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Speaker 1: You needn't help dunk in that basketball on that hoop?

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Speaker 1: Did behind you? Do you have that set up for

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Speaker 1: your for your kids, dude, just to fucking go full

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Speaker 1: Anthony Edwards on them. I bring it out every once

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Speaker 1: in a while. Yeah. Then when there's yeah, when they're sleeping,

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Speaker 1: I put at the top of their bed and wake

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Speaker 1: them up with a fucking poster. Damn, bro, they're not

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Speaker 1: going This would be a closed casket. What I like, just.

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Speaker 2: Waking up fan you got going on? That's the fan,

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Speaker 2: the type of fan that your your kids should be

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Speaker 2: able to convince themselves can propel them.

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Speaker 1: Oh yeah, yeah, put.

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Speaker 3: Them on a skateboard, put that behind them.

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Speaker 1: But first put a series of thin objects in to

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Speaker 1: see just how powerful the fan blade is. As any

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Speaker 1: true fan, explore.

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Speaker 3: An umbrella next to it.

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Speaker 1: Oh yeah, I would just throw toothpicks. I remember my

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Speaker 1: grandma would have him at her house. I would just

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Speaker 1: throw them into to watch him get fucking whipped out

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Speaker 1: like a crazy speed. Oh my god. Yeah, well, you're like,

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Speaker 1: you're learning cause and effect at a young age.

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Speaker 3: This is what we had instead of smartphones.

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Speaker 1: Yeah, exactly, throwing toothpicks in a big ass blade.

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Speaker 2: Yeah, basically any cell phone game works like a fruit ninja.

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Speaker 1: Did you ever do the thing to see how much

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Speaker 1: you can handle, like put your fingers in to see

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Speaker 1: if you can stop the fan blade?

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Speaker 3: Oh yeah, yeah, I've stopped. I've stopped fans with my tongue.

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Speaker 1: I've stopped them all here, yeah for real?

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Speaker 3: Like yeah, yeah, and I rocked them all, baby, to

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Speaker 3: send you a million open face fans, and I've rocked

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Speaker 3: them all. Hello the Internet, and welcome to Season three fifty,

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Speaker 3: episode two of Daily I Guys See production of iHeart Radio.

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Speaker 3: This is a podcast where we take a deep dive

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Speaker 3: into America's shared consciousness. And it is Tuesday, August sixth,

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Speaker 3: twenty twenty four.

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Speaker 1: Hell yeah, man, National Night Out Day? Why is there

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Speaker 1: hop in the image for this? Actually no, I take

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Speaker 1: that back.

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Speaker 3: Night Out Day is like they go full staff on

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Speaker 3: like DUI traps, like it promotes police community. Oh boy, yeah,

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Speaker 3: compaganda day, Little night out you deserve it.

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Speaker 1: Hey, you know, hey, you know who else can pop

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Speaker 1: and lock officer fucking rivers over here? Why don't you

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Speaker 1: show them a little locking?

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Speaker 4: Bro?

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Speaker 1: Yeah? There he goes Egyptian lover doing the most old schoolshid.

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Speaker 1: It's also National Fresh Breath Day, National root Beer Float Day,

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Speaker 1: National Wiggle your Toesday.

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Speaker 2: Wiggle them, okay, wiggle Yeah.

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Speaker 3: I'm at the point where my wife is officially like,

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Speaker 3: you need a pedicure like food.

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Speaker 1: We're not.

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Speaker 3: We need to take these out of your hands.

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Speaker 1: Wait, what is happening? What is going dry?

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Speaker 3: You know?

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Speaker 1: I mean my feed? I'm not. I'm acting like I

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Speaker 1: have like fucking award winning feet. I don't. I have

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Speaker 1: feet that have I think asbestos on the bottom. Because

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Speaker 1: I'm walking out here.

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Speaker 2: Something for the This is for the people to decide.

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Speaker 2: You got to show got to show feet. Yeah, see

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Speaker 2: what happens.

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Speaker 1: It's it's beyond that, Andrew. I can't. I mustn't put

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Speaker 1: many us. I mustn't. Definitely, I don't know.

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Speaker 3: This is this is me trying to create some demand.

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Speaker 1: I mustn't. I simply mustn't. Oh me, Oh you caught

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Speaker 1: me as I was darning my stockings in the night,

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Speaker 1: and I guess my feet were uncovered. Yeah, wait are they?

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Speaker 1: It's just got like you got them dry pads and

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Speaker 1: ship like. I like you put the lighter underneath. Yeah,

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Speaker 1: put the lighter underneath. I feel that ship as a kid,

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Speaker 1: just to like kind of prove to myself. I was like, yeah, bro,

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Speaker 1: this ship is thick. I can like five seconds create a.

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Speaker 3: Nice little okay without anybody noticed feeling youmi, Yeah, get.

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Speaker 1: The microplane out any.

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Speaker 3: Because I get the microplane out because that's when I

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Speaker 3: brainstorm in my foot cell storm. Anyways, my name is

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Speaker 3: Jack O'Brien aka Robert. Brought a dead bear cub. He

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Speaker 3: bought it for a dime. His sister had another one.

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Speaker 3: She paid it for a bike. He put the bike

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Speaker 3: with the dead bear cub. He put in New York City.

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Speaker 3: Put the bike with the dead bear cub. He put

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Speaker 3: in Central Park. He put the bike with the dead

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Speaker 3: bear cub. He put in New York City. Put the

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Speaker 3: bike with the dead bear cub. He called New Yorker

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Speaker 3: said write it up, and said New Yorker, that is it.

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Speaker 3: That is courtesy a Lacaroni on the discord. Great great job, Locarni,

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Speaker 3: What a time, A great job. Robert Kennett, Robert Afghnnedy Junior,

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Speaker 3: What a time to be alive you guys, Jesus Christ.

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Speaker 1: It gets worse. I love it. It's probably going to

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Speaker 1: be a bad story. About me, and then the New

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Speaker 1: York or post the photo of him with the dead

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Speaker 1: bear cub, and.

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Speaker 3: You're like, these haters, it's probably gonna be a bad story.

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Speaker 1: You're holding a dead animal pretending like that's clearly been

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Speaker 1: like it's bleeding from its injuries, and you're like, it's

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Speaker 1: biting me. No, all right, let's let's go to Lugers.

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Speaker 2: Huh, gotta go to Keynes by the way, when you're

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Speaker 2: up in Central Park?

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Speaker 3: Does this say at least the last time he was like,

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Speaker 3: last time there was a horrible picture of him with

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Speaker 3: a dead animal, he was pretending to eat a dog. Yeah,

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Speaker 3: this time he had the bear pretending to eat him,

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Speaker 3: which would be an improvement if his alibi was not.

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Speaker 3: I was gonna cut it up for meat and eat

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Speaker 3: it bear fur so that I could have like baby

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Speaker 3: bear meat and fur.

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Speaker 1: Like No, one's really really kind of sussing that part out,

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Speaker 1: or it's like, hold on, so you just want to

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Speaker 1: eat dead bear cub meat? Yeah?

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Speaker 3: Is this gonna be a big enough story though, Like

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Speaker 3: we don't even have to explain what the fuck we're

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Speaker 3: talking about, or do we need to tell people? I

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Speaker 3: don't know, I mean, listen, We talked about it on

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Speaker 3: Monday's episode.

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Speaker 1: You can listen to it there. Look RFK threw away

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Speaker 1: a dead bear cub in Central Park, dead.

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Speaker 3: Bear cub in his car first of all, threw it

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Speaker 3: away in Central Park. Big mystery for a decade how

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Speaker 3: bear get into Central Park? And he was like, out

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Speaker 3: of nowhere? Okay, it was me.

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Speaker 1: Ten years later, all right, my niece wrote that piece. Yeah,

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Speaker 1: that's a weird incidence. Huh. Okay, well I didn't.

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Speaker 2: I didn't see anything besides the headline is he's he

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Speaker 2: was going to get caught, is why he came up

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Speaker 2: with or it was the New.

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Speaker 3: Yorker was about to report on it, and he was like,

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Speaker 3: I don't know, that's probably gonna probably gonna slander me,

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Speaker 3: you know, meant.

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Speaker 2: Feel like he he is kind of like in that

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Speaker 2: like Internet scammer territory where like like like the shit

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Speaker 2: with like intentional or who cares typo's which filters out

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Speaker 2: anyone remotely skeptical, So like anyone who's still on the

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Speaker 2: RFK junior hook is I mean those hooks are in

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Speaker 2: your brain.

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Speaker 1: Oh yeah, almost.

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Speaker 3: Like the tentacles of a worm, like like this is

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Speaker 3: just like yeah, he got whatever.

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Speaker 1: Like four percent of the population believes him, like really

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Speaker 1: believes in him. Yeah, yeah, Yeah, that's a potent group

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Speaker 1: though that are behind him. Yeah, Like they're like, Bro,

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Speaker 1: I don't care about the bear stuff or the dog

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Speaker 1: stuff or the everything stuff.

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Speaker 3: I'm just saying, let's hear him Outston, what a cool guy.

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Speaker 3: He acknowledges the climate change is real. Let's hear him

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Speaker 3: out on everything else having to do with science, no

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Speaker 3: matter what he says. All right, I am thrilled to

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Speaker 3: be joined as always by my co host mister Miles

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Speaker 3: grad Ak.

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Speaker 1: Heyvance, you don't have to fit in that glove time.

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Speaker 1: Those days are over. You don't have to about it

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Speaker 1: to the right. Hey, verts, you don't have to fuck

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Speaker 1: that couch tonight, struck. The same for money. You don't

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Speaker 1: care if it's swaying or corder roy hey vants. Okay,

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Speaker 1: shout out to Steaming Chuck on the discord for that

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Speaker 1: one of my favorite karaoke jams.

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Speaker 3: So thank you for I was gonna say, shout out

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Speaker 3: to Steaming Chuck and shout out to uh Miles Gray. Yeah,

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Speaker 3: the vocal performance of of a lifetime.

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Speaker 1: Those beautiful The second verse two would have you can

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Speaker 1: hear him singing to a couch and he's like, I

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Speaker 1: love you since hog. Yeah, I wouldn't talk down to

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Speaker 1: ya because he's it's a sofa that he's you know, yeah, yeah, sofa.

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Speaker 2: You're no to Web one point zero or did you

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Speaker 2: guys already talk about this? Like remember like like like

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Speaker 2: one of the earliest viral videos was those like fucking

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Speaker 2: six dudes like humping that autumn in the couch.

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Speaker 1: Yeah pretty ricky?

