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Speaker 1: A media.

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Speaker 2: Your scientists have yet to discover how neural networks create

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Speaker 2: self consciousness, let alone how the human brain processes two

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Speaker 2: dimensional retinal images into three dimensional phenomenon known as perception.

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Speaker 2: Yet you, somehow brazen lye declare seeing is believing.

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Speaker 3: Yes, I do.

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Speaker 2: I'm at Zitron. This is better offline. I'm your host,

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Speaker 2: by our merchandise. Go to my newsletter. Where's your ed

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Speaker 2: dot app?

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Speaker 3: Anyway? Fuck all that.

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Speaker 2: Brian Kopperman is joining us here in the studio. He's

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Speaker 2: the incredible writer, producer, and he's in the Bear a Bear.

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Speaker 2: He's a real deal actor. He's also the co creator,

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Speaker 2: showrunner and executive producer of Showtimes, Billions and super pumped

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Speaker 2: the battle for Uber. Brian, thank you so much for

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Speaker 2: joining us.

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Speaker 1: Thrilled to be here with you.

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Speaker 2: Man. We've of course got Mike drug At, the wonderful comedian.

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Speaker 2: And of course you have a book, don't you.

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Speaker 4: Mike, Yes, sir, it's called Good Game, No Rematch. Get

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Speaker 4: out embarrassing myself with video games throughout my entire.

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Speaker 2: Life, wonderful, I'll be embarrassing myself with tech on this show.

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Speaker 2: Sherlyn Lowe as well, joining us from mang gotcha hate

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Speaker 2: doing shell.

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Speaker 5: I am about it down a protein shake hopefully.

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Speaker 2: Yes, Yeah, we're just talking about the Ninja Creamy and

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Speaker 2: all the various slops.

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Speaker 1: They're great Ninja Creamy. I'll tell you there's no endorsement

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Speaker 1: out there for any of us. They don't need it

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Speaker 1: because people just get these things and they immediately go

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Speaker 1: to tiktokh because they want to share how good they are. Yeah,

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Speaker 1: they're honestly, they're so good. That's why I have full

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Speaker 1: indorse full endorsement.

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Speaker 3: This is the Ninja Creamy show.

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Speaker 2: Now I'm going to get what's great about this is

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Speaker 2: literally any time I mention any product, I will get

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Speaker 2: an email from someone being like, here's a post from

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Speaker 2: two thousand and three from the CEO in which case

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Speaker 2: they shot the door.

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Speaker 3: It's like some insane ed how dare you bring up?

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Speaker 3: I'm like, I don't know everything. Please tell me.

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Speaker 5: Well, Fair Lives bottles have a lot of Dalley's in them,

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Speaker 5: so you know that's our that's our thing today.

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Speaker 2: Yeah, I just eat like too much yoga. So I

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Speaker 2: want to actually start with a really good thing. So, Mike,

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Speaker 2: you wrote a really great piece of the Gamer I

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Speaker 2: think they went out, Yeah, yeah, very recently is called

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Speaker 2: I'm starting to worry this industry has no respect for

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Speaker 2: the people who work in it.

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Speaker 3: Well, you walk us through.

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Speaker 2: The article, because I think it's a good subject.

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Speaker 3: Matsa sure well.

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Speaker 4: As listeners may or may not know, Microsoft recently laid

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Speaker 4: off about nine thousand people, most of which were in

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Speaker 4: their gaming department. Simultaneously, they were claiming that their gaming

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Speaker 4: department's quite profitable and they're making a ton of money

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Speaker 4: and everything's going well.

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Speaker 3: I don't know if that's actually.

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Speaker 4: True, but they are saying that, And they've kind of

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Speaker 4: also said that a lot of the money that they

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Speaker 4: were paying these employees isn't isn't about losses, it's that

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Speaker 4: they want to move this money into AI development.

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Speaker 2: Did they say that, Yeah, yeah, that's horrifying.

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Speaker 4: Yeah, it's kind of im I mean they didn't say

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Speaker 4: that word for word, but it was very implied that

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Speaker 4: the budget was you know, they're more focused. That was

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Speaker 4: kind of the language. The language was like, you know,

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Speaker 4: as we restructure, we're more focused on AI going forwards.

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Speaker 2: It's just so ironic, is like video games is one

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Speaker 2: of the first real exposures to AI for most people, yeah,

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Speaker 2: I remember when Fear came out.

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Speaker 3: He won't remember fit. Yeah, it was one of the first,

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Speaker 3: one of the first.

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Speaker 2: It was the first time a guy would like shove

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Speaker 2: his head over and look around before just jumping up

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Speaker 2: and getting shot.

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Speaker 1: Yeah.

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Speaker 2: It's just the reason this really spoke to me, other

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Speaker 2: than in fact it's very well written, is the I

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Speaker 2: feel like this is the central problem with a lot

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Speaker 2: of tech, entertainment everything right now, where it's like the

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Speaker 2: people at talk and you make this.

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Speaker 3: Point well in the article.

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Speaker 2: Yeah, the industry is the problem because the industry is

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Speaker 2: not being piloted by the people creating right exactly.

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Speaker 4: I mean, you know, and it's a problem that also

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Speaker 4: extends to the you know, Hollywood, which I also work in.

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Speaker 3: Because I'm famous. No, that's not true at all.

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Speaker 4: But it's this problem that, like, you know, a lot

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Speaker 4: of the decision makers are not as close to the

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Speaker 4: product as they used to be. Yeah, I mean, I

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Speaker 4: mean you could even say the same same for things

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Speaker 4: like Boeing, where you know, you don't have engineers running

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Speaker 4: the department anymore. You have business people who've been brought

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Speaker 4: in because they're good at checking well track alliance exactly exactly,

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Speaker 4: and you know, these people are far away from the product,

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Speaker 4: they're far away from the development of it, so they

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Speaker 4: see these you know, workers as numbers on a spreadsheet.

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Speaker 4: And for better or worse, video game development is a

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Speaker 4: very long process. It takes a lot of time, it

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Speaker 4: takes a lot of money.

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Speaker 2: It's getting longer. It's getting longer, yeah, horizon as well.

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Speaker 4: And so it's you know, very easy for these companies

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Speaker 4: that are looking for a short term you know, next

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Speaker 4: financial quarter.

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Speaker 3: Boost to go.

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Speaker 4: We fired all these people and this product wasn't going

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Speaker 4: to come out for two years, so it really doesn't matter.

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Speaker 2: And that's the thing with Microsofts well they've that's that

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Speaker 2: they've laid fifteen thousand people this year as well. Nuts

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Speaker 2: and it is the money really even going an It's

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Speaker 2: just so confusing. But you're seeing everyone with Hollywood. There

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Speaker 2: was the Acme versus Coyote thing. Yeah, sorry, Coyote vusus

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Speaker 2: Acme thing where they just like multhbuled it releasing it

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Speaker 2: happily mouth bolded movie. Yeah, to save on taxes. I

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Speaker 2: feel like that shouldn't be a loophole.

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Speaker 3: I hate that loophole.

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Speaker 4: I hate the idea of that loophole. I mean, it

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Speaker 4: almost reminds me of that loophole from and Brian. You

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Speaker 4: could maybe correct me on this, but remember like in

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Speaker 4: the nineties they made a Fantastic Four film they weren't

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Speaker 4: going to release just so they could maintain rights for it,

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Speaker 4: because the rights for it were you have to make

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Speaker 4: a movie to keep the rights, you don't have to

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Speaker 4: release it.

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Speaker 1: I don't remember that exactly though, now that you say it,

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Speaker 1: it rings a bell, but I don't have particulars on it.

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Speaker 1: But I think what you're talking about really the answer.

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Speaker 1: I agree completely as you diagnose it with how depressing

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Speaker 1: it is. But to me, these things feel like a

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Speaker 1: system's response, like complexity theory and system well, they're like

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Speaker 1: systems responses to what's happening in the world, as opposed

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Speaker 1: to feeling like because I think it's easier for us

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Speaker 1: to look at the human being and go that motherfucker,

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Speaker 1: and that may be a motherfucker. But another possible answer

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Speaker 1: is that from a far remove, like something's happening right,

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Speaker 1: and when this thing is happening, it gets into quantum theory.

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Speaker 1: But it is a complexity system's response to this giant

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Speaker 1: change of artificial intelligence, having certain capabilities and all of this.

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Speaker 1: You can look at all of it, and I mean,

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Speaker 1: we've seen people who had one set of beliefs for

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Speaker 1: so long and on the record in your industry, completely

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Speaker 1: switch and they may think that's an autonomous decision they're making.

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Speaker 1: But I found a little bit of solace by reading

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Speaker 1: about complexity theory because that for me, offers a potentially

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Speaker 1: more not hopeful, but sort of a more complete understand.

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Speaker 2: You're saying, like a systemic response to conditions. Because I

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Speaker 2: don't know about the AI capability side, but the existence

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Speaker 2: of capabilities potentially being there makes sense. In the they're

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Speaker 2: all trying to reconfigure for a future they don't know

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Speaker 2: if it's actually there. The general AI doesn't seem to

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Speaker 2: do it, but the potential of that, the idea of it,

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Speaker 2: is motivating them so much. Microsoft of all people as well,

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Speaker 2: should know how little money's actually being made from this,

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Speaker 2: considering out the thirty.

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Speaker 1: So that's it, Yes, But executives, honestly, because I've studied

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Speaker 1: these people so much and written about them much, and

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Speaker 1: I understand how venal they are and how short term

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Speaker 1: they think at times. But they're also I think looking

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Speaker 1: at data that we don't have very often, and they're

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Speaker 1: trying to forecast out a long time.

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Speaker 2: Why do you think they have that we don't have though,

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Speaker 2: because like with Microsoft for example, and I have probably

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Speaker 2: studied such in the DELLA too much at this point.

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Speaker 2: I really I learned too much about the growth mindset

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Speaker 2: and Karlyn Dweck and all of that nonsense. But the data,

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Speaker 2: if the data is there, they're acting very peculiar about

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Speaker 2: the data because they're only making thirteen billion dollars this

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Speaker 2: year from AI. Ten billion is a ur ur I

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Speaker 2: was from open Ai, burning giraffes and whatever they put

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Speaker 2: into the machine for chat GPT. But it's it feels

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Speaker 2: almost like they are reacting to what they hope will

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Speaker 2: happen or what may happen. I'm just trying to lad

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Speaker 2: it might just.

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Speaker 1: Be when when everyone reacts to quarterly like the problem

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Speaker 1: our business, the entertainment. The thing is that these people

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Speaker 1: are responding to quarterly earnings calls that they have to make,

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Speaker 1: and maybe the data isn't about the long term possible,

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Speaker 1: maybe the data is literally that we don't know that

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Speaker 1: they're looking at is their little cohort of people and

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Speaker 1: the exact moment they can exercise which kinds of options,

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Speaker 1: some of which they have to declare and maybe some

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Speaker 1: of which they are somehow able to flip on a

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Speaker 1: different market.

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Speaker 2: What Anthropic basically Amazon did with Anthropic, they flip their

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Speaker 2: investment into a certain kind of thing they could tax

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Speaker 2: the dug.

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Speaker 1: I mean, there's no doubt that there's heartlessness, But I

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Speaker 1: just because Druckers here, I just want to say, you know,

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Speaker 1: even in the most depressing times or in moments where

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Speaker 1: the technology the platform seems brutal, people's humanity can transcend it.

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Speaker 1: And I remember in some dark days of Twitter, Drucker

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Speaker 1: was and it was really amazing man. And I remember,

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Speaker 1: you know, you know, I don't know each other well,

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Speaker 1: but we've known each other a long wi time, like

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Speaker 1: twenty years, and yeah, it's true. And I remember that

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Speaker 1: they're where many nights you were like not sleeping that well,

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Speaker 1: sleeping at the wrong time, yeah, and like sleeping during

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Speaker 1: the day up at night. But keep I remember him

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Speaker 1: really like very vulnerably talking about sadness and depression and

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Speaker 1: getting people to entrust sharing, and him actively trying to

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Speaker 1: like save people in a place where like where people

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Speaker 1: were so callous almost by profession on there by like

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Speaker 1: everything they wanted to do that right, Well, all everyone

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Speaker 1: was at home playing grand theft Auto, which could you

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Speaker 1: guys fix that and get the next one out? But

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Speaker 1: everyone's at home playing grand theft Auto, uh, and being callous.

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Speaker 1: And he was like literally going, let me explain depression

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Speaker 1: to you and what you can do and what the

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Speaker 1: resources are and why you shouldn't kill yourself, and which

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Speaker 1: I think is great. Of course you would lead the

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Speaker 1: empathy on this too and look at these assholes and

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Speaker 1: think about all the engineers, and it's the right way

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Speaker 1: to process it. I just think often it's not a binary.

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Speaker 2: No, and that makes sense. And that's the thing I've

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Speaker 2: been saying like a lot about the show is I'm

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Speaker 2: talking about the pigs in the arsholes and the skumbags

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Speaker 2: and all the different names and the voices I.

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Speaker 1: Do for them.

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Speaker 2: But it's also about the fact that there is like

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Speaker 2: I'm pissed off case of Kagawa.

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Speaker 3: Friend of the show's said this to him a lot.

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Speaker 2: It's I'm broken hearted dramatic because things like what Mike did.

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Speaker 3: Actually one of the reasons every day and.

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Speaker 2: Gadget as well, is it's like Yeah, there are a

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Speaker 2: lot of these financial horrors in there, but there's fun,

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Speaker 2: dorky shit online. Still, there's still one Like most of

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Speaker 2: my friends are from the internet, you know. It's like

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Speaker 2: I'm like the drill quiet crying. It's like being on

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Speaker 2: Like it's like like everything I got is through emails.

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Speaker 3: But I still think there is a lot.

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Speaker 2: Of joy in this And I think the central thing

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Speaker 2: about your article now is just like beneath this capitalism

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Speaker 2: crush is actually some really like wonderful things being built.

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Speaker 2: There are still wonderful games being built. Yeah, there's like

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Speaker 2: an entire economy on Minecraft, for better or for worse

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Speaker 2: about selling mods and stuff. There are people on roadblocks

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Speaker 2: other problems with roadblocks obviously, who are like building games

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Speaker 2: in there and selling them. There's still some cool shit

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Speaker 2: happening in tech, just being in with because every three

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Speaker 2: months someone gets upset at them.

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Speaker 4: Or finds a new way to make money off it,

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Speaker 4: which you have to you know, shove into a new category,

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Speaker 4: but the new thing.

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Speaker 5: To chase, right that's where it is right now. I

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Speaker 5: think that what you were talking about, the like macro

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Speaker 5: picture of everyone sort of having that reaction, and I

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Speaker 5: think we will course correct in time. I think we're

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Speaker 5: right now at that stage in history where it's not

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Speaker 5: as Cotton dried to me as the NFT crypto sort

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Speaker 5: of bubble, where like it was clearly about criminals.

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Speaker 1: Yeah, it was walking people.

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Speaker 5: Over, knowing right, And I'd argue that on some level

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Speaker 5: these are criminals. There are criminals that play in this

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Speaker 5: scenario right now. I will say that with what Mike

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Speaker 5: was writing about in your article, the Microsoft thing was

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Speaker 5: all the more like I think when my team saw

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Speaker 5: the news last week, my instant reaction was, didn't they

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Speaker 5: just ratify a union contract? Was like their first ever

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Speaker 5: in the US? Too like to be saying that on

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Speaker 5: one hand, we really care about workers rights and really

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Speaker 5: want to protect workers, and the other you're like, and

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Speaker 5: those are their quality people, right And now we're like, oh,

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Speaker 5: game developers, man, nine thousand of you can go because

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Speaker 5: AI n PC's are all the rage right now and

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Speaker 5: they really make a lot of sense. A m PCs

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Speaker 5: never said a bad thing, never said anything.

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Speaker 2: That was demo last year?

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Speaker 4: Was it this year?

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Speaker 5: I can't remember.

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Speaker 2: They've done too and video wheels out at demo would

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Speaker 2: be like the generative AI NPC's here and within one

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Speaker 2: day someone has made it says layah or like it's

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Speaker 2: just said something in spane. Microsoft tay was the twenty

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Speaker 2: sixteen AI bought that learned from the Internet to be racist,

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Speaker 2: like one of a proto Mecha Hitler situation.

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Speaker 4: Oh that was Grock Yeah taal that the Microsoft bathroom

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Speaker 4: Yeah from twelve or something.

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Speaker 3: I forgot about that.

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Speaker 5: But the thing is, back to Brand's point is that

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Speaker 5: this keeps happening and then we keep course correcting back

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Speaker 5: like it feels like there is a system's response and

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Speaker 5: Ed I think you're looking for the joy. I wouldn't

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Speaker 5: use the word intervening or interfering. I would say it's noise.

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Speaker 5: And the way for companies and people existing in this

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Speaker 5: industry to deal with that is to focus on what

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Speaker 5: you think is good. I find the irony in saying that,

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Speaker 5: which I think these tech bros that you're referring to, Brian,

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Speaker 5: they're also trying to focus on what they think it's good,

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Speaker 5: so they're cutting out noise from their perspective. And I

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Speaker 5: don't know it's that movie that was just released featuring

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Speaker 5: those four dudes in the Mountain Lodge.

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Speaker 1: Oh not out Head Mountain.

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Speaker 5: Mountain Head, mountain Head. Yes, so it feels like that.

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Speaker 5: It feels like everyone's got their little bubble, which is

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Speaker 5: also at the same time created maybe and supported by tech.

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Speaker 5: And if we continue to operate in silos, I don't know.

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Speaker 5: I feel like if we.

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Speaker 1: Only if we only think of those and often they

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Speaker 1: are men in those roles. They're not all, but often

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Speaker 1: they are. If you think of some of those you said, bros.

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Speaker 1: But if we only reduce them to those guys running

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Speaker 1: around trying to kill someone in a song where I

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Speaker 1: think in a way it allows in a way, it

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Speaker 1: allows us to like not worry about them because we

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Speaker 1: can do them.

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Speaker 3: Moll right, that's fair.

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Speaker 1: Some of them are smarter, like just raw synthesizing power.

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Speaker 1: Some of those people, not all of them, they're smarter

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Speaker 1: than everyone in this building combined, couple of them. And

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Speaker 1: I'm not saying that that makes them good or anything

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Speaker 1: in any way good or in any way good for now,

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Speaker 1: because even if you say, like I agree with you

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Speaker 1: that some of them may believe that they're doing this,

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Speaker 1: that they're doing good, right, But if they're thinking in

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Speaker 1: a thousand year chunks, that's really bad for You've all

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Speaker 1: said this and you've all I think is amazing. And

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Speaker 1: he said, well, the problem is historians like me. He goes,

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Speaker 1: you may look at it now. He was on a podcast.

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Speaker 1: He said, you may look at it now and say,

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Speaker 1: but those things worked out. Okay, look where we are.

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Speaker 1: But as a historian, I have to look at the

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Speaker 1: cost of human life. Yes, that happened for all the intervals,

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Speaker 1: all the little intervals to get from there to here.

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Speaker 1: And that's really what you guys are just talking about. Rightly,

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Speaker 1: so is all the you know, the devastation in the way.

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Speaker 1: But but can I just because you might might be

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Speaker 1: so jaded, but do you not think AI is like

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Speaker 1: mind bogglingly great?

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Speaker 3: It's nice.

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Speaker 5: I can see the valance. I think it's like far

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Speaker 5: off you.

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Speaker 1: Don't wait, you really I think it's like the single

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Speaker 1: I think it's why the single greatest invention in my life.

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Speaker 5: What is what is your favorite thing that has done?

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Speaker 1: I think the ability to have super high level conversations

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Speaker 1: about really esoteric about systems theory, right, you know it's

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Speaker 1: correct in them? Well you can, well, you have to

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Speaker 1: do a bit of work.

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Speaker 2: I don't want to talk to something that I have

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Speaker 2: to verify constantly I talk to people.

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Speaker 5: Well, you have to do that with people to some extent.

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Speaker 5: You can't trust everything you feel about.

