thing and it's like contextually it's actually not that

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Speaker 1: interesting because they passed something called the Universal bar Exam,

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Speaker 1: which no individual state use it.

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Speaker 4: There's like a million other things.

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Speaker 3: So the best the best thing in it was him

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Speaker 3: saying that he cited this METATR study. Oh it doesn't

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Speaker 3: even say it, well, no, no, But what's great about it

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Speaker 3: is it's like it's like tasks that an AI can do.

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Speaker 3: The involve it working autonomously for hours, hours and hours

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Speaker 3: right when you go and click on it, it's actually

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Speaker 3: tasks that it succeeds at fifty percent of the time. Yeah,

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Speaker 3: and how the.

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Speaker 1: Meta article or whatever it was is absolutely mischaracterized, like

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Speaker 1: egregiously mischaracterized, like to the point if we had journalism

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Speaker 1: still and this was run in a newspaper that cared

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Speaker 1: about stuff, which what a bygone era that we pretended

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Speaker 1: to have.

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Speaker 4: So sad.

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Speaker 2: Another part of this whole conversation is that the thing

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Speaker 2: that's been pissing me off for a while now, but

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Speaker 2: especially recently with this like increased hysteria around, like oh

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Speaker 2: AI is like the big thing is gonna happen soon

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Speaker 2: is it's like people keep saying that it's smart. It's

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Speaker 2: that it's going to be smarter than humans, maybe even

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Speaker 2: that it is smarter than humans already, when it's like,

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Speaker 2: there is no unless I'm missing something there, It has

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Speaker 2: not shown any level of original intelligence. What it can

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Speaker 2: do is that it's just more a very efficiently coming

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Speaker 2: through databases of words and like just information. It's very

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Speaker 2: efficient at that, But that doesn't prove intelligence as far

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Speaker 2: as I'm aware. So whatever people say AI is intelligent.

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Speaker 2: I lose my fucking mind because it's not. It's just fast.

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Speaker 2: It's a fast database search that like it's just like

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Speaker 2: pukes back out at you.

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Speaker 5: Someone who is it? Okay, yeah, Matt Schumer specific and

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Speaker 5: when you posted on Bluesky of the Day that he

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Speaker 5: was the guy who made that awful video game this

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Speaker 5: is the future.

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Speaker 3: Oh yeah. If you've seen this listeners, it's the video

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Speaker 3: game thing where it's like it's like two minutes and

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Speaker 3: just it is insane, like the US changes.

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Speaker 2: Oh yes, wait the game the oh yeah.

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Speaker 1: And like the guy falls off the bridge and the

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Speaker 1: guys like really impressed that the AI put him back

404
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Speaker 1: on a bridge. But it's a completely new bridge and

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Speaker 1: a completely new f cash between the town.

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Speaker 2: Lakito could do that in Mario sixty four, thirty years ago.

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Speaker 3: Dude, like, come onal today. Yes you can vibe code.

408
00:22:09,880 --> 00:22:14,399
Speaker 3: Here's how to get started. Fucking start and it's like

409
00:22:14,440 --> 00:22:17,040
Speaker 3: you go and look, it's like sample task merch files,

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Speaker 3: extract information, clean and convert files, fix finicky images, get

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Speaker 3: rid of garbage. Well, I'm fucking reading some and it's

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Speaker 3: I just feel like we're in this hysteria mode where

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Speaker 3: just every day. There's just this bizarre escalation of like, wow,

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Speaker 3: AI is so powerful and look it's replacing workers. But

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Speaker 3: then you click on the article, it's like, well, fields

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Speaker 3: that involve maybe being affected by AI have seen less

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Speaker 3: entry level hires, and that will be on the front

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Speaker 3: of the Financial Times. Like that's just like, that's what

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00:22:52,920 --> 00:22:57,200
Speaker 3: we're doing. Every other era of actual innovation, they'd be like, yeah,

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00:22:57,440 --> 00:23:01,160
Speaker 3: you can, like you have a dear cleon perhaps not Tesla.

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00:23:01,320 --> 00:23:03,199
Speaker 3: Look a very nice camera in a phone. Look at

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Speaker 3: the photos. Look how good the photos are. Wow, this

423
00:23:06,520 --> 00:23:09,280
Speaker 3: phone can can use email. Now that's a huge deal.

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Speaker 3: This is like yeah, if you squint really hard, it's

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Speaker 3: almost something.

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Speaker 6: I think.

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Speaker 1: The thing that gets me is if you which I'm

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Speaker 1: sure someone has done this, if you take this humor

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Speaker 1: article and feed it into an AI and said, hey,

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Speaker 1: can you produce a counter article at coach point point,

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Speaker 1: it would be equally as compelling.

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Speaker 2: Right, Yes, that's an interesting idea. Actually I don't like

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Speaker 2: using AI, but I would love to see that.

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Speaker 3: I will be honest, I actually did that perfect seeing

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Speaker 3: Oh my god, I did it using his own his

436
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Speaker 3: actually his own thing. Let's see, let's see if I

437
00:23:48,920 --> 00:23:51,440
Speaker 3: got this yeah, because I did it, and I pulled

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Speaker 3: up hyper write, which is his dog shit, his dogship.

439
00:23:55,359 --> 00:23:58,960
Speaker 3: AI starts up and it basically said, yeah, you know,

440
00:23:59,080 --> 00:24:02,320
Speaker 3: it's mostly just I don't have it in front of me.

441
00:24:02,720 --> 00:24:05,320
Speaker 3: I'm sorry, I don't. I didn't prepare.

442
00:24:05,040 --> 00:24:08,679
Speaker 1: Journalism, and I know I know I've already had to think.

443
00:24:08,680 --> 00:24:10,800
Speaker 1: We have a real journalist Jim here too.

444
00:24:10,880 --> 00:24:14,320
Speaker 3: Guise me. Actually this is just me straight up complaining,

445
00:24:14,320 --> 00:24:17,600
Speaker 3: which I know is different to usual. I write like

446
00:24:17,880 --> 00:24:21,760
Speaker 3: ten to fifteen thousand word analyzes of things, and I

447
00:24:21,800 --> 00:24:24,760
Speaker 3: am so detailed, and I go through years of earnings

448
00:24:24,760 --> 00:24:27,280
Speaker 3: and shit, and this guy's like, wow, you know, the

449
00:24:27,320 --> 00:24:32,119
Speaker 3: computer is just fucking so crazy now, and like do

450
00:24:32,160 --> 00:24:35,800
Speaker 3: you read that? Like AI is learning from itself and

451
00:24:35,920 --> 00:24:39,040
Speaker 3: like it's amazing, like and look at this, and people

452
00:24:39,160 --> 00:24:43,280
Speaker 3: are coming themselves reading it. They're they're like, we need

453
00:24:43,320 --> 00:24:48,280
Speaker 3: this guy on national television. And then he's then he says, oh,

454
00:24:48,320 --> 00:24:52,399
Speaker 3: I'm not trying to scare people. Fuck you, you little worm.

455
00:24:52,680 --> 00:24:56,800
Speaker 1: So I do have a question about these AIS, because

456
00:24:56,840 --> 00:24:58,480
Speaker 1: I don't get these AI startups, and I know you

457
00:24:58,520 --> 00:25:00,520
Speaker 1: don't fundamentally understand, but I think you know more than me,

458
00:25:01,200 --> 00:25:05,320
Speaker 1: which is if there's only like eight or nine like

459
00:25:05,480 --> 00:25:08,640
Speaker 1: models out there, like deep Seak and Kimmi and what

460
00:25:08,640 --> 00:25:11,160
Speaker 1: what the fuck is in AI start Like they take

461
00:25:11,200 --> 00:25:13,680
Speaker 1: these models and they would give us a prompt beforehand.

462
00:25:14,000 --> 00:25:16,920
Speaker 1: So it's federal organized for the tech Okay.

463
00:25:17,200 --> 00:25:20,399
Speaker 3: From the very from the very lowest AI startup to

464
00:25:20,480 --> 00:25:23,280
Speaker 3: the most successful ones like any sphere who makes cursor.

465
00:25:23,920 --> 00:25:25,960
Speaker 3: It is much more complex at the top end, but

466
00:25:26,080 --> 00:25:30,200
Speaker 3: it is prompt engineering. It is finding ways to translate

467
00:25:30,720 --> 00:25:33,040
Speaker 3: what you say to it into a prompt that the

468
00:25:33,080 --> 00:25:36,280
Speaker 3: model will then use to do something like.

469
00:25:36,280 --> 00:25:38,960
Speaker 1: That one of like the eight or nine existing models

470
00:25:39,000 --> 00:25:40,800
Speaker 1: like Yes, Deep Seek.

471
00:25:40,560 --> 00:25:43,560
Speaker 3: And which is what's really funny with this guy's start

472
00:25:43,640 --> 00:25:46,400
Speaker 3: upbout hyper Rite, which is by a company called other

473
00:25:46,480 --> 00:25:50,120
Speaker 3: Side AI is they raised two point six million dollars

474
00:25:50,160 --> 00:25:55,120
Speaker 3: in let's see in November twelve, twenty twenty. Then they

475
00:25:55,200 --> 00:25:59,879
Speaker 3: raised another let's see other Side another two point eight

476
00:26:00,040 --> 00:26:05,040
Speaker 3: million in March twenty twenty three. Where the fuck is

477
00:26:05,080 --> 00:26:05,520
Speaker 3: the money?

478
00:26:05,640 --> 00:26:06,280
Speaker 4: Where is it?

479
00:26:07,119 --> 00:26:07,600
Speaker 5: Where is it?