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Speaker 2: Yeah yeah is that not you know jd Vance's face

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Speaker 2: like not just like that video if you want a

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Speaker 2: quick viral video.

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Speaker 1: I can't believe no one's put his face floating PG

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Speaker 1: or whatever. Yeah, yeah, I'm just surprised that's a automan humpers. Yeah,

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Speaker 1: he's probably gonna hit them with like a seasoned desist

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Speaker 1: like the way things are going, because he only knows

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Speaker 1: how to make things worse for himself.

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Speaker 3: Super producer Justin did bring it right. Yeah I knew it. Yeah,

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Speaker 3: I knew I could feel it in the ether. We

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Speaker 3: got too much content to make without that having Come on, no,

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Speaker 3: I get it, I get it. I'm just cut his

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Speaker 3: own good joke.

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Speaker 1: Yeah, come on, justin humility, like we said, this is

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Speaker 1: the summer of humility. To see that humility.

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Speaker 3: That's right, all right, Andrew, We're going to get to

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Speaker 3: know you yet.

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Speaker 1: I just I got to We're always talking to each

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Speaker 1: other anyway, so it was like, yeah anyway, so oh.

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Speaker 3: Yeah, anyway, oh yeah, this guy. Anyways, we are thrilled

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Speaker 3: in our third Yes, one of the very faces on

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Speaker 3: Mount Zeitmore a hilarious and brilliant writer producer. You know

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Speaker 3: him from the jos this racist podcast.

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Speaker 1: It's Andrew t. M.

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Speaker 2: I give it to you with much trivia, like hot

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Speaker 2: take straight from iHeartMedia.

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Speaker 1: That was ten seconds before we started recording. I was like,

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Speaker 1: I think I could pull this one off. That was flawless.

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Speaker 3: I could sense the COVID in your voice. The grave,

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Speaker 3: the grave.

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Speaker 2: Still y'all keep it, keep it safe, maskop COVID. This

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Speaker 2: COVID is is as the realist. Yet I feel for

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Speaker 2: me personally. Wow, did you go packed LOVID for COVID?

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Speaker 1: No?

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Speaker 2: I probably should have. I I just was like, I'm fine, right, uh, probably.

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Speaker 1: I don't know that version. No one in particular. Yeah,

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Speaker 1: my dog mostly right, Hey man, who are you talking to? Huh?

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Speaker 2: Mostly mostly my dog. I was telling these guys off. Mike,

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Speaker 2: my last screen time check in clocked in it. Hey, Andrew,

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Speaker 2: your screen time is a little bit up. You average

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Speaker 2: sixteen point five hours a day last week.

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Speaker 3: That's we're gonna need you to check into some sort

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Speaker 3: of rehab.

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Speaker 2: Yeah, two full union shifts of being on YouTube per day?

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Speaker 1: Right, what does that do to your soul? Like, how

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Speaker 1: do you feel?

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Speaker 2: I feel? Well, we can talk about it because all

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Speaker 2: of my over under rate.

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Speaker 3: History and like so deep and obscure.

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Speaker 2: I guess I will just say, broadly speaking, I'm an

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Speaker 2: old man and I finally found like I finally think

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Speaker 2: I get YouTube for me.

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Speaker 3: Oh okay, the algorithm has finally been bent to or

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Speaker 3: will I?

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Speaker 1: Yes, sort of, I need to get forty hours of

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Speaker 1: non stop watching. I think it might have an idea

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Speaker 1: of what I mean.

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Speaker 2: Well, I think what happened was I just like hated

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Speaker 2: the idea of YouTube stuff, and so when I like,

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Speaker 2: don't like engage with it, YouTube would just keep like

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Speaker 2: suggesting to me, Okay, well, the average person who likes

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Speaker 2: quote unquote YouTube likes this stuff, which is all like

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Speaker 2: right wing garbage.

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Speaker 1: So I was like, oh, I hate this, I hate this,

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Speaker 1: I hate.

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Speaker 2: This and then I finally watched enough of the Internet that, like,

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Speaker 2: I found people who un ironically call themselves YouTubers and

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Speaker 2: call the product that they make content and who I

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Speaker 2: don't hate. Ok So I started watching some of those folks,

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Speaker 2: and then it started suggesting me more of that, because

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Speaker 2: I assume YouTube is internally incentivized.

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Speaker 1: To who's a creator? We like to use the term

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Speaker 1: creator exactly. Yeah, yeah, he's a creator. Who like who

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Speaker 1: Who's Who's Who's one of the creators that cut your eye?

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Speaker 1: That's gonna come up?

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Speaker 2: And now I think the first one, Oh my god,

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Speaker 2: what's his name is? This is a very very famous person.

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Speaker 2: It's just new to me. But this guy, Jarvis Johnson, Okay,

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Speaker 2: who it's close enough to the circles of like people

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Speaker 2: who have been on Daily's guys have been on his

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Speaker 2: show before. But he is just a you know, fucking YouTuber,

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Speaker 2: talks to the camera, hota knows everything about YouTube. But

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Speaker 2: he's he's like mixed, and he is by virtue of

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Speaker 2: not being white. I feel like has at least like

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Speaker 2: takes that I can understand. And I'm just like, oh,

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Speaker 2: this is like much better. This is what I assume

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Speaker 2: people like who just watch like fucking mister Beast or

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Speaker 2: whatever without thinking about it, like get out of it anyway.

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Speaker 2: I was just I was like okay, and then once

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Speaker 2: I start on that train, the algorithm was like, Okay,

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Speaker 2: he finally has engaged.

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Speaker 1: With YouTube without it. Yeah.

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Speaker 2: So I've just been hit with a like a you know,

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Speaker 2: and it's good.

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Speaker 1: You know, I'm glad.

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Speaker 2: I'm glad I figured out a piece of this like

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Speaker 2: ecosystem that I can stand.

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Speaker 1: But yeah, that's how That's how sick I was. YouTube

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Speaker 1: video That's how sick I was, and YouTube videos.

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Speaker 3: I feel like the second so I went to his

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Speaker 3: Twitter and he's got like some something going on with

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Speaker 3: Cody co Like yeah, you know, and it's just immediately

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Speaker 3: like there's a beef with somebody who I'm not familiar with.

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Speaker 2: It is all internal, It is all part of this

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Speaker 2: like ecosystem that I don't know. It is interesting because

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Speaker 2: it's like I don't know any of the people he's

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Speaker 2: talking about except for like the most famous breakthay to

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Speaker 2: the household name ish people. Yeah, and I'm not gonna

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Speaker 2: learn probably, but it is like I'm like, Okay, this

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Speaker 2: guy at least has the right opinions on the basic

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Speaker 2: social things.

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Speaker 1: So I'm like, Okay, I can engage with this person. Yeah,

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Speaker 1: it's not going to say turn into like yeah, some

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Speaker 1: weird genocide apology or something. Yeah. Yeah.

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Speaker 3: We were in Boston stand with some friends last week

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Speaker 3: and their kids are like from say eight up through

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Speaker 3: like eighth grade, and they their mom was like, they

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Speaker 3: live in LA They've probably seen like some famous people,

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Speaker 3: do you guys want to ask them? But we were like, oh, yeah,

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Speaker 3: we saw Tom Cruise and they were like, oh my god,

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Speaker 3: that's so cool.

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Speaker 1: Did you see mister Beast?

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Speaker 3: And then like asked us if we'd see mister Beast

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Speaker 3: like five more times after we'd said no, they're like,

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Speaker 3: but what about mister Beat?

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Speaker 1: Like an actual famous person?

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Speaker 3: So you haven't you you haven't seen a famous interesting

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Speaker 3: you see Elon Musk? All right, let's uh, we're gonna

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Speaker 3: get to know you a little bit better in a moment. First,

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Speaker 3: a couple of things that we're talking about in today's episode.

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Speaker 3: Kamalin now leads Trump in the national polls. Trump continues

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Speaker 3: to still he's finding his footing finding well, we'll see,

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Speaker 3: And then the beepstakes is should be today and people

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Speaker 3: probably know by the time you're listening to this who

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Speaker 3: the heap is. But there's been a bit of a

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Speaker 3: controversy around the leading candidate, Josh Shapiro, and the mainstream

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Speaker 3: media always talks about it as like due to pressure

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Speaker 3: from progressive wing of the Democratic Party, he's got some

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Speaker 3: got some doubters, but they're they're very vague about why

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Speaker 3: that is, where that pressure comes from in some cases.

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Speaker 3: In some cases they're just like the left wing is

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Speaker 3: bad and scary, and people will be scary. Anyways, we'll

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Speaker 3: talk about that. We'll talk about the market correction that

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Speaker 3: is happening around AI might be upon us. We've been

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Speaker 3: talking about this for a while that a lot of

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Speaker 3: the stock market is puffed up on like AI hype

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Speaker 3: and what they think AI does doesn't seem to be

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Speaker 3: in line with reality, and whether they're would be a

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Speaker 3: market correction. The stock market is taking a ship, and

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Speaker 3: at least one of the narratives that's being spun around

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Speaker 3: that particular ship is that they have lost patience with AI.

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Speaker 3: So where's my money, says the shareholders? Right, AI peor Hey,

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Speaker 3: I I know it's like he's just having a tough one,

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Speaker 3: but we've all been.

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Speaker 1: You know, you know what that's like, Like we're talking

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Speaker 1: about practice. That's what I say.

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Speaker 3: All of that plenty more. But first, Andrew, we do

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Speaker 3: like to ask our guest, what is something from your

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Speaker 3: search history?

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Speaker 2: My my search history again fever dream for the last

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Speaker 2: two weeks, although you know, I don't think I should

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Speaker 2: had a fever anyway, My search history is did they

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Speaker 2: make that Korean sharpshooter into a Fortnite skin? Which seems

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Speaker 2: like the most obvious, like money on the table, Yeah,

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Speaker 2: thing I've ever and they did.

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Speaker 3: Not given so many billion dollar ideas, right, I know,

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Speaker 3: did they do that?

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Speaker 1: Turkish dude? Dude, that's the one who he was so

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Speaker 1: cat someone was saying, like he was explaining, He's like

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Speaker 1: I like to use both eyes open, so I don't

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Speaker 1: like that kind of stuff. And then a lot of

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Speaker 1: people are like, that's how like military people talk like

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Speaker 1: about shooting people Like that's sort of like yeah, I mean,

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Speaker 1: I don't know if that's a direct line, but they're like,

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Speaker 1: that's that's like the kind of training you get when

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Speaker 1: you're using like arms for hurting. But I think that

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Speaker 1: was also part of like the hyperbolic excitement around the

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Speaker 1: guy who had his hand in his pocket while winning

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Speaker 1: a silver medal.