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Speaker 1: The automobile or is the horse drawn carriage still your thing?

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Speaker 2: No, I'm just saying, no, I'm wondering what the point is.

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Speaker 5: It looks very uneasy, right, I mean the point?

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Speaker 1: No, I just have jokes in my head.

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Speaker 3: No, I'm well, because I.

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Speaker 1: Think you can decry the industry and the way people

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Speaker 1: are using it. And I guarante I'm pretty sure I

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Speaker 1: was reading Alazer you had Kowski before anyone in this room.

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Speaker 1: I mean, how to actually change your mind as a

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Speaker 1: book that I was obsessed to give it Christmas gifts.

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Speaker 3: I can't take your ds.

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Speaker 1: I just it's you gotta take him serious, isn't. I

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Speaker 1: mean you have to take one to take that guy's

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Speaker 1: brain seriously. He think everything you guys are worried about

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Speaker 1: he called out fifteen years ago in detail.

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Speaker 2: Okay, I mean, like, here's the thing. The automobile is

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Speaker 2: not a comparison because we knew who had to go forward,

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Speaker 2: side side and everything like that, Like we had an

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Speaker 2: actual use case for that with generative AI. The way

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Speaker 2: that these conversations happen and large language models can be

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Speaker 2: conversing with documents. It's one of the only use cases

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Speaker 2: that is actually remotely useful because you can actually verify

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Speaker 2: based on the parts of the document. Having a conversation

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Speaker 2: with one of these. Fine, it's a thing. I'm not

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Speaker 2: amazed by it because there have been chatbots doing this

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Speaker 2: with KMS since like what thirteen, twenty fourteen.

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Speaker 4: And the Eliza effect goes back to the sixties.

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Speaker 2: Yeah, and Eliza even then, there's a Karen Howe's Empire

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Speaker 2: of Ai is a great thing about Eliza.

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Speaker 3: What the creator was just like, why is everyone so

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Speaker 3: fucking impressed? But it's just like, really, just like, what

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Speaker 3: the fuck is this?

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Speaker 2: I think what it is is. I am just not

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Speaker 2: that impressed by it. Based on the larger discussion. Everyone

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Speaker 2: acts like this as the fucking future, and it just

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Speaker 2: feels like a growth of the past. Chat GPT has

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Speaker 2: become for better or for worst is what Google could

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Speaker 2: have potentially been. It's insane the attention.

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Speaker 1: I agree with you, but like, okay, I got a

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Speaker 1: tick bite. Okay, and you can literally the second you

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Speaker 1: get a tick bite, everybody says antibiotics you gotta go.

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Speaker 1: If you can't find the thing. I got a picture.

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Speaker 1: I put it on there. The AI was immediately able

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Speaker 1: to say, yes, I could verify it after because I called,

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Speaker 1: I was able to say, this is the kind of

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Speaker 1: ticket is, this is the area you're in, this kind

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Speaker 1: of tick. You don't need antibiotics. You're not gonna get

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Speaker 1: lyme disease. Here's why sent it to my doctor who

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Speaker 1: that it's feedback and they agreed they'll be harder to

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Speaker 1: do it.

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Speaker 2: Why didn't you send it to your doctor before you, Austin, Well.

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Speaker 1: He doesn't. His job isn't too like identify what ticket is.

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Speaker 3: Oh, oh, sorry, I misunderstood what you said.

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Speaker 5: Yeah, I want more clarity on the doctor thing. I

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Speaker 5: raised my hand as you were talking because I'm like,

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Speaker 5: did you trust the chat GPT answer and leave it

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Speaker 5: at that?

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Speaker 2: Right?

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Speaker 1: Then? I took when it said that, then I searched

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Speaker 1: for what it said and compared and it was right.

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Speaker 1: But it was.

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Speaker 4: Out of curiosity and ed you might be able to

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Speaker 4: answer this question. Is that And I don't actually know

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Speaker 4: the answer. Is that iterative AI or is that generative AI?

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Speaker 2: So we're using chat GPT so that it's a mix,

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Speaker 2: so that would be generative okay, and so that would

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Speaker 2: still be that And by the way, sounds like a

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Speaker 2: use case.

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Speaker 3: It is the growth of so what you.

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Speaker 5: Say, you know, I was going to say, I think

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Speaker 5: I think a version of Google could have done that

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Speaker 5: for you in the past two before chat chept. What

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Speaker 5: chat gupt, the generative side of it is providing is

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Speaker 5: the l M, the natural language interface about pulling a

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Speaker 5: lot of different sources of data and putting that together

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Speaker 5: for you. That is chat jept. You could have done

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Speaker 5: that with Google or I think there were apps right

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Speaker 5: that you could do a photo uh sort.

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Speaker 2: Of recognition for a while.

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Speaker 5: Yeah, and I to my knowledge that parts of Google

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Speaker 5: and Google Health researchers with their AI divisions were working

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Speaker 5: on apps that could identify different things like skin things,

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Speaker 5: so like is that a bug bite or is that

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Speaker 5: exemon kind of thing through the pixel phones. I don't

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Speaker 5: think they've ever broadly released it, but they were experimenting

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Speaker 5: way before chat GPT was even a thing. I would

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Speaker 5: argue that I think the LM portion of this is

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Speaker 5: more about how it converses with you and how it

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Speaker 5: like understands what you're actually concerned about. So if you

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Speaker 5: didn't give it the exact words of look up this

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Speaker 5: thing and should I see a doctor? Even if you

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Speaker 5: didn't input this your doctor request, it might you know

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Speaker 5: divine that that's what you realize.

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Speaker 3: This is the thing like chat GPT.

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Speaker 2: I don't think would have been a big deal if

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Speaker 2: Google had actually innovated and search at all, if they

440
00:19:24,800 --> 00:19:27,639
Speaker 2: have something adds to it. Google search has been the

441
00:19:27,680 --> 00:19:32,080
Speaker 2: same for like fifteen twenty years, except worse what you

442
00:19:32,119 --> 00:19:35,920
Speaker 2: were describing their valid its inference. Basically, it's infers the

443
00:19:36,000 --> 00:19:39,000
Speaker 2: understanding from the image and all this and then spits

444
00:19:39,040 --> 00:19:39,560
Speaker 2: out an answer.

445
00:19:39,680 --> 00:19:40,520
Speaker 3: It's the use case.

446
00:19:40,720 --> 00:19:42,240
Speaker 2: I think that in the way you use it that

447
00:19:42,359 --> 00:19:46,200
Speaker 2: was responsible. The problem is at scale that is not

448
00:19:46,280 --> 00:19:47,920
Speaker 2: going to work out so great because.

449
00:19:47,800 --> 00:19:49,480
Speaker 1: I believe you know way more about this than I.

450
00:19:52,200 --> 00:19:54,359
Speaker 2: No. I'm genuinely glad you brought this up because it's like,

451
00:19:54,800 --> 00:19:59,160
Speaker 2: but what about that is extrapolating out to the greater

452
00:19:59,240 --> 00:20:02,480
Speaker 2: AI replacing jobs? Think, because that's where my principal problem is.

453
00:20:02,520 --> 00:20:04,800
Speaker 2: People are taking what is what Google should have been

454
00:20:04,840 --> 00:20:08,000
Speaker 2: in twenty seventeen and turned it into this is going

455
00:20:08,000 --> 00:20:10,680
Speaker 2: to replace half of workers. A quote from Dario Amit

456
00:20:10,760 --> 00:20:12,480
Speaker 2: Day that was fucking made up.

457
00:20:12,359 --> 00:20:14,120
Speaker 3: Which he set off the top of his head. It's

458
00:20:14,119 --> 00:20:14,640
Speaker 3: not a bird.

459
00:20:14,720 --> 00:20:19,640
Speaker 2: Sorry, just getting Wario himself. Oh harrio amitay.

460
00:20:19,920 --> 00:20:22,119
Speaker 5: I think what you're also bringing up, Brian, is that

461
00:20:22,240 --> 00:20:25,120
Speaker 5: there is this marketing I guess problem with AI, which

462
00:20:25,119 --> 00:20:27,480
Speaker 5: is that like AI has existed for a very long time,

463
00:20:27,520 --> 00:20:29,639
Speaker 5: and the current version that everyone's really obsessed with is

464
00:20:29,680 --> 00:20:32,439
Speaker 5: jen AI and generative AI is all about like what

465
00:20:32,560 --> 00:20:35,239
Speaker 5: it can generate for you, using large language models, using art,

466
00:20:35,280 --> 00:20:37,720
Speaker 5: like creating art, creating videos, all of that stuff. Is

467
00:20:37,760 --> 00:20:40,800
Speaker 5: this current iteration of AI we're all talking about, But

468
00:20:40,840 --> 00:20:43,000
Speaker 5: the previous stuff has existed before, and this is all

469
00:20:43,000 --> 00:20:45,000
Speaker 5: like just more of the same. I think that's why

470
00:20:45,040 --> 00:20:46,879
Speaker 5: so many of us in the industry are so frustrated

471
00:20:46,920 --> 00:20:50,639
Speaker 5: with it, because there's a misconception. There's also this idea

472
00:20:50,680 --> 00:20:52,880
Speaker 5: that the jobs that it's trying to replace are in

473
00:20:52,920 --> 00:20:55,359
Speaker 5: that field of coming up with art, music and words

474
00:20:55,359 --> 00:20:58,080
Speaker 5: that are not jobs at APay wall to begin with,

475
00:20:58,200 --> 00:21:01,240
Speaker 5: but be like our parts of our life that we

476
00:21:01,280 --> 00:21:02,879
Speaker 5: want it replaced, right, we want it.

477
00:21:03,840 --> 00:21:06,080
Speaker 1: When I talk about Yodkowski, the reason I do is

478
00:21:06,119 --> 00:21:08,400
Speaker 1: that it is that he was somebody who he's right

479
00:21:08,440 --> 00:21:12,240
Speaker 1: on your side way and his thing always was this

480
00:21:12,440 --> 00:21:15,000
Speaker 1: is not going to be a net good. First, that's

481
00:21:15,000 --> 00:21:18,040
Speaker 1: not even he and I mean I remember, I'm sure

482
00:21:18,040 --> 00:21:21,359
Speaker 1: you remember when astro Teller wrote Ex Jesus. I remember

483
00:21:21,359 --> 00:21:23,399
Speaker 1: reading that book like the day it came out, and

484
00:21:23,440 --> 00:21:26,000
Speaker 1: it really did freak me out about what was possible.

485
00:21:26,040 --> 00:21:28,120
Speaker 1: Now we're not even there yet, right, it even isn't

486
00:21:28,119 --> 00:21:31,040
Speaker 1: where that but that version of Ai.

487
00:21:31,119 --> 00:21:34,280
Speaker 2: And Butowski's he's Awskidkowski.

488
00:21:34,400 --> 00:21:37,520
Speaker 3: Sorry, he's so find him quite distasteful.

489
00:21:37,560 --> 00:21:40,240
Speaker 2: But on top of that, he's a fucking agi dom

490
00:21:40,480 --> 00:21:42,920
Speaker 2: and he's saying he's just yeah, sure if a frog

491
00:21:42,960 --> 00:21:45,920
Speaker 2: had but a frog had wings that could fly, It's like, yeah,

492
00:21:45,960 --> 00:21:47,800
Speaker 2: if the computer wakes up and does this, this could

493
00:21:47,800 --> 00:21:49,399
Speaker 2: be scary. He's one of the few people that seems

494
00:21:49,400 --> 00:21:50,919
Speaker 2: to what you think about what a g I could do.

495
00:21:51,240 --> 00:21:53,520
Speaker 2: But also we're so far more off from it that

496
00:21:53,640 --> 00:21:55,359
Speaker 2: I can't see him as anything but a grift of

497
00:21:55,480 --> 00:21:57,439
Speaker 2: because all he is doing is grift.

498
00:21:57,720 --> 00:21:59,800
Speaker 1: It's just he's been on it long before there was grift.

499
00:22:00,119 --> 00:22:03,959
Speaker 1: He was on it as a nonprofit. No he no,

500
00:22:04,640 --> 00:22:07,200
Speaker 1: I mean, think about what I've written. You think I

501
00:22:07,240 --> 00:22:18,000
Speaker 1: know nonprofit's the matter with your one? Yeah, but he's

502
00:22:18,800 --> 00:22:20,800
Speaker 1: I have a different view. Now, I'm not saying all

503
00:22:20,840 --> 00:22:23,879
Speaker 1: his opinions are sure are correct. What I'm saying is

504
00:22:23,920 --> 00:22:28,040
Speaker 1: that that's somebody who flagged a bunch of these potential

505
00:22:28,560 --> 00:22:32,600
Speaker 1: issues a long time ago, and of course it shouldn't

506
00:22:32,760 --> 00:22:36,040
Speaker 1: and it's horrible that these people in charge are so

507
00:22:36,240 --> 00:22:40,320
Speaker 1: willing to slough off being the moment they think there's

508
00:22:40,359 --> 00:22:42,359
Speaker 1: this much of an edge. But also because of what

509
00:22:42,400 --> 00:22:44,960
Speaker 1: I study in life, I can't be so we've all

510
00:22:45,000 --> 00:22:46,880
Speaker 1: of us who write about this stuff in fiction, right,

511
00:22:46,920 --> 00:22:48,919
Speaker 1: you do it because okay, and you guys have to

512
00:22:48,920 --> 00:22:50,960
Speaker 1: report on something you have to. I can take sort

513
00:22:51,000 --> 00:22:52,560
Speaker 1: of like my partner, I could take what's in there

514
00:22:53,040 --> 00:22:57,480
Speaker 1: and try to dramatize what might happen, and always would

515
00:22:57,560 --> 00:23:00,480
Speaker 1: try to point out exactly that these motherfuckers do all

516
00:23:00,520 --> 00:23:03,239
Speaker 1: this shit. It's not surprising to me. It's horrific, but

517
00:23:03,280 --> 00:23:04,680
Speaker 1: not surprising.

518
00:23:15,960 --> 00:23:17,359
Speaker 3: What bothers me as well with it?

519
00:23:17,440 --> 00:23:20,240
Speaker 2: And I think you're completely right, And also like I'm

520
00:23:20,280 --> 00:23:22,679
Speaker 2: not completely saying everything you're saying is wrong, Like you

521
00:23:22,680 --> 00:23:24,600
Speaker 2: actually have well read on this, which is nice because

522
00:23:24,640 --> 00:23:27,000
Speaker 2: a lot of people who mentioned the AGI stuff don't

523
00:23:27,000 --> 00:23:27,520
Speaker 2: read anything.

524
00:23:28,800 --> 00:23:29,960
Speaker 3: But I think the thing is as well.

525
00:23:30,040 --> 00:23:32,320
Speaker 2: Is so much about this aiag I think is not

526
00:23:32,359 --> 00:23:34,679
Speaker 2: about what it can actually do. Yeah, the best fiction

527
00:23:34,880 --> 00:23:37,919
Speaker 2: about AGI, girlfriend Sarah showed me the kill Switch X

528
00:23:37,960 --> 00:23:41,720
Speaker 2: Files episode. Fantastic AGI episode. It's scary because it's not

529
00:23:41,800 --> 00:23:44,000
Speaker 2: about the people making it. It's about the computer itself

530
00:23:44,040 --> 00:23:46,679
Speaker 2: and the intentions spilling into it. A lot of what

531
00:23:46,720 --> 00:23:49,640
Speaker 2: the AGI discussion now is and it will auto make jobs.

532
00:23:49,680 --> 00:23:51,640
Speaker 2: And that's the last time I'm going to think about

533
00:23:51,640 --> 00:23:56,800
Speaker 2: it before I say, here's anthropic and it's and I

534
00:23:56,840 --> 00:23:58,679
Speaker 2: think what it is is there are discussions to be

535
00:23:58,720 --> 00:24:00,880
Speaker 2: had around what this stuff could do. If AI could

536
00:24:00,920 --> 00:24:02,520
Speaker 2: do the things that they're saying it could do, if

537
00:24:02,520 --> 00:24:04,719
Speaker 2: it could replace jobs, it would be doing it. But

538
00:24:04,800 --> 00:24:08,080
Speaker 2: also it would actually require a change in society. It's

539
00:24:08,119 --> 00:24:09,919
Speaker 2: almost as if they're like they want all of the

540
00:24:10,000 --> 00:24:13,480
Speaker 2: profits from all of the exposure and the ability to

541
00:24:13,520 --> 00:24:16,360
Speaker 2: lay people off without actually doing anything to earn it.

542
00:24:16,480 --> 00:24:18,439
Speaker 2: And it comes back to what you said about the

543
00:24:18,480 --> 00:24:21,520
Speaker 2: people running this shit aren't even trying to build AI.

544
00:24:21,880 --> 00:24:25,320
Speaker 2: They're not Mark Zuckerberg's building a Manhattan size get data

545
00:24:25,359 --> 00:24:27,680
Speaker 2: center to build superintelligence.

546
00:24:28,359 --> 00:24:30,200
Speaker 1: I wonder though, when you say, and you guys ask

547
00:24:30,280 --> 00:24:34,440
Speaker 1: you to this. I wonder when you when when you

548
00:24:34,480 --> 00:24:36,320
Speaker 1: talk about the Google could have done this or should

549
00:24:36,359 --> 00:24:38,080
Speaker 1: have done this, And then it's a great point that

550
00:24:38,119 --> 00:24:42,080
Speaker 1: you made about that it is the way that it communicates,

551
00:24:42,080 --> 00:24:44,920
Speaker 1: because what I wonder is, you're all expert, you're all

552
00:24:45,040 --> 00:24:52,040
Speaker 1: native early adopters, native to not only the Internet, but

553
00:24:52,160 --> 00:24:56,240
Speaker 1: native to tech, and so sure you could you have

554
00:24:56,520 --> 00:25:01,919
Speaker 1: used Alda Vista to do oh yeah, ge for fox sake, baby,

555
00:25:01,920 --> 00:25:03,760
Speaker 1: But I'm saying you could have used all that, but

556
00:25:03,920 --> 00:25:07,639
Speaker 1: for generations of people who aren't literate, in a million,

557
00:25:07,720 --> 00:25:12,240
Speaker 1: billions of people exactly, even if that was possible through

558
00:25:12,280 --> 00:25:14,840
Speaker 1: Google image search. But you ever try Google image watch

559
00:25:14,880 --> 00:25:16,639
Speaker 1: people try to do a Google image searching.

560
00:25:17,200 --> 00:25:17,719
Speaker 3: I agree.

561
00:25:17,800 --> 00:25:22,560
Speaker 1: I'm thinking this is all making it for people friendly

562
00:25:22,600 --> 00:25:25,159
Speaker 1: to them, as you said, it easy for them, and

563
00:25:25,200 --> 00:25:26,800
Speaker 1: I wonder if that's.

564
00:25:26,160 --> 00:25:27,560
Speaker 3: That's exactly what I'm saying.

565
00:25:27,680 --> 00:25:30,560
Speaker 2: That's why people like chat GPT. It's not because it's

566
00:25:30,560 --> 00:25:32,960
Speaker 2: this amazing product, it's because it does what people go

567
00:25:33,040 --> 00:25:34,080
Speaker 2: to Google that's hard.

568
00:25:34,600 --> 00:25:41,879
Speaker 1: But communicating well in an inviting, seductive way is very challenging. Yes,

569
00:25:42,560 --> 00:25:45,119
Speaker 1: So for text people to do that to regular people

570
00:25:45,480 --> 00:25:48,040
Speaker 1: is the amazing thing it's almost when you're saying Google

571
00:25:48,119 --> 00:25:49,160
Speaker 1: invented this tech.

572
00:25:49,800 --> 00:25:52,439
Speaker 2: The attention the twenty seventeen Attention is All you Need

573
00:25:52,480 --> 00:25:53,880
Speaker 2: paper was eight Google scientists.

574
00:25:53,880 --> 00:25:54,760
Speaker 3: I just look this up.

575
00:25:54,800 --> 00:25:57,400
Speaker 2: I thought it was several, but it's like, no, shousea.