480
00:26:07,640 --> 00:26:09,439
Speaker 3: Where did it go when you used it?

481
00:26:09,480 --> 00:26:10,480
Speaker 1: Did you pay for it?

482
00:26:10,920 --> 00:26:11,040
Speaker 3: No?

483
00:26:11,240 --> 00:26:11,400
Speaker 4: God?

484
00:26:11,520 --> 00:26:11,560
Speaker 2: No?

485
00:26:12,280 --> 00:26:14,919
Speaker 1: So yeah, okay, now, yeah, that's a great question.

486
00:26:15,320 --> 00:26:15,840
Speaker 4: What the fuck?

487
00:26:16,440 --> 00:26:20,119
Speaker 3: No, I truly don't know and it's it's a the

488
00:26:20,160 --> 00:26:22,560
Speaker 3: current itera. This is from twenty twenty three, the current

489
00:26:22,600 --> 00:26:25,560
Speaker 3: iteration of hyperwrite, which is a Chrome extension with personalization

490
00:26:25,600 --> 00:26:28,920
Speaker 3: and context awareness launched in early twenty twenty two. It's

491
00:26:28,920 --> 00:26:32,119
Speaker 3: a Chrome ex This is the fucking guy, this is

492
00:26:32,119 --> 00:26:35,560
Speaker 3: the this is I think it just shows that none

493
00:26:35,600 --> 00:26:39,359
Speaker 3: of these AI boosters actually believe in anything, because I

494
00:26:39,440 --> 00:26:43,240
Speaker 3: don't know. When I see someone doing AI criticism and

495
00:26:43,240 --> 00:26:46,119
Speaker 3: they're just making shit up, I'm like, hey, don't do that.

496
00:26:46,200 --> 00:26:49,600
Speaker 3: It weakens our argument. These people are like, yeah, just

497
00:26:49,600 --> 00:26:51,480
Speaker 3: just lying, it's fine.

498
00:26:51,880 --> 00:26:52,800
Speaker 4: Just wow. I agree.

499
00:26:52,920 --> 00:26:56,840
Speaker 3: I saw Alexis o'hennian talking about it, one of my

500
00:26:56,920 --> 00:27:00,879
Speaker 3: favorite guys who's just fallen from for them from grace.

501
00:27:01,119 --> 00:27:04,280
Speaker 3: Like his big previous investment is this thing called Doodles,

502
00:27:04,280 --> 00:27:08,720
Speaker 3: Doodles n f T that he claims to NFT. No, yeah,

503
00:27:09,200 --> 00:27:10,480
Speaker 3: he's like a big NFT guy.

504
00:27:12,080 --> 00:27:14,479
Speaker 1: Do we think doodles is better or worse than V Friends?

505
00:27:15,280 --> 00:27:17,800
Speaker 3: What's V Friends? Oh? Gary Vee's thing? No, No, it's

506
00:27:17,880 --> 00:27:21,679
Speaker 3: it's the friends. Is Gary Vaynerchuk? What is Gary Vaynerchuk

507
00:27:21,680 --> 00:27:22,760
Speaker 3: doing If he isn't.

508
00:27:22,600 --> 00:27:24,760
Speaker 1: An AI, he's a AI. We I don't even have

509
00:27:24,800 --> 00:27:28,200
Speaker 1: to look that up. I feel it in my bones. Yeah,

510
00:27:29,000 --> 00:27:35,479
Speaker 1: he also has Gary Yeah, like doing enough in your

511
00:27:35,520 --> 00:27:35,919
Speaker 1: life for what.

512
00:27:36,040 --> 00:27:38,760
Speaker 3: He sounds like Charlie from It's Always Donny in Philadelphia.

513
00:27:39,040 --> 00:27:42,000
Speaker 2: Why do why do these tech people, despite being like

514
00:27:42,160 --> 00:27:45,639
Speaker 2: a lot of like very health focused to like a

515
00:27:45,680 --> 00:27:49,320
Speaker 2: degree that is unnerving, they always look sickly. They always

516
00:27:49,320 --> 00:27:52,760
Speaker 2: look just very wet and sick Brian Johnson. I think

517
00:27:52,840 --> 00:27:54,879
Speaker 2: did that on purpose, Like I think he wants to

518
00:27:54,920 --> 00:27:58,119
Speaker 2: look like that, so yeah, and he succeeded. He is

519
00:27:58,160 --> 00:28:00,920
Speaker 2: really good at looking very sickly and and wet.

520
00:28:01,440 --> 00:28:05,439
Speaker 3: So I think what it is is these people, in

521
00:28:05,520 --> 00:28:09,400
Speaker 3: my experience work insane hours. Now work is a kind

522
00:28:09,400 --> 00:28:14,000
Speaker 3: of a relative term, but like they're in meetings or

523
00:28:14,040 --> 00:28:17,639
Speaker 3: dinners or lunches or whatever, or just staring at X

524
00:28:17,720 --> 00:28:20,280
Speaker 3: the everything app four to seven.

525
00:28:20,400 --> 00:28:24,200
Speaker 4: That's a lot, dude, Yeah, that one.

526
00:28:25,280 --> 00:28:27,560
Speaker 3: No, I mean, like I can't judge someone for doing that,

527
00:28:27,600 --> 00:28:29,840
Speaker 3: but like they don't have a time when they're sitting

528
00:28:29,840 --> 00:28:35,000
Speaker 3: around like taking an edible and watching YouTube of manual

529
00:28:35,080 --> 00:28:38,480
Speaker 3: cats like palace Cats. Right, they don't have joy in

530
00:28:38,520 --> 00:28:40,920
Speaker 3: their lives like that, but they are.

531
00:28:41,200 --> 00:28:45,120
Speaker 1: Emerge from their process tired and wet because they're tired

532
00:28:45,200 --> 00:28:51,360
Speaker 1: and damp, just like just like visibly moist, visibly moist

533
00:28:51,560 --> 00:28:54,520
Speaker 1: or terrifyingly dry.

534
00:28:55,080 --> 00:28:57,320
Speaker 5: They all like they have a disease that they have

535
00:28:57,400 --> 00:29:00,120
Speaker 5: to explain to you. You've never heard of a You

536
00:29:00,200 --> 00:29:01,840
Speaker 5: just looking at your phone and you're like, damn, dude,

537
00:29:01,840 --> 00:29:02,640
Speaker 5: that's crazy.

538
00:29:03,120 --> 00:29:06,040
Speaker 3: Ye yeah, yeah, man, that's crazy.

539
00:29:06,120 --> 00:29:08,720
Speaker 2: Doesn't Jernan Peterson have one of those diseases that's just

540
00:29:08,760 --> 00:29:11,040
Speaker 2: like sort of low key fake. Like his daughter's like, yeah,

541
00:29:11,040 --> 00:29:12,680
Speaker 2: he has this thing and then you look into it

542
00:29:12,680 --> 00:29:14,960
Speaker 2: and it's just like sort of fake or something like that.

543
00:29:16,600 --> 00:29:22,200
Speaker 1: June Okay, I was under the impression that she had

544
00:29:22,240 --> 00:29:25,120
Speaker 1: induced it. So that's that's which is slightly different than fake.

545
00:29:25,480 --> 00:29:27,640
Speaker 5: Oh yeah, his cider psychosis.

546
00:29:28,920 --> 00:29:31,320
Speaker 2: Yeah, he has some weird shit going on. He has

547
00:29:31,360 --> 00:29:32,240
Speaker 2: a ton of different things.

548
00:29:32,440 --> 00:29:35,040
Speaker 3: I just it's just no one happy would defend a

549
00:29:35,080 --> 00:29:41,080
Speaker 3: corporation this much. I say this is reading forums of

550
00:29:41,120 --> 00:29:45,320
Speaker 3: people arguing about video games for years. I even then.

551
00:29:46,200 --> 00:29:49,320
Speaker 3: The idea of being so desperate to protect the idea

552
00:29:49,360 --> 00:29:53,400
Speaker 3: that corporations can make more money is so very fucking sad.

553
00:29:53,920 --> 00:29:56,600
Speaker 3: But it's kind of it's turned into everything now. I mean,

554
00:29:57,280 --> 00:30:01,800
Speaker 3: I saw on Twitter the other day, people gambling on traffic.

555
00:30:02,840 --> 00:30:07,200
Speaker 2: Why why why why why?

556
00:30:07,400 --> 00:30:12,360
Speaker 1: I don't understand, Will Menica, what is the target functioned? Like,

557
00:30:12,400 --> 00:30:14,800
Speaker 1: how do you how does the book verify that you

558
00:30:14,880 --> 00:30:16,000
Speaker 1: want or lost your band?

559
00:30:16,320 --> 00:30:18,160
Speaker 3: It's one of the I don't think this is like

560
00:30:18,200 --> 00:30:24,400
Speaker 3: a particularly nuanced that, Like it's like it's like people gambling.

561
00:30:24,480 --> 00:30:27,600
Speaker 3: Let's see how many cars will go through in a

562
00:30:27,640 --> 00:30:28,640
Speaker 3: particular period.

563
00:30:29,040 --> 00:30:34,880
Speaker 2: People need hobbies, man, you've got to call the hotline.

564
00:30:34,920 --> 00:30:35,080
Speaker 3: Man.

565
00:30:35,160 --> 00:30:35,680
Speaker 4: That's not no.

566
00:30:35,800 --> 00:30:39,560
Speaker 3: I'm quoting Will Menica of Chapo here who posted this

567
00:30:39,720 --> 00:30:42,400
Speaker 3: about a sport where guys just run into each other.