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Speaker 3: Yeah, people were trying to like hate on it, and

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Speaker 3: they were like, this is actually a very normal way

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Speaker 3: to aim, and there are some people who aim that way.

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Speaker 3: It's just very uncommon. But everything they said still made

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Speaker 3: me be like, the guy's kind of cool, Like that's

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Speaker 3: kind of I like, I liked that dude.

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Speaker 2: He did as this morning just before we logged in

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Speaker 2: tweet very earnest fanboy stuff to Elon Musk that I think,

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Speaker 2: which if you if you rewind and just even look

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Speaker 2: at him, like if you showed that image to someone

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Speaker 2: and just like yes or no, this guy loves Elon Musk. Yeah,

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Speaker 2: before you you know, knew what under that this guy

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Speaker 2: wants to drive a Tesla. Yeah, the answer is so

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Speaker 2: we're just that's just he's just regressing to the obvious

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Speaker 2: mean he presented.

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Speaker 1: Yeh, look, we contain multitudes, you know, That's what I'm saying.

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Speaker 2: Though I don't he doesn't contain multitudes. That guy just

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Speaker 2: nail the gun. Guy who looks like that, it acts

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Speaker 2: like that. Of course the guy loves Elon Musk.

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Speaker 3: Yeah, the guy who got stuck on his dick while

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Speaker 3: pole vaulting actually just came out as big Trump supporter.

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Speaker 1: Sucks.

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Speaker 3: What is something you say is underrated?

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Speaker 1: Andrew underrated?

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Speaker 2: Okay, this was my other fever dream in COVID, I

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Speaker 2: genuinely started like outlining this as an essay that I

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Speaker 2: think I'm not going to do so Silicon Valley. I'm

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Speaker 2: sure you guys have covered it in some level, but

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Speaker 2: but there's been a broad spectrum of like failed or

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Speaker 2: failing AI wearables. Yeah right, that like humane pin and

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Speaker 2: this this new thing called friend and that's like the.

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Speaker 1: Little speaker you wear on your neck, right.

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Speaker 2: Yeah, my friend called it the AI amulet, and I'm like, yeah,

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00:20:32,080 --> 00:20:33,239
Speaker 2: that's basically what it is.

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Speaker 1: And I think.

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Speaker 2: My my like thesis, that my quarter baked thesis is

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Speaker 2: that like Silicon Valley, because it has been emptied of

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Speaker 2: anyone normal or middle class lower middle class, there is

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Speaker 2: not enough like street social pressure to have these people

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Speaker 2: realize that they are like unpopular dorks like the in

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00:21:00,240 --> 00:21:03,439
Speaker 2: an AI amulate seems like a good business idea to

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Speaker 2: a Silicon Valley person is because they live in a

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Speaker 2: bubble where conspicuously participating in Silicon Valley shit is cool

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Speaker 2: and they don't realize that humanity does not agree with

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Speaker 2: them anymore, and so like, yeah, so underrated is bullying

395
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Speaker 2: dorks for their own good because they have no like

396
00:21:26,400 --> 00:21:30,600
Speaker 2: like there's no like actual focus group more real than

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Speaker 2: like a bunch of fucking teenagers on the corner. And

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Speaker 2: if someone had, like if there were normal pockets of

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Speaker 2: normal people in San Francisco and Palo Alto, if you

400
00:21:41,200 --> 00:21:45,040
Speaker 2: walked around with an Apple Vision pro, your industrial designers

401
00:21:45,080 --> 00:21:47,600
Speaker 2: would quickly realize, oh, this is not going to work

402
00:21:47,640 --> 00:21:50,600
Speaker 2: as a consumer product or a cyber truck or any

403
00:21:50,640 --> 00:21:54,480
Speaker 2: of it. Right, But there's I think the market, like

404
00:21:54,560 --> 00:21:57,800
Speaker 2: really cheap market testing where your new wearable tech to

405
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Speaker 2: public high school when it gets out of there PM, Yeah,

406
00:22:01,640 --> 00:22:05,080
Speaker 2: stand outside and just hear the kind of flaming you

407
00:22:05,160 --> 00:22:05,880
Speaker 2: get from.

408
00:22:05,800 --> 00:22:07,679
Speaker 1: An actual public high school.

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Speaker 2: High no public high school where the property tax is

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00:22:12,840 --> 00:22:17,040
Speaker 2: less than whatever number would make a normal high school.

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Speaker 1: Sure, sure, sure, yeah, yeah exactly. But I think, yeah,

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Speaker 1: it's true, there is like so many of the ideas

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00:22:22,960 --> 00:22:26,560
Speaker 1: are like this only sounds good to like y'all, Yeah,

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00:22:26,760 --> 00:22:28,920
Speaker 1: like you're not in the whole thing is like, what

415
00:22:29,160 --> 00:22:32,040
Speaker 1: problem are you solving with this? Because it's not like

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00:22:32,359 --> 00:22:35,080
Speaker 1: I wish Star Trek was real. That's my problem that

417
00:22:35,200 --> 00:22:37,840
Speaker 1: I need solving, like which, I get that part, But

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00:22:38,240 --> 00:22:40,280
Speaker 1: aside from that, that's not the kind of thing where

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00:22:40,280 --> 00:22:42,520
Speaker 1: suddenly it's like you get that kind of ubiquity where

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Speaker 1: it's like, well, now everyone needs the aim.

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00:22:44,560 --> 00:22:47,760
Speaker 2: Yeah, well, or like, like I mean, the problem doesn't

422
00:22:47,800 --> 00:22:49,840
Speaker 2: have to be a technical problem. You know, it can

423
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Speaker 2: be I'm a fucking wristwatch guy. The problem can just

424
00:22:53,280 --> 00:22:55,760
Speaker 2: be I want a piece of jewelry that shows that

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Speaker 2: I'm part of this group. They just don't realize their group.

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Speaker 1: Is not cool. An our group was cool for like, you.

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Speaker 2: Know whatever, a small window from like twenty eleven, maybe

428
00:23:08,000 --> 00:23:10,520
Speaker 2: for like a year, because it's like, oh, you can

429
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Speaker 2: be rich and you're not.

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Speaker 1: You're not. It's not obvious to everyone that you're evil yet, right.

431
00:23:18,040 --> 00:23:22,199
Speaker 3: We actually wrote don't be evil on our letterhead, so

432
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Speaker 3: where it.

433
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Speaker 2: Worked, Yeah, as long as that worked for people, But

434
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Speaker 2: it doesn't work anymore and they don't realize it.

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Speaker 3: But then they all got so rich from that that

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Speaker 3: they were able to just expel literally everybody else from

437
00:23:34,240 --> 00:23:37,040
Speaker 3: the city in which they live. So now they're just

438
00:23:37,880 --> 00:23:42,000
Speaker 3: all by themselves. Being impressed by each other and while

439
00:23:42,040 --> 00:23:45,080
Speaker 3: doing things that believe you get your ass kicked for

440
00:23:45,160 --> 00:23:47,960
Speaker 3: doing something like that. Just Cincinnati, Ohio.

441
00:23:48,280 --> 00:23:49,879
Speaker 1: I mean, I know enough people in the Bay that

442
00:23:49,960 --> 00:23:54,080
Speaker 1: are are very sufficiently outraged enough by tech people. So like, yeah,

443
00:23:54,119 --> 00:23:56,880
Speaker 1: y'all were counting on you, really make go harder on them,

444
00:23:57,000 --> 00:23:59,159
Speaker 1: go harder on them. Let them just normal roast.

445
00:23:59,359 --> 00:24:01,000
Speaker 2: Just you know, you don't have to you have to

446
00:24:01,000 --> 00:24:03,840
Speaker 2: beat anyone's ass. You don't have to not feed anyone's ass.

447
00:24:03,920 --> 00:24:06,520
Speaker 2: But you don't have to be or just say, oh,

448
00:24:06,600 --> 00:24:08,840
Speaker 2: so you're a tough guy. Huh, and that's it. Just

449
00:24:08,840 --> 00:24:10,639
Speaker 2: say that and let it keep it moving. So you

450
00:24:10,680 --> 00:24:13,640
Speaker 2: tell me what what and then they'll be thinking about

451
00:24:13,640 --> 00:24:14,120
Speaker 2: that all day.

452
00:24:14,720 --> 00:24:16,000
Speaker 1: Anyway, Hey, I wear a bolt.

453
00:24:16,040 --> 00:24:19,400
Speaker 2: I watched I watch I have so many images saved

454
00:24:19,920 --> 00:24:22,679
Speaker 2: for a again, like a substack that I'll never write

455
00:24:22,680 --> 00:24:24,480
Speaker 2: of people wearing a couple visions.

456
00:24:24,560 --> 00:24:29,960
Speaker 3: And I did see somebody with the glasses again, glasses

457
00:24:30,000 --> 00:24:34,479
Speaker 3: that are like the camera glasses like glass the camera

458
00:24:34,880 --> 00:24:38,520
Speaker 3: on it, and they were like bragging about how good

459
00:24:38,520 --> 00:24:40,080
Speaker 3: the quality of the video is.

460
00:24:40,400 --> 00:24:42,520
Speaker 1: So for what though, Like what do you what are

461
00:24:42,520 --> 00:24:46,920
Speaker 1: you surreptitiously videoing exactly. You know, it's better because then

462
00:24:46,960 --> 00:24:50,480
Speaker 1: no one knows I'm taking video. Pretty cool. I'm just

463
00:24:50,560 --> 00:24:53,840
Speaker 1: a guy with sunglasses at a at a volleyball practice,

464
00:24:54,080 --> 00:24:58,719
Speaker 1: that's all. Yeah. Yeah. It's like, do you guys just

465
00:24:58,720 --> 00:24:59,520
Speaker 1: like spend.

466
00:24:59,280 --> 00:25:02,520
Speaker 2: Ten minutes thinking about the worst use case of this

467
00:25:02,600 --> 00:25:06,240
Speaker 2: before you fucking release it and spell Nope, nope.

468
00:25:06,560 --> 00:25:07,600
Speaker 1: I'm gonna stop you right there.

469
00:25:07,640 --> 00:25:11,840
Speaker 2: Nope, we do not and ten minutes talking yeah, Like honestly,

470
00:25:11,920 --> 00:25:15,560
Speaker 2: like like every tech company just needs one hater.

471
00:25:16,119 --> 00:25:20,959
Speaker 1: They don't have any haters, and like, just one. Listen.

472
00:25:21,080 --> 00:25:23,359
Speaker 1: I don't want to be Silicon Valley's hater, but someone

473
00:25:23,400 --> 00:25:25,640
Speaker 1: needs to be the Silicon Valley. Can you imagine from this?