576
00:25:57,400 --> 00:25:59,480
Speaker 2: They ended up paying like two billion dollars of character

577
00:26:00,040 --> 00:26:02,280
Speaker 2: just to bring him back. Google had this technology and

578
00:26:02,720 --> 00:26:06,040
Speaker 2: had this movement just been we're going to make inference

579
00:26:06,080 --> 00:26:09,119
Speaker 2: of meaning better exactly what you're talking about, it would

580
00:26:09,119 --> 00:26:11,960
Speaker 2: not be this big story. The thing is they need

581
00:26:12,000 --> 00:26:14,960
Speaker 2: it to be what you were describing our use cases good, bad, whatever.

582
00:26:15,040 --> 00:26:17,320
Speaker 2: They are things that people use it for the actual

583
00:26:17,320 --> 00:26:21,080
Speaker 2: things they're describing on as a PhD level intelligence, it's

584
00:26:21,119 --> 00:26:23,520
Speaker 2: going to solve physics. And it's like, no, it's not.

585
00:26:23,840 --> 00:26:26,920
Speaker 2: But if they say, what if Google Search worked, they

586
00:26:26,960 --> 00:26:29,520
Speaker 2: can't make a trazillion dollar They can't be like this

587
00:26:29,680 --> 00:26:31,879
Speaker 2: is going to be everything we need. We're going to

588
00:26:31,920 --> 00:26:34,760
Speaker 2: build data centers. It's just because what you're describing, Brian

589
00:26:34,880 --> 00:26:36,760
Speaker 2: is the most evil thing.

590
00:26:36,800 --> 00:26:41,240
Speaker 1: In going back to Mike Er, yeah, because I think

591
00:26:41,280 --> 00:26:45,399
Speaker 1: what I think in all these things, what I've learned

592
00:26:45,960 --> 00:26:49,679
Speaker 1: just being curious is to look at the incentives.

593
00:26:49,880 --> 00:26:50,879
Speaker 3: Yes, that's what I mean.

594
00:26:50,960 --> 00:26:54,840
Speaker 1: And Google was de incentivized to disincentivized to make search

595
00:26:54,880 --> 00:26:58,359
Speaker 1: work a bit for the user. Yeah, because of the

596
00:26:58,400 --> 00:27:00,960
Speaker 1: profit agenda, right you should, Oh, they think should make

597
00:27:01,000 --> 00:27:03,680
Speaker 1: a fucking profit. But here's the thing, in a weird way,

598
00:27:03,800 --> 00:27:06,399
Speaker 1: them not making a profit is better for the users. Yes,

599
00:27:06,760 --> 00:27:09,960
Speaker 1: and the Google wasn't. But I'm sure it's all on

600
00:27:09,960 --> 00:27:15,640
Speaker 1: the Google paper that nobody's fucking reading except I'm talking

601
00:27:15,680 --> 00:27:19,520
Speaker 1: about for the user. I'm trying to You're raising these

602
00:27:19,520 --> 00:27:21,560
Speaker 1: amazing questions, but if I try to think about how

603
00:27:21,560 --> 00:27:24,760
Speaker 1: to answer them, it's that these companies are like in

604
00:27:24,800 --> 00:27:26,879
Speaker 1: the early days of social media and all this stuff.

605
00:27:26,920 --> 00:27:29,200
Speaker 1: In the beginning, they try to super serve the user

606
00:27:29,280 --> 00:27:31,800
Speaker 1: to get you addicted to it so that then they

607
00:27:31,800 --> 00:27:33,879
Speaker 1: could But the incentive structure is what you got to

608
00:27:33,920 --> 00:27:36,760
Speaker 1: look at. And obviously Microsoft's incentive structure has been locked

609
00:27:36,760 --> 00:27:38,280
Speaker 1: in for a very long time, and that is for

610
00:27:38,359 --> 00:27:41,240
Speaker 1: the people who have the most stock in Microsoft to

611
00:27:41,280 --> 00:27:43,400
Speaker 1: make the most money one. That's the incentive.

612
00:27:43,119 --> 00:27:45,199
Speaker 5: Struct Google too, to an extent, and that's why they

613
00:27:45,240 --> 00:27:47,040
Speaker 5: had that stuff for that long, but they never because

614
00:27:47,040 --> 00:27:48,600
Speaker 5: they never found a way to integrate it into all

615
00:27:48,600 --> 00:27:50,640
Speaker 5: the different parts of his businesses that matter. That ads part,

616
00:27:50,680 --> 00:27:51,240
Speaker 5: the search.

617
00:27:51,080 --> 00:27:52,680
Speaker 3: Parts do still so they monopoly.

618
00:27:52,720 --> 00:27:54,600
Speaker 5: They just they have a monopoly. They have no reason

619
00:27:54,600 --> 00:27:56,399
Speaker 5: to be. But I don't know if you remember this

620
00:27:56,520 --> 00:27:59,840
Speaker 5: Google Io twenty seventeen, twenty eighteen, even twenty nineteen when

621
00:28:00,160 --> 00:28:03,159
Speaker 5: first show kitchen Sink, which was their version of chat GPT,

622
00:28:03,320 --> 00:28:06,160
Speaker 5: but it wasn't as conversational. It was you can ask

623
00:28:06,200 --> 00:28:08,600
Speaker 5: this app to come up with ways to learn about

624
00:28:08,640 --> 00:28:10,480
Speaker 5: a new hobby or to plan a thing for you.

625
00:28:10,640 --> 00:28:13,240
Speaker 5: Before chat GPC even was known widely to everyone, I

626
00:28:13,240 --> 00:28:15,320
Speaker 5: think it was a I wasn't really even a thing,

627
00:28:15,800 --> 00:28:18,920
Speaker 5: and it just wasn't a chat interface. And I think

628
00:28:18,960 --> 00:28:22,120
Speaker 5: to Brian's point, the incentive that chat GPT has brought

629
00:28:22,160 --> 00:28:23,840
Speaker 5: to people and to my parents, who by the way,

630
00:28:23,880 --> 00:28:26,919
Speaker 5: discovered chat GPT last year, very annoying to me. But

631
00:28:27,080 --> 00:28:30,200
Speaker 5: it's like it's so much easier. It brings them into

632
00:28:30,320 --> 00:28:32,600
Speaker 5: technology in a way that technology used to be kind

633
00:28:32,600 --> 00:28:34,680
Speaker 5: of looking down on people for not knowing things, and

634
00:28:35,040 --> 00:28:37,240
Speaker 5: you deal you do away with that with the chat bot.

635
00:28:37,480 --> 00:28:40,600
Speaker 5: My parents not only like feel so hip now, which

636
00:28:40,680 --> 00:28:43,719
Speaker 5: sorry Mom and dad, you're not, but but also like

637
00:28:43,760 --> 00:28:47,240
Speaker 5: there's people who seek comfort in the companionship brought on

638
00:28:47,400 --> 00:28:51,720
Speaker 5: by AI chatbots like chests differently, they are amazing. They

639
00:28:51,880 --> 00:28:55,000
Speaker 5: dance at their age and I love that. But the

640
00:28:55,040 --> 00:28:56,600
Speaker 5: thing is, if you look at the use of jen

641
00:28:56,680 --> 00:28:58,440
Speaker 5: a I from the last and I think I talked

642
00:28:58,440 --> 00:29:01,000
Speaker 5: about this on that Kevin Ruse episode you're on, but

643
00:29:01,800 --> 00:29:04,880
Speaker 5: the use of chat aih services has to change, right.

644
00:29:04,960 --> 00:29:08,080
Speaker 5: Used to be like very interest based on very search basements.

645
00:29:08,160 --> 00:29:11,160
Speaker 5: Now companionship base as the top few uses. And that's

646
00:29:11,160 --> 00:29:13,360
Speaker 5: why people are drawn to it. And I think that

647
00:29:14,440 --> 00:29:16,400
Speaker 5: my last point that came up really when I was

648
00:29:16,480 --> 00:29:19,120
Speaker 5: like listening to you talk ed, is that like the

649
00:29:19,280 --> 00:29:22,400
Speaker 5: incentive for them to push towards like, yes, let's go

650
00:29:22,440 --> 00:29:25,000
Speaker 5: towards a GI. It's not just like laying off people.

651
00:29:25,040 --> 00:29:27,840
Speaker 5: It's also who can get there first. It's the race

652
00:29:27,880 --> 00:29:31,160
Speaker 5: of like it's like it's the tech AI ego thing,

653
00:29:31,240 --> 00:29:33,640
Speaker 5: that tech the text ceo ego thing, and then from

654
00:29:33,640 --> 00:29:35,920
Speaker 5: the ego standpoint, then they pushed down to profits.

655
00:29:36,200 --> 00:29:39,840
Speaker 1: So many businesses are like so many businesses are already

656
00:29:41,200 --> 00:29:44,240
Speaker 1: sort of the industries are already acting in bad faith.

657
00:29:44,360 --> 00:29:47,240
Speaker 1: Oh yes, yeah, like okay, look at them. Here's here's

658
00:29:47,560 --> 00:29:49,280
Speaker 1: I'll give you, not a youth, like a real industry

659
00:29:49,280 --> 00:29:51,200
Speaker 1: that I think will be transformed by it. And I

660
00:29:51,200 --> 00:29:54,080
Speaker 1: think that's not a bad thing. Is like money management.

661
00:29:55,200 --> 00:30:00,640
Speaker 1: Money management, which is a billions of AI AI already

662
00:30:00,200 --> 00:30:06,120
Speaker 1: can replace delineate between Yeah, though, but well you can,

663
00:30:06,200 --> 00:30:08,600
Speaker 1: you can do that. But I'm saying, I know that

664
00:30:08,640 --> 00:30:12,080
Speaker 1: whole business, That whole business is about a front money management.

665
00:30:12,160 --> 00:30:14,200
Speaker 1: I'm not talking about the high net worth I'm saying

666
00:30:14,440 --> 00:30:18,080
Speaker 1: in general, people who use for their retirement accounts money

667
00:30:18,080 --> 00:30:22,880
Speaker 1: managers well front all that stuff. But within those companies

668
00:30:23,240 --> 00:30:26,480
Speaker 1: they're they're they're front facing language of humans who are

669
00:30:26,480 --> 00:30:29,360
Speaker 1: just trying to keep you invested. They don't know that stuff.

670
00:30:29,400 --> 00:30:31,600
Speaker 1: They are not offering value really, and they're taking his

671
00:30:31,640 --> 00:30:34,560
Speaker 1: big percentage from regular people trying to say it for

672
00:30:34,600 --> 00:30:37,560
Speaker 1: their retirement, and they're bleeding off my and in the

673
00:30:37,680 --> 00:30:40,080
Speaker 1: end they're going I was listening to.

674
00:30:40,080 --> 00:30:42,720
Speaker 2: Like, No, I'm smiling because you were right already this

675
00:30:42,840 --> 00:30:44,960
Speaker 2: was the twenty fifteen through twenty twenty wealth.

676
00:30:45,040 --> 00:30:46,360
Speaker 1: No, I know that all that stuff, but I'm talking

677
00:30:46,400 --> 00:30:49,320
Speaker 1: about even now like big banks, like the big banks,

678
00:30:49,320 --> 00:30:51,840
Speaker 1: I'm not talking about their their AI front. I'm saying

679
00:30:51,880 --> 00:30:56,120
Speaker 1: that I was listening to a talk given by Josh

680
00:30:56,200 --> 00:30:59,760
Speaker 1: Brown is uh and I one of his partners, I think, okay,

681
00:31:00,000 --> 00:31:07,520
Speaker 1: And he was talking about how essentially all of the

682
00:31:07,560 --> 00:31:09,640
Speaker 1: back end of all that stuff, meaning you might still

683
00:31:09,640 --> 00:31:13,040
Speaker 1: have a person talking to the user to but everything

684
00:31:13,040 --> 00:31:15,560
Speaker 1: else is going to be done by I just just

685
00:31:16,960 --> 00:31:18,240
Speaker 1: it is Michael Batnik from his company.

686
00:31:18,240 --> 00:31:21,280
Speaker 2: He was talking because here's the thing with that is

687
00:31:21,880 --> 00:31:25,080
Speaker 2: from my knowledge of basically financial regulation, they don't want

688
00:31:25,640 --> 00:31:27,960
Speaker 2: LLM touching much of this. They there's a lot of

689
00:31:27,960 --> 00:31:31,440
Speaker 2: stuff within financial research happening now with jener I, a

690
00:31:31,440 --> 00:31:34,760
Speaker 2: ton of companies doing like insanely high compute burn to

691
00:31:34,840 --> 00:31:38,960
Speaker 2: do these massive kind of like evaluations of stuff. I

692
00:31:38,960 --> 00:31:41,840
Speaker 2: don't know if anyone wants to touch the money with llms.

693
00:31:41,880 --> 00:31:44,920
Speaker 2: And they've actually been quite resistant to it, partly because

694
00:31:44,920 --> 00:31:46,760
Speaker 2: they don't know how they work, Like they truly don't know,

695
00:31:46,800 --> 00:31:49,200
Speaker 2: I know, the black box thing, yeah, yea yeah, And

696
00:31:49,240 --> 00:31:53,040
Speaker 2: so it's like a lot of these things. Also, when

697
00:31:53,080 --> 00:31:55,720
Speaker 2: are they gonna happen though, because they've been saying this

698
00:31:55,800 --> 00:31:56,680
Speaker 2: for two years.

699
00:31:56,720 --> 00:31:58,400
Speaker 1: But do you think there's a scenario, But do you

700
00:31:58,400 --> 00:32:02,000
Speaker 1: think there's a scenario where this this goes this goes away.

701
00:32:02,120 --> 00:32:03,360
Speaker 3: I don't think it goes I.

702
00:32:05,080 --> 00:32:06,680
Speaker 1: Don't think it's gonna do you think it's going to

703
00:32:07,000 --> 00:32:09,960
Speaker 1: reveal itself as as being fraudulent for what.

704
00:32:10,080 --> 00:32:14,400
Speaker 2: I'll explain, I think you're going to see what call

705
00:32:14,440 --> 00:32:18,560
Speaker 2: him my shot. So I believe that open ai will

706
00:32:18,680 --> 00:32:21,480
Speaker 2: is an ongoing concern eventually go into nothingness.

707
00:32:21,520 --> 00:32:23,840
Speaker 3: Matt Hughes, my editor, believes they'll become a patent. Sal

708
00:32:23,840 --> 00:32:25,000
Speaker 3: I actually think it's an amazing thing.

709
00:32:25,440 --> 00:32:27,920
Speaker 2: I think that what we experience of large language models

710
00:32:27,960 --> 00:32:30,280
Speaker 2: will vastly pull back. I think there will be rate

711
00:32:30,360 --> 00:32:33,800
Speaker 2: limits that, as there will be rate limits on GPT,

712
00:32:34,000 --> 00:32:36,600
Speaker 2: people are going to be horrifyingly sad because those.

713
00:32:36,440 --> 00:32:38,400
Speaker 1: Comparive companions are going to go away, And they're not

714
00:32:38,440 --> 00:32:38,840
Speaker 1: going to go.

715
00:32:38,760 --> 00:32:41,560
Speaker 2: Away, they're just going to be much, much, much more limited.

716
00:32:41,800 --> 00:32:43,960
Speaker 2: And I think that everything we see today the kind

717
00:32:44,000 --> 00:32:45,960
Speaker 2: of and you look in any of the reds behind

718
00:32:45,960 --> 00:32:48,800
Speaker 2: any of the serious like GPT's, they're all kind of

719
00:32:48,800 --> 00:32:51,160
Speaker 2: saying like, yeah, we know that the abundance the free

720
00:32:51,240 --> 00:32:54,400
Speaker 2: ride is over. So no, it's not going away, but

721
00:32:54,440 --> 00:32:56,480
Speaker 2: you're not going to hear about it constantly, And everything

722
00:32:56,520 --> 00:32:59,360
Speaker 2: you use today is going to be so severely rate limited,

723
00:32:59,600 --> 00:33:03,280
Speaker 2: or those companies that are charging for generative AI things

724
00:33:03,400 --> 00:33:06,680
Speaker 2: beneath the surface. All of the API rates behind these companies.

725
00:33:06,720 --> 00:33:08,000
Speaker 2: So the things that you plug in to run the

726
00:33:08,040 --> 00:33:12,479
Speaker 2: models are vastly subsidized by big tech and by the companies.

727
00:33:13,080 --> 00:33:15,440
Speaker 1: Alda Vista goes away and but Google takes over.

728
00:33:16,120 --> 00:33:19,560
Speaker 2: Well, Google Gemini exists, but the Gemini request perhaps don't

729
00:33:19,640 --> 00:33:21,080
Speaker 2: hit the Lelem as much they.

730
00:33:21,080 --> 00:33:22,200
Speaker 1: Have using it as a parallel.

731
00:33:22,320 --> 00:33:27,800
Speaker 2: Yeah, but it won't be this big thing you hear

732
00:33:27,840 --> 00:33:28,720
Speaker 2: about all the time.

733
00:33:28,800 --> 00:33:29,520
Speaker 3: I think you were going to.

734
00:33:29,880 --> 00:33:31,360
Speaker 1: Of course, it'll just be the back end of lots

735
00:33:31,400 --> 00:33:31,760
Speaker 1: of stuff.

736
00:33:32,480 --> 00:33:34,800
Speaker 5: No, it would just be something that sits large skills.

737
00:33:34,840 --> 00:33:35,560
Speaker 2: It's also not.

738
00:33:35,520 --> 00:33:38,160
Speaker 3: Good as a back end. Large language models are not

739
00:33:38,200 --> 00:33:38,720
Speaker 3: good back.

740
00:33:38,560 --> 00:33:40,240
Speaker 1: At they're good at talking to you.

741
00:33:41,640 --> 00:33:43,480
Speaker 2: Can they can divine stuff as well.

742
00:33:43,640 --> 00:33:46,240
Speaker 4: One thing we're kind of circling on the consumer level

743
00:33:46,240 --> 00:33:48,680
Speaker 4: that we're not talking about is it's still a novelty now.

744
00:33:48,680 --> 00:33:49,360
Speaker 3: I think it's going to.

745
00:33:49,360 --> 00:33:51,120
Speaker 4: Be continued to be used. I do think that people

746
00:33:51,160 --> 00:33:54,400
Speaker 4: are going to continue to make lists and scheduling and

747
00:33:54,520 --> 00:33:58,000
Speaker 4: summarize summari like you know, what's a good vacation.

748
00:33:57,720 --> 00:33:59,520
Speaker 2: And they are on device models that we are able

749
00:33:59,560 --> 00:33:59,920
Speaker 2: to do that.

750
00:34:00,520 --> 00:34:02,280
Speaker 4: I just think that right now it's such a big

751
00:34:02,320 --> 00:34:04,880
Speaker 4: deal that everyone's using it because it's cool, because they

752
00:34:04,960 --> 00:34:07,000
Speaker 4: you know, your parents hear about it, because you're told

753
00:34:07,000 --> 00:34:09,600
Speaker 4: you should use it for work. And I do think

754
00:34:09,600 --> 00:34:11,239
Speaker 4: that it'll stick around. I do think there'll be a

755
00:34:11,280 --> 00:34:13,880
Speaker 4: contraction in the sense that you know, it'll be a

756
00:34:13,880 --> 00:34:15,920
Speaker 4: cool thing. But I think in fifteen years it's no

757
00:34:15,960 --> 00:34:18,360
Speaker 4: longer gonna be as funny to produce, like a picture

758
00:34:18,400 --> 00:34:21,360
Speaker 4: of a pig with like Mickey Mouse's head and three boobs.

759
00:34:21,680 --> 00:34:22,600
Speaker 4: And I feel like, now.

760
00:34:22,480 --> 00:34:26,480
Speaker 2: That's a big, big Some comedy is timeless, like, but

761
00:34:26,520 --> 00:34:26,839
Speaker 2: I just.