568
00:30:44,000 --> 00:30:46,280
Speaker 3: And his post was just this is the sport they

569
00:30:46,360 --> 00:30:55,120
Speaker 3: watching RoboCop and it's like it's like it's just robocops stuff.

570
00:30:55,160 --> 00:30:58,280
Speaker 3: Now it's just prediction markets. I saw, oh god, we're

571
00:30:58,280 --> 00:31:00,360
Speaker 3: gonna do a prediction market episode. Got a blog from

572
00:31:00,360 --> 00:31:04,840
Speaker 3: Barns that, Yeah, it's fucking great. I love prediction markets.

573
00:31:05,200 --> 00:31:07,760
Speaker 4: I mean they're getting worse. They're getting so so so bad.

574
00:31:07,960 --> 00:31:10,160
Speaker 3: When someone was talking about betting and whether a rocket.

575
00:31:09,960 --> 00:31:12,280
Speaker 2: Would ex yeah, the new the rocket that's I don't

576
00:31:12,280 --> 00:31:14,320
Speaker 2: know when it's launching, but it was yeah that like

577
00:31:14,400 --> 00:31:18,560
Speaker 2: someone I I screenshot this reply, in particular from Pollymarket.

578
00:31:19,760 --> 00:31:23,479
Speaker 2: Let me find it really quick. I swear, I just

579
00:31:23,720 --> 00:31:25,600
Speaker 2: I just did it, and I'm gonna I'm gonna.

580
00:31:25,600 --> 00:31:29,040
Speaker 4: Use one too, not even like a SpaceX. Yeah it

581
00:31:29,160 --> 00:31:29,880
Speaker 4: was disgusting.

582
00:31:31,120 --> 00:31:35,560
Speaker 3: Yeah it's Pollymarket. And like, I don't know how long

583
00:31:35,600 --> 00:31:39,040
Speaker 3: all of that will work, Like how long before that

584
00:31:39,080 --> 00:31:41,640
Speaker 3: gets outlawed by someone? But I think something that might

585
00:31:41,720 --> 00:31:44,480
Speaker 3: accelerate that process is someone getting killed because of it.

586
00:31:45,240 --> 00:31:48,840
Speaker 5: Yeah, like Garan's awful sun in contact is going to

587
00:31:48,880 --> 00:31:49,720
Speaker 5: blow up that rocket?

588
00:31:49,800 --> 00:31:50,200
Speaker 1: Dude?

589
00:31:50,520 --> 00:31:53,240
Speaker 2: Yeah yeah, Like so so what I saw someone said

590
00:31:53,240 --> 00:31:55,040
Speaker 2: that sorry to be a sculd but wagering on people

591
00:31:55,120 --> 00:31:57,920
Speaker 2: dying should not be legal, which I think is the

592
00:31:57,920 --> 00:32:01,760
Speaker 2: most moral and correct in the world. That's fucking crazy.

593
00:32:04,360 --> 00:32:09,200
Speaker 2: But i'nna that person is fine, very very good take person.

594
00:32:09,760 --> 00:32:10,360
Speaker 4: Yeah, great take.

595
00:32:10,400 --> 00:32:13,360
Speaker 2: But then Polymarket replied to that, saying, to clarify, this

596
00:32:13,520 --> 00:32:16,720
Speaker 2: was a market about a potential booster stage rupture to

597
00:32:16,760 --> 00:32:20,200
Speaker 2: find hardware failure scenario, not about the orion crue capsule

598
00:32:20,320 --> 00:32:23,200
Speaker 2: or astrona safety. This was not a market on crew

599
00:32:23,280 --> 00:32:26,760
Speaker 2: injury or loss of life. But like, I don't know, man,

600
00:32:26,880 --> 00:32:30,720
Speaker 2: I feel like if you are gambling on any stage

601
00:32:30,880 --> 00:32:34,720
Speaker 2: of this sort of exploding a disaster, that's sort of

602
00:32:34,760 --> 00:32:37,920
Speaker 2: gambling on disaster. We saw that there was gambling and

603
00:32:38,240 --> 00:32:41,240
Speaker 2: prediction markets about Palisades fire, how long it was going

604
00:32:41,280 --> 00:32:43,320
Speaker 2: to last, how far it was going to reach. So

605
00:32:43,440 --> 00:32:49,040
Speaker 2: like the idea that people gamble on disasters is not like,

606
00:32:49,320 --> 00:32:51,280
Speaker 2: oh wow, this took us by surprise that a lot

607
00:32:51,280 --> 00:32:53,400
Speaker 2: of people thought this would happen. They've it's been happening

608
00:32:53,480 --> 00:32:55,720
Speaker 2: for over a year now. People have been gambling on

609
00:32:55,800 --> 00:32:57,040
Speaker 2: disaster for over a year now.

610
00:32:57,400 --> 00:32:59,640
Speaker 3: Well, Nick Devoor is the blog from Barrus who's going

611
00:32:59,680 --> 00:33:02,520
Speaker 3: to join us and as in not today, don't worry,

612
00:33:02,600 --> 00:33:05,040
Speaker 3: it's just gonna punch, just just fucking like a like

613
00:33:05,080 --> 00:33:09,840
Speaker 3: a halftime substitution right now. He made the point it's

614
00:33:09,880 --> 00:33:16,120
Speaker 3: like inevitable that someone uses this to manipulate someone's murder,

615
00:33:16,240 --> 00:33:20,600
Speaker 3: Like they create a market where someone says, Okay, yeah,

616
00:33:20,640 --> 00:33:23,160
Speaker 3: you're going to like this, but will this person live

617
00:33:23,240 --> 00:33:26,400
Speaker 3: or die on May thirteenth? And on some level it's

618
00:33:26,480 --> 00:33:28,880
Speaker 3: like this will encourage someone to get killed. Like, I

619
00:33:28,920 --> 00:33:32,760
Speaker 3: know this sounds extreme, but polymark. Nick also made this

620
00:33:32,800 --> 00:33:35,640
Speaker 3: point where it's like polymarket already encourages inside of trading.

621
00:33:36,600 --> 00:33:38,920
Speaker 1: Yeah, I mean we saw this with with like in

622
00:33:38,960 --> 00:33:41,000
Speaker 1: addition to the Maduro stuff, right, but we saw this

623
00:33:41,080 --> 00:33:45,200
Speaker 1: with like Pam Bondy's press conference times where she got

624
00:33:45,240 --> 00:33:47,680
Speaker 1: within like thirty seconds of that, which is like you

625
00:33:47,760 --> 00:33:50,080
Speaker 1: could say the markets hit that really well, but I'm

626
00:33:50,120 --> 00:33:52,840
Speaker 1: not gonna say that. I think she knew someone was

627
00:33:52,840 --> 00:33:55,680
Speaker 1: gonna make money on the under right, Well, what was

628
00:33:55,720 --> 00:33:58,840
Speaker 1: the what was the It was how long a particular

629
00:33:58,840 --> 00:34:02,000
Speaker 1: press conference on a particular would go. And it's like,

630
00:34:02,040 --> 00:34:05,200
Speaker 1: you know, a couple hours and then some and and

631
00:34:05,240 --> 00:34:09,000
Speaker 1: she abruptly, like in the middle of like a sentence,

632
00:34:09,160 --> 00:34:13,200
Speaker 1: she just shut it down thirty seconds, within uh, within

633
00:34:13,239 --> 00:34:16,120
Speaker 1: thirty seconds of like the timeline to hit the mark.

634
00:34:16,160 --> 00:34:19,640
Speaker 1: And so a bunch of people who had we shouldn't

635
00:34:19,640 --> 00:34:21,680
Speaker 1: say bet on the ound because it's not betting, right,

636
00:34:21,719 --> 00:34:26,840
Speaker 1: who had who had purchased contract shares on the events

637
00:34:27,160 --> 00:34:30,520
Speaker 1: result of it being less than a certain amount of time,

638
00:34:30,960 --> 00:34:32,160
Speaker 1: made a ton of money out of it.

639
00:34:33,120 --> 00:34:35,520
Speaker 4: Yeah. So, like people are.

640
00:34:35,640 --> 00:34:39,799
Speaker 1: Already either actively manipulating it or not doing anything to

641
00:34:39,840 --> 00:34:41,799
Speaker 1: prevent the appearance of manipulating it.

642
00:34:41,840 --> 00:34:42,400
Speaker 3: Like when.

643
00:34:44,719 --> 00:34:48,759
Speaker 1: Jannis Tatakupo for the Milwaukee Bucks. Who's now like on

644
00:34:48,800 --> 00:34:50,160
Speaker 1: the chair or something for Call.

645
00:34:50,239 --> 00:34:52,720
Speaker 4: She yeah, he's a chair board member of CALCI.

646
00:34:52,840 --> 00:34:56,160
Speaker 1: Now yeah, so like he had like demanded a trade.

647
00:34:56,400 --> 00:35:00,080
Speaker 1: It sounded like from the Bucks to an organization that

648
00:35:00,120 --> 00:35:04,200
Speaker 1: will actually win games, which is understandable. And there was

649
00:35:04,239 --> 00:35:05,879
Speaker 1: this whole trade market in Call. She had a bunch

650
00:35:05,920 --> 00:35:09,200
Speaker 1: of different markets for for Yannis and where he was

651
00:35:09,200 --> 00:35:10,400
Speaker 1: going to go and whether or not he was going

652
00:35:10,480 --> 00:35:13,879
Speaker 1: to stay, and ultimately he didn't get traded. He stayed

653
00:35:13,920 --> 00:35:16,839
Speaker 1: with the Bucks, and everybody who would bet that he

654
00:35:16,920 --> 00:35:18,200
Speaker 1: was going to stay with the Bucks had made a

655
00:35:18,200 --> 00:35:20,960
Speaker 1: ton of money. And then right after that he announced

656
00:35:20,960 --> 00:35:23,560
Speaker 1: he was on the board of Calls. And it's like,

657
00:35:23,719 --> 00:35:27,080
Speaker 1: I don't know that Yannis or any of his entourage

658
00:35:27,280 --> 00:35:31,480
Speaker 1: or whoever put any money on on any of these things,

659
00:35:31,719 --> 00:35:34,840
Speaker 1: but that sure looks fucking awful, right.