474
00:25:25,720 --> 00:25:28,639
Speaker 1: They're like, Andrew, we'd love for you to come up

475
00:25:28,680 --> 00:25:31,679
Speaker 1: to Palalo, right, We'd love to show you sort of

476
00:25:31,720 --> 00:25:33,600
Speaker 1: like the new line of products we're thinking about and

477
00:25:33,720 --> 00:25:36,280
Speaker 1: just fucking go to rip our heads off. Dude.

478
00:25:36,600 --> 00:25:39,359
Speaker 3: It's much It's much cheaper than sending the street teams

479
00:25:39,400 --> 00:25:42,960
Speaker 3: that we usually do out to Louisville, Kentucky to get

480
00:25:42,960 --> 00:25:43,520
Speaker 3: made fun of.

481
00:25:43,760 --> 00:25:46,960
Speaker 2: Truly, like, like someone there just needs to be like

482
00:25:47,000 --> 00:25:49,679
Speaker 2: a consumer focus group company said, this is this is

483
00:25:49,720 --> 00:25:52,520
Speaker 2: my actual billion nine dollar idea someone else could take

484
00:25:52,600 --> 00:25:57,040
Speaker 2: in the Bay Area, which is like just like like

485
00:25:57,280 --> 00:26:01,480
Speaker 2: your focus group is just like twenty diverse teams from

486
00:26:01,560 --> 00:26:02,360
Speaker 2: lower middle.

487
00:26:02,119 --> 00:26:06,280
Speaker 1: Class background ryes. Yeah, not people who like it's in Like,

488
00:26:06,440 --> 00:26:09,000
Speaker 1: price isn't a barrier, like the people they talk to.

489
00:26:09,200 --> 00:26:11,920
Speaker 1: Price isn't a barrier to like ninety nine percent of

490
00:26:11,960 --> 00:26:14,080
Speaker 1: the products that they're hawking versus other people are like,

491
00:26:14,400 --> 00:26:17,240
Speaker 1: first of all, do I even want to aspire to that? No?

492
00:26:17,560 --> 00:26:19,840
Speaker 2: Or But even even if they're not representative, what they

493
00:26:19,840 --> 00:26:23,120
Speaker 2: can do is find your vulnerability quickly.

494
00:26:23,240 --> 00:26:26,920
Speaker 1: Right right right, Like ooh, so these they call these

495
00:26:27,000 --> 00:26:30,760
Speaker 1: pedal goggles these yeah camera, yeah, okay.

496
00:26:30,720 --> 00:26:32,760
Speaker 2: Oh if I saw someone wearing these, I would call

497
00:26:32,800 --> 00:26:34,720
Speaker 2: the police. I would like to make sure I know

498
00:26:34,760 --> 00:26:38,240
Speaker 2: why because my high school is, yeah, weird.

499
00:26:38,040 --> 00:26:40,119
Speaker 1: They're weird. Okay, good to know, Good to know.

500
00:26:40,359 --> 00:26:43,160
Speaker 3: Trying to think of, like what the other than the smartphone?

501
00:26:43,200 --> 00:26:46,639
Speaker 3: What have been the successful products out of like that

502
00:26:46,720 --> 00:26:49,199
Speaker 3: have like broken through the main stream that you like

503
00:26:49,280 --> 00:26:52,359
Speaker 3: see on a regular basis out of Silicon Valley. The

504
00:26:52,400 --> 00:26:56,879
Speaker 3: only thing I can think of is those hoverboards, But like,

505
00:26:56,920 --> 00:26:58,680
Speaker 3: are those even out of Silicon Valley?

506
00:26:58,960 --> 00:27:00,440
Speaker 1: Like those are everywhere they are.

507
00:27:00,359 --> 00:27:03,760
Speaker 2: Out of shin in China. I'm pretty sure that's because

508
00:27:03,800 --> 00:27:05,159
Speaker 2: that's like a gyroscope.

509
00:27:05,960 --> 00:27:11,199
Speaker 1: It's not like that's not even the from yeah yeah yeah, right,

510
00:27:11,520 --> 00:27:15,480
Speaker 1: aside from making like little gimb like stabilizing gimbal arms

511
00:27:15,480 --> 00:27:17,800
Speaker 1: for like cell phones. That's like something I always see

512
00:27:17,840 --> 00:27:20,480
Speaker 1: people hawking online. I feel like, honestly, I feel like

513
00:27:20,520 --> 00:27:23,760
Speaker 1: the tablet was like the last thing that was new,

514
00:27:23,920 --> 00:27:25,680
Speaker 1: and everyone's like, oh ship, yeah.

515
00:27:25,520 --> 00:27:28,919
Speaker 3: Tablets not cool to walk around in any way, No,

516
00:27:29,119 --> 00:27:31,119
Speaker 3: or take photos of at a sporting event.

517
00:27:32,119 --> 00:27:36,000
Speaker 1: I would say Apple Watch kind of yeah. Yeah. It's

518
00:27:36,119 --> 00:27:41,560
Speaker 1: like mainly known for being a gift people return, Yeah yeah,

519
00:27:41,600 --> 00:27:45,280
Speaker 1: yeah yeah. Is it well, it's it's like attack. I

520
00:27:45,320 --> 00:27:46,440
Speaker 1: feel like yeah, because.

521
00:27:46,240 --> 00:27:49,000
Speaker 2: It's like it's like, you know, this couldn't really count,

522
00:27:49,119 --> 00:27:51,320
Speaker 2: Like like there's no reason a kid needs an Apple Watch.

523
00:27:51,840 --> 00:27:54,640
Speaker 1: People need Apple Watch other people who like need their

524
00:27:54,680 --> 00:27:57,960
Speaker 1: fucking like steps measured and hearts. I was saying, my niece,

525
00:27:58,480 --> 00:28:01,520
Speaker 1: my niece, if she get my knees, got an Apple Watch,

526
00:28:01,600 --> 00:28:04,720
Speaker 1: so she so they didn't have to get her a phone, right,

527
00:28:05,280 --> 00:28:07,520
Speaker 1: and so that way there is a way. I was saying,

528
00:28:07,520 --> 00:28:09,919
Speaker 1: like it looks like the twenty twenties version of like

529
00:28:09,920 --> 00:28:12,000
Speaker 1: when a kid got a pager back in the day,

530
00:28:12,280 --> 00:28:14,080
Speaker 1: right right, which is sort of like, yeah, you really

531
00:28:14,119 --> 00:28:16,400
Speaker 1: can't do much dirt on this thing, but at least

532
00:28:16,440 --> 00:28:18,320
Speaker 1: we can like talk Nokia bricks.

533
00:28:18,600 --> 00:28:21,720
Speaker 3: Those are coming back in a big way with the kids.

534
00:28:22,200 --> 00:28:22,639
Speaker 1: I've been on.

535
00:28:22,840 --> 00:28:26,680
Speaker 2: I've been on a wired on my my my barely

536
00:28:26,800 --> 00:28:30,000
Speaker 2: surviving constitutionals, evening constitutionals.

537
00:28:30,200 --> 00:28:34,360
Speaker 1: I switched back to wired headphones. Oh yeah, yeah, which ones?

538
00:28:34,400 --> 00:28:35,040
Speaker 1: What do you got?

539
00:28:35,359 --> 00:28:39,200
Speaker 2: Just the regular ass because I just have an iPhone,

540
00:28:37,960 --> 00:28:40,240
Speaker 2: so that came to the phone.

541
00:28:40,680 --> 00:28:43,880
Speaker 1: Well, when you're marathon and like that, the battery can't

542
00:28:43,920 --> 00:28:46,960
Speaker 1: keep up with your lifestyle. Andrew sixteen hours on the

543
00:28:47,000 --> 00:28:50,760
Speaker 1: fucking block. No, bro, I know that you understand.

544
00:28:51,000 --> 00:28:55,400
Speaker 2: Yeah, how my my phone battery like doesn't exist anymore.

545
00:28:55,440 --> 00:28:58,640
Speaker 2: It's just a suggestion. It's just been like power all

546
00:28:58,640 --> 00:28:59,600
Speaker 2: the way up and down.

547
00:29:00,640 --> 00:29:00,880
Speaker 1: Yeah.

548
00:29:01,840 --> 00:29:05,800
Speaker 3: That means, what's something you think is overrated?

549
00:29:05,840 --> 00:29:10,800
Speaker 1: Andrew overrated once again? Format purity Okay.

550
00:29:10,920 --> 00:29:14,120
Speaker 2: As someone who was like, Okay, I found some YouTube

551
00:29:14,120 --> 00:29:17,760
Speaker 2: I like or like, I mean, it's just like this

552
00:29:17,880 --> 00:29:22,080
Speaker 2: kind of thing, like I've been like thinking about this

553
00:29:22,120 --> 00:29:24,480
Speaker 2: for someone, but like, like the people who like I'm

554
00:29:24,520 --> 00:29:28,360
Speaker 2: a fan of YouTube or like, like you know, the

555
00:29:28,400 --> 00:29:32,880
Speaker 2: prior version of this was like streaming, Like, oh, streaming

556
00:29:32,960 --> 00:29:38,040
Speaker 2: is materially different. Netflix is different than fucking broadcast TV.

557
00:29:38,680 --> 00:29:40,400
Speaker 2: And it's the thing that like I remember when I

558
00:29:40,400 --> 00:29:42,880
Speaker 2: worked at Comedy Central, I worked in the digital department,

559
00:29:44,160 --> 00:29:46,920
Speaker 2: you know, years and years and years ago before Netflix

560
00:29:47,000 --> 00:29:50,800
Speaker 2: was a huge thing, and I remember all of those

561
00:29:50,840 --> 00:29:54,000
Speaker 2: people being like, one day we're gonna run this place.

562
00:29:54,120 --> 00:29:57,640
Speaker 2: The whole and company is going to be Internet, and

563
00:29:57,800 --> 00:30:01,560
Speaker 2: like they're like worship of like the fiber optic cable

564
00:30:01,760 --> 00:30:03,760
Speaker 2: or the different fiber optic cable. I was like, this

565
00:30:03,840 --> 00:30:07,040
Speaker 2: is fucking crazy, Like you just don't know shit about

566
00:30:07,280 --> 00:30:11,760
Speaker 2: like developing TV shows, right, Oh, I remember you're saying that.

567
00:30:11,800 --> 00:30:13,800
Speaker 2: I'm like, they're just gonna push you out once the

568
00:30:13,840 --> 00:30:17,200
Speaker 2: medium is like lucrative and yeah, oh okay, yeah yeah yeah.