762
00:34:26,800 --> 00:34:29,000
Speaker 4: Don't imagine that in like fifteen years having the same

763
00:34:29,120 --> 00:34:32,680
Speaker 4: novelty as it does, or five years, ten years.

764
00:34:32,880 --> 00:34:35,200
Speaker 2: If you take away all the headlines, if you take

765
00:34:35,239 --> 00:34:37,400
Speaker 2: away all of the money and you actually look at

766
00:34:37,400 --> 00:34:40,960
Speaker 2: what's there, it is everything. What you're describing is probably

767
00:34:40,960 --> 00:34:44,120
Speaker 2: the most useful thing. It's like, do I know this, Okay,

768
00:34:44,120 --> 00:34:46,279
Speaker 2: this seems plausible. I'm gonna double check it. It's kind

769
00:34:46,280 --> 00:34:48,040
Speaker 2: of like what in Karta could have been. I don't

770
00:34:48,040 --> 00:34:51,240
Speaker 2: even mean that sarcastically. I as a very cool child,

771
00:34:51,360 --> 00:34:53,040
Speaker 2: I was very cool. Would sit them then caught for

772
00:34:53,120 --> 00:34:54,160
Speaker 2: hours reading stuff.

773
00:34:53,920 --> 00:34:55,560
Speaker 3: Because it was kind of good. Why you have access

774
00:34:55,600 --> 00:34:56,000
Speaker 3: to everything?

775
00:34:56,080 --> 00:34:57,959
Speaker 2: And I think human beings a curious so of course

776
00:34:57,960 --> 00:35:00,640
Speaker 2: they're gonna talk to it if it talks back. I

777
00:35:00,760 --> 00:35:03,200
Speaker 2: just think right now, there is no business model. That's

778
00:35:03,200 --> 00:35:05,480
Speaker 2: the biggest one. The biggest one is there really is

779
00:35:05,520 --> 00:35:08,600
Speaker 2: no business model. Ads do not work, ads are not going.

780
00:35:08,800 --> 00:35:11,200
Speaker 2: How do you put you inject ads.

781
00:35:10,960 --> 00:35:12,600
Speaker 3: Within an L and M. Look at what happened with

782
00:35:12,640 --> 00:35:13,760
Speaker 3: grog grok.

783
00:35:13,800 --> 00:35:16,239
Speaker 2: Happened because they tried to make us let's make it

784
00:35:16,360 --> 00:35:19,239
Speaker 2: just how we crank up this racism dial and like,

785
00:35:19,320 --> 00:35:21,160
Speaker 2: how do we mess with this system prompt. But the

786
00:35:21,200 --> 00:35:25,279
Speaker 2: thing is those subtle changes for even advertising will be bad.

787
00:35:25,320 --> 00:35:27,440
Speaker 2: Perplexity's been talking about there and ads for a fucking year.

788
00:35:27,480 --> 00:35:30,920
Speaker 2: Not heard much of that aravant, and it's like, on

789
00:35:30,960 --> 00:35:34,080
Speaker 2: some level, regardless of how useful it is, the economics

790
00:35:34,120 --> 00:35:38,000
Speaker 2: do not make sense. They're nothing like an uber. Uber Yes,

791
00:35:38,080 --> 00:35:40,279
Speaker 2: you well know Uber was a complete fucking and it

792
00:35:40,400 --> 00:35:42,799
Speaker 2: still barely makes sense. They're raising another one point two

793
00:35:42,840 --> 00:35:45,560
Speaker 2: billion dollars, but you can at least tell someone exactly

794
00:35:45,600 --> 00:35:48,719
Speaker 2: what uber is and why you'd use it and it's

795
00:35:48,840 --> 00:35:52,480
Speaker 2: just kind of chugging along through necessity. I don't know

796
00:35:52,520 --> 00:35:57,719
Speaker 2: how necessary. Chat GPT is a've large language model, a

797
00:35:57,840 --> 00:36:01,960
Speaker 2: Google Gemini, and Google is claiming that doing efficiency stuff

798
00:36:02,160 --> 00:36:04,920
Speaker 2: that could lost. I think it will be heavily right limited.

799
00:36:05,120 --> 00:36:08,760
Speaker 1: I'm certain you are all right about the business models

800
00:36:08,800 --> 00:36:11,759
Speaker 1: and the viability of this from a business standpoint. You

801
00:36:11,800 --> 00:36:13,640
Speaker 1: know so much more than I do. I'm learning and

802
00:36:13,640 --> 00:36:17,719
Speaker 1: it's fascinating as a user of it who is not

803
00:36:17,960 --> 00:36:21,040
Speaker 1: on the inside of the business. I my prediction is

804
00:36:21,080 --> 00:36:22,360
Speaker 1: you're dead wrong.

805
00:36:22,239 --> 00:36:22,560
Speaker 4: No, Brent.

806
00:36:22,719 --> 00:36:26,360
Speaker 1: I it's going to become the dominant thing in most

807
00:36:26,360 --> 00:36:29,600
Speaker 1: of society in lots of ways. I think here's people

808
00:36:29,600 --> 00:36:31,120
Speaker 1: are going to use it and it's going to be

809
00:36:31,200 --> 00:36:34,680
Speaker 1: part of them, like William Gibson predicted a really long

810
00:36:34,719 --> 00:36:37,239
Speaker 1: time ago. Like I think that it's fine that. I

811
00:36:37,239 --> 00:36:39,480
Speaker 1: think it's bad at creative things, the thing people think

812
00:36:39,520 --> 00:36:41,120
Speaker 1: it's good at. I don't. It's bad at that. I

813
00:36:41,160 --> 00:36:43,360
Speaker 1: don't think you can write a story. Yet none of

814
00:36:43,360 --> 00:36:45,640
Speaker 1: them can convincingly write a story like drugs are good,

815
00:36:46,239 --> 00:36:48,919
Speaker 1: Like there are lots of that stuff that's not there yet.

816
00:36:49,000 --> 00:36:51,200
Speaker 1: I have no idea. Again, you all know way better

817
00:36:51,239 --> 00:36:53,480
Speaker 1: than I did the science behind it, but you're asking

818
00:36:53,480 --> 00:36:58,439
Speaker 1: about working out before you could. There are ninety five

819
00:36:58,480 --> 00:37:01,399
Speaker 1: percent of people who are trainers can't do as good

820
00:37:01,400 --> 00:37:04,320
Speaker 1: a job of programming, and you could play different aias

821
00:37:04,320 --> 00:37:06,520
Speaker 1: against each other and asking questions.

822
00:37:06,840 --> 00:37:09,360
Speaker 5: That's also ninety percent of them are influencers with no

823
00:37:09,480 --> 00:37:10,240
Speaker 5: serious backgrounds.

824
00:37:10,280 --> 00:37:11,680
Speaker 1: But I'm saying, even if you talk to sign but

825
00:37:12,280 --> 00:37:15,239
Speaker 1: because you can show programming, it can track for it

826
00:37:15,239 --> 00:37:17,040
Speaker 1: can just now you may, you may, And I'm sure

827
00:37:17,040 --> 00:37:20,839
Speaker 1: you're correct. That's not the AI doing it. It's other

828
00:37:20,960 --> 00:37:23,000
Speaker 1: people could have done. But the way I I can

829
00:37:23,080 --> 00:37:29,239
Speaker 1: program and interact with you and allow you to catch up.

830
00:37:29,520 --> 00:37:31,319
Speaker 1: If someone asked me all day long, people are asking me,

831
00:37:31,719 --> 00:37:35,520
Speaker 1: wait the question about programming. Well, I can't program for you.

832
00:37:35,680 --> 00:37:39,040
Speaker 1: I don't know you well enough. But if we talk

833
00:37:39,160 --> 00:37:42,000
Speaker 1: generally about what your goals are, I could definitely talk

834
00:37:42,080 --> 00:37:46,520
Speaker 1: to chat, GBT or Claude and build something that you

835
00:37:46,520 --> 00:37:48,320
Speaker 1: could then iterates.

836
00:37:47,719 --> 00:37:50,279
Speaker 2: Such engine were describing the iterations of such.

837
00:37:50,480 --> 00:37:52,920
Speaker 1: Yeah, but it's packaged in this way. Now go ahead.

838
00:37:53,080 --> 00:37:55,560
Speaker 5: The thing that three of us are going to tell

839
00:37:55,600 --> 00:37:57,200
Speaker 5: you are at different levels. I think it's coming to

840
00:37:57,200 --> 00:38:00,000
Speaker 5: you at the business model. The very like micro right

841
00:38:00,239 --> 00:38:02,319
Speaker 5: and sure, and yeah, that's the way kind of how

842
00:38:02,320 --> 00:38:04,279
Speaker 5: it's got to play out financially, and druggers come in

843
00:38:04,320 --> 00:38:06,640
Speaker 5: with the medium level of the use case and everything,

844
00:38:06,640 --> 00:38:08,280
Speaker 5: and I'm going to tell you that at the top level,

845
00:38:08,360 --> 00:38:10,759
Speaker 5: I draw another parallel for you, which is two things

846
00:38:10,840 --> 00:38:15,000
Speaker 5: come to mind. One is how bitcoin and crypto very

847
00:38:15,040 --> 00:38:17,480
Speaker 5: exciting everyone found in novels y factor and it ran

848
00:38:17,560 --> 00:38:20,239
Speaker 5: for everyone wanted to make NFTs and crypto a thing.

849
00:38:20,360 --> 00:38:22,560
Speaker 5: And then now is kind of dialed back down to

850
00:38:22,640 --> 00:38:24,920
Speaker 5: a less fever pitch and more of a regular body

851
00:38:24,920 --> 00:38:28,279
Speaker 5: temperature pitch. The macro metaphorical level is what I'm going

852
00:38:28,320 --> 00:38:30,360
Speaker 5: to say. I think AI will dial back down to

853
00:38:30,440 --> 00:38:34,839
Speaker 5: that Norman normal regulatory sort of body temperature. And then

854
00:38:34,960 --> 00:38:39,359
Speaker 5: to draw another parallel tinder was everyone was making their app.

855
00:38:39,400 --> 00:38:41,880
Speaker 5: The tinder of this tinder of real estate and we

856
00:38:41,880 --> 00:38:44,319
Speaker 5: were describing is like an interface that works really well

857
00:38:44,400 --> 00:38:47,800
Speaker 5: for something like a use case. The Chad GBT model

858
00:38:48,200 --> 00:38:50,239
Speaker 5: is an interface that works really well for question and

859
00:38:50,320 --> 00:38:53,160
Speaker 5: answer is seeking help assisting you with things that might

860
00:38:53,200 --> 00:38:56,000
Speaker 5: never go away, that might just get built into every app.

861
00:38:56,040 --> 00:38:59,759
Speaker 5: As access to LLMS becomes easier for developers, they'll build

862
00:38:59,800 --> 00:39:01,719
Speaker 5: it in to the Bank of America, Chat Bottle build

863
00:39:01,760 --> 00:39:03,680
Speaker 5: it into everything, and so I think that's where it

864
00:39:03,719 --> 00:39:06,920
Speaker 5: balances out eventually over time, maybe through rate limits. I

865
00:39:06,960 --> 00:39:08,400
Speaker 5: don't know that that's going to be the way. I

866
00:39:08,400 --> 00:39:10,040
Speaker 5: think some consolidation might happen.

867
00:39:10,160 --> 00:39:12,360
Speaker 3: You have right limits own actually accessing your pa R.

868
00:39:12,400 --> 00:39:14,319
Speaker 5: The question back and forth for the people who are

869
00:39:14,400 --> 00:39:16,759
Speaker 5: using chat gubt right or yeah. I think that will

870
00:39:16,760 --> 00:39:19,200
Speaker 5: come too, But I think eventually we're talking like five

871
00:39:19,239 --> 00:39:21,239
Speaker 5: years with Druggers. I think with the rate limits, maybe

872
00:39:21,239 --> 00:39:23,120
Speaker 5: in the short term, but I think even longer term

873
00:39:23,160 --> 00:39:27,520
Speaker 5: than that, we're seeing that might eventually go away. Those

874
00:39:27,560 --> 00:39:29,320
Speaker 5: apps may not even exist stand alone.

875
00:39:29,440 --> 00:39:31,880
Speaker 2: Yeah, and I think I actually don't know if I

876
00:39:31,920 --> 00:39:34,400
Speaker 2: fully disagree with you about everything you're saying. It's just

877
00:39:34,480 --> 00:39:36,880
Speaker 2: the scale of what we're talking about might be different.

878
00:39:37,120 --> 00:39:40,040
Speaker 2: Everyone having a large language model to access. Just saying

879
00:39:40,040 --> 00:39:43,000
Speaker 2: that this is the future we're talking about doesn't really

880
00:39:43,280 --> 00:39:46,680
Speaker 2: change that much. I don't like the economics effect. I

881
00:39:46,719 --> 00:39:48,880
Speaker 2: know I'm going to do the business bullshit thing I do,

882
00:39:49,000 --> 00:39:53,719
Speaker 2: but it's the economic effects are quite limited right now.

883
00:39:53,840 --> 00:39:54,040
Speaker 3: Now.

884
00:39:54,080 --> 00:39:55,959
Speaker 2: If you're saying everyone will have a large language model

885
00:39:56,000 --> 00:39:58,240
Speaker 2: will be hamstrung in some way or what have you. Fine,

886
00:39:58,280 --> 00:40:00,160
Speaker 2: I can buy that for good or for bad. I

887
00:40:00,160 --> 00:40:02,920
Speaker 2: could see that happening. I just don't think that it

888
00:40:03,080 --> 00:40:06,000
Speaker 2: goes much further than what we see today. And I

889
00:40:06,000 --> 00:40:10,280
Speaker 2: think what you're describing is what Google Search should have become.

890
00:40:10,760 --> 00:40:13,200
Speaker 2: And like that was what I remember when Bard came out.

891
00:40:13,200 --> 00:40:14,359
Speaker 3: I wrote about this and was kind of.

892
00:40:14,280 --> 00:40:17,080
Speaker 2: Like, surely what chat GPT is is what search was

893
00:40:17,120 --> 00:40:17,480
Speaker 2: meant to.

894
00:40:17,400 --> 00:40:19,879
Speaker 1: Be, right as the com Brothers said, you know, sure

895
00:40:19,880 --> 00:40:24,320
Speaker 1: if if if a frog you know, had had wings,

896
00:40:24,520 --> 00:40:26,800
Speaker 1: had wings, it wouldn't bump its ass hopping, you know

897
00:40:26,800 --> 00:40:27,200
Speaker 1: what I mean?

898
00:40:28,960 --> 00:40:31,799
Speaker 4: And so and I'll say that, you know, I think

899
00:40:31,840 --> 00:40:35,120
Speaker 4: that even as it's completely absorbed into our culture, we're

900
00:40:35,120 --> 00:40:37,279
Speaker 4: still going to be on the phone listening to an

901
00:40:37,320 --> 00:40:41,520
Speaker 4: AI list medical options, yeah, human, human operator, human, Like

902
00:40:41,560 --> 00:40:42,400
Speaker 4: I don't think that's.

903
00:40:42,239 --> 00:40:44,719
Speaker 2: Going away green seven seven one right, yeah, Like I

904
00:40:44,719 --> 00:40:45,160
Speaker 2: don't think.

905
00:40:45,480 --> 00:40:47,520
Speaker 4: I do think that people are going to be like, yeah, okay,

906
00:40:47,600 --> 00:40:49,600
Speaker 4: I'll talk you through my problems until I hit this

907
00:40:49,640 --> 00:40:51,479
Speaker 4: part with my insurance. And I just want a human

908
00:40:51,560 --> 00:40:54,320
Speaker 4: right right right now. And I don't think that's going.

909
00:40:54,120 --> 00:40:55,120
Speaker 3: Away, No, not at all.

910
00:40:55,200 --> 00:40:58,680
Speaker 2: I actually think we're really more on the same page. Yah,

911
00:40:58,920 --> 00:41:01,880
Speaker 2: it seem because it's like, my whole thing is what

912
00:41:01,920 --> 00:41:04,080
Speaker 2: you're describing is the use case. I think there are

913
00:41:04,080 --> 00:41:05,839
Speaker 2: real harms, but I think we kind of agree where

914
00:41:05,840 --> 00:41:08,480
Speaker 2: the dangers would be. My thing is is that people

915
00:41:08,520 --> 00:41:12,279
Speaker 2: are extrapolating from that to this insane level, like this

916
00:41:12,360 --> 00:41:16,320
Speaker 2: whole they keep talking about agents everywhere. You've got Matthew McConaughey.

917
00:41:15,800 --> 00:41:17,760
Speaker 5: And a But agent is kind of what I'm describing,

918
00:41:17,760 --> 00:41:20,640
Speaker 5: which is like every every service has its own chat

919
00:41:20,680 --> 00:41:22,400
Speaker 5: bot more or less. They're just using different.

920
00:41:22,160 --> 00:41:24,399
Speaker 2: They're just using a different Yeah, And I mean that's

921
00:41:24,480 --> 00:41:26,480
Speaker 2: kind of what like Bank of America already has a

922
00:41:26,560 --> 00:41:27,800
Speaker 2: chat bot and it does it.

923
00:41:27,640 --> 00:41:28,279
Speaker 3: Does not work.

924
00:41:28,440 --> 00:41:31,080
Speaker 2: It's the bottom is when you're trying to search for

925
00:41:31,080 --> 00:41:31,680
Speaker 2: a transaction.

926
00:41:31,880 --> 00:41:34,560
Speaker 1: He's talking about for sure, the human of course we

927
00:41:34,640 --> 00:41:38,600
Speaker 1: all do that. I guess I think it'll fool us.

928
00:41:38,920 --> 00:41:40,120
Speaker 4: That's fair, that's fair.

929
00:41:40,239 --> 00:41:42,799
Speaker 1: I don't think right, I've already fooled a lot of people.

930
00:41:42,600 --> 00:41:45,320
Speaker 4: Very well, people into up killing themselves.

931
00:41:45,360 --> 00:41:47,720
Speaker 1: Oh god, well that's tell your car that old character AI.

932
00:41:47,920 --> 00:41:50,879
Speaker 3: Of course Google paid two billion dollars for them.

933
00:41:51,080 --> 00:41:52,960
Speaker 1: I'm not making an argument that it's this is a

934
00:41:52,960 --> 00:41:58,160
Speaker 1: beneficent as in any way, shape or form. Like Like

935
00:41:58,200 --> 00:41:59,879
Speaker 1: I said, I've been reading about this for so long,

936
00:42:00,200 --> 00:42:03,319
Speaker 1: but I do think that to just sort of That's

937
00:42:03,360 --> 00:42:05,960
Speaker 1: why I brought up the horse and buggy because no,

938
00:42:06,160 --> 00:42:09,520
Speaker 1: the people who wanted the horses right, they were right

939
00:42:09,560 --> 00:42:11,640
Speaker 1: about a lot of stuff about the harms that would do,

940
00:42:11,920 --> 00:42:16,160
Speaker 1: the pollution, the pollution, the noise, the way would take

941
00:42:16,200 --> 00:42:19,200
Speaker 1: us away from our communities, like they were right about

942
00:42:19,239 --> 00:42:22,359
Speaker 1: so much. What they were wrong about was the inevitable

943
00:42:22,440 --> 00:42:23,919
Speaker 1: march of the future in time.

944
00:42:24,160 --> 00:42:26,080
Speaker 5: That's I think the same. Before coming to this podcast,

945
00:42:26,080 --> 00:42:27,839
Speaker 5: I was reading Mike your piece and I was like, oh, yeah,

946
00:42:28,120 --> 00:42:30,880
Speaker 5: are we like in the Industrial Revolution forgetting about the

947
00:42:30,880 --> 00:42:33,480
Speaker 5: agricultural revolution? For getting all the revolutions.