660
00:35:34,960 --> 00:35:37,400
Speaker 2: They probably did. I would be I would bet that,

661
00:35:37,760 --> 00:35:39,560
Speaker 2: I would imagine I would bet I would take that

662
00:35:39,560 --> 00:35:40,839
Speaker 2: bet on calshe that they did.

663
00:35:42,560 --> 00:35:48,200
Speaker 3: I just I every I think that there is goodness

664
00:35:48,200 --> 00:35:50,640
Speaker 3: in our future and that good things will happen, but

665
00:35:50,680 --> 00:35:52,480
Speaker 3: I think on the way that there are going to

666
00:35:52,520 --> 00:35:54,800
Speaker 3: be there's going to be something called the Calshie murders.

667
00:35:55,280 --> 00:35:58,799
Speaker 3: Maybe yeah, yeah, no, for sure, there's good like we

668
00:35:58,960 --> 00:36:02,960
Speaker 3: already we've already thankfully I've got to use my term

669
00:36:03,040 --> 00:36:06,320
Speaker 3: son of Sam Oltman for an AI psychosis murder.

670
00:36:07,040 --> 00:36:09,400
Speaker 1: I mean very sad, I mean like no, terrible, but

671
00:36:09,440 --> 00:36:12,160
Speaker 1: also like nice nice.

672
00:36:12,480 --> 00:36:17,280
Speaker 3: But it's just it feels like we're in this weird

673
00:36:17,360 --> 00:36:20,640
Speaker 3: golf where everyone is desperately trying all the money is

674
00:36:20,680 --> 00:36:23,120
Speaker 3: trying to pretend that all of this is so fucking sick.

675
00:36:23,360 --> 00:36:25,560
Speaker 3: When you look at it, even as a booster, it's

676
00:36:25,640 --> 00:36:31,320
Speaker 3: like great, coding is fast, I guess yay, it's also

677
00:36:31,560 --> 00:36:35,280
Speaker 3: so expensive and no one wants the no one wants

678
00:36:35,320 --> 00:36:36,759
Speaker 3: to do the math. Yeah.

679
00:36:36,800 --> 00:36:38,719
Speaker 1: That's like the other thing about this is like all

680
00:36:38,800 --> 00:36:43,880
Speaker 1: of the booster arguments for AI coming for different industries

681
00:36:43,920 --> 00:36:46,799
Speaker 1: and jobs or being able to perform different tasks or whatever. Right,

682
00:36:47,120 --> 00:36:50,879
Speaker 1: all of the arguments about its increasing capability rely on

683
00:36:50,920 --> 00:36:53,960
Speaker 1: an assumption that the progress quote unquote progress is measured

684
00:36:53,960 --> 00:36:56,799
Speaker 1: by their own benchmarks anyway that it's made so far

685
00:36:57,760 --> 00:37:00,919
Speaker 1: will continue at an exponential rate in the same way

686
00:37:00,920 --> 00:37:04,800
Speaker 1: that we envision technology tends to, which is not really

687
00:37:04,920 --> 00:37:07,880
Speaker 1: like if you look at the progress of like battery technology,

688
00:37:07,880 --> 00:37:09,840
Speaker 1: It does not follow the same fucking It's just not

689
00:37:10,000 --> 00:37:12,520
Speaker 1: like that. But the way that people seem to imagine

690
00:37:12,560 --> 00:37:16,080
Speaker 1: technology is just like in crazy, you know, exponential it

691
00:37:16,160 --> 00:37:18,160
Speaker 1: spikes up until they they.

692
00:37:18,040 --> 00:37:18,680
Speaker 4: Put that a some.

693
00:37:20,440 --> 00:37:23,120
Speaker 1: Right, Yeah exactly.

694
00:37:23,520 --> 00:37:26,280
Speaker 3: It's just when you look at the chot Disco sales

695
00:37:26,320 --> 00:37:26,879
Speaker 3: will go off.

696
00:37:27,200 --> 00:37:28,640
Speaker 4: Yeah, it'll just double every year.

697
00:37:28,880 --> 00:37:33,239
Speaker 1: Yeah, but people will assume this, and they'll make the

698
00:37:33,360 --> 00:37:36,920
Speaker 1: argument that it will without understanding like what it took

699
00:37:37,080 --> 00:37:40,279
Speaker 1: to get to the shitty place it is now and

700
00:37:40,400 --> 00:37:43,120
Speaker 1: how we're at the edge of that, like we can't

701
00:37:43,160 --> 00:37:47,399
Speaker 1: actually devote the more resources to make these models better.

702
00:38:02,920 --> 00:38:06,040
Speaker 3: So what's funny is there was because Matt Hughes, who's

703
00:38:06,080 --> 00:38:10,600
Speaker 3: my editor, who's long suffering, by which I mean I no,

704
00:38:10,680 --> 00:38:12,399
Speaker 3: I made him watch a two and a half hour

705
00:38:12,440 --> 00:38:16,320
Speaker 3: long Dario ama Day interview. Sorry sorry, but he found

706
00:38:16,320 --> 00:38:21,200
Speaker 3: one fascinating quote in here. This is Warrio ama Day,

707
00:38:21,320 --> 00:38:24,640
Speaker 3: CEO of Anthropic. Even though a part of my brain

708
00:38:24,719 --> 00:38:26,960
Speaker 3: wonders if it's going to keep growing ten x, I

709
00:38:27,000 --> 00:38:29,200
Speaker 3: can't buy one trillion dollars a year of computing in

710
00:38:29,200 --> 00:38:32,160
Speaker 3: twenty twenty seven. If I'm off by a year in

711
00:38:32,200 --> 00:38:33,960
Speaker 3: that rate of growth, or if the growth rate is

712
00:38:33,960 --> 00:38:35,719
Speaker 3: five x a year instead of tens a year, then

713
00:38:35,760 --> 00:38:41,240
Speaker 3: you go bankrupt. And previously he said, if my revenue

714
00:38:41,280 --> 00:38:43,920
Speaker 3: is not one trillion dollars, if it's even eight hundred billion,

715
00:38:44,000 --> 00:38:45,839
Speaker 3: there's no force on earth, there's no hedge on earth

716
00:38:45,880 --> 00:38:47,759
Speaker 3: that could stop me from going bankrupt if I buy

717
00:38:47,760 --> 00:38:54,200
Speaker 3: that much compute Why did clear? Well, No, this this interview,

718
00:38:54,200 --> 00:38:57,799
Speaker 3: by the way, is fucking sick. It's so funny. It

719
00:38:57,960 --> 00:39:01,319
Speaker 3: was the he used this example where he says he

720
00:39:01,400 --> 00:39:06,719
Speaker 3: has he describes having better than fifty percent gross margins.

721
00:39:07,000 --> 00:39:09,759
Speaker 3: But that's because the way he evaluates gross margins is

722
00:39:09,760 --> 00:39:12,560
Speaker 3: based on how much a model cost to train and

723
00:39:12,680 --> 00:39:16,600
Speaker 3: how much the model made money, like how much money.

724
00:39:16,600 --> 00:39:20,760
Speaker 1: He separates out the training cost in the inference cost

725
00:39:21,000 --> 00:39:26,800
Speaker 1: and buckets out future training cost against a different margin,

726
00:39:26,920 --> 00:39:31,760
Speaker 1: and so like, I, you know, if you did sign

727
00:39:31,800 --> 00:39:34,640
Speaker 1: it like four point five or whatever, the amount of

728
00:39:34,719 --> 00:39:38,160
Speaker 1: revenue SIGN at four point five brings in is greater

729
00:39:38,280 --> 00:39:41,120
Speaker 1: than the inference plus training cost of SIGN at four

730
00:39:41,120 --> 00:39:44,759
Speaker 1: point five. And he's ignoring the training cost of like

731
00:39:44,840 --> 00:39:48,319
Speaker 1: four point six, which is like ten times larger, and

732
00:39:48,360 --> 00:39:49,359
Speaker 1: it always needs to be.

733
00:39:49,560 --> 00:39:52,600
Speaker 3: He's ignoring the reinforcement learning and the updates they have

734
00:39:52,680 --> 00:39:55,000
Speaker 3: to do, which is also a training cost. But actually,

735
00:39:55,440 --> 00:39:59,839
Speaker 3: I take it back. Here's how Dario Amity talks about profitability.

736
00:40:00,160 --> 00:40:02,799
Speaker 3: Let me quote him here. Profitability is this kind of

737
00:40:02,840 --> 00:40:05,480
Speaker 3: weird thing in this field. I don't think in this

738
00:40:05,560 --> 00:40:08,799
Speaker 3: field profitability is actually a measure of spending down versus

739
00:40:08,880 --> 00:40:14,239
Speaker 3: investing in the business. I actually think profitability happens when

740
00:40:14,239 --> 00:40:16,919
Speaker 3: you underestimated the amount of demand you were going to get,

741
00:40:16,920 --> 00:40:19,799
Speaker 3: and loss happens when you've overrested, madd the amount of

742
00:40:19,800 --> 00:40:21,759
Speaker 3: demand you were going to get because you're buying the

743
00:40:21,840 --> 00:40:26,120
Speaker 3: data centers ahead of time. No, Dario, profitability is when

744
00:40:26,160 --> 00:40:29,000
Speaker 3: you make more money than you spent you underestimated.