569
00:30:17,240 --> 00:30:18,800
Speaker 2: This was like but I think that to me, it's

570
00:30:18,840 --> 00:30:20,680
Speaker 2: the same thing that I was like, I don't like YouTube,

571
00:30:20,680 --> 00:30:24,479
Speaker 2: and I was like, I do like some YouTube. This

572
00:30:24,600 --> 00:30:26,080
Speaker 2: was my like same revelation.

573
00:30:26,280 --> 00:30:28,120
Speaker 1: It was like right ready to come around. Yet it

574
00:30:28,120 --> 00:30:31,920
Speaker 1: doesn't like mean anything, like it's just like.

575
00:30:32,880 --> 00:30:35,720
Speaker 3: I mean, everything in his content is what you're seah,

576
00:30:35,840 --> 00:30:36,320
Speaker 3: I get it.

577
00:30:36,440 --> 00:30:39,120
Speaker 1: Yeah, I feel man exactly.

578
00:30:39,440 --> 00:30:41,800
Speaker 2: But also I will say this, this was my like

579
00:30:41,880 --> 00:30:48,360
Speaker 2: first like like realization that like the cool thing of

580
00:30:48,440 --> 00:30:52,680
Speaker 2: being like like gen Z millennials like just like and

581
00:30:52,800 --> 00:30:57,120
Speaker 2: ironically using marketing speak to like describe themselves used to

582
00:30:57,120 --> 00:30:59,080
Speaker 2: bum me out so much. And now I'm like, yeah,

583
00:30:59,120 --> 00:31:04,320
Speaker 2: we all make content guys, Yeah, creators, Yeah.

584
00:31:04,000 --> 00:31:07,040
Speaker 1: Get in on this synergy. Man. Oh god, So.

585
00:31:07,000 --> 00:31:10,080
Speaker 3: You're saying like format purity, being like, oh, I I

586
00:31:10,200 --> 00:31:11,440
Speaker 3: only write for TV.

587
00:31:11,720 --> 00:31:16,840
Speaker 1: Yeah, yeah, I don't care for you too. I love right.

588
00:31:17,720 --> 00:31:20,760
Speaker 1: I'm just like really like music on it's really all

589
00:31:20,800 --> 00:31:26,120
Speaker 1: the same. Yeah, and it's all your heart. Your heart

590
00:31:26,240 --> 00:31:29,000
Speaker 1: is opening. I like to see that. Yeah. Yeah.

591
00:31:29,000 --> 00:31:32,160
Speaker 2: And again that maybe because of cardiovascular damage, but we'll

592
00:31:32,160 --> 00:31:32,560
Speaker 2: find out.

593
00:31:33,760 --> 00:31:35,360
Speaker 1: We will find out. Report back.

594
00:31:35,720 --> 00:31:38,360
Speaker 3: All right, let's take a quick break. We'll come back

595
00:31:38,400 --> 00:31:43,680
Speaker 3: and we'll talk about some news.

596
00:31:50,280 --> 00:31:51,720
Speaker 1: And we're back.

597
00:31:51,840 --> 00:31:55,640
Speaker 3: We're back, and all right, let's check out with the polls,

598
00:31:55,800 --> 00:31:58,880
Speaker 3: because they are in no way indicative of what's going

599
00:31:58,960 --> 00:32:01,080
Speaker 3: to happen in the election, but they are indicative of

600
00:32:01,360 --> 00:32:05,200
Speaker 3: which side is freaking out.

601
00:32:04,880 --> 00:32:09,880
Speaker 1: We got some we got some intel. Yeah, yeah, she's up.

602
00:32:10,240 --> 00:32:15,680
Speaker 1: Now about three points. I believe Kamala Harris. Yes, yes, yes, yes,

603
00:32:16,040 --> 00:32:21,000
Speaker 1: she's up. The polls nationally have swung her way forty

604
00:32:21,000 --> 00:32:23,960
Speaker 1: six points to forty three points. At the beginning of

605
00:32:24,000 --> 00:32:26,560
Speaker 1: January when it was Biden Trump Trump had a four

606
00:32:26,600 --> 00:32:31,800
Speaker 1: point lead over Biden. So that's pretty substantial. Like a

607
00:32:31,840 --> 00:32:34,280
Speaker 1: lot of people are like, oh it pretty much. Her

608
00:32:34,560 --> 00:32:37,600
Speaker 1: entry into the race has negated like all the negatives

609
00:32:37,600 --> 00:32:40,640
Speaker 1: that Biden had or lack of enthusiasm. So in a way,

610
00:32:40,760 --> 00:32:43,800
Speaker 1: like a lot of the map looks different, more states

611
00:32:43,800 --> 00:32:49,360
Speaker 1: are in play than previously believed. So yeah, a lot

612
00:32:49,360 --> 00:32:54,200
Speaker 1: of confidence for Democrats, for Republicans at mainly Trump, a

613
00:32:54,240 --> 00:32:59,560
Speaker 1: lot of freaking out. Trump is like, these polls are fake. Okay, dude,

614
00:33:00,000 --> 00:33:02,400
Speaker 1: And he's like he chickened out of like debating her,

615
00:33:02,880 --> 00:33:04,960
Speaker 1: and he's like was also using logic, He's like, well,

616
00:33:04,960 --> 00:33:06,480
Speaker 1: I lead in the polls, so why should I even

617
00:33:06,560 --> 00:33:08,600
Speaker 1: have to debate. It's like, I'm sorry, when was that

618
00:33:08,640 --> 00:33:11,240
Speaker 1: a condition of having a debate based on who was

619
00:33:11,320 --> 00:33:14,160
Speaker 1: up or down in the polls? But okay, even people

620
00:33:14,160 --> 00:33:16,760
Speaker 1: in truth social were calling him a coward for trying

621
00:33:16,840 --> 00:33:19,520
Speaker 1: to avoid this debate with Kamala Harris. Which is a

622
00:33:19,520 --> 00:33:21,880
Speaker 1: bit weird for them. I don't know if that's like

623
00:33:21,920 --> 00:33:25,680
Speaker 1: also just people like having a you know, coordinated troll

624
00:33:25,760 --> 00:33:28,280
Speaker 1: effort on truth social but like a hashtag was trending

625
00:33:28,280 --> 00:33:30,560
Speaker 1: about like Trump's a coward, and I'm like, that can't

626
00:33:30,560 --> 00:33:33,480
Speaker 1: be their people. Then he had a rally in Atlanta

627
00:33:33,720 --> 00:33:36,600
Speaker 1: over the weekend, complained that I could have had a

628
00:33:36,600 --> 00:33:41,320
Speaker 1: bigger crowd as Kamala, but the school organizers they're trying

629
00:33:41,360 --> 00:33:43,560
Speaker 1: to shut us down like a bunch of communists. Like

630
00:33:43,600 --> 00:33:45,320
Speaker 1: he had this whole thing about how they could have

631
00:33:45,360 --> 00:33:47,880
Speaker 1: had more people in there, Like he was really fucking

632
00:33:47,960 --> 00:33:50,440
Speaker 1: kept talking about how many more people could have been

633
00:33:50,480 --> 00:33:53,840
Speaker 1: in this arena. And also like he added a new

634
00:33:53,880 --> 00:33:56,440
Speaker 1: bit of like flair to the rally in the form

635
00:33:56,480 --> 00:34:00,719
Speaker 1: of like these like gas air cannons that blew up

636
00:34:00,760 --> 00:34:02,320
Speaker 1: on the side of the runway, like it was some

637
00:34:02,400 --> 00:34:05,960
Speaker 1: kind of fucking WrestleMania entrance. I think this is clear

638
00:34:06,040 --> 00:34:08,360
Speaker 1: He's like, we need better vibes. Do we have making

639
00:34:08,360 --> 00:34:11,799
Speaker 1: the stat why do we have Amber Rose? Boy? All right,

640
00:34:11,920 --> 00:34:14,719
Speaker 1: get the air cannons. So it looks like something is

641
00:34:14,760 --> 00:34:18,799
Speaker 1: fucking excited. Are exciting about this? You know, these appearances

642
00:34:19,000 --> 00:34:22,560
Speaker 1: but he continues now to just like still slur his

643
00:34:22,600 --> 00:34:26,040
Speaker 1: way through speeches. I will play some clips from this

644
00:34:26,120 --> 00:34:29,240
Speaker 1: Atlanta rally because you know, our man is still having

645
00:34:29,560 --> 00:34:32,720
Speaker 1: the trouble with reading and words.

646
00:34:32,840 --> 00:34:34,640
Speaker 3: It was wild, Like we talked about this last week,

647
00:34:34,640 --> 00:34:36,839
Speaker 3: but it was wild how quickly when Biden dropped out,

648
00:34:36,880 --> 00:34:38,680
Speaker 3: he was like, I don't have a cognitive defect.

649
00:34:38,760 --> 00:34:38,960
Speaker 1: You do?

650
00:34:39,320 --> 00:34:42,920
Speaker 3: Like yeah, he immediately got so insecure about that.

651
00:34:43,160 --> 00:34:45,239
Speaker 1: It was also like Biden wasn't too old to run

652
00:34:45,800 --> 00:34:50,520
Speaker 1: and you're like, sir, yeah, yo, bruh, shut up to me.

653
00:34:50,920 --> 00:34:54,400
Speaker 1: Another eight year old Trump slurs at the rally. This

654
00:34:54,560 --> 00:34:59,120
Speaker 1: is by far the most version. Exactly they aren't. They're

655
00:34:59,160 --> 00:35:02,360
Speaker 1: just messpeech an area. But yeah, this is when where

656
00:35:02,160 --> 00:35:05,080
Speaker 1: he does this thing though too, where it's like half

657
00:35:05,320 --> 00:35:08,319
Speaker 1: year old but also half so insecure that like when

658
00:35:08,360 --> 00:35:11,960
Speaker 1: he fucks up mid sentence, he completely pivots to try

659
00:35:12,000 --> 00:35:13,960
Speaker 1: and make another point rather than be like, oh, sorry,

660
00:35:14,280 --> 00:35:16,480
Speaker 1: did it like correcting anyway. Here's an example of this,

661
00:35:16,520 --> 00:35:20,239
Speaker 1: when he says national wrecting anyway. You'll hear it, appreciate it.

662
00:35:22,200 --> 00:35:26,440
Speaker 4: Together, we will stop Kamala Harris's nation wreckting. I'll tell

663
00:35:26,440 --> 00:35:28,200
Speaker 4: you what when you see what she's done.

664
00:35:28,080 --> 00:35:28,640
Speaker 1: To our nation.