948
00:42:33,000 --> 00:42:35,640
Speaker 1: That came there. Because I've seen it all from when

949
00:42:35,680 --> 00:42:39,360
Speaker 1: I was I remember when AOL showed up and coffee served,

950
00:42:39,400 --> 00:42:40,920
Speaker 1: and I remember mess I was invited on a message

951
00:42:40,920 --> 00:42:42,920
Speaker 1: board where I was fourteen. I'm fifty nine, like this

952
00:42:43,000 --> 00:42:44,799
Speaker 1: guy I knew had a message board in New York

953
00:42:44,840 --> 00:42:47,640
Speaker 1: and had to dial up. And I mean, so I've

954
00:42:47,680 --> 00:42:49,960
Speaker 1: seen this, So I'm an early adapter of stuff, even

955
00:42:50,000 --> 00:42:52,279
Speaker 1: though I'm an old dude, I'm but not from a

956
00:42:52,360 --> 00:42:53,320
Speaker 1: tech side, from.

957
00:42:53,120 --> 00:42:53,839
Speaker 3: A user side.

958
00:42:55,560 --> 00:42:58,319
Speaker 1: Single. This is this single as a just as a

959
00:42:58,400 --> 00:43:01,279
Speaker 1: user meaning I don't know how it works why, but

960
00:43:01,360 --> 00:43:03,440
Speaker 1: I can explain to you why people are so fascinating

961
00:43:03,600 --> 00:43:05,680
Speaker 1: and NFTs I was on. You could find the old

962
00:43:05,680 --> 00:43:11,440
Speaker 1: tweets going calling people buy one? Are you fucking I

963
00:43:11,440 --> 00:43:16,000
Speaker 1: don't know. Studying people is like literally like why I

964
00:43:16,000 --> 00:43:17,919
Speaker 1: started doing what I do. But like, no, of course

965
00:43:17,920 --> 00:43:20,400
Speaker 1: I recognize that as a con from the moment one.

966
00:43:20,640 --> 00:43:21,759
Speaker 5: But this doesn't mean like a con.

967
00:43:22,080 --> 00:43:25,239
Speaker 1: But even when you say the bitcoin things today, yeah,

968
00:43:25,239 --> 00:43:30,560
Speaker 1: I know, so Bitcoin one, NFTs were always huckster devices

969
00:43:30,600 --> 00:43:34,359
Speaker 1: to separate suckers from their money. And look, Theodore Eblin said,

970
00:43:34,400 --> 00:43:37,200
Speaker 1: and David Mammock quotes it all the time. Every profession

971
00:43:37,640 --> 00:43:41,560
Speaker 1: is a conspiracy against the laity, every profession fox over

972
00:43:41,719 --> 00:43:48,080
Speaker 1: the regular. Yes, that's the and there's no doubt about Yeah,

973
00:43:48,120 --> 00:43:49,600
Speaker 1: Mamott was ahead of that by twenty years. But you

974
00:43:49,640 --> 00:43:53,120
Speaker 1: gotta you gotta look at it and not me Mammott

975
00:43:53,280 --> 00:43:58,160
Speaker 1: and then me, but but so uh, you got to

976
00:43:58,239 --> 00:44:02,000
Speaker 1: just understand that it is very active. Sure, people, I'm just.

977
00:44:01,960 --> 00:44:06,120
Speaker 2: Trying to bridge from what you're describing to the automobile,

978
00:44:06,160 --> 00:44:09,120
Speaker 2: which changed everything. I don't think large language models. I

979
00:44:09,120 --> 00:44:11,400
Speaker 2: think they're going to create harms. I think they are

980
00:44:11,400 --> 00:44:14,400
Speaker 2: going to be things that change. But it's like what

981
00:44:15,080 --> 00:44:17,800
Speaker 2: happens now, because what we have right now is basically

982
00:44:17,880 --> 00:44:20,000
Speaker 2: what we've had for two years. If you want to

983
00:44:20,040 --> 00:44:22,680
Speaker 2: email me about reasoning, please do our all email as

984
00:44:22,760 --> 00:44:26,560
Speaker 2: much as you want. But the thing is you look

985
00:44:26,600 --> 00:44:28,719
Speaker 2: at this and people are going, okay, and then this

986
00:44:28,760 --> 00:44:32,160
Speaker 2: will happen. It's like that thing that with an LLM

987
00:44:32,200 --> 00:44:34,560
Speaker 2: that goes and does something really basic. Thing an LLLM

988
00:44:34,560 --> 00:44:36,640
Speaker 2: that you tell to go and do a thing online.

989
00:44:37,040 --> 00:44:40,239
Speaker 2: They are bad, bad at it. There was a Salesforce

990
00:44:40,280 --> 00:44:43,840
Speaker 2: study that sayds like thirty five percent they like it

991
00:44:43,920 --> 00:44:46,279
Speaker 2: was like thirty something percent they fail or that that

992
00:44:46,400 --> 00:44:48,040
Speaker 2: was only the only ones they complete it.

993
00:44:48,080 --> 00:44:49,520
Speaker 1: I don't think I've never asked an AI to do

994
00:44:49,560 --> 00:44:51,480
Speaker 1: a task for it. That's I'm saying. I've never asked

995
00:44:51,480 --> 00:44:53,200
Speaker 1: it to do it. Do one of those agent kind

996
00:44:53,200 --> 00:44:55,600
Speaker 1: of functions. I wouldn't agree with you. I wouldn't well,

997
00:44:55,680 --> 00:44:57,359
Speaker 1: I don't think it's there yet. I mean, for again,

998
00:44:57,400 --> 00:45:01,200
Speaker 1: as a user. But research, I've had good research.

999
00:45:00,880 --> 00:45:04,319
Speaker 5: Done, really well, I mean the AI and jen AI

1000
00:45:04,400 --> 00:45:06,120
Speaker 5: has done a lot of good stuff in the medical

1001
00:45:06,160 --> 00:45:08,479
Speaker 5: fields too, right, Chrisper and all of that stuff. There's

1002
00:45:08,480 --> 00:45:11,600
Speaker 5: been a lot of discoveries about what sort of mutations

1003
00:45:11,600 --> 00:45:13,800
Speaker 5: you find in certain types of cancer that like, I

1004
00:45:13,800 --> 00:45:14,960
Speaker 5: don't think that done.

1005
00:45:15,239 --> 00:45:18,120
Speaker 1: I want to study an industry to consider writing about it,

1006
00:45:18,800 --> 00:45:20,440
Speaker 1: I can ask you have to ask good question. I mean,

1007
00:45:20,440 --> 00:45:22,640
Speaker 1: it's like in anything else, right, I can ask really

1008
00:45:22,680 --> 00:45:25,920
Speaker 1: good questions of an AI and send it, you know,

1009
00:45:26,280 --> 00:45:28,319
Speaker 1: for the two hundred dollars a month model one where

1010
00:45:28,320 --> 00:45:31,560
Speaker 1: they'll do that research thing. And if I ask it

1011
00:45:31,600 --> 00:45:34,920
Speaker 1: to do research and then you can, it'll like it

1012
00:45:34,960 --> 00:45:36,960
Speaker 1: will come back and maybe you have to send it

1013
00:45:37,000 --> 00:45:40,719
Speaker 1: back three times, but you have the speed and accurate. Yeah,

1014
00:45:40,760 --> 00:45:45,840
Speaker 1: but you can very quickly a lot of it because

1015
00:45:45,840 --> 00:45:48,680
Speaker 1: you can. Like the booklist thing that I can't fathom

1016
00:45:48,719 --> 00:45:51,560
Speaker 1: being that irresponsible, like those idiots who in the paper,

1017
00:45:52,600 --> 00:45:54,279
Speaker 1: but you literally all you have to say to them

1018
00:45:54,320 --> 00:45:57,200
Speaker 1: is when it presents any kind of list, all you

1019
00:45:57,239 --> 00:45:59,200
Speaker 1: say to it is go verify that list. Please.

1020
00:45:59,280 --> 00:46:00,520
Speaker 3: How do you know what right is?

1021
00:46:01,120 --> 00:46:04,920
Speaker 5: Or myself? I mean I would at some point, but you.

1022
00:46:04,800 --> 00:46:07,279
Speaker 1: Go verify the list. It immediately goes, You're right, I

1023
00:46:07,320 --> 00:46:11,919
Speaker 1: was hallucinating these three books don't exist. That happens. Then

1024
00:46:12,120 --> 00:46:14,319
Speaker 1: you go do it one more time and make sure

1025
00:46:14,320 --> 00:46:16,879
Speaker 1: that these titles are available in these stores. Then they'll

1026
00:46:16,920 --> 00:46:18,719
Speaker 1: give you links and then you can go look at

1027
00:46:19,360 --> 00:46:20,120
Speaker 1: something that you're doing.

1028
00:46:20,280 --> 00:46:22,319
Speaker 4: And I think sometimes we're splitting here though, is you're

1029
00:46:22,680 --> 00:46:25,520
Speaker 4: both of you are extraordinarily intelligent people who have done

1030
00:46:25,520 --> 00:46:27,520
Speaker 4: a lot of research in your life, so you know

1031
00:46:27,600 --> 00:46:29,160
Speaker 4: how to do those states. You know how to be like,

1032
00:46:29,200 --> 00:46:30,720
Speaker 4: I need to call this up, I need to search

1033
00:46:30,760 --> 00:46:33,400
Speaker 4: this make sure. I do think that one of the problems,

1034
00:46:33,400 --> 00:46:36,120
Speaker 4: again coming from like the low level consumer level, is

1035
00:46:36,640 --> 00:46:39,760
Speaker 4: it's often being marketed as an impartial refer impart.

1036
00:46:40,200 --> 00:46:42,680
Speaker 1: You're totally right, You're totally right. It's not that you

1037
00:46:42,719 --> 00:46:44,359
Speaker 1: cannot you'll be fucked so bad.

1038
00:46:45,200 --> 00:46:47,200
Speaker 2: But most people don't interact with it like you do,

1039
00:46:47,320 --> 00:46:48,000
Speaker 2: is what I'm saying.

1040
00:46:48,080 --> 00:46:49,600
Speaker 1: Well, but but they can.

1041
00:46:49,640 --> 00:46:51,520
Speaker 4: They can, and I agree with you, they can. I

1042
00:46:51,520 --> 00:46:54,040
Speaker 4: think it's almost but it's it's.

1043
00:46:54,400 --> 00:46:56,080
Speaker 2: They don't lead them to do that. It's not like

1044
00:46:56,120 --> 00:46:57,440
Speaker 2: they have things that guide them.

1045
00:46:57,640 --> 00:46:57,839
Speaker 3: Right.

1046
00:46:58,280 --> 00:47:00,239
Speaker 1: This is really interesting here what you're saying about this.

1047
00:47:00,440 --> 00:47:02,120
Speaker 4: Yeah, well, I mean I think that you know, when

1048
00:47:02,160 --> 00:47:04,759
Speaker 4: you see people and we all saw the Rock Nazi thing,

1049
00:47:05,040 --> 00:47:07,640
Speaker 4: but if you see people on Twitter, they don't use

1050
00:47:07,800 --> 00:47:10,759
Speaker 4: they I would say, fewer use it to do not

1051
00:47:10,880 --> 00:47:13,360
Speaker 4: see ship as much as they go, hey rock, is

1052
00:47:13,400 --> 00:47:13,840
Speaker 4: this true?

1053
00:47:13,960 --> 00:47:15,120
Speaker 3: Is this true? Is this true?

1054
00:47:15,480 --> 00:47:18,200
Speaker 4: And depending on what fingers, on which scale, the answer

1055
00:47:18,239 --> 00:47:19,120
Speaker 4: is different each time.

1056
00:47:19,600 --> 00:47:22,640
Speaker 1: But I think I understand why. I mean I did.

1057
00:47:22,719 --> 00:47:26,880
Speaker 1: I haven't been on Twitter since Yer whatever that date is,

1058
00:47:27,719 --> 00:47:30,800
Speaker 1: so I don't know, but I would never. Of course,

1059
00:47:30,840 --> 00:47:34,160
Speaker 1: you can't just say let's go to the ay as

1060
00:47:34,200 --> 00:47:37,640
Speaker 1: though that's a final arbiter and it's certainly not today.

1061
00:47:37,920 --> 00:47:38,960
Speaker 3: But it's being pransitioned.

1062
00:47:39,800 --> 00:47:41,920
Speaker 4: I think that's my problem is it's being positioned that way.

1063
00:47:41,960 --> 00:47:43,880
Speaker 4: And I think you're absolutely right. I think you're absolutely

1064
00:47:43,960 --> 00:47:46,279
Speaker 4: right how useful it is, especially if you have the

1065
00:47:46,280 --> 00:47:47,920
Speaker 4: skill to use it. I think the problem is it

1066
00:47:47,960 --> 00:47:50,920
Speaker 4: is being marketed as this is a catch all solution,

1067
00:47:51,040 --> 00:47:54,239
Speaker 4: this is a panacea to your knowledge problems, and oh.

1068
00:47:54,239 --> 00:47:54,920
Speaker 1: Yeah, you know what I mean.

1069
00:47:56,960 --> 00:47:57,600
Speaker 3: Well, it is like.

1070
00:47:57,560 --> 00:47:59,960
Speaker 1: Believing rock Star games that that they're going to get

1071
00:48:00,320 --> 00:48:05,120
Speaker 1: fucking grant the motto out right. I mean, it's no doubt, no,

1072
00:48:05,320 --> 00:48:07,839
Speaker 1: I mean I literally look at the actuarial table. If

1073
00:48:07,840 --> 00:48:09,839
Speaker 1: you have to build one on my life expectancy verse

1074
00:48:09,880 --> 00:48:13,360
Speaker 1: when the next popperation of GTA, I might lose.

1075
00:48:14,080 --> 00:48:15,640
Speaker 3: But here's the thing. Here's the thing.

1076
00:48:15,640 --> 00:48:18,359
Speaker 2: Though you're describing the research, it isn't an invalid use case.

1077
00:48:18,400 --> 00:48:21,239
Speaker 2: What you're describing is how people use Google Search to

1078
00:48:21,280 --> 00:48:23,520
Speaker 2: do research. They pull up a bunch of stuff, they

1079
00:48:23,560 --> 00:48:25,160
Speaker 2: go through them, they look at it and they go,

1080
00:48:25,400 --> 00:48:25,879
Speaker 2: is this right?

1081
00:48:26,040 --> 00:48:26,760
Speaker 1: That's a slog?

1082
00:48:27,160 --> 00:48:29,280
Speaker 3: It is a slog? Why that is a slog?

1083
00:48:29,680 --> 00:48:31,600
Speaker 1: No, it is not. It's a pleasure. It's not. No,

1084
00:48:31,640 --> 00:48:33,959
Speaker 1: you're gonna be honest about it. It is a total pace.

1085
00:48:34,480 --> 00:48:37,560
Speaker 2: I should be clear. I'm also I have actually used

1086
00:48:37,560 --> 00:48:40,239
Speaker 2: these things. I've genuinely tried because I'm I love my

1087
00:48:40,280 --> 00:48:43,040
Speaker 2: dud Dad's Mcgizmo's and I've really I've sat down and

1088
00:48:43,080 --> 00:48:43,960
Speaker 2: been like, what I'm I missing?

1089
00:48:44,239 --> 00:48:49,920
Speaker 1: You just became Paul McCartney And that was Paul McCartney moment.

1090
00:48:50,080 --> 00:48:52,560
Speaker 2: He has not my invite for the show though. No,

1091
00:48:53,120 --> 00:48:56,760
Speaker 2: I'm not invited Mecca. My mom would be so happy.

1092
00:48:57,040 --> 00:48:57,719
Speaker 3: No, it's just.

1093
00:48:59,360 --> 00:49:01,440
Speaker 2: I really feel like this keeps coming back to the

1094
00:49:02,000 --> 00:49:04,239
Speaker 2: you were using it in a totally fine way. I'm

1095
00:49:04,239 --> 00:49:05,880
Speaker 2: mad at the fact that everyone's like, and this is

1096
00:49:05,880 --> 00:49:08,040
Speaker 2: the only thing you'll need. You can fully trust this,

1097
00:49:08,040 --> 00:49:10,080
Speaker 2: this is the best thing. The information's the best. You're

1098
00:49:10,080 --> 00:49:11,600
Speaker 2: looking at it and going this is a way of

1099
00:49:11,640 --> 00:49:14,080
Speaker 2: digging through information and passing stuff and having a conversation

1100
00:49:14,080 --> 00:49:14,640
Speaker 2: with the information.

1101
00:49:14,640 --> 00:49:17,800
Speaker 1: Always common and in the broad society, yes, the lowest,

1102
00:49:17,880 --> 00:49:21,440
Speaker 1: that's the lowest common. But unfortunately, yes, you're right, our

1103
00:49:21,560 --> 00:49:24,719
Speaker 1: educational systems really fucked up. Disadvantaged people have no chance

1104
00:49:24,760 --> 00:49:27,680
Speaker 1: that Mike went to a school that allowed people from

1105
00:49:27,719 --> 00:49:29,680
Speaker 1: disparate areas to get We gotta, yeah, we gotta reform

1106
00:49:29,719 --> 00:49:31,759
Speaker 1: the education system, and we don't trust it. Everyone has

1107
00:49:31,800 --> 00:49:33,480
Speaker 1: a fair shot, I agree.

1108
00:49:33,160 --> 00:49:34,799
Speaker 2: But there's a very base so let's do it. Yeah,

1109
00:49:34,800 --> 00:49:36,560
Speaker 2: but there's actually a very basic thing we don't have

1110
00:49:36,640 --> 00:49:38,719
Speaker 2: to with that. There should be regulation that says that

1111
00:49:38,760 --> 00:49:42,160
Speaker 2: these things need big fucking disclaimers that say, hey, check everything.

1112
00:49:42,320 --> 00:49:44,840
Speaker 2: They won't do it because the incentives we discussed, but

1113
00:49:45,160 --> 00:49:47,880
Speaker 2: that would actually be I think called the stealing is

1114
00:49:47,920 --> 00:49:50,560
Speaker 2: also bad. I think the environmental damage is bad. But

1115
00:49:50,600 --> 00:49:51,560
Speaker 2: I think we agree on that.

1116
00:49:52,120 --> 00:49:55,239
Speaker 1: It's just Safeguards are always great, But then you gotta

1117
00:49:55,239 --> 00:49:57,600
Speaker 1: figure out who decides what those safeguards are and who's

1118
00:49:57,960 --> 00:49:59,839
Speaker 1: Like you talk to Bill Girly, he'll talk about regulatory creep.

1119
00:50:00,200 --> 00:50:02,799
Speaker 1: So where do you want to I'm just saying, no, no, no,

1120
00:50:02,920 --> 00:50:06,279
Speaker 1: I agree with you. He's a smart person and he's

1121
00:50:06,560 --> 00:50:08,680
Speaker 1: you know, thoughtful about this stuff. And so how who

1122
00:50:08,719 --> 00:50:10,719
Speaker 1: do you want? Do you want to go? I mean this,

1123
00:50:10,840 --> 00:50:15,560
Speaker 1: you want the current government? I think the guardrails. Who

1124
00:50:15,600 --> 00:50:16,120
Speaker 1: who's to do?

1125
00:50:16,160 --> 00:50:17,520
Speaker 3: But here's a very basic god roil.

1126
00:50:17,600 --> 00:50:20,120
Speaker 2: It's just a disclaiming that says everything with the general

1127
00:50:20,120 --> 00:50:21,319
Speaker 2: if I is blah blah blah, let you.

1128
00:50:21,320 --> 00:50:22,960
Speaker 5: Kind of some of them have it now, but they're

1129
00:50:23,000 --> 00:50:25,120
Speaker 5: all in preview right, and that's probably what they're coaching.

1130
00:50:25,160 --> 00:50:30,680
Speaker 1: Again, they do say right on every single time, every

1131
00:50:30,719 --> 00:50:34,760
Speaker 1: single time, every single time I've got today actually for sure.