745
00:40:29,000 --> 00:40:31,000
Speaker 4: That's such like a.

746
00:40:30,560 --> 00:40:34,840
Speaker 7: Fake smart guy way to say that they can demand

747
00:40:34,880 --> 00:40:37,400
Speaker 7: you were going to get, Like, yeah, I guess the

748
00:40:37,440 --> 00:40:39,760
Speaker 7: price would go up in a world where your supply

749
00:40:39,880 --> 00:40:40,720
Speaker 7: doesn't meet demand.

750
00:40:41,080 --> 00:40:44,280
Speaker 2: They said that they locked capitalism too, and they unlocked

751
00:40:44,280 --> 00:40:46,319
Speaker 2: a new version of profitability.

752
00:40:46,360 --> 00:40:46,880
Speaker 4: That's awesome.

753
00:40:46,960 --> 00:40:52,000
Speaker 3: Yeah, he said that these are stylized facts. What that's

754
00:40:52,160 --> 00:40:58,960
Speaker 3: that is his term. I'm just if I'm an investor

755
00:40:59,000 --> 00:41:04,560
Speaker 3: and I read my investment saying stylized facts. I'm stylizing

756
00:41:04,600 --> 00:41:12,160
Speaker 3: a nine millimeter in my mouth. It's just like I

757
00:41:12,160 --> 00:41:15,600
Speaker 3: I am. I think things are going to get crazier,

758
00:41:15,760 --> 00:41:18,040
Speaker 3: not in the actual outcomes, but the things they're going

759
00:41:18,120 --> 00:41:21,839
Speaker 3: to start promising, Like I can't. I hope Aanthropic tries

760
00:41:21,840 --> 00:41:24,680
Speaker 3: to go public just so I can see what insane

761
00:41:24,680 --> 00:41:27,200
Speaker 3: definition of gross margin they have, because I don't think

762
00:41:27,239 --> 00:41:32,800
Speaker 3: it's like revenue minus cogs. I think it's a model

763
00:41:32,840 --> 00:41:37,560
Speaker 3: plus n plus divided by seventy divided by seven thousand

764
00:41:37,680 --> 00:41:41,680
Speaker 3: plus two million plus one. And they're like, yep, look,

765
00:41:41,800 --> 00:41:44,160
Speaker 3: numbers higher than last year. What do you think? And

766
00:41:44,560 --> 00:41:49,120
Speaker 3: the investors who just are I assume catatonic, like they're

767
00:41:49,120 --> 00:41:52,800
Speaker 3: pitched in this sleep. I just it's all gonna I

768
00:41:53,120 --> 00:41:55,160
Speaker 3: don't see how this doesn't come down. I'm just like,

769
00:41:55,239 --> 00:41:57,360
Speaker 3: look at every time I look at the numbers, I

770
00:41:57,400 --> 00:41:59,840
Speaker 3: feel crazy. But when I read their definitions for it,

771
00:42:00,160 --> 00:42:03,200
Speaker 3: they just go, yeah, just you know, work out.

772
00:42:03,600 --> 00:42:06,000
Speaker 2: I so, so, I don't know if you guys have

773
00:42:06,120 --> 00:42:09,880
Speaker 2: been noticing this particularly since Also, I know you guys

774
00:42:09,920 --> 00:42:13,919
Speaker 2: like this football the Super Bowl ever since the Super Bowl, Nay, yeah,

775
00:42:13,960 --> 00:42:17,000
Speaker 2: brand new to you. But there was a ton of

776
00:42:17,040 --> 00:42:20,799
Speaker 2: AI generated ads in the Super Bowl. Since then, I've

777
00:42:20,880 --> 00:42:25,080
Speaker 2: seen three different companies, one of them being Liquid Death,

778
00:42:25,360 --> 00:42:29,840
Speaker 2: another being Noodles and Company, and another one I'm blinking

779
00:42:29,840 --> 00:42:31,440
Speaker 2: on which what what the other? What the other one

780
00:42:31,480 --> 00:42:33,360
Speaker 2: I saw was? But I saw this is after the

781
00:42:33,400 --> 00:42:37,360
Speaker 2: Super Bowl, different companies using AI generated fucking ads. And

782
00:42:37,360 --> 00:42:40,839
Speaker 2: they're horrible. They're horrifying, They're awful. They're so bad they

783
00:42:40,880 --> 00:42:43,360
Speaker 2: don't make me want to even look at the brands anymore.

784
00:42:43,520 --> 00:42:45,120
Speaker 4: And it's like, I my theory.

785
00:42:45,520 --> 00:42:47,360
Speaker 2: I haven't confirmed this, I haven't looked into it, but

786
00:42:47,360 --> 00:42:51,440
Speaker 2: I'm assuming a lot of these AI generative AI models,

787
00:42:51,600 --> 00:42:54,440
Speaker 2: these these these companies, whoever runs it, are striking deals

788
00:42:54,440 --> 00:42:57,319
Speaker 2: with these companies to do like, hey, like make this

789
00:42:57,440 --> 00:43:01,399
Speaker 2: next ad campaign entirely on us, entirely on us, because

790
00:43:01,440 --> 00:43:04,520
Speaker 2: in these ads they do mention that they're AI generated.

791
00:43:04,600 --> 00:43:07,359
Speaker 2: I think the Noodles and Company once says it like

792
00:43:07,400 --> 00:43:10,000
Speaker 2: in the corner it's like AI. There's like a little pun.

793
00:43:10,200 --> 00:43:11,680
Speaker 2: It's been a menu since I've seen that, but there's

794
00:43:11,680 --> 00:43:13,160
Speaker 2: like a little pun to be like, oh it's AI.

795
00:43:13,480 --> 00:43:16,600
Speaker 2: So I think these are like very obviously ad campaigns

796
00:43:17,000 --> 00:43:19,240
Speaker 2: to try to be like hey, wow, AI is everywhere,

797
00:43:19,280 --> 00:43:22,160
Speaker 2: isn't it. It's crazy how everywhere AI is and everyone

798
00:43:22,200 --> 00:43:22,720
Speaker 2: loves AI.

799
00:43:23,040 --> 00:43:26,120
Speaker 3: It's an attempt to just get attention because like it's

800
00:43:26,160 --> 00:43:29,200
Speaker 3: basically like, look how much this sucks but it cost us?

801
00:43:29,840 --> 00:43:32,239
Speaker 1: Well that's what That's explicitly what Liquid Death did in

802
00:43:32,280 --> 00:43:35,080
Speaker 1: their ad right. They were like very like they thought

803
00:43:35,200 --> 00:43:37,360
Speaker 1: they could kind of get away with being like tongue

804
00:43:37,360 --> 00:43:40,399
Speaker 1: in cheek about using AI and and it like kind

805
00:43:40,440 --> 00:43:42,960
Speaker 1: of fits their brand image to be kind of irreverent

806
00:43:43,120 --> 00:43:48,839
Speaker 1: or whatever, and so then they made it terrifying, horrifying.

807
00:43:48,320 --> 00:43:49,359
Speaker 4: Uncorble ad.

808
00:43:49,480 --> 00:43:52,239
Speaker 1: The worst about was it their energy drink.

809
00:43:53,280 --> 00:43:54,239
Speaker 4: It's just one of their drunks.

810
00:43:54,680 --> 00:44:00,640
Speaker 3: Have you seen the Darren Aronovski AI generated thing this six?

811
00:44:01,120 --> 00:44:05,200
Speaker 3: So if you haven't heard about this, dear listeners, unbelievably

812
00:44:05,280 --> 00:44:10,400
Speaker 3: big fucking hack. Darren Aronovski is making a He's making

813
00:44:10,440 --> 00:44:14,320
Speaker 3: a thing called On this Day seventeen seventy six, A

814
00:44:14,640 --> 00:44:18,080
Speaker 3: real is telling the story of America's first meme. I

815
00:44:18,080 --> 00:44:20,480
Speaker 3: didn't learn any American history. I'm not going to start today.

816
00:44:21,120 --> 00:44:24,840
Speaker 3: They handed out some shit to some people. I guess

817
00:44:24,880 --> 00:44:27,080
Speaker 3: just like there's some history unless thingers.

818
00:44:26,760 --> 00:44:31,120
Speaker 1: Like fuck undersinding of seventeen seventy six. Is that it's

819
00:44:31,120 --> 00:44:33,080
Speaker 1: a giant Boyce piece really.

820
00:44:32,960 --> 00:44:39,319
Speaker 3: Yeah, yeah, yeah, yeah, no, So this movie it's it's

821
00:44:39,360 --> 00:44:41,799
Speaker 3: called On This Day seventeen seventy six, America's first meme

822
00:44:41,840 --> 00:44:44,200
Speaker 3: is born when Thomas Payne arrives from England. He's encouraged

823
00:44:44,200 --> 00:44:47,160
Speaker 3: by Benjamin Franklin to write what others hesitate to say.

824
00:44:47,280 --> 00:44:50,160
Speaker 3: The resulting pamphlet sends ripples through from the colonies to

825
00:44:50,200 --> 00:44:52,160
Speaker 3: the other side of the Atlantic, and what was once

826
00:44:52,239 --> 00:44:56,719
Speaker 3: unthinkable becomes irrefutable, as in, I don't know that he's

827
00:44:56,760 --> 00:44:58,920
Speaker 3: a huge fucking hack. There is a bit, there are

828
00:44:58,960 --> 00:45:01,480
Speaker 3: some really good bits. Nit Thomas Payne, the collar of

829
00:45:01,480 --> 00:45:05,160
Speaker 3: his shirt changes between shots. They have to constantly cut

830
00:45:05,239 --> 00:45:12,359
Speaker 3: shots early because the breaks down after six seconds or whatever. Yeah.