665
00:35:28,800 --> 00:35:32,799
Speaker 4: She's wrecking our nation. The radicalism will take back our

666
00:35:32,840 --> 00:35:36,000
Speaker 4: country from the worst administration in American history.

667
00:35:36,239 --> 00:35:41,680
Speaker 1: Uh huh nation wrecked. Yeah, i'll tell you what. That

668
00:35:41,680 --> 00:35:44,719
Speaker 1: didn't happen. You did. Yeah, what do you think you

669
00:35:44,760 --> 00:35:47,200
Speaker 1: are proud of himself? On the second on the second

670
00:35:47,239 --> 00:35:51,640
Speaker 1: take wrecking. Yeah, I'll tell you what. Yeah, yeah, wreck

671
00:35:52,840 --> 00:35:55,880
Speaker 1: I've got an erecting. Here's the other thing. He also

672
00:35:56,040 --> 00:36:00,640
Speaker 1: goes in on her saying something about violent marbs or mobs.

673
00:36:00,680 --> 00:36:02,960
Speaker 1: I don't know, you should not stop.

674
00:36:03,000 --> 00:36:06,960
Speaker 4: She said she was endorsing to fund the place and

675
00:36:07,080 --> 00:36:11,920
Speaker 4: the police, and she said violent mobs. Let the violent

676
00:36:12,080 --> 00:36:13,200
Speaker 4: mobs keep going.

677
00:36:13,760 --> 00:36:15,759
Speaker 1: Okay, he need another one, he need another stab at

678
00:36:15,760 --> 00:36:16,080
Speaker 1: that one.

679
00:36:16,320 --> 00:36:16,520
Speaker 3: Yeah.

680
00:36:16,560 --> 00:36:19,799
Speaker 1: Yeah. It's such a stark contrast, Like even from that

681
00:36:19,920 --> 00:36:23,239
Speaker 1: rally that Kamala Harris had, like it was like wow, man,

682
00:36:23,600 --> 00:36:26,759
Speaker 1: like they're saying there's like there's real political messaging in

683
00:36:26,800 --> 00:36:30,080
Speaker 1: that speech where it's like they got the marbs, I

684
00:36:30,160 --> 00:36:33,080
Speaker 1: need the mobs, and they wanted to fund the place

685
00:36:33,680 --> 00:36:38,360
Speaker 1: in this place, I mean police just yeah. So uh.

686
00:36:38,440 --> 00:36:40,000
Speaker 1: And then also there was like this moment too where

687
00:36:40,040 --> 00:36:43,200
Speaker 1: Kyle Rittenhouse was like I can't support Donald Trump anymore

688
00:36:43,200 --> 00:36:45,480
Speaker 1: because he's not about the Second Amendment. And then very

689
00:36:45,560 --> 00:36:47,960
Speaker 1: quickly he like had a Maya kolpa and it was like,

690
00:36:48,400 --> 00:36:52,279
Speaker 1: I'm I got some bad advice from people. Obviously I'm

691
00:36:52,320 --> 00:36:55,000
Speaker 1: going to be voting for Trump because they came for

692
00:36:55,120 --> 00:36:57,880
Speaker 1: his ass when he tweeted that. So there's a lot

693
00:36:58,719 --> 00:37:04,640
Speaker 1: unsure sureness happening. But you know, we'll see because there's

694
00:37:04,680 --> 00:37:07,360
Speaker 1: a there's a there's a real good chance that a

695
00:37:07,400 --> 00:37:11,200
Speaker 1: lot of the enthusiasm could be just erased with the

696
00:37:11,280 --> 00:37:14,400
Speaker 1: wrong VP pick, at least from my perspective. Yeah, I

697
00:37:14,440 --> 00:37:16,680
Speaker 1: did most people. But yeah, the VP pick is really

698
00:37:16,680 --> 00:37:19,279
Speaker 1: the next like I said, that's the final not one

699
00:37:19,320 --> 00:37:22,360
Speaker 1: of the final things to put the ticket together where

700
00:37:22,600 --> 00:37:24,560
Speaker 1: either people go or.

701
00:37:26,360 --> 00:37:30,040
Speaker 3: Yeah, so it's I will just say like it. It

702
00:37:30,080 --> 00:37:33,879
Speaker 3: does make it clearer and clear, Like, how what a

703
00:37:33,960 --> 00:37:37,200
Speaker 3: weird position the Republicans are in. And there is a

704
00:37:37,800 --> 00:37:41,600
Speaker 3: lawnmower just going outside my window, just going off, so.

705
00:37:41,760 --> 00:37:45,000
Speaker 1: I can't can't even hear it sounds good and that's.

706
00:37:46,920 --> 00:37:49,440
Speaker 3: It might be a riding mower, all right, John, I

707
00:37:49,440 --> 00:37:52,200
Speaker 3: can't see it, but I just hear it. What a

708
00:37:52,239 --> 00:37:56,440
Speaker 3: weird position The Republicans are in because both parties had

709
00:37:56,560 --> 00:38:00,359
Speaker 3: this same terrible problem and that their candidates are like

710
00:38:00,560 --> 00:38:03,120
Speaker 3: way too old and kind of losing it, and the

711
00:38:03,160 --> 00:38:07,160
Speaker 3: Democrats had it worse, and then like one one of

712
00:38:07,200 --> 00:38:11,600
Speaker 3: them fixes the problem. It's just like, really, and like

713
00:38:11,680 --> 00:38:15,120
Speaker 3: all their talking points were drawing attention to that. It's

714
00:38:15,160 --> 00:38:18,360
Speaker 3: just a weird I don't know. I guess everybody's like

715
00:38:18,400 --> 00:38:21,279
Speaker 3: pointing this out already, but it is just I don't know,

716
00:38:21,480 --> 00:38:26,120
Speaker 3: like they both have the exact same horrorfule problem happened,

717
00:38:26,400 --> 00:38:30,040
Speaker 3: and one of them just magically fixes it and he

718
00:38:30,360 --> 00:38:32,000
Speaker 3: obviously can't fix it.

719
00:38:32,280 --> 00:38:34,640
Speaker 1: I mean, Nikki Haley was like when I remember in

720
00:38:34,680 --> 00:38:36,600
Speaker 1: the primary, she's like, the party that's gonna win is

721
00:38:36,640 --> 00:38:39,360
Speaker 1: the one that ditches their elderly person, which you know

722
00:38:39,400 --> 00:38:42,040
Speaker 1: when now that you look at you're like, huh, well, yeah,

723
00:38:42,040 --> 00:38:43,279
Speaker 1: I may have had. I know, you were saying that

724
00:38:43,440 --> 00:38:45,359
Speaker 1: in the process of a primary to be like get

725
00:38:45,480 --> 00:38:47,600
Speaker 1: rid of Trump and that's how we beat them. But

726
00:38:47,719 --> 00:38:51,800
Speaker 1: now it really does feel like, oh yeah, just whoever

727
00:38:51,880 --> 00:38:55,279
Speaker 1: gets real about like the leadership, it will help in

728
00:38:55,360 --> 00:38:57,439
Speaker 1: terms of support, but we'll see.

729
00:38:57,560 --> 00:39:00,279
Speaker 2: It's I mean, they were just so certain it was

730
00:39:00,320 --> 00:39:03,680
Speaker 2: not gonna happen. Yeah, yeah, yeah, yeah, yeah, and and

731
00:39:04,440 --> 00:39:07,000
Speaker 2: pretty rightful. I like the fact that it happened at

732
00:39:07,040 --> 00:39:08,759
Speaker 2: all is like fucking amazing.

733
00:39:09,239 --> 00:39:10,320
Speaker 1: It is really amazing.

734
00:39:10,840 --> 00:39:14,439
Speaker 3: And yeah, they I think it's really hard for them

735
00:39:14,480 --> 00:39:18,520
Speaker 3: to them to imagine h Trump doing it because it

736
00:39:18,880 --> 00:39:22,600
Speaker 3: could never possibly ever happen. Like, just trying to imagine

737
00:39:22,640 --> 00:39:24,359
Speaker 3: him being like, I'm gonna do it's best for the

738
00:39:24,400 --> 00:39:26,839
Speaker 3: country is like makes you laugh.

739
00:39:27,040 --> 00:39:29,319
Speaker 1: Like if he does that, it will be from a

740
00:39:29,480 --> 00:39:33,960
Speaker 1: jet like en Root to Moscow. He's like, and I'm

741
00:39:34,000 --> 00:39:36,040
Speaker 1: doing the best thing, which is I'll never come back

742
00:39:36,080 --> 00:39:39,440
Speaker 1: because it's so quickd there, and I'm now kicking it

743
00:39:39,440 --> 00:39:41,960
Speaker 1: with Steven Sagal, one of the coolest guys ever, so

744
00:39:42,280 --> 00:39:45,040
Speaker 1: I know he's also like if he didn't, he for

745
00:39:45,160 --> 00:39:48,800
Speaker 1: sure would then defer to or like like nominate fucking

746
00:39:49,120 --> 00:39:53,960
Speaker 1: Don Junior. Right, oh yeah, yeah, yeah, you think that

747
00:39:54,000 --> 00:39:57,239
Speaker 1: would be incredible? Could you imagine that debate? You know

748
00:39:57,280 --> 00:39:59,839
Speaker 1: how much fucking blow that guy would probably be doing

749
00:40:00,080 --> 00:40:03,480
Speaker 1: to be like, I gotta be confident there man for.

750
00:40:03,480 --> 00:40:07,520
Speaker 3: The smallest appearance, Ye mar A Lago an interview with

751
00:40:07,680 --> 00:40:11,799
Speaker 3: jd Vance before jd Vance was like a VP like

752
00:40:12,080 --> 00:40:17,959
Speaker 3: that's but yeah, all coine, the VP race, it's really

753
00:40:18,000 --> 00:40:20,840
Speaker 3: it's still it looks like it's down to Shapiro, Waltz

754
00:40:21,000 --> 00:40:21,720
Speaker 3: and Kelly.