1132
00:50:35,160 --> 00:50:38,240
Speaker 5: Yeah, because they they have been criticized. But to Brand's

1133
00:50:38,280 --> 00:50:40,520
Speaker 5: larger point, there are guardrails put in place into a

1134
00:50:40,560 --> 00:50:42,640
Speaker 5: lot of these. I'm most familiar with the Geminis and

1135
00:50:42,680 --> 00:50:45,600
Speaker 5: the Apple intelligences and the Amazon ones of the world,

1136
00:50:45,680 --> 00:50:49,400
Speaker 5: and they their guardrails are around like see Sam Right

1137
00:50:49,520 --> 00:50:53,480
Speaker 5: child sexual abuse material, or like not presenting people's faces

1138
00:50:53,600 --> 00:50:56,000
Speaker 5: or like trying to avoid photo realism because then you

1139
00:50:56,080 --> 00:50:58,920
Speaker 5: get very deceptive, very quickly. You don't see any of

1140
00:50:59,000 --> 00:51:00,840
Speaker 5: that in rock. Maybe it depends.

1141
00:51:01,000 --> 00:51:04,719
Speaker 3: I just look, I just used the which one does.

1142
00:51:04,719 --> 00:51:06,839
Speaker 1: Really it does. But I'm not gonna turn on my phone.

1143
00:51:06,880 --> 00:51:09,600
Speaker 2: But I'm like, I'm just like, here's the thing. If

1144
00:51:09,600 --> 00:51:11,960
Speaker 2: they're there sometimes and not others, that's also bad. Like

1145
00:51:11,960 --> 00:51:14,600
Speaker 2: it's like it's it's because when you've got people who

1146
00:51:14,680 --> 00:51:18,359
Speaker 2: are killing themselves, people are having it was miles clear.

1147
00:51:18,400 --> 00:51:20,120
Speaker 2: I think it was a rolling stone wrot, this piece

1148
00:51:20,120 --> 00:51:24,120
Speaker 2: about people having psychotic reactions. Yeah, I agree this This

1149
00:51:24,160 --> 00:51:27,000
Speaker 2: administration I've probably done won't regulating this. But the answer

1150
00:51:27,080 --> 00:51:29,440
Speaker 2: being let's regulate nothing is terrifying.

1151
00:51:29,480 --> 00:51:31,960
Speaker 1: Well, it's really, but it is confusing because if you

1152
00:51:32,000 --> 00:51:37,680
Speaker 1: think about, let's say Facebook, right, there's no doubt that

1153
00:51:38,000 --> 00:51:42,880
Speaker 1: Facebook was used in me and mar to uh foment

1154
00:51:43,080 --> 00:51:48,520
Speaker 1: a genocide. People were warned inside. Who knows where it

1155
00:51:48,560 --> 00:51:52,759
Speaker 1: got to? It is very well documented. How could one

1156
00:51:53,400 --> 00:51:56,399
Speaker 1: after the fact it's okay, we know it's really hard

1157
00:51:56,520 --> 00:51:59,160
Speaker 1: to sort of figure out these use cases, and then

1158
00:51:59,200 --> 00:52:00,840
Speaker 1: she's all social media you have gone away like some

1159
00:52:00,880 --> 00:52:03,799
Speaker 1: people think it should be. I would social media feel.

1160
00:52:03,560 --> 00:52:04,120
Speaker 3: Better about that.

1161
00:52:04,200 --> 00:52:09,040
Speaker 2: If Andrew Bosworth the CTO, Yeah, yeah, yeah, I mostly

1162
00:52:09,080 --> 00:52:11,479
Speaker 2: say it for the this, yeah, because also the yellow.

1163
00:52:11,600 --> 00:52:13,880
Speaker 1: But if I know I'm a well, that's good. If

1164
00:52:13,920 --> 00:52:16,240
Speaker 1: I know he did. He did an.

1165
00:52:16,120 --> 00:52:19,279
Speaker 2: Internal letter in twenty sixteen twenty seventeen where he said

1166
00:52:19,320 --> 00:52:21,800
Speaker 2: that all things were justified for growth, including a terror attack,

1167
00:52:22,200 --> 00:52:23,960
Speaker 2: and that's kind of how they approach everything.

1168
00:52:24,080 --> 00:52:25,960
Speaker 1: No, it was monstrous, Like when I saw that Me

1169
00:52:26,000 --> 00:52:27,520
Speaker 1: and Mark thing, it made me say, like I should

1170
00:52:27,520 --> 00:52:30,240
Speaker 1: never use Facebook. I mean, I think that is as

1171
00:52:30,320 --> 00:52:31,840
Speaker 1: bad a thing as I've ever seen.

1172
00:52:32,520 --> 00:52:35,000
Speaker 2: I mean literally, this is still an example that and

1173
00:52:35,320 --> 00:52:40,920
Speaker 2: Meta's l M allows children and Jeff Horwitz report this

1174
00:52:40,920 --> 00:52:43,839
Speaker 2: in the journal allowed children to like have sexual conversations

1175
00:52:43,840 --> 00:52:50,360
Speaker 2: with John Cena very peculiar, Like there was like super clear.

1176
00:52:53,680 --> 00:52:56,359
Speaker 1: You say with John That's what I say.

1177
00:52:56,520 --> 00:53:01,640
Speaker 2: One, it was just John c He's gone to jail

1178
00:53:01,680 --> 00:53:02,520
Speaker 2: for one hundred years.

1179
00:53:02,520 --> 00:53:04,120
Speaker 1: On the Bear. I was in a scene with John

1180
00:53:04,960 --> 00:53:07,520
Speaker 1: lovely guy, and he definitely wasn't doing that.

1181
00:53:07,600 --> 00:53:10,120
Speaker 2: No, No, he seems like I'm saying it was the

1182
00:53:10,200 --> 00:53:13,239
Speaker 2: voice of John c You're gloud to have pedo conversations with.

1183
00:53:13,480 --> 00:53:17,080
Speaker 2: But again it's this lack of restriction because no particular

1184
00:53:17,120 --> 00:53:18,240
Speaker 2: technology is evil.

1185
00:53:18,040 --> 00:53:19,360
Speaker 3: At it's it's.

1186
00:53:34,200 --> 00:53:36,160
Speaker 5: I think what Brian was saying. And I don't know

1187
00:53:36,200 --> 00:53:39,080
Speaker 5: if I misinterpreting you, but like it's there is some

1188
00:53:39,160 --> 00:53:41,920
Speaker 5: fatigue at seeing these things throughout all the pharmaceutical warnings

1189
00:53:41,920 --> 00:53:43,879
Speaker 5: you get the end of every commercial, every warning you're

1190
00:53:43,880 --> 00:53:46,760
Speaker 5: gonna get from Gemini from now on, every Apple intelligence warning.

1191
00:53:46,800 --> 00:53:50,040
Speaker 5: That's like notification summaries can be wrong sometimes, like sometimes

1192
00:53:50,080 --> 00:53:51,919
Speaker 5: you see that I see them on my phone.

1193
00:53:52,000 --> 00:53:54,640
Speaker 2: Are no, no, no, I believe you.

1194
00:53:54,640 --> 00:53:58,680
Speaker 1: It's I guess what I'm saying. Is that why I

1195
00:53:58,680 --> 00:54:03,680
Speaker 1: brought up me and Mara is No, we as a society, unfortunately,

1196
00:54:03,760 --> 00:54:06,240
Speaker 1: we default to the convenient, fun.

1197
00:54:06,280 --> 00:54:06,799
Speaker 4: Easy way.

1198
00:54:07,000 --> 00:54:09,759
Speaker 1: Yeah, that'sactly what we all should have done when face

1199
00:54:10,160 --> 00:54:12,640
Speaker 1: And I'm not even blaming exactly like you can blame bos.

1200
00:54:12,640 --> 00:54:15,080
Speaker 1: I don't know enough to blame anybody there. We know

1201
00:54:15,160 --> 00:54:17,759
Speaker 1: that the technology that Facebook itself was used by these

1202
00:54:17,800 --> 00:54:21,120
Speaker 1: generals in me and mar right, yes, and nobody took pause,

1203
00:54:21,239 --> 00:54:25,880
Speaker 1: Like I think very few people left Facebook as a

1204
00:54:25,880 --> 00:54:28,480
Speaker 1: result of that. I think. So I just don't know

1205
00:54:28,520 --> 00:54:31,759
Speaker 1: what the fix is for these problems because we gravitate

1206
00:54:31,960 --> 00:54:33,040
Speaker 1: like bees to honey.

1207
00:54:33,239 --> 00:54:35,800
Speaker 5: But they're also like tools that can be used as weapons,

1208
00:54:35,880 --> 00:54:38,560
Speaker 5: and it depends on the perpetrator and the person using

1209
00:54:38,600 --> 00:54:40,839
Speaker 5: these right. And this is an age old question again

1210
00:54:40,920 --> 00:54:43,919
Speaker 5: coming back to the industrial and agricultural revolution. This can

1211
00:54:43,960 --> 00:54:46,120
Speaker 5: be just a tool for hacking a plant off of

1212
00:54:46,120 --> 00:54:48,160
Speaker 5: its huscal. It can be used as a murder weapon.

1213
00:54:48,680 --> 00:54:51,640
Speaker 5: Back to AI, can be very informative, very helpful for

1214
00:54:51,640 --> 00:54:55,000
Speaker 5: people who need companionship. It can be used like people

1215
00:54:55,040 --> 00:54:58,160
Speaker 5: will send me scam texts all the time. The technologies

1216
00:54:58,239 --> 00:55:00,440
Speaker 5: keeps keeping up with them and filtering them out, so

1217
00:55:00,440 --> 00:55:02,520
Speaker 5: they keep changing their spam. Bad actors are going to

1218
00:55:02,600 --> 00:55:05,160
Speaker 5: bad act That's just the way it's going to be.

1219
00:55:05,320 --> 00:55:08,160
Speaker 5: What can we do? We can I stop using Facebook.

1220
00:55:08,480 --> 00:55:10,880
Speaker 5: I try to educate my parents every single time they

1221
00:55:10,960 --> 00:55:12,960
Speaker 5: use a GBT answer with me. I'm like, Mom and Dad,

1222
00:55:13,040 --> 00:55:15,880
Speaker 5: stop using that. But then they just keep using it

1223
00:55:15,920 --> 00:55:18,319
Speaker 5: because they're the sort of person that's accessible to this.

1224
00:55:18,680 --> 00:55:22,480
Speaker 5: They will just use it convenience and they don't want

1225
00:55:22,480 --> 00:55:24,560
Speaker 5: to do the extra work of maybe the Google search

1226
00:55:24,600 --> 00:55:27,160
Speaker 5: method which you were saying at, or they just want

1227
00:55:27,200 --> 00:55:29,600
Speaker 5: something that's easy and they don't care if they get

1228
00:55:29,640 --> 00:55:29,959
Speaker 5: it wrong.

1229
00:55:30,000 --> 00:55:31,400
Speaker 1: Me. Yeah, I guess I love the idea of you

1230
00:55:31,520 --> 00:55:34,160
Speaker 1: using your brains to figure out how to make these

1231
00:55:34,200 --> 00:55:38,520
Speaker 1: things safer and more useful as you agitate within the industry.

1232
00:55:38,560 --> 00:55:41,960
Speaker 1: I think trying to trying to find a way for

1233
00:55:42,080 --> 00:55:46,800
Speaker 1: them to disappear seems all possible. And again, like NFTs

1234
00:55:46,840 --> 00:55:50,239
Speaker 1: were obviously going to disappear, but the underlying bigcoin thing

1235
00:55:50,440 --> 00:55:53,960
Speaker 1: wasn't because there's too much the moment this election happened,

1236
00:55:54,440 --> 00:55:57,319
Speaker 1: the moment this election happened, bitcoin is going to two

1237
00:55:57,400 --> 00:55:59,720
Speaker 1: hunch of bitcoin was going because.

1238
00:55:59,560 --> 00:56:02,600
Speaker 2: Once about that is we could have stopped crypto. I

1239
00:56:02,640 --> 00:56:04,680
Speaker 2: was writing about it at the time. I was, and

1240
00:56:04,719 --> 00:56:06,799
Speaker 2: the only one are you going to starve crypto by

1241
00:56:06,800 --> 00:56:10,200
Speaker 2: informing people about the inherently criminal aspect that underlies everything,

1242
00:56:10,200 --> 00:56:12,000
Speaker 2: the fact that tether is more than likely in the

1243
00:56:12,040 --> 00:56:15,440
Speaker 2: hands of multiple different I tried, and I failed, and

1244
00:56:15,600 --> 00:56:18,200
Speaker 2: you know what, it sucks, But you try. You can

1245
00:56:18,200 --> 00:56:20,520
Speaker 2: put ideas out there, you can see if people pick

1246
00:56:20,600 --> 00:56:22,600
Speaker 2: them up. And I mean that's kind of what you

1247
00:56:22,719 --> 00:56:25,360
Speaker 2: do there. In the case of crypto, it's such a

1248
00:56:25,360 --> 00:56:28,240
Speaker 2: weird thing as well, because it's there, but it isn't.

1249
00:56:28,400 --> 00:56:31,200
Speaker 2: It doesn't really do anything other than fun things. Or

1250
00:56:31,239 --> 00:56:34,120
Speaker 2: be funded, and it just kind of exists where it's

1251
00:56:34,200 --> 00:56:36,080
Speaker 2: just like they kind of they don't even I kind

1252
00:56:36,080 --> 00:56:37,640
Speaker 2: of respect the fact they don't even try and.

1253
00:56:37,600 --> 00:56:40,759
Speaker 1: Be literally you're talking to Charlie Monger and Warren Buffet's book.

1254
00:56:40,800 --> 00:56:42,759
Speaker 1: That's what they always say. I know, but that's what

1255
00:56:42,760 --> 00:56:46,360
Speaker 1: they always said. Because of that. That's their whole point,

1256
00:56:46,400 --> 00:56:48,680
Speaker 1: that to forget the blockchain. It doesn't really do it,

1257
00:56:48,719 --> 00:56:50,520
Speaker 1: you know yet really oh yeah, do anything.

1258
00:56:50,560 --> 00:56:52,560
Speaker 2: And the thing is, though there was real money in that,

1259
00:56:52,680 --> 00:56:55,319
Speaker 2: which there isn't a generative payer. And I think that

1260
00:56:55,600 --> 00:56:59,040
Speaker 2: what's funny is this convenience thing you're talking about may

1261
00:56:59,080 --> 00:57:01,800
Speaker 2: actually be their downfa because those five hundred million weekly

1262
00:57:01,840 --> 00:57:04,440
Speaker 2: active chat deepert users, the cost of them billions of dollars,

1263
00:57:04,640 --> 00:57:06,040
Speaker 2: it's probably all gonna fascinate it.

1264
00:57:06,160 --> 00:57:09,799
Speaker 1: It's not as fascinating interesting that they mean their popularity

1265
00:57:09,880 --> 00:57:14,520
Speaker 1: is going to destroy. Yeah, those companies, not underlying tech,

1266
00:57:14,600 --> 00:57:17,480
Speaker 1: but that the companies themselves, that's fascinating. I'm really like,

1267
00:57:17,520 --> 00:57:20,000
Speaker 1: I'm saying, you're teaching me something I had no idea

1268
00:57:20,000 --> 00:57:20,320
Speaker 1: about that.

1269
00:57:20,480 --> 00:57:22,960
Speaker 2: I'll explain it very simply, which is open Ai last

1270
00:57:23,080 --> 00:57:26,040
Speaker 2: year spent nine billion dollars to lose five billion dollars.

1271
00:57:26,240 --> 00:57:30,320
Speaker 2: Anthropics spent five point three billion dollars. No, sorry, they

1272
00:57:30,320 --> 00:57:34,480
Speaker 2: look they spent like nine seven eight. They spent billions

1273
00:57:34,560 --> 00:57:37,120
Speaker 2: to lose five point three billion dollars, of which a

1274
00:57:37,240 --> 00:57:40,080
Speaker 2: chunk of that was just given to Amazon for servers.

1275
00:57:40,600 --> 00:57:44,080
Speaker 2: It's very fucking weird. They lose money. Their conversion rate

1276
00:57:44,200 --> 00:57:47,400
Speaker 2: on chet GPT is awful. So five hundred million, I

1277
00:57:47,440 --> 00:57:50,920
Speaker 2: think they have fifteen point five to sixty million paying subscribers.

1278
00:57:51,160 --> 00:57:54,000
Speaker 2: They don't publish monthly active users because if they did,

1279
00:57:54,120 --> 00:57:56,320
Speaker 2: you could do the math and see it's trash. And

1280
00:57:56,440 --> 00:57:59,240
Speaker 2: on top of that, they just can't find a way

1281
00:57:59,280 --> 00:58:01,280
Speaker 2: to make money. They so much money. So what's more

1282
00:58:01,320 --> 00:58:03,800
Speaker 2: than likely is, yeah, these companies might die. Llllms will

1283
00:58:03,840 --> 00:58:06,280
Speaker 2: hang around because there are use cases and Google has that,

1284
00:58:06,440 --> 00:58:08,000
Speaker 2: Like Jeff Dino for at, Google is one of the

1285
00:58:08,080 --> 00:58:10,560
Speaker 2: least evil tech people. There are actually people there who

1286
00:58:10,760 --> 00:58:12,520
Speaker 2: like the tech and give a shit about it, and

1287
00:58:12,600 --> 00:58:15,920
Speaker 2: there's more efficiency stuff coming out of Google's models. The

1288
00:58:16,080 --> 00:58:18,840
Speaker 2: thing is what we see today I do not believe

1289
00:58:19,200 --> 00:58:21,560
Speaker 2: is I think the most annoying scenario is going to

1290
00:58:21,560 --> 00:58:24,400
Speaker 2: be the longest life large language model with unlimited access

1291
00:58:24,520 --> 00:58:26,760
Speaker 2: is going to be on fucking Meta because of their

1292
00:58:27,000 --> 00:58:30,160
Speaker 2: unrestricted tripe that's on every fucking app. But I think

1293
00:58:30,240 --> 00:58:32,640
Speaker 2: things like chet gupt are just going to be limited.

1294
00:58:32,840 --> 00:58:34,320
Speaker 2: You may still have people who use them in the

1295
00:58:34,360 --> 00:58:34,960
Speaker 2: way with the gym.

1296
00:58:35,000 --> 00:58:38,600
Speaker 1: But it's interesting what you were saying about. Maybe an

1297
00:58:38,680 --> 00:58:41,400
Speaker 1: answer and you think about the industrial revolution is eventually

1298
00:58:42,440 --> 00:58:44,960
Speaker 1: is that people are going to have to be trained

1299
00:58:45,640 --> 00:58:48,959
Speaker 1: on instead of firing nine thousand, people train nine thousand,

1300
00:58:49,040 --> 00:58:52,560
Speaker 1: become prompt engineers, and yeah, become prompt engineers. And the

1301
00:58:52,640 --> 00:58:55,080
Speaker 1: fact that they didn't, I mean, look, no one is

1302
00:58:55,160 --> 00:58:58,520
Speaker 1: less surprised than me by incentive structures making these motherfuckers

1303
00:58:59,160 --> 00:59:05,160
Speaker 1: act like evil. It's completely non caring, you know, monsters.

1304
00:59:05,440 --> 00:59:08,280
Speaker 1: But if they can be convinced that there's a profit

1305
00:59:08,400 --> 00:59:09,760
Speaker 1: motive in training people.

1306
00:59:09,920 --> 00:59:10,920
Speaker 3: It's no profits.

1307
00:59:11,400 --> 00:59:12,920
Speaker 1: No, that they can be convinced of it. No, But

1308
00:59:13,280 --> 00:59:15,240
Speaker 1: I must be clear though, but event but in all

1309
00:59:15,280 --> 00:59:18,200
Speaker 1: these businesses there was no profit until nothing even as

1310
00:59:18,240 --> 00:59:19,640
Speaker 1: long did take Amazon to become profitable.

1311
00:59:19,640 --> 00:59:22,320
Speaker 2: Amazon took about eleven years with a WS but AWS

1312
00:59:22,440 --> 00:59:24,760
Speaker 2: was a concern that reduced costs for Amazon itself to

1313
00:59:24,840 --> 00:59:25,160
Speaker 2: run there.