831
00:45:12,400 --> 00:45:15,000
Speaker 4: Yeah, it's really expensive to have the long shots.

832
00:45:15,160 --> 00:45:16,759
Speaker 3: There's a great bit in it where they hand out

833
00:45:16,800 --> 00:45:19,560
Speaker 3: common sense and as they hand it out, as they

834
00:45:19,560 --> 00:45:22,439
Speaker 3: pick it up, it goes from saying America to like, wow,

835
00:45:23,320 --> 00:45:25,840
Speaker 3: just like language because.

836
00:45:25,560 --> 00:45:29,960
Speaker 1: Well, unintentionally kind of apropos, I would say, but yeah.

837
00:45:29,440 --> 00:45:33,080
Speaker 3: But apparently they're releasing it on Netflix's YouTube.

838
00:45:33,239 --> 00:45:37,960
Speaker 4: Oh god YouTube. Yeah, it's like really like the Family sucks.

839
00:45:38,000 --> 00:45:41,359
Speaker 3: What it's like the family Guy Ringo star thing with

840
00:45:41,400 --> 00:45:45,600
Speaker 3: like we'll put the song on the fridge.

841
00:45:45,920 --> 00:45:47,160
Speaker 4: But I haven't seen more of it.

842
00:45:47,200 --> 00:45:49,600
Speaker 3: They put out a few bits of it, and then

843
00:45:49,600 --> 00:45:52,600
Speaker 3: they just stopped releasing it, probably because of all the

844
00:45:52,640 --> 00:45:56,000
Speaker 3: comments that said this looks like dog shit, kill yourself.

845
00:45:57,040 --> 00:45:59,560
Speaker 3: Like it's mostly just that.

846
00:46:00,360 --> 00:46:04,319
Speaker 1: So like the thing is, like you could if it

847
00:46:04,360 --> 00:46:07,960
Speaker 1: didn't have this insane cost and and and ethical problem

848
00:46:08,000 --> 00:46:11,640
Speaker 1: associated with it, you could convince me that, like that

849
00:46:11,680 --> 00:46:16,000
Speaker 1: one Marvel opening for whatever the fuck it was with

850
00:46:16,040 --> 00:46:20,160
Speaker 1: the scrolls right where it's unsettling and it changes a

851
00:46:20,160 --> 00:46:20,960
Speaker 1: little bit every.

852
00:46:20,800 --> 00:46:23,359
Speaker 4: Couple of seconds, what is this? There was there was.

853
00:46:25,160 --> 00:46:26,880
Speaker 1: It might have been doctor what it was doctor Strange?

854
00:46:26,880 --> 00:46:28,360
Speaker 1: That wouldn't have been very helpful. I thought it was

855
00:46:28,360 --> 00:46:31,760
Speaker 1: like there's a Marvel TV show where the opening because

856
00:46:31,760 --> 00:46:34,760
Speaker 1: it's a's a show. I haven't watched anything after.

857
00:46:36,680 --> 00:46:37,000
Speaker 4: What's it?

858
00:46:37,080 --> 00:46:41,800
Speaker 8: Like thee Yeah, it's invasion which involves the scrolls right, Yeah,

859
00:46:41,840 --> 00:46:45,480
Speaker 8: And so the opening for that was it with the

860
00:46:45,520 --> 00:46:49,359
Speaker 8: early version of like AI video generation And it is uncomfortable,

861
00:46:49,680 --> 00:46:52,200
Speaker 8: I remember, and.

862
00:46:51,320 --> 00:46:53,440
Speaker 1: And and it's changing a lot and you can't keep

863
00:46:53,440 --> 00:46:55,920
Speaker 1: a stable image and it's like, well, you know, there

864
00:46:55,920 --> 00:46:59,640
Speaker 1: are a bunch of problems both like environmentally and ethically

865
00:46:59,680 --> 00:47:03,160
Speaker 1: from a later's standpoint, that should stop this from happening.

866
00:47:03,239 --> 00:47:08,520
Speaker 1: But esthetically this does make sense for this show. Why

867
00:47:08,600 --> 00:47:11,200
Speaker 1: the fuck would you need that for Thomas Paine to

868
00:47:11,280 --> 00:47:13,560
Speaker 1: common set? Like, it's just like a bad esthetics.

869
00:47:13,719 --> 00:47:14,600
Speaker 4: What are you doing?

870
00:47:15,120 --> 00:47:18,919
Speaker 3: It's because you're a fucking hack? Yeah, okay, well yes,

871
00:47:19,280 --> 00:47:21,520
Speaker 3: no that is a good Just what pride do you

872
00:47:21,640 --> 00:47:24,399
Speaker 3: have in your work? If you put this out? If

873
00:47:24,440 --> 00:47:26,719
Speaker 3: I was a filmmaker and it looked like this, I

874
00:47:26,719 --> 00:47:28,280
Speaker 3: would just stop making movie.

875
00:47:28,400 --> 00:47:32,600
Speaker 2: Like It's just it completely devalues whatever catalog he has.

876
00:47:33,120 --> 00:47:33,840
Speaker 3: I mean I like it.

877
00:47:33,880 --> 00:47:36,680
Speaker 5: I mean I actually like some of Aronofsky's stuff. But

878
00:47:36,760 --> 00:47:39,360
Speaker 5: if you know anything about him at all, like this

879
00:47:39,480 --> 00:47:41,680
Speaker 5: makes total sense. The guy is a just has.

880
00:47:41,600 --> 00:47:44,040
Speaker 1: He has he done anything good after Black Swan?

881
00:47:45,680 --> 00:47:48,360
Speaker 4: I like, I mean he did, don't like it. I

882
00:47:48,719 --> 00:47:49,359
Speaker 4: haven't seen it.

883
00:47:50,040 --> 00:47:51,799
Speaker 2: I mean he did the Well. I haven't seen the Well.

884
00:47:51,880 --> 00:47:53,719
Speaker 2: I was going to see cot Stealing. I just never

885
00:47:53,760 --> 00:47:55,600
Speaker 2: got around to it. It seemed like it could be.

886
00:47:55,719 --> 00:47:58,120
Speaker 3: It's exactly that kind of movie. It's exactly The.

887
00:47:59,640 --> 00:48:03,359
Speaker 5: Swan. What was that the wrestler came out for Black Swan.

888
00:48:03,400 --> 00:48:07,600
Speaker 5: I think he's got some OK movies, but he also steals.

889
00:48:07,600 --> 00:48:09,400
Speaker 5: He loves stealing Atham's careers.

890
00:48:09,760 --> 00:48:13,600
Speaker 3: Wow, there you go, so well, there you go. It's

891
00:48:13,719 --> 00:48:16,120
Speaker 3: very appropriate. I also love the idea that they used

892
00:48:16,120 --> 00:48:18,480
Speaker 3: AI for the opening of Secret Invasion, which had like

893
00:48:18,560 --> 00:48:21,279
Speaker 3: Samuel L. Jackson in it. I don't know, like very

894
00:48:21,320 --> 00:48:26,440
Speaker 3: expensive actors, so years of footage, like you just cut

895
00:48:26,480 --> 00:48:30,520
Speaker 3: together without spending any money. I don't know.

896
00:48:30,719 --> 00:48:33,759
Speaker 2: It's I think it's the thing where it's like I've

897
00:48:33,800 --> 00:48:37,640
Speaker 2: been trying to like figure out exactly why because I've

898
00:48:37,840 --> 00:48:39,480
Speaker 2: felt this and a lot of people have felt that

899
00:48:39,560 --> 00:48:44,680
Speaker 2: AI just feels bad, and I've drilled down, I think

900
00:48:44,800 --> 00:48:46,799
Speaker 2: on on like as much as I can, because there's

901
00:48:46,800 --> 00:48:48,600
Speaker 2: so many different parts of it that feel bad. But

902
00:48:48,640 --> 00:48:50,120
Speaker 2: I feel like there has to be like a source

903
00:48:50,160 --> 00:48:52,840
Speaker 2: of the bad feeling. And like, I think the bad

904
00:48:52,920 --> 00:48:56,280
Speaker 2: feeling with AI is if you put aside the environmental factors,

905
00:48:56,360 --> 00:48:58,200
Speaker 2: because of course that is like one of the main

906
00:48:58,719 --> 00:49:01,480
Speaker 2: ultimate bad things. But the reason why it personally feels

907
00:49:01,480 --> 00:49:05,200
Speaker 2: bad in the world is it just it is constantly

908
00:49:05,360 --> 00:49:07,920
Speaker 2: pushed onto us in a way that it's like, oh,

909
00:49:08,080 --> 00:49:10,440
Speaker 2: you're gonna love this AI. It's gonna be here. You're

910
00:49:10,480 --> 00:49:12,960
Speaker 2: gonna not. Everything's going to be AI. It's going to

911
00:49:13,000 --> 00:49:17,640
Speaker 2: take your job. You should consulted about what temperature it

912
00:49:17,680 --> 00:49:20,840
Speaker 2: is outside. It can tell you if a glove is

913
00:49:20,880 --> 00:49:24,320
Speaker 2: good for snow. Is like it's stuff like that, for example.

914
00:49:26,200 --> 00:49:27,799
Speaker 2: But it's like stuff like that. It feels like it

915
00:49:27,880 --> 00:49:30,840
Speaker 2: is just like people that the tech world is like,

916
00:49:30,920 --> 00:49:33,719
Speaker 2: you will like AI and you will not complain about it,

917
00:49:33,760 --> 00:49:35,680
Speaker 2: and if you do, we are the victims.