755
00:40:22,080 --> 00:40:24,400
Speaker 1: There was something with some speculation when Mark Kelly tweeted

756
00:40:24,400 --> 00:40:26,359
Speaker 1: something and deleted. I didn't read the headline. People oh

757
00:40:26,400 --> 00:40:28,160
Speaker 1: my god, maybe they've had something to do with him

758
00:40:28,200 --> 00:40:31,719
Speaker 1: being picked. Whatever, you know, I think there seems to

759
00:40:31,719 --> 00:40:34,920
Speaker 1: be a lot of talk about who it should be,

760
00:40:35,360 --> 00:40:39,360
Speaker 1: and right now I'm seeing more about who it shouldn't be,

761
00:40:40,239 --> 00:40:43,400
Speaker 1: which is Josh Shapiro. And there are a few things

762
00:40:43,480 --> 00:40:45,719
Speaker 1: happened towards the end of last week. There was a

763
00:40:45,719 --> 00:40:48,840
Speaker 1: woman's group that's like advocates for safer workplaces like urged

764
00:40:48,880 --> 00:40:52,600
Speaker 1: Kamala Harris to reconsider like picking Josh Shapiro because of

765
00:40:52,600 --> 00:40:56,600
Speaker 1: the way he handled a sexual harassment case like within

766
00:40:56,640 --> 00:40:59,320
Speaker 1: his own office and like paying this woman like a

767
00:40:59,440 --> 00:41:01,880
Speaker 1: two hundred nin twenty five thousand dollars settlement from like

768
00:41:01,920 --> 00:41:04,799
Speaker 1: public funds. And people were like, are you kind of

769
00:41:04,800 --> 00:41:08,560
Speaker 1: like backing up, like your your homie that was perpetrating

770
00:41:08,560 --> 00:41:11,480
Speaker 1: this stuff, which wasn't a great look. The UAW, the

771
00:41:11,560 --> 00:41:16,080
Speaker 1: United Auto Workers, they said we'd rather not have Shapiro either,

772
00:41:16,480 --> 00:41:19,920
Speaker 1: they said, uh, Kentucky Governor Andy Basheer, in Minnesota Governor

773
00:41:19,960 --> 00:41:23,760
Speaker 1: Tim Waltz. Those were the top choices for the union.

774
00:41:24,160 --> 00:41:26,960
Speaker 1: They pointed at. One of the big things was that

775
00:41:27,040 --> 00:41:31,680
Speaker 1: Governor Shapiro is like big on school vouchers, and you know,

776
00:41:31,960 --> 00:41:35,080
Speaker 1: like Sean Fayn was, like public education has been under

777
00:41:35,160 --> 00:41:38,600
Speaker 1: attack for like from a Republican administrations forever. Like having

778
00:41:38,640 --> 00:41:41,880
Speaker 1: somebody who's like less in on public education is a

779
00:41:41,920 --> 00:41:45,360
Speaker 1: no for US dog Bernie Sanders and other unions with

780
00:41:45,520 --> 00:41:48,280
Speaker 1: like Tim Waltz as well for his like working class

781
00:41:48,320 --> 00:41:51,800
Speaker 1: cred you know, like Waltz like joined the auto workers

782
00:41:51,800 --> 00:41:54,359
Speaker 1: on a picket line. People just like that. But it's

783
00:41:54,400 --> 00:41:58,120
Speaker 1: interesting to see that like the establishment pick seems to

784
00:41:58,160 --> 00:42:01,440
Speaker 1: be Shapiro, or at least a lot of pundits, both

785
00:42:01,560 --> 00:42:05,600
Speaker 1: like the you know, center right and center center right

786
00:42:05,719 --> 00:42:08,800
Speaker 1: ish like Republicans that now speak on places like MSNBC

787
00:42:09,239 --> 00:42:13,440
Speaker 1: are also being like, obviously the the person it should

788
00:42:13,480 --> 00:42:16,680
Speaker 1: be is Josh Shapiro and using the same sort of

789
00:42:16,719 --> 00:42:19,160
Speaker 1: debunked logic we talked about last week, which is like,

790
00:42:19,280 --> 00:42:23,279
Speaker 1: well he's from Pennsylvania. You need Pennsylvania to win. And

791
00:42:23,360 --> 00:42:28,120
Speaker 1: because they like him in Pennsylvania, he can win Pennsylvania.

792
00:42:28,200 --> 00:42:30,879
Speaker 1: And a lot of research shows that that the VP

793
00:42:30,960 --> 00:42:33,880
Speaker 1: pick has really very little impact on something or like

794
00:42:33,960 --> 00:42:36,760
Speaker 1: that logic really isn't born out in the data. Yeah,

795
00:42:36,800 --> 00:42:38,200
Speaker 1: but here we are today.

796
00:42:38,320 --> 00:42:41,160
Speaker 2: I saw a stat probably on Twitter somewhere that was

797
00:42:41,239 --> 00:42:44,240
Speaker 2: like part of his big approval rating is because Mega

798
00:42:44,280 --> 00:42:46,840
Speaker 2: people love him, like a third of He has like

799
00:42:47,160 --> 00:42:53,000
Speaker 2: approval from like a third of MEGA voters. And it's like, hey, guys,

800
00:42:53,600 --> 00:42:56,680
Speaker 2: those people are not going to vote for kawbo Ara, Like.

801
00:42:57,480 --> 00:43:01,200
Speaker 1: Yeah, to happen. Well, it's like this goal with him,

802
00:43:01,400 --> 00:43:03,399
Speaker 1: yeah right. And it's the same way we have these

803
00:43:03,440 --> 00:43:06,040
Speaker 1: other sort of purple states where like you'll have like

804
00:43:06,080 --> 00:43:09,399
Speaker 1: a Democrat win the governor the governor's mansion, and then

805
00:43:09,440 --> 00:43:11,880
Speaker 1: down ballot they're like yeah, but Republicans for other stuff,

806
00:43:12,360 --> 00:43:14,480
Speaker 1: you know, like where they're like between the two, I

807
00:43:14,480 --> 00:43:19,040
Speaker 1: can make this choice or whatever. But the deep steaks,

808
00:43:19,480 --> 00:43:22,719
Speaker 1: it's very touch and go. I mean, I think it

809
00:43:22,760 --> 00:43:26,000
Speaker 1: feels like you're gonna you're gonna gain more or they'll

810
00:43:26,040 --> 00:43:29,200
Speaker 1: be less hand ringing and less fracturing if you go

811
00:43:29,280 --> 00:43:31,239
Speaker 1: as someone that is in Shapiro, when you already have

812
00:43:31,320 --> 00:43:34,160
Speaker 1: people that are part of the coalition of voters Democrats

813
00:43:34,160 --> 00:43:36,839
Speaker 1: recording like coming out and being like explicit and being

814
00:43:36,880 --> 00:43:40,160
Speaker 1: like maybe not him, maybe not him. Yeah, Like there

815
00:43:40,200 --> 00:43:42,200
Speaker 1: was another thing like people are pointing out Like when

816
00:43:42,200 --> 00:43:44,319
Speaker 1: he was in college he wrote this like paper talking

817
00:43:44,360 --> 00:43:47,920
Speaker 1: about Palestinians like being unable to like self govern, very

818
00:43:48,239 --> 00:43:52,440
Speaker 1: very fucking backwards take on Palestinian people. His office was

819
00:43:52,480 --> 00:43:56,439
Speaker 1: like he was twenty one in writing that. Also, don't

820
00:43:56,480 --> 00:43:58,799
Speaker 1: ask him to comment on what is happening there now.

821
00:43:58,840 --> 00:44:02,960
Speaker 2: But he is different, right, he is a different age now. Yeah,

822
00:44:03,000 --> 00:44:04,280
Speaker 2: and that is indisputable.

823
00:44:06,360 --> 00:44:10,400
Speaker 3: He did compare student protesters to the KKK, but otherwise

824
00:44:10,520 --> 00:44:12,360
Speaker 3: it seems to be seems to have both feet on

825
00:44:12,360 --> 00:44:12,920
Speaker 3: the ground there.

826
00:44:13,040 --> 00:44:14,520
Speaker 1: Yeah, yeah, he does seem to.

827
00:44:14,680 --> 00:44:18,839
Speaker 3: So according to odds makers, who are true overlords can

828
00:44:18,920 --> 00:44:21,920
Speaker 3: take bets on this, they said that he was he

829
00:44:22,040 --> 00:44:25,880
Speaker 3: was like way out in front last Wednesday, but due

830
00:44:25,920 --> 00:44:29,960
Speaker 3: to the pressure from some of the progressive wings of

831
00:44:30,000 --> 00:44:34,839
Speaker 3: the party, Tim Waltz has been the betting benefactor of

832
00:44:34,840 --> 00:44:38,080
Speaker 3: that push, going from plus fourteen hundred at UK book

833
00:44:38,080 --> 00:44:41,439
Speaker 3: maker bet three sixty five to plus two seventy five

834
00:44:41,520 --> 00:44:45,200
Speaker 3: on Sunday. So in a matter of like three days.

835
00:44:45,239 --> 00:44:51,640
Speaker 3: He became pretty close with Shapiro fairly rapidly. I feel

836
00:44:51,680 --> 00:44:55,280
Speaker 3: like gamblers are pretty good at like getting inside information

837
00:44:55,440 --> 00:44:58,040
Speaker 3: on shit. So that's the one thing that's giving me hope.

838
00:44:58,280 --> 00:45:01,520
Speaker 3: The history of the Democratic Party, like tacking towards the

839
00:45:01,600 --> 00:45:04,480
Speaker 3: center is the thing that is not giving me hope.

840
00:45:04,600 --> 00:45:05,080
Speaker 1: Yeah.

841
00:45:05,080 --> 00:45:07,840
Speaker 2: The election market, though, is such a like like the

842
00:45:07,960 --> 00:45:10,240
Speaker 2: election choices market.

843
00:45:11,040 --> 00:45:13,000
Speaker 1: Yeah, it is wild.

844
00:45:12,680 --> 00:45:16,279
Speaker 2: That this is because it's like, like why, it's not like,

845
00:45:16,400 --> 00:45:19,360
Speaker 2: you know, there's like a like a like a like

846
00:45:19,400 --> 00:45:22,560
Speaker 2: a player's association or something like what is to stop

847
00:45:22,600 --> 00:45:26,440
Speaker 2: someone from the Kamala Harris administration to just put a

848
00:45:26,440 --> 00:45:32,000
Speaker 2: bunch of money into this market because they know the answer, right.

849
00:45:31,360 --> 00:45:33,720
Speaker 1: So I almost like, yeah, like we're seeing some betting

850
00:45:33,719 --> 00:45:39,000
Speaker 1: irregularities around the VP pick Like why wouldn't like this

851
00:45:39,160 --> 00:45:42,680
Speaker 1: just feels like whoever it is, including Kamla herself, just

852
00:45:42,840 --> 00:45:46,319
Speaker 1: fucking clean up would seem odd because it's not something

853
00:45:46,360 --> 00:45:49,480
Speaker 1: that's I guess, you know, like an athletic competition seemingly

854
00:45:49,560 --> 00:45:52,960
Speaker 1: up to chance or whoever the best performance zuro chance

855
00:45:53,040 --> 00:45:56,080
Speaker 1: involved here? Who knows what this person is thinking except

856
00:45:56,080 --> 00:45:59,640
Speaker 1: for maybe forty people who know was what she's thinking,

857
00:46:00,080 --> 00:46:02,400
Speaker 1: maybe you tell people, but like it's.