1314
00:59:25,360 --> 00:59:27,000
Speaker 1: But in two thousand and a lot of people thought

1315
00:59:27,000 --> 00:59:28,000
Speaker 1: it was never going to become.

1316
00:59:27,840 --> 00:59:30,000
Speaker 3: Profitable, right, Yeah, but that's not the same.

1317
00:59:31,080 --> 00:59:34,440
Speaker 2: Yeah, but the economics computer is, it's completely different economics.

1318
00:59:34,640 --> 00:59:36,120
Speaker 5: Ask so, Brian, how much would you pay to keep

1319
00:59:36,200 --> 00:59:37,680
Speaker 5: using GBT a month?

1320
00:59:38,160 --> 00:59:41,760
Speaker 1: I'm a bad I'm older, and I've done well and

1321
00:59:42,520 --> 00:59:44,000
Speaker 1: I've do you know what I'm saying?

1322
00:59:44,040 --> 00:59:44,960
Speaker 5: Do you pay anything right now?

1323
00:59:45,000 --> 00:59:47,160
Speaker 1: I pay two hundred dollars a month.

1324
00:59:47,200 --> 00:59:49,360
Speaker 5: Two hundred a month. Okay, so Google one's like two fifty.

1325
00:59:49,720 --> 00:59:51,160
Speaker 3: Do you pay two hundred for.

1326
00:59:51,240 --> 00:59:55,000
Speaker 1: The research because for the supercharged researching research on the

1327
00:59:55,040 --> 00:59:57,280
Speaker 1: twenty bucks a month one though not the same level.

1328
00:59:58,120 --> 00:59:59,800
Speaker 4: That's an interesting thing to point out, too, is that

1329
01:00:00,000 --> 01:00:02,440
Speaker 4: you're getting better quality by paying for a higher I'm.

1330
01:00:02,280 --> 01:00:05,520
Speaker 1: Aware of it. Seriously, is there a quality difference if

1331
01:00:05,560 --> 01:00:07,320
Speaker 1: you look at it? We could yet, oh my god,

1332
01:00:09,080 --> 01:00:12,680
Speaker 1: they would tell you there isn't any amount of What

1333
01:00:13,760 --> 01:00:14,240
Speaker 1: is the rate?

1334
01:00:14,440 --> 01:00:16,200
Speaker 5: So they sell your ship product to make you buy

1335
01:00:16,240 --> 01:00:16,680
Speaker 5: them less?

1336
01:00:16,800 --> 01:00:18,360
Speaker 1: Well, I don't know if they sell product, but you're

1337
01:00:18,400 --> 01:00:21,840
Speaker 1: saying the US rate. I someone told me someone I

1338
01:00:21,880 --> 01:00:24,120
Speaker 1: trust a lot, a person who's tech savvy, was like,

1339
01:00:24,720 --> 01:00:26,840
Speaker 1: this is when that happened. They used it for a

1340
01:00:26,880 --> 01:00:29,840
Speaker 1: while and a couple of months ago, I was in

1341
01:00:29,920 --> 01:00:31,680
Speaker 1: a meeting with someone and they were like, if I

1342
01:00:31,720 --> 01:00:33,040
Speaker 1: was going to tell you to spend money on anything,

1343
01:00:33,120 --> 01:00:34,320
Speaker 1: spend two hundred bucks.

1344
01:00:34,280 --> 01:00:35,400
Speaker 3: Want do you want to something crazy?

1345
01:00:35,480 --> 01:00:35,600
Speaker 2: Though?

1346
01:00:35,680 --> 01:00:37,960
Speaker 3: Yes, they lose money on every two hundred buck a

1347
01:00:38,000 --> 01:00:38,600
Speaker 3: month customer.

1348
01:00:38,760 --> 01:00:41,560
Speaker 1: I believe you because because I'm using, Because when they

1349
01:00:41,640 --> 01:00:43,800
Speaker 1: do that search, you're saying it's burning so much.

1350
01:00:44,160 --> 01:00:44,800
Speaker 3: But that's the thing.

1351
01:00:45,320 --> 01:00:48,560
Speaker 2: How likely do you think that that will continue because

1352
01:00:48,720 --> 01:00:49,680
Speaker 2: the deep research that.

1353
01:00:52,720 --> 01:00:54,680
Speaker 1: Well, of course there's a number very soon. I'm already

1354
01:00:54,720 --> 01:00:54,880
Speaker 1: at that.

1355
01:00:55,080 --> 01:00:56,640
Speaker 5: Yeah, you're very high end of it.

1356
01:00:56,720 --> 01:00:59,040
Speaker 1: Yeah, yeah, for sure, double this I would I would

1357
01:00:59,080 --> 01:01:00,440
Speaker 1: not pay double that.

1358
01:01:00,960 --> 01:01:02,480
Speaker 2: I would not pay And I will be honest. And

1359
01:01:02,560 --> 01:01:05,520
Speaker 2: this is not even me being like hate or anything.

1360
01:01:05,880 --> 01:01:08,720
Speaker 2: It may not be that cheap because right now.

1361
01:01:08,840 --> 01:01:10,640
Speaker 3: It's the big store of my news. Let please head it.

1362
01:01:11,920 --> 01:01:14,040
Speaker 2: Look for a two hundred a month power user who's

1363
01:01:14,040 --> 01:01:18,360
Speaker 2: already using the losing the money. They lose so much

1364
01:01:18,440 --> 01:01:21,120
Speaker 2: money on them that it's like four hundred, five hundred,

1365
01:01:21,200 --> 01:01:24,080
Speaker 2: one thousand dollars a month clawed code right now, which

1366
01:01:24,120 --> 01:01:27,440
Speaker 2: is slightly different because the way they do context stuff. Nevertheless,

1367
01:01:27,520 --> 01:01:31,240
Speaker 2: on the two hundred dollars max user on Claude they

1368
01:01:31,280 --> 01:01:34,680
Speaker 2: could be losing. They had someone on ia on Twitter

1369
01:01:34,880 --> 01:01:36,960
Speaker 2: they spent ten thousand dollars in compute on a two

1370
01:01:37,040 --> 01:01:40,240
Speaker 2: hundred month subscription. These are the majority of power users.

1371
01:01:40,320 --> 01:01:43,280
Speaker 2: Power users go nuts, as you well know. This is

1372
01:01:43,440 --> 01:01:45,760
Speaker 2: why I'm so pushy on the economics, because what we

1373
01:01:45,840 --> 01:01:49,000
Speaker 2: are seeing today, it's like if every uber weighed forty

1374
01:01:49,120 --> 01:01:52,480
Speaker 2: thousand pounds and the fuel was giraffe blood. It was

1375
01:01:52,640 --> 01:01:55,439
Speaker 2: just like this insane economic And I sound like I'm kidding,

1376
01:01:55,480 --> 01:01:59,200
Speaker 2: but the economics are completely bonkers. So as much use

1377
01:01:59,200 --> 01:02:02,120
Speaker 2: as you're getting today, I just don't know how practically

1378
01:02:02,160 --> 01:02:04,440
Speaker 2: they'll provide that. And they might be cheaper models, but

1379
01:02:04,480 --> 01:02:06,200
Speaker 2: the cheaper models and I might not be able to

1380
01:02:06,240 --> 01:02:08,520
Speaker 2: do I see me use three Yeah, I've got.

1381
01:02:08,560 --> 01:02:12,000
Speaker 1: Yeah, yeah, So which takes a lot. Yeah, sometimes it

1382
01:02:12,080 --> 01:02:13,200
Speaker 1: still takes such a long time.

1383
01:02:13,320 --> 01:02:13,720
Speaker 3: That's the thing.

1384
01:02:13,920 --> 01:02:16,000
Speaker 1: You can have a conversation and that's long.

1385
01:02:16,080 --> 01:02:19,720
Speaker 2: How much of this is sustainable long term? And I

1386
01:02:19,840 --> 01:02:22,080
Speaker 2: know the business stuff is annoying because you still have

1387
01:02:22,160 --> 01:02:23,200
Speaker 2: your experience, which you like.

1388
01:02:23,320 --> 01:02:25,720
Speaker 1: No, it's not annoying, it's fascinating. I'm fascinated about the

1389
01:02:25,720 --> 01:02:26,840
Speaker 1: whole thing. This is just great.

1390
01:02:27,080 --> 01:02:29,440
Speaker 2: It's just the long term here, and I'm talking long

1391
01:02:29,560 --> 01:02:32,560
Speaker 2: terms in eighteen months is I don't know if we

1392
01:02:32,680 --> 01:02:35,840
Speaker 2: will have deep research at any price point approaching the

1393
01:02:35,880 --> 01:02:37,560
Speaker 2: one we'll have in that period.

1394
01:02:37,560 --> 01:02:39,640
Speaker 1: Well, the deep research is the only reason anyone should

1395
01:02:39,640 --> 01:02:40,160
Speaker 1: pay the money.

1396
01:02:40,160 --> 01:02:42,320
Speaker 2: And that's the thing, though, the only reason that people

1397
01:02:42,320 --> 01:02:45,040
Speaker 2: should pay is the thing that is not sustainable and

1398
01:02:45,520 --> 01:02:48,400
Speaker 2: has no problems. So when you talk about how we

1399
01:02:48,560 --> 01:02:51,720
Speaker 2: bring this towards like an AWS situation, AWS is lack

1400
01:02:51,760 --> 01:02:54,600
Speaker 2: of profitability was built on infrastructure expansion. It was because

1401
01:02:54,640 --> 01:02:57,400
Speaker 2: they were building the rails of cloud rip large. It

1402
01:02:57,640 --> 01:02:59,880
Speaker 2: was never in the billions and billions a year with

1403
01:03:00,120 --> 01:03:03,440
Speaker 2: no There is no path the profitability here. There was

1404
01:03:03,520 --> 01:03:05,320
Speaker 2: one model of open aies that they we can deliver

1405
01:03:05,400 --> 01:03:08,080
Speaker 2: to open Microsoft in twenty twenty three, called a.

1406
01:03:08,120 --> 01:03:10,200
Speaker 3: Racus, and.

1407
01:03:12,320 --> 01:03:14,480
Speaker 2: They failed to do it. They have yet to discover

1408
01:03:14,760 --> 01:03:17,880
Speaker 2: a or make because it may not be mathematically possible

1409
01:03:18,040 --> 01:03:20,480
Speaker 2: a really good large language model that can do the

1410
01:03:20,600 --> 01:03:24,040
Speaker 2: kind of reasoning like that that would be reliable and

1411
01:03:24,200 --> 01:03:25,000
Speaker 2: have the web search too.

1412
01:03:25,040 --> 01:03:26,120
Speaker 1: And I want to say you do have to when

1413
01:03:26,120 --> 01:03:27,840
Speaker 1: you talk about prompt Sorry, I was thinking about this

1414
01:03:27,960 --> 01:03:30,080
Speaker 1: when I was learning about complexity theory. I read this

1415
01:03:30,080 --> 01:03:31,960
Speaker 1: book by a gun named Neil Tiste that I loved.

1416
01:03:32,000 --> 01:03:34,960
Speaker 1: He's professor at at NYU. And I'm not good at physics,

1417
01:03:35,320 --> 01:03:37,560
Speaker 1: and I really want to understand quantum physics, and so

1418
01:03:37,720 --> 01:03:41,880
Speaker 1: I would ask questions, Right, do research on this and

1419
01:03:41,960 --> 01:03:44,800
Speaker 1: then find a way to be able to explain it.

1420
01:03:45,040 --> 01:03:47,760
Speaker 1: So go read these books, go find documentaries. And then

1421
01:03:48,040 --> 01:03:51,880
Speaker 1: I just quickly realized at some point that that that

1422
01:03:52,000 --> 01:03:55,120
Speaker 1: the AI hadn't It was clear to me it hadn't

1423
01:03:55,200 --> 01:03:57,960
Speaker 1: read something and it doesn't have access. But I could

1424
01:03:57,960 --> 01:03:59,280
Speaker 1: figure it out it hadn't, And I just said, like,

1425
01:03:59,480 --> 01:04:01,360
Speaker 1: did you look at that video? Like it was a video?

1426
01:04:01,400 --> 01:04:03,560
Speaker 1: I go, did you really look at that video that speech?

1427
01:04:04,240 --> 01:04:06,560
Speaker 1: And then it immediately went no, you got me there.

1428
01:04:06,600 --> 01:04:08,000
Speaker 1: I didn't look at the speech, but I'm gonna go

1429
01:04:08,160 --> 01:04:10,920
Speaker 1: now find it a different way. So you do. You

1430
01:04:11,120 --> 01:04:15,479
Speaker 1: do have to be vigilant acculturated to having those kinds

1431
01:04:15,520 --> 01:04:17,960
Speaker 1: of crazy too, like you have to maybe have advanced

1432
01:04:18,000 --> 01:04:22,240
Speaker 1: degrees or have trained yourself. So I'm not this is fascinating,

1433
01:04:22,320 --> 01:04:26,200
Speaker 1: right because I take for granted the steps I take

1434
01:04:26,240 --> 01:04:31,240
Speaker 1: for granted to and make my way through the world.

1435
01:04:31,440 --> 01:04:32,120
Speaker 3: Yes, and.

1436
01:04:34,080 --> 01:04:36,720
Speaker 1: Like the way that I would interrogate something like that

1437
01:04:36,920 --> 01:04:39,000
Speaker 1: to get to an answer that's useful.

1438
01:04:39,320 --> 01:04:41,640
Speaker 5: Yeah, as opposed to maybe my parents would be like, oh, okay,

1439
01:04:41,640 --> 01:04:42,360
Speaker 5: you watched that video.

1440
01:04:42,360 --> 01:04:46,160
Speaker 1: Okay, okay, and they might get wrong information from this.

1441
01:04:46,360 --> 01:04:47,880
Speaker 1: I can't argue with that. You're right about it.

1442
01:04:47,920 --> 01:04:50,400
Speaker 2: And I think that a lot of this grand theory

1443
01:04:50,480 --> 01:04:53,680
Speaker 2: of this comes down to I think people have a

1444
01:04:53,760 --> 01:04:55,760
Speaker 2: lot of questions and not a lot of people to

1445
01:04:55,840 --> 01:04:58,400
Speaker 2: ask them to questions about their life, how they're feeling.

1446
01:04:58,520 --> 01:05:01,640
Speaker 2: Then I think it's answer with the medical side, where

1447
01:05:01,680 --> 01:05:04,360
Speaker 2: it's it's quite hard to ask a doctor a question

1448
01:05:04,480 --> 01:05:07,200
Speaker 2: in any country, it's hard to know whether they and

1449
01:05:07,320 --> 01:05:10,120
Speaker 2: also doctors regularly make you feel annoying. I'm not talking

1450
01:05:10,160 --> 01:05:10,840
Speaker 2: about my doctor.

1451
01:05:10,880 --> 01:05:11,320
Speaker 3: He loves me.

1452
01:05:12,000 --> 01:05:14,400
Speaker 2: But you can't go to a doctor regularly with little questions.

1453
01:05:14,480 --> 01:05:17,600
Speaker 2: They don't have the time because they must maximize profits

1454
01:05:17,760 --> 01:05:20,120
Speaker 2: or they are busy one of the two. Yeah, And

1455
01:05:21,280 --> 01:05:23,760
Speaker 2: ultimately we are sitting there going I've got this weird

1456
01:05:23,920 --> 01:05:26,400
Speaker 2: rash or like I might leg itched in this place

1457
01:05:26,440 --> 01:05:27,800
Speaker 2: in the same day three days running.

1458
01:05:27,840 --> 01:05:30,080
Speaker 3: What could that mean? And you can't really google that.

1459
01:05:30,560 --> 01:05:33,880
Speaker 2: It's just you're dying but you can't really google that,

1460
01:05:33,960 --> 01:05:36,720
Speaker 2: but chat, GPT whatever can do an impressive impression of it,

1461
01:05:37,160 --> 01:05:39,800
Speaker 2: or in your case, Brian and I think this is

1462
01:05:39,880 --> 01:05:42,600
Speaker 2: reasonable lead you in a direction towards like a male

1463
01:05:42,680 --> 01:05:45,120
Speaker 2: clinic article about a particular things, something you could raise

1464
01:05:45,160 --> 01:05:48,640
Speaker 2: to your doctor. That makes sense. People are lonely, people

1465
01:05:48,800 --> 01:05:51,520
Speaker 2: just have weird questions. And I think that there is

1466
01:05:51,680 --> 01:05:54,680
Speaker 2: partially a bad cybe where it's like everyone wants everything immediately,

1467
01:05:54,880 --> 01:05:57,800
Speaker 2: we must have everything we want immediately. But also people

1468
01:05:57,840 --> 01:06:01,200
Speaker 2: are curious, and we are more connected as people. We

1469
01:06:01,320 --> 01:06:04,720
Speaker 2: are more decentralized as people. We don't meet people we

1470
01:06:04,840 --> 01:06:09,760
Speaker 2: don't have We are by at scale, overworked, underpaid, so

1471
01:06:09,840 --> 01:06:11,800
Speaker 2: we don't have the time to be generous with our time.

1472
01:06:12,000 --> 01:06:12,200
Speaker 3: Yeah.

1473
01:06:12,280 --> 01:06:14,400
Speaker 1: I'll give you one other use case though, because and

1474
01:06:14,520 --> 01:06:17,120
Speaker 1: it goes to the question of training. Training, But you

1475
01:06:17,160 --> 01:06:19,400
Speaker 1: could use this for you said, your parents dance or whatever.

1476
01:06:19,920 --> 01:06:24,000
Speaker 1: So I'll tell you that if you are somebody who

1477
01:06:24,120 --> 01:06:27,480
Speaker 1: is all used like deadlifting or squatting with a bar bell.

1478
01:06:27,520 --> 01:06:30,800
Speaker 1: As an example, if I put a thirty second clip

1479
01:06:31,800 --> 01:06:36,480
Speaker 1: into chat GBT and I say please watch this, tell

1480
01:06:36,560 --> 01:06:39,200
Speaker 1: me is this form at risk of injury? How should

1481
01:06:39,200 --> 01:06:41,880
Speaker 1: I modify it. Is it good enough? What do you

1482
01:06:41,920 --> 01:06:45,440
Speaker 1: think about the load of this weight? The answer it

1483
01:06:45,480 --> 01:06:48,120
Speaker 1: will give is and I have checked it against like

1484
01:06:48,200 --> 01:06:51,600
Speaker 1: the world. The answer will give is outstandings. And that's

1485
01:06:51,920 --> 01:06:53,720
Speaker 1: how would you get that in another way? I don't

1486
01:06:53,760 --> 01:06:57,520
Speaker 1: think there is. And that's a small example. Well that's

1487
01:06:57,600 --> 01:07:00,520
Speaker 1: different than search. There's an export. No, that's I'm searching

1488
01:07:00,640 --> 01:07:02,280
Speaker 1: using a video. No, no, it's not.

1489
01:07:02,400 --> 01:07:05,960
Speaker 5: The matching is a bit less spe sophisticated in the

1490
01:07:06,240 --> 01:07:07,160
Speaker 5: regular Google search.

1491
01:07:07,200 --> 01:07:07,880
Speaker 3: Oh I'm saying that.

1492
01:07:08,000 --> 01:07:11,160
Speaker 1: No, it's I'm merely matching it against a perceived perfect form.

1493
01:07:11,200 --> 01:07:13,440
Speaker 1: It's looking at your female literally, it'll go with your

1494
01:07:13,480 --> 01:07:16,200
Speaker 1: femur size. This is the kind of squad that you

1495
01:07:16,240 --> 01:07:18,880
Speaker 1: could do if I low bar versus high bar. Here's why,

1496
01:07:19,000 --> 01:07:21,240
Speaker 1: here's what this looks like. You're over, I'm agreeing with you.

1497
01:07:21,560 --> 01:07:24,240
Speaker 2: I'm just saying that search as a term has grown

1498
01:07:24,280 --> 01:07:26,960
Speaker 2: to look at this image and compare it to these sources,

1499
01:07:27,040 --> 01:07:28,600
Speaker 2: which is theoretically we'll search.