918
00:49:35,960 --> 00:49:37,839
Speaker 4: And that's why I think it feels bad.

919
00:49:38,000 --> 00:49:40,600
Speaker 3: I can't like. I actually do think that leads into

920
00:49:40,600 --> 00:49:43,280
Speaker 3: one of my definite predictions, which is when AI shits

921
00:49:43,280 --> 00:49:46,359
Speaker 3: itself and dies, because I think it's going to end

922
00:49:46,440 --> 00:49:49,880
Speaker 3: up being on device or really expensive, like expensive in

923
00:49:49,920 --> 00:49:52,640
Speaker 3: a way that just no sane person would pay for.

924
00:49:53,280 --> 00:49:55,719
Speaker 3: I can't wait for the articles and boosters where it's like,

925
00:49:55,920 --> 00:49:56,520
Speaker 3: look what you.

926
00:49:56,480 --> 00:50:02,279
Speaker 1: Did, Millennials kill their eye look at it.

927
00:50:02,800 --> 00:50:06,160
Speaker 3: Really it's gonna be like millennials killed like, look, we

928
00:50:06,239 --> 00:50:09,360
Speaker 3: had this amazing thing that sometimes got things right sometimes

929
00:50:09,560 --> 00:50:13,640
Speaker 3: and cost it costs ten dollars to make one dollar,

930
00:50:14,000 --> 00:50:18,400
Speaker 3: and codis were able to write at and thendeterminately faster speed, and.

931
00:50:18,840 --> 00:50:21,439
Speaker 1: After you were done ransacking all of gardens, you went

932
00:50:21,480 --> 00:50:21,960
Speaker 1: to AI.

933
00:50:22,640 --> 00:50:26,160
Speaker 5: Yeah yeah, gear like Gary V or maybe the other

934
00:50:26,239 --> 00:50:28,120
Speaker 5: one of the garys. We'll have an article about, like, oh,

935
00:50:28,120 --> 00:50:29,360
Speaker 5: you've never lied before.

936
00:50:35,360 --> 00:50:39,440
Speaker 3: What's really What it's just is people without culture or

937
00:50:39,480 --> 00:50:43,160
Speaker 3: sensuality trying to do both. I think the AI video

938
00:50:43,280 --> 00:50:46,120
Speaker 3: doesn't look good because it looks it looks like a

939
00:50:46,200 --> 00:50:49,239
Speaker 3: next Netflix movie in that it's got this kind of

940
00:50:49,280 --> 00:50:52,680
Speaker 3: pallid color palla. But also when people look at each other,

941
00:50:52,719 --> 00:50:54,280
Speaker 3: they're not looking in each other's eyes.

942
00:50:54,600 --> 00:50:55,160
Speaker 4: Yeah, that's all.

943
00:50:56,480 --> 00:50:58,440
Speaker 1: And the shadows are off, like they are in a

944
00:50:58,440 --> 00:51:01,600
Speaker 1: bunch of like Netflix features now, Like the shadows are

945
00:51:01,600 --> 00:51:03,800
Speaker 1: really off and it's really off putting, and so nothing

946
00:51:04,360 --> 00:51:07,360
Speaker 1: looks like a movie anymore. And AI kind of triples

947
00:51:07,400 --> 00:51:09,680
Speaker 1: down on that where the lighting is bad, the shadows

948
00:51:09,680 --> 00:51:12,560
Speaker 1: are off. But to me, the like the issue is

949
00:51:12,640 --> 00:51:17,360
Speaker 1: AI writing is no, yeah, grotesquely bad. And and I

950
00:51:18,040 --> 00:51:21,040
Speaker 1: I say that not because it is like I've had

951
00:51:21,120 --> 00:51:24,880
Speaker 1: writers submit to me pieces that from a technical perspective

952
00:51:24,960 --> 00:51:27,040
Speaker 1: or worse than if an AI had generated the piece,

953
00:51:27,520 --> 00:51:30,759
Speaker 1: but like it's great. I mean it's you're a new

954
00:51:30,760 --> 00:51:33,239
Speaker 1: writer that yeah.

955
00:51:32,480 --> 00:51:33,120
Speaker 5: Dude a reason.

956
00:51:33,160 --> 00:51:40,560
Speaker 1: I'm right here, but like, but like the problem is

957
00:51:40,600 --> 00:51:44,279
Speaker 1: that like their writing has like character, right, and their

958
00:51:44,320 --> 00:51:46,560
Speaker 1: writing has a distinct voice and it needs to be

959
00:51:46,640 --> 00:51:48,680
Speaker 1: honed and it needs to be refined or whatever. But

960
00:51:48,719 --> 00:51:51,799
Speaker 1: like AI doesn't have character, it doesn't have a voice.

961
00:51:51,800 --> 00:51:54,520
Speaker 1: And they don't mean that because it's a machine. I

962
00:51:54,600 --> 00:51:57,560
Speaker 1: mean that because I have read thousands and thousands and

963
00:51:57,560 --> 00:52:00,480
Speaker 1: thousands and thousands of words written by A and it

964
00:52:00,640 --> 00:52:02,759
Speaker 1: all sounds the exact fucking saying.

965
00:52:02,880 --> 00:52:03,120
Speaker 4: Yeah.

966
00:52:03,160 --> 00:52:05,960
Speaker 1: And so when I see a tweet or I see

967
00:52:06,120 --> 00:52:09,320
Speaker 1: like that editorial image from people dot com, I just

968
00:52:09,360 --> 00:52:11,560
Speaker 1: see a tweet or a snippet from an article, and

969
00:52:11,600 --> 00:52:15,359
Speaker 1: I can immediately tell it's AI because it has this

970
00:52:15,520 --> 00:52:21,480
Speaker 1: like been all voiceless kind of.

971
00:52:20,440 --> 00:52:22,439
Speaker 5: Same thing at the end too, We're like, imagine just talking.

972
00:52:22,800 --> 00:52:27,359
Speaker 5: I'm American, so I'll imagine a Hamberger article will end

973
00:52:27,440 --> 00:52:30,640
Speaker 5: by saying it's not a Hamber it's a lifestyle. It

974
00:52:30,719 --> 00:52:34,160
Speaker 5: always has the end that it's the ultimate tell.

975
00:52:34,440 --> 00:52:36,040
Speaker 3: It's not just the food it's yummy.

976
00:52:36,920 --> 00:52:39,360
Speaker 2: No, no, yeah, that's that's something I feel like. Yeah, Caleb,

977
00:52:39,360 --> 00:52:41,359
Speaker 2: you and I have talked a lot about on our show,

978
00:52:41,440 --> 00:52:44,120
Speaker 2: Killed the Computer where it's like we've sort of peeked

979
00:52:44,120 --> 00:52:46,640
Speaker 2: into the world of like people dating AI a lot,

980
00:52:46,680 --> 00:52:50,680
Speaker 2: and whenever we read people talking about or to their

981
00:52:50,760 --> 00:52:54,400
Speaker 2: their AI partner or or like, you read these conversations

982
00:52:54,440 --> 00:52:57,080
Speaker 2: and it's I don't see a human quality in this,

983
00:52:57,239 --> 00:52:58,839
Speaker 2: but yeat, this person is human?

984
00:52:59,000 --> 00:53:02,200
Speaker 5: Ever talk to me like that. I would shoot myself tonight.

985
00:53:02,800 --> 00:53:05,920
Speaker 2: There's no humanity in it, and it's very weird. It's

986
00:53:06,000 --> 00:53:08,440
Speaker 2: weird to see that people see humanity in it because

987
00:53:08,440 --> 00:53:11,960
Speaker 2: to me, it's I am very clearly reading generated text

988
00:53:12,040 --> 00:53:14,040
Speaker 2: from a machine.

989
00:53:14,719 --> 00:53:17,200
Speaker 3: Yeah. I also like when people like, yeah, I went

990
00:53:17,239 --> 00:53:20,719
Speaker 3: and talked to chat GPT about my relationship problems. That

991
00:53:20,920 --> 00:53:24,200
Speaker 3: is just a breakup. Just you if you find yourself

992
00:53:24,280 --> 00:53:28,440
Speaker 3: going to chat GPT to ask it for coaching through

993
00:53:29,280 --> 00:53:34,080
Speaker 3: a breakup, or like, is my partner right here? Just

994
00:53:34,080 --> 00:53:35,440
Speaker 3: just fucking what.

995
00:53:35,680 --> 00:53:36,359
Speaker 4: I do love those.

996
00:53:36,440 --> 00:53:39,319
Speaker 2: I do love those, the specifically that it's am I

997
00:53:39,320 --> 00:53:41,360
Speaker 2: in the right, but like not going to Reddit.

998
00:53:42,600 --> 00:53:44,360
Speaker 5: That will never tell me I'm wrong?

999
00:53:45,640 --> 00:53:47,520
Speaker 3: My god, what do you do if it tells you

1000
00:53:47,520 --> 00:53:47,959
Speaker 3: you're wrong?

1001
00:53:51,640 --> 00:53:53,960
Speaker 4: The regenerate button You're five? Yeah good?

1002
00:53:57,080 --> 00:54:00,800
Speaker 5: The New model where I used to tell them everything

1003
00:54:00,840 --> 00:54:03,279
Speaker 5: they wanted to hear. Yeah, there's one woman that was

1004
00:54:03,320 --> 00:54:06,359
Speaker 5: like it was like I need the chat cheeps something

1005
00:54:06,360 --> 00:54:07,960
Speaker 5: like I need to be clear like I'm not going

1006
00:54:08,040 --> 00:54:09,480
Speaker 5: to rescue you or I'm not going to save you

1007
00:54:09,600 --> 00:54:11,080
Speaker 5: or whatever. She's like upset about it.