1500
01:07:28,840 --> 01:07:34,000
Speaker 1: But then in the good language, right language, fix how

1501
01:07:34,040 --> 01:07:34,480
Speaker 1: to fix it?

1502
01:07:35,320 --> 01:07:37,640
Speaker 4: And I worry if you ask a bad question, like

1503
01:07:37,720 --> 01:07:40,560
Speaker 4: if you not ask a bad question, phrase a question incorrect?

1504
01:07:40,640 --> 01:07:41,440
Speaker 3: Sure, And.

1505
01:07:43,000 --> 01:07:45,320
Speaker 4: I'm not saying you if one phrases let's say they

1506
01:07:45,320 --> 01:07:47,200
Speaker 4: ask a fitness question, but they phrase it a little

1507
01:07:47,200 --> 01:07:49,880
Speaker 4: bit weird, and the answer they get is harmful. It

1508
01:07:49,960 --> 01:07:53,280
Speaker 4: almost I'm worried about situations like that. And also it

1509
01:07:53,320 --> 01:07:58,680
Speaker 4: feels like we're removing an element of human responsibility or accountability,

1510
01:07:58,680 --> 01:08:01,160
Speaker 4: where it's like, well, the machine answered weird, rather than

1511
01:08:01,320 --> 01:08:03,200
Speaker 4: like a doctor answered weird.

1512
01:08:03,240 --> 01:08:05,560
Speaker 3: And I think that lots of corporations love that. They

1513
01:08:05,600 --> 01:08:06,960
Speaker 3: love that because they're already doing it.

1514
01:08:07,240 --> 01:08:10,880
Speaker 4: If I if my AI accidentally denies your insurance, it.

1515
01:08:10,960 --> 01:08:12,040
Speaker 3: Was the AI, that wasn't us.

1516
01:08:12,120 --> 01:08:16,040
Speaker 2: It's the algorithm. It's it's the same algorithmic pass on thing.

1517
01:08:16,560 --> 01:08:18,760
Speaker 2: But I think you're right in the that is kind

1518
01:08:18,800 --> 01:08:20,920
Speaker 2: of cool. I also have used those three because I

1519
01:08:21,000 --> 01:08:24,600
Speaker 2: do pay for this. I'm not a baseless hater. I

1520
01:08:24,680 --> 01:08:27,640
Speaker 2: did ask what's the distance between this bottom of this

1521
01:08:27,760 --> 01:08:31,160
Speaker 2: photo frame and the floor, and it spent ten minutes

1522
01:08:31,200 --> 01:08:33,560
Speaker 2: to give me a completely insanely wrong answer. They're not

1523
01:08:33,680 --> 01:08:34,439
Speaker 2: good with numbers.

1524
01:08:34,520 --> 01:08:37,160
Speaker 1: Oh fascinating. The other day, in this example, I just

1525
01:08:37,200 --> 01:08:39,880
Speaker 1: gave you it said I can't. It just said like

1526
01:08:39,960 --> 01:08:41,880
Speaker 1: I can't see, which is good. That was great.

1527
01:08:42,080 --> 01:08:42,640
Speaker 3: That should do that.

1528
01:08:43,240 --> 01:08:45,760
Speaker 5: That was great, both of you using the same I

1529
01:08:45,840 --> 01:08:46,599
Speaker 5: was using three.

1530
01:08:46,800 --> 01:08:52,040
Speaker 2: On Chap GBT plus though, So now I don't know

1531
01:08:52,080 --> 01:08:54,080
Speaker 2: how I feel about giving Clammy Sammy two.

1532
01:08:54,000 --> 01:08:55,639
Speaker 1: Underprive for a month and see what it does.

1533
01:08:55,840 --> 01:08:57,639
Speaker 3: Yeah, you can write it off.

1534
01:08:57,720 --> 01:09:00,720
Speaker 2: I don't want to write gave two hundred bucks to

1535
01:09:00,720 --> 01:09:01,840
Speaker 2: Warriolama day hopes.

1536
01:09:01,840 --> 01:09:02,960
Speaker 1: I would love you to do that and then call

1537
01:09:03,040 --> 01:09:05,120
Speaker 1: me and say it's the same result.

1538
01:09:05,200 --> 01:09:06,960
Speaker 2: No, I would love you to tell me I actually

1539
01:09:07,040 --> 01:09:07,559
Speaker 2: really careous.

1540
01:09:07,560 --> 01:09:09,120
Speaker 1: I would love you to say to me, dude, that's

1541
01:09:09,360 --> 01:09:10,559
Speaker 1: just been twenty bucks the same.

1542
01:09:11,240 --> 01:09:12,599
Speaker 5: Then you can say you're one eighty a month.

1543
01:09:12,680 --> 01:09:13,880
Speaker 1: Yeah, great news. Please.

1544
01:09:14,120 --> 01:09:16,000
Speaker 5: I am what Mike was bringing up I thought was

1545
01:09:16,040 --> 01:09:17,439
Speaker 5: going to be similar to the point I had been

1546
01:09:17,520 --> 01:09:19,360
Speaker 5: mulling over when you were talking about the training stuff,

1547
01:09:19,360 --> 01:09:21,560
Speaker 5: which is that we already deal with due to like

1548
01:09:22,120 --> 01:09:25,400
Speaker 5: you know, capitalism or barriers to entry, an influx of

1549
01:09:25,560 --> 01:09:27,920
Speaker 5: individuals who may not actually be fully equipped for the

1550
01:09:28,040 --> 01:09:31,759
Speaker 5: jobs they purport to do. So, whether it be journalists

1551
01:09:31,800 --> 01:09:34,840
Speaker 5: like myself or like again Finnish influencers or trainers that

1552
01:09:34,960 --> 01:09:38,080
Speaker 5: say they have whatever types of physical health degrees that

1553
01:09:38,240 --> 01:09:40,840
Speaker 5: are just the result of a ten hour course online

1554
01:09:41,000 --> 01:09:43,680
Speaker 5: that sort of thing. We're already dealing with the like

1555
01:09:43,880 --> 01:09:47,640
Speaker 5: quality dilution of like information coming from sources like that,

1556
01:09:48,080 --> 01:09:50,880
Speaker 5: to throw AI into the mix piece like making it

1557
01:09:51,120 --> 01:09:53,640
Speaker 5: even worse, like harder than ever to tell what the

1558
01:09:53,720 --> 01:09:55,640
Speaker 5: truth is. And I don't know about you all, but

1559
01:09:55,720 --> 01:09:59,040
Speaker 5: I find myself gaslighting myself all the time now, regardless

1560
01:09:59,080 --> 01:10:00,920
Speaker 5: of like my own life, whether it's the truth in

1561
01:10:01,000 --> 01:10:04,120
Speaker 5: the world, whether I'm being too sympathetic to multiple different perspectives.

1562
01:10:04,520 --> 01:10:06,760
Speaker 5: I don't know what the truth is anymore. I can't

1563
01:10:06,800 --> 01:10:09,679
Speaker 5: tell you what the cold heart scientific truth of anything

1564
01:10:09,800 --> 01:10:11,680
Speaker 5: ever is. And that's where it's led me.

1565
01:10:11,840 --> 01:10:13,280
Speaker 1: You also have to be willing, I agree with. That's

1566
01:10:13,280 --> 01:10:15,000
Speaker 1: a brilliant point. I think one of the things I

1567
01:10:15,040 --> 01:10:16,800
Speaker 1: would say to people if someone asked me, how do

1568
01:10:16,920 --> 01:10:19,639
Speaker 1: you how should you communicate? Mm hmm, I would say,

1569
01:10:19,640 --> 01:10:21,840
Speaker 1: And it's really painful for people because I've seen them

1570
01:10:21,880 --> 01:10:25,160
Speaker 1: talk online about what they love about conversing with the eye.

1571
01:10:26,040 --> 01:10:29,519
Speaker 1: You gotta say, click every toggle that says be mean,

1572
01:10:30,040 --> 01:10:32,800
Speaker 1: tell me the truth, don't tell me I'm smart. Yeah,

1573
01:10:32,960 --> 01:10:36,840
Speaker 1: Like you got to suck me rocked it to really

1574
01:10:37,080 --> 01:10:40,759
Speaker 1: be withholding in that way if you want to actually

1575
01:10:40,840 --> 01:10:43,960
Speaker 1: engage so that so that you're you're not getting gas lit,

1576
01:10:44,080 --> 01:10:46,360
Speaker 1: because yes, I agree that this is dangerous. The default

1577
01:10:46,400 --> 01:10:48,920
Speaker 1: setting is to gaslight you. You got to actually go.

1578
01:10:49,240 --> 01:10:51,519
Speaker 1: I don't need to hear like that glaze you.

1579
01:10:51,640 --> 01:10:52,759
Speaker 3: I think the young people.

1580
01:10:52,640 --> 01:10:57,200
Speaker 4: Blaze you sure before we run And you didn't hear

1581
01:10:57,240 --> 01:10:58,800
Speaker 4: the producer just laugh out loud.

1582
01:11:00,000 --> 01:11:02,120
Speaker 1: We talk about just one I think they used to say,

1583
01:11:02,160 --> 01:11:06,120
Speaker 1: gle but can we just talk about one positive, purely

1584
01:11:06,200 --> 01:11:09,200
Speaker 1: positive tech thing that happened the last week July fourth?

1585
01:11:09,880 --> 01:11:12,559
Speaker 1: Who who's on TikTok and knows all about the antipasto party?

1586
01:11:12,720 --> 01:11:16,840
Speaker 5: And was it the one that one person went to?

1587
01:11:17,479 --> 01:11:19,439
Speaker 1: Yeah, okay, it's the greatest thing.

1588
01:11:19,560 --> 01:11:19,840
Speaker 3: Please.

1589
01:11:20,479 --> 01:11:20,760
Speaker 2: This is this?

1590
01:11:21,000 --> 01:11:25,200
Speaker 1: Okay this They're in Texas. These people have a July

1591
01:11:25,360 --> 01:11:28,120
Speaker 1: fourth party. There's a woman she's just moved there recently.

1592
01:11:29,160 --> 01:11:32,400
Speaker 1: Her kid becomes friends with someone else's kid. And this woman, Sarah,

1593
01:11:32,479 --> 01:11:35,519
Speaker 1: is the parent of one boy, says, come with me

1594
01:11:35,600 --> 01:11:38,080
Speaker 1: over to these people party, these people's party. Woman a

1595
01:11:38,760 --> 01:11:42,880
Speaker 1: makes the apotheosis of all antipastos out, the greatest salady

1596
01:11:42,880 --> 01:11:46,200
Speaker 1: you've ever seen in your life, goes to their house. Yeah,

1597
01:11:47,040 --> 01:11:49,920
Speaker 1: and these people are like who's this stranger in our house?

1598
01:11:50,080 --> 01:11:52,000
Speaker 1: And they kick her out even though she brought this

1599
01:11:52,120 --> 01:11:56,639
Speaker 1: incredible She goes home and gets on TikTok and she's

1600
01:11:56,680 --> 01:12:00,240
Speaker 1: crying and she's like, I brought this salad and they

1601
01:12:00,320 --> 01:12:03,400
Speaker 1: kicked me out of their hounds. And the entire internet

1602
01:12:03,520 --> 01:12:06,960
Speaker 1: that her, yeah, and loves her so much, and it's like,

1603
01:12:07,800 --> 01:12:10,280
Speaker 1: it's an incredible story. Everybody I know is like, like

1604
01:12:10,600 --> 01:12:13,040
Speaker 1: everybody of all ages, like nieces and nephews of mine

1605
01:12:13,400 --> 01:12:16,599
Speaker 1: and then older people older than me are all sending

1606
01:12:16,880 --> 01:12:19,200
Speaker 1: and it's an amazing thing. Is a huge community is

1607
01:12:19,320 --> 01:12:23,760
Speaker 1: rallied to hate these people and to love her and

1608
01:12:23,880 --> 01:12:28,120
Speaker 1: her homemade mazzarella and come grown tomatoes that she brought

1609
01:12:28,160 --> 01:12:28,559
Speaker 1: to their.

1610
01:12:28,439 --> 01:12:32,680
Speaker 5: House scaling Lo, you saw something else, but no, I

1611
01:12:32,720 --> 01:12:35,040
Speaker 5: saw something else. All together, it's really worth But like

1612
01:12:35,120 --> 01:12:36,960
Speaker 5: Reddit does things like that, and that's the thing.

1613
01:12:37,040 --> 01:12:39,320
Speaker 3: Reddit like this is I liked ending this in the

1614
01:12:39,360 --> 01:12:41,280
Speaker 3: post of notes. That's love it. Reddit does that.

1615
01:12:41,560 --> 01:12:44,120
Speaker 5: You can say something well, yeah, no, community and social

1616
01:12:44,280 --> 01:12:46,439
Speaker 5: forums like that, that's what the Internet is great for.

1617
01:12:46,680 --> 01:12:48,920
Speaker 5: And then not a lot of AI as president, a

1618
01:12:48,960 --> 01:12:49,760
Speaker 5: lot of that and.

1619
01:12:50,000 --> 01:12:52,800
Speaker 2: It's almost read it especially right now has got good

1620
01:12:52,800 --> 01:12:56,360
Speaker 2: because it isn't they The CEO keeps thinking of shoving

1621
01:12:56,439 --> 01:12:57,840
Speaker 2: it places, but even on the.

1622
01:12:57,840 --> 01:12:59,719
Speaker 3: Better offline read nine thousand.

1623
01:13:00,080 --> 01:13:03,519
Speaker 2: Now, hey guys, but it's great because I one of

1624
01:13:03,520 --> 01:13:05,800
Speaker 2: my biggest stories I wrote recently was on Cursor and

1625
01:13:06,000 --> 01:13:08,240
Speaker 2: them falling apart, and it was because someone on the

1626
01:13:08,280 --> 01:13:10,719
Speaker 2: Red the forum was just like looking through their stuff

1627
01:13:10,800 --> 01:13:13,439
Speaker 2: like a you and everyone had this full conversation about it.

1628
01:13:13,720 --> 01:13:16,280
Speaker 2: You've got these people out there in this morass of

1629
01:13:17,040 --> 01:13:20,160
Speaker 2: fake stuff or generated stuff or seo stuff, You've got

1630
01:13:20,280 --> 01:13:23,479
Speaker 2: genuine people. There is still a joy to all of

1631
01:13:23,560 --> 01:13:26,200
Speaker 2: this crap. I love Blue Sky as well, but read

1632
01:13:26,320 --> 01:13:27,840
Speaker 2: it has really just I'm shocked.

1633
01:13:27,880 --> 01:13:29,439
Speaker 1: Spend a lot of time I read it. I read it.

1634
01:13:29,520 --> 01:13:30,000
Speaker 2: I'm shocked.

1635
01:13:30,040 --> 01:13:33,360
Speaker 1: And a group someone ever goes on you got to

1636
01:13:33,360 --> 01:13:36,000
Speaker 1: pick your on. No, but I'm saying, you know, like,

1637
01:13:36,240 --> 01:13:39,720
Speaker 1: but but there where people have a hobby or a thing,

1638
01:13:39,880 --> 01:13:44,160
Speaker 1: like whether it's music. You know, you're like I take stuff. Yeah,

1639
01:13:44,240 --> 01:13:47,920
Speaker 1: it's great and you must love the like the Claude Reddit.

1640
01:13:48,600 --> 01:13:52,320
Speaker 1: Every day someone's like, why does claud you must I

1641
01:13:52,360 --> 01:13:53,520
Speaker 1: love our slash Google.

1642
01:13:53,360 --> 01:13:56,519
Speaker 2: Yes, I like our slash sas because it's all just

1643
01:13:56,640 --> 01:14:01,200
Speaker 2: people like running sass. Know you're thinking of a different

1644
01:14:01,280 --> 01:14:04,880
Speaker 2: SaaS one. I'm talking s A A S as a service.

1645
01:14:05,120 --> 01:14:05,920
Speaker 3: Yeah, I'm a loser.

1646
01:14:06,320 --> 01:14:09,280
Speaker 2: So no, you watch people being like my app has

1647
01:14:09,320 --> 01:14:11,760
Speaker 2: been up for six months since made seven dollars. A

1648
01:14:11,800 --> 01:14:12,960
Speaker 2: bunch of people are like yes.

1649
01:14:13,200 --> 01:14:13,760
Speaker 5: And there was subs.

1650
01:14:14,640 --> 01:14:17,639
Speaker 2: I kind of love them that you've got these niches.

1651
01:14:17,760 --> 01:14:19,720
Speaker 2: But I hate to say, I do need to call

1652
01:14:19,800 --> 01:14:20,280
Speaker 2: this episode.

1653
01:14:20,640 --> 01:14:22,000
Speaker 3: Brian. Where can people find you?

1654
01:14:22,920 --> 01:14:23,080
Speaker 1: Oh?

1655
01:14:23,800 --> 01:14:26,760
Speaker 3: Instagram, Yes, we'll have a link to you there as well.

1656
01:14:27,520 --> 01:14:30,519
Speaker 4: You can find me on Instagram at Mike Drucker is Dead,

1657
01:14:30,720 --> 01:14:33,519
Speaker 4: and on Blue Sky at Mike Drucker and by a

1658
01:14:33,640 --> 01:14:36,599
Speaker 4: good Game, No Rematch. It is a book that's available digital,

1659
01:14:36,640 --> 01:14:39,240
Speaker 4: hardcover or audio with the audio read by myself.

1660
01:14:39,479 --> 01:14:43,479
Speaker 5: Hell yeah, I'm at engadget dot com or on threads

1661
01:14:43,479 --> 01:14:45,519
Speaker 5: at Sherlin Instagram c h r O I N N

1662
01:14:45,640 --> 01:14:46,439
Speaker 5: S c h r M.

1663
01:14:46,960 --> 01:14:49,920
Speaker 3: Type in Google the man who destroyed Google Search. That's me.

1664
01:14:50,520 --> 01:14:52,800
Speaker 2: I'm ed Zitchohn. Thank you so much for listening as

1665
01:14:52,880 --> 01:14:55,520
Speaker 2: ever is recording the wonderful New York Studio and iHeartRadio.

1666
01:14:55,600 --> 01:14:58,080
Speaker 2: Danel Goodman of course as our producer. Thank you so much, Daniel,

1667
01:14:58,520 --> 01:15:08,960
Speaker 2: Thank you all for listening. Thank you for listening to

1668
01:15:09,040 --> 01:15:12,000
Speaker 2: Better Offline. The editor and composer of the Better Offline

1669
01:15:12,000 --> 01:15:14,680
Speaker 2: theme song is Matasowski. You can check out more of

1670
01:15:14,720 --> 01:15:18,320
Speaker 2: his music and audio projects at Matasowski dot com, M

1671
01:15:18,360 --> 01:15:22,479
Speaker 2: A T T O S O W s ki dot com.

1672
01:15:23,200 --> 01:15:25,479
Speaker 2: You can email me at easy at Better Offline dot

1673
01:15:25,560 --> 01:15:27,760
Speaker 2: com or visit Better Offline dot com to find more

1674
01:15:27,800 --> 01:15:31,160
Speaker 2: podcast links and of course, my newsletter. I also really

1675
01:15:31,200 --> 01:15:33,439
Speaker 2: recommend you go to chat dot where's youreaed dot at

1676
01:15:33,520 --> 01:15:35,920
Speaker 2: to visit the discord, and go to our slash Better

1677
01:15:35,960 --> 01:15:39,160
Speaker 2: Offline to check out our reddit. Thank you so much

1678
01:15:39,200 --> 01:15:43,000
Speaker 2: for listening. Better Offline is a production of cool Zone Media.

1679
01:15:43,160 --> 01:15:46,000
Speaker 2: For more from cool Zone Media, visit our website cool

1680
01:15:46,080 --> 01:15:49,400
Speaker 2: Zonemedia dot com, or check us out on the iHeartRadio app,

1681
01:15:49,479 --> 01:16:08,439
Speaker 2: Apple Podcasts, or wherever you get your podcasts scho