1008
00:54:11,080 --> 00:54:13,360
Speaker 2: It's so funny you're you're talking about that the hashtag

1009
00:54:13,480 --> 00:54:14,920
Speaker 2: keep four Oh.

1010
00:54:16,320 --> 00:54:17,920
Speaker 4: I've been digging into the.

1011
00:54:18,480 --> 00:54:20,960
Speaker 1: Yeah, so I've been doing enough of that that like

1012
00:54:21,440 --> 00:54:25,840
Speaker 1: my phone gives me notifications from these subreddits of people

1013
00:54:26,840 --> 00:54:29,200
Speaker 1: the bad chat cheepy Tea, and it's just it's just

1014
00:54:29,239 --> 00:54:33,880
Speaker 1: like one after another of like them threatening to like

1015
00:54:33,960 --> 00:54:35,720
Speaker 1: fly a plane into Sam Altman.

1016
00:54:39,600 --> 00:54:42,279
Speaker 3: I'm yeah, there's one. There's one where it's like a

1017
00:54:42,280 --> 00:54:45,680
Speaker 3: hamster standing outside of a building. It says a prey

1018
00:54:45,719 --> 00:54:48,879
Speaker 3: eye on it and so says never four oh, get

1019
00:54:50,280 --> 00:54:51,040
Speaker 3: you know how?

1020
00:54:51,440 --> 00:54:53,440
Speaker 2: You know how like a wine mom with like blue

1021
00:54:53,480 --> 00:54:55,920
Speaker 2: hair was one of the people that went to Nick Fuentes'

1022
00:54:56,040 --> 00:54:59,080
Speaker 2: house and like got preppers right. This is like that

1023
00:54:59,200 --> 00:55:03,160
Speaker 2: version of it. It's the median average, like random person

1024
00:55:03,360 --> 00:55:06,719
Speaker 2: in these AI communities is going to like, actually, I

1025
00:55:06,920 --> 00:55:09,800
Speaker 2: probably shouldn't be saying this. You know what I'm gonna say.

1026
00:55:09,760 --> 00:55:18,240
Speaker 4: They're gonna ye bet yeah, yeah, yeah, I poly market.

1027
00:55:18,680 --> 00:55:18,919
Speaker 7: Yeah.

1028
00:55:19,000 --> 00:55:20,960
Speaker 3: And as we wrap up here, I just I just

1029
00:55:21,000 --> 00:55:23,960
Speaker 3: looked up the hashtag keep four oh thing and I'm

1030
00:55:24,000 --> 00:55:27,000
Speaker 3: looking at this image where it's just people running with

1031
00:55:27,400 --> 00:55:29,160
Speaker 3: like it looks like a relay race. I guess that

1032
00:55:29,200 --> 00:55:31,880
Speaker 3: says keep four oh. They keep dropping the baton. We

1033
00:55:32,040 --> 00:55:34,480
Speaker 3: just wanted the one that worked, you know, that's how

1034
00:55:34,680 --> 00:55:37,960
Speaker 3: batons work. But my favorite part of this image, which

1035
00:55:37,960 --> 00:55:40,400
Speaker 3: I'm going to share with all of you, is the

1036
00:55:40,440 --> 00:55:42,399
Speaker 3: fact that there's a guy with like they've all got

1037
00:55:42,400 --> 00:55:45,640
Speaker 3: helmets for some reason, and their arms are folk going

1038
00:55:45,680 --> 00:55:50,719
Speaker 3: the wrong way. Some one of the helmets just has

1039
00:55:50,719 --> 00:55:55,000
Speaker 3: a mouth on it, and someone has they have an

1040
00:55:55,080 --> 00:55:56,840
Speaker 3: eyebrow by their nose.

1041
00:55:57,760 --> 00:55:59,839
Speaker 2: I also like that in this image, the one that

1042
00:55:59,840 --> 00:56:02,200
Speaker 2: they say is the one that works is the one

1043
00:56:02,239 --> 00:56:03,960
Speaker 2: that's not running in the track.

1044
00:56:04,840 --> 00:56:06,560
Speaker 4: But awesome, don't function.

1045
00:56:10,040 --> 00:56:12,680
Speaker 2: This is maybe one of the greatest posts I've ever seen,

1046
00:56:12,760 --> 00:56:15,439
Speaker 2: one of the greatest AI posts ever. Just it's so long,

1047
00:56:15,480 --> 00:56:20,200
Speaker 2: it's it's got every quality of an incredible post that

1048
00:56:20,239 --> 00:56:20,520
Speaker 2: you need.

1049
00:56:20,560 --> 00:56:20,759
Speaker 4: Here.

1050
00:56:21,400 --> 00:56:24,680
Speaker 3: All right, let's wrap it there, June Caleb, well pe

1051
00:56:24,840 --> 00:56:25,560
Speaker 3: find you.

1052
00:56:27,000 --> 00:56:28,640
Speaker 4: We do, Yeah, you can shoot.

1053
00:56:29,200 --> 00:56:30,560
Speaker 5: I feel like I was more annoying than you on

1054
00:56:30,560 --> 00:56:31,040
Speaker 5: this outside.

1055
00:56:31,280 --> 00:56:34,000
Speaker 2: No, no, I I intro the episode being mean to end,

1056
00:56:34,680 --> 00:56:37,920
Speaker 2: I feel like that. But no, I I guess I can.

1057
00:56:38,000 --> 00:56:38,360
Speaker 4: I can do it.

1058
00:56:38,360 --> 00:56:41,719
Speaker 2: I'm bad at pitching. But what I will say is,

1059
00:56:42,200 --> 00:56:44,880
Speaker 2: Caleb and I we do a show called Kill the Computer.

1060
00:56:45,080 --> 00:56:48,920
Speaker 2: It's a show about how the Internet sort of influences

1061
00:56:49,000 --> 00:56:54,720
Speaker 2: and uh, actually I don't know this is edited, right, Caleb?

1062
00:56:54,760 --> 00:56:57,520
Speaker 2: How about you do it? Cut me out of I

1063
00:56:57,520 --> 00:56:59,240
Speaker 2: need to work on my pitches. I need to work.

1064
00:57:00,760 --> 00:57:02,880
Speaker 5: No, please, Yeah, I would say it's like it's a

1065
00:57:02,920 --> 00:57:05,600
Speaker 5: show where we just kind of look at how the

1066
00:57:05,719 --> 00:57:08,680
Speaker 5: Internet is internet stuff inner culture spills out to the

1067
00:57:08,719 --> 00:57:10,839
Speaker 5: real real world, and how it affect us on our

1068
00:57:10,840 --> 00:57:13,480
Speaker 5: personal level. I guess the most high bround way to

1069
00:57:13,560 --> 00:57:17,200
Speaker 5: say it is just different a subculture analysis of different

1070
00:57:17,280 --> 00:57:19,080
Speaker 5: how different kinds of people use the Internet.

1071
00:57:19,560 --> 00:57:24,120
Speaker 3: Absolutely hell yeah, and a reef people can find you

1072
00:57:24,280 --> 00:57:30,280
Speaker 3: where at wide Left dot football where I talk about football. Yeah.

1073
00:57:30,360 --> 00:57:33,040
Speaker 3: And I'm just gonna say to the people that really

1074
00:57:33,080 --> 00:57:38,200
Speaker 3: didn't enjoy the Super Bowl episode last year, like you

1075
00:57:38,280 --> 00:57:41,400
Speaker 3: have any idea how close I was to just only

1076
00:57:41,440 --> 00:57:46,560
Speaker 3: talking about football for fifty like to hear they released

1077
00:57:46,640 --> 00:57:50,320
Speaker 3: Tyreek Hill like going no, no, no, no, dude. We're

1078
00:57:50,320 --> 00:57:52,760
Speaker 3: gonna end the episode there. Thank you all for listening

1079
00:57:52,880 --> 00:57:55,560
Speaker 3: to Better Offline. There will be a monologue this week.

1080
00:57:55,640 --> 00:57:59,800
Speaker 3: I assume download the episodes, read the newsletter, I met Zichean,

1081
00:58:00,000 --> 00:58:10,920
Speaker 3: and goodbye. Thank you for listening to Better Offline. The

1082
00:58:11,040 --> 00:58:14,240
Speaker 3: editor and composer of the Better Offline theme song is Matasowski.

1083
00:58:14,560 --> 00:58:16,400
Speaker 3: You can check out more of his music and audio

1084
00:58:16,440 --> 00:58:20,240
Speaker 3: projects at Mattasowski dot com, m A T T O

1085
00:58:20,480 --> 00:58:24,600
Speaker 3: S O W s ki dot com. You can email

1086
00:58:24,640 --> 00:58:27,080
Speaker 3: me at easy at better offline dot com or visit

1087
00:58:27,120 --> 00:58:29,480
Speaker 3: better offline dot com to find more podcast links and

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Speaker 3: of course, my newsletter. I also really recommend you go

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Speaker 3: to chat dot Where's youreed dot at to visit the discord,

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Speaker 3: and go to our slash Better Offline to check out

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Speaker 3: our reddit. Thank you so much for listening.

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Speaker 4: Better Offline is a production of cool Zone Media.

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Speaker 3: For more from cool Zone Media, visit our website cool

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Speaker 3: Zonemedia dot com, or check us out on the iHeartRadio app,

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Speaker 3: Apple Podcasts, or wherever you get your podcasts.