858
00:46:02,280 --> 00:46:04,960
Speaker 3: Interesting, amazing if Kamala was just in it for the

859
00:46:05,000 --> 00:46:10,360
Speaker 3: action the whole, just like trying to get an edge

860
00:46:11,000 --> 00:46:13,279
Speaker 3: on the betting markets.

861
00:46:13,800 --> 00:46:20,359
Speaker 1: Murray, shit, yeah yeah. But the other thing that's like

862
00:46:20,800 --> 00:46:24,560
Speaker 1: seeing like the like sort of center right punditry talk

863
00:46:24,640 --> 00:46:27,319
Speaker 1: about it like they've they've said again they're like one

864
00:46:27,320 --> 00:46:30,200
Speaker 1: of her vulnerabilities, like this is obviously coming from a

865
00:46:30,239 --> 00:46:33,759
Speaker 1: center right perspective. Quote is the multitude of leftist positions

866
00:46:33,760 --> 00:46:37,040
Speaker 1: she foolishly took in the twenty nineteen campaign. A Shapiro

867
00:46:37,200 --> 00:46:41,040
Speaker 1: selection unambiguously moves the ticket to the center. Then they

868
00:46:41,080 --> 00:46:43,200
Speaker 1: all go on. It's like and if Harris pulls back

869
00:46:43,239 --> 00:46:46,640
Speaker 1: from Shapiro after the campaign against him from the left,

870
00:46:47,200 --> 00:46:49,399
Speaker 1: it will look weak. Huh.

871
00:46:49,480 --> 00:46:56,000
Speaker 3: It's it's the standard Democrat and mainstream media narrative that

872
00:46:56,400 --> 00:46:59,439
Speaker 3: the Democrats have to It's the same thing that every

873
00:46:59,480 --> 00:47:02,960
Speaker 3: time what's his name was writing about the saying that

874
00:47:02,960 --> 00:47:07,759
Speaker 3: they should nominate Mitt Romney, It's like the Republicans, but say,

875
00:47:07,800 --> 00:47:10,719
Speaker 3: you're Democrats. How could that ever backfire?

876
00:47:11,120 --> 00:47:11,759
Speaker 1: Right? Right? Right?

877
00:47:12,000 --> 00:47:14,719
Speaker 2: Yeah, I mean I will say like that though, was

878
00:47:14,760 --> 00:47:16,920
Speaker 2: also the train of thought that was like, stick with Biden,

879
00:47:17,000 --> 00:47:20,919
Speaker 2: stick with Biden. And I'm wondering if the very short term,

880
00:47:21,160 --> 00:47:25,120
Speaker 2: like hey, not doing what these dummies always suggest, it

881
00:47:25,200 --> 00:47:26,160
Speaker 2: seems to have worked.

882
00:47:26,640 --> 00:47:31,000
Speaker 1: Yes, right, finally think in yeah, I mean in a way,

883
00:47:31,040 --> 00:47:32,640
Speaker 1: it's like, I mean, we thought this was an l

884
00:47:32,719 --> 00:47:35,040
Speaker 1: going into it. But do we do we do we

885
00:47:35,160 --> 00:47:38,759
Speaker 1: keep going against what accepted wisdom is that should be

886
00:47:39,040 --> 00:47:41,399
Speaker 1: all this time? Do you guys want to try to win?

887
00:47:42,680 --> 00:47:45,560
Speaker 2: I'm just I'm just fucking around here. Call me crazy.

888
00:47:45,760 --> 00:47:47,480
Speaker 2: Maybe we should try to win the selection this.

889
00:47:47,440 --> 00:47:54,440
Speaker 1: Time, but hey, give me some of whatever Andrew's smoking.

890
00:47:56,760 --> 00:47:59,640
Speaker 1: I don't know. Do we want to win? Thought, he's try,

891
00:48:00,480 --> 00:48:03,200
Speaker 1: let's try. Let's just try. I don't know, Miles, who

892
00:48:03,239 --> 00:48:06,799
Speaker 1: am I? Do we want to win? You're some guy

893
00:48:06,800 --> 00:48:13,560
Speaker 1: who's all fucked up off whatever Andrew's smoking. This guy's

894
00:48:13,640 --> 00:48:14,399
Speaker 1: high as a kite.

895
00:48:15,239 --> 00:48:18,440
Speaker 3: All right, let's take a quick break and we'll come back.

896
00:48:29,200 --> 00:48:33,640
Speaker 3: And we're back. We're and we've been talking for a

897
00:48:33,680 --> 00:48:37,520
Speaker 3: while to the delight of our audience. Some of our

898
00:48:37,560 --> 00:48:41,200
Speaker 3: audience are like, we get it. AI is bullshit. Stop

899
00:48:41,320 --> 00:48:44,200
Speaker 3: stop talking about this. But we have been talking for

900
00:48:44,239 --> 00:48:47,600
Speaker 3: a while about how much of the mainstream media narrative

901
00:48:47,640 --> 00:48:50,440
Speaker 3: around AI is kind of out of line with reality.

902
00:48:51,120 --> 00:48:56,040
Speaker 3: How this idea of the artificial general intelligence that they

903
00:48:56,040 --> 00:49:01,160
Speaker 3: are promising that I think the theory that they want

904
00:49:01,239 --> 00:49:03,880
Speaker 3: us to keep in mind that they might have in

905
00:49:03,960 --> 00:49:07,040
Speaker 3: mind is just like, yeah, it's like a person brain

906
00:49:07,160 --> 00:49:09,960
Speaker 3: you stick in a washing machine and now you're washing machines.

907
00:49:10,120 --> 00:49:11,120
Speaker 3: So fucking smart, bro.

908
00:49:11,280 --> 00:49:13,800
Speaker 1: Yeah oh oh WHOA maybe a little less detergent on

909
00:49:13,840 --> 00:49:16,320
Speaker 1: this one. Is this cop Okay, I don't want to handle.

910
00:49:16,120 --> 00:49:19,080
Speaker 3: This, Yeah exactly, I don't mind to have a conversation

911
00:49:19,160 --> 00:49:21,280
Speaker 3: with it. You never have to talk to people again.

912
00:49:21,719 --> 00:49:23,600
Speaker 3: You just have a washing machine.

913
00:49:23,280 --> 00:49:24,560
Speaker 1: That you're in love with.

914
00:49:24,960 --> 00:49:30,160
Speaker 3: Anyways, It's all just a sophisticated version of the autocomplete

915
00:49:30,160 --> 00:49:35,959
Speaker 3: fun function when you're texting is basically what they're working with.

916
00:49:36,120 --> 00:49:40,799
Speaker 3: In the AGI we had AI experts on, they were like,

917
00:49:40,880 --> 00:49:43,319
Speaker 3: this is all bullshit, Like it's not going to get

918
00:49:43,360 --> 00:49:46,880
Speaker 3: you to a place where it's it's all just marketing.

919
00:49:46,840 --> 00:49:48,560
Speaker 1: Right, And they're like, but they said it will. It's

920
00:49:48,560 --> 00:49:51,200
Speaker 1: like yeah, so they can inflate the price of like

921
00:49:51,239 --> 00:49:54,799
Speaker 1: the value of their companies, right, And I'm like, oh yeah, yeah,

922
00:49:55,040 --> 00:49:55,200
Speaker 1: is this.

923
00:49:55,320 --> 00:49:58,200
Speaker 2: The tipping point that like Wall Street kind of starts

924
00:49:58,239 --> 00:50:00,880
Speaker 2: to realize that Silicon Valley just liars.

925
00:50:01,800 --> 00:50:05,160
Speaker 3: I can't imagine because it would lose so many of

926
00:50:05,200 --> 00:50:08,880
Speaker 3: them so much money, right to realize that, Like I

927
00:50:08,920 --> 00:50:11,920
Speaker 3: feel like they're seeing the same shit we are. But yeah, so,

928
00:50:12,000 --> 00:50:14,640
Speaker 3: like the one side is like people who are actual

929
00:50:14,680 --> 00:50:17,960
Speaker 3: experts on how these AGI work are like, it's not

930
00:50:18,080 --> 00:50:20,320
Speaker 3: what they claim it is. The other thing is like

931
00:50:20,360 --> 00:50:22,160
Speaker 3: when you talk to people who pay attention to the

932
00:50:22,200 --> 00:50:24,799
Speaker 3: stock market, they're like, a lot of value is tied

933
00:50:24,880 --> 00:50:27,799
Speaker 3: up in AGI and like AI being what they say

934
00:50:27,880 --> 00:50:30,759
Speaker 3: it is. And so it was like, on the one hand,

935
00:50:30,800 --> 00:50:33,240
Speaker 3: you're like something has to give. On the other hand,

936
00:50:33,600 --> 00:50:37,040
Speaker 3: you're like or not because they've been able to just

937
00:50:37,080 --> 00:50:42,680
Speaker 3: like be heads down and maintain their like high stock

938
00:50:42,719 --> 00:50:48,480
Speaker 3: market value throughout an entire pandemic, you know, like without

939
00:50:49,200 --> 00:50:51,480
Speaker 3: really letting any of the reality in in a lot

940
00:50:51,480 --> 00:50:55,840
Speaker 3: of cases. So anyways, stock markets around the world are tumbling,

941
00:50:56,360 --> 00:50:59,920
Speaker 3: and one of the narratives that's coming up in line

942
00:51:00,040 --> 00:51:05,320
Speaker 3: and with this fall is that people are losing patients

943
00:51:05,760 --> 00:51:09,560
Speaker 3: with AI and AI companies, Like when are you going

944
00:51:09,600 --> 00:51:12,200
Speaker 3: to be able to do the thing that you claimed

945
00:51:12,320 --> 00:51:12,960
Speaker 3: this could do?

946
00:51:13,360 --> 00:51:18,520
Speaker 2: Just a little bit longer, dude, exact four billion dollars

947
00:51:18,520 --> 00:51:20,400
Speaker 2: a quarter, just like just a tiny.

948
00:51:20,320 --> 00:51:23,200
Speaker 1: Four for b a q. Okay when you turn like

949
00:51:23,239 --> 00:51:28,760
Speaker 1: the four billion dollars like inded, like any money broke, patient, dude,

950
00:51:28,920 --> 00:51:33,440
Speaker 1: it's cooking man, just like, don't even invest if you

951
00:51:33,480 --> 00:51:35,239
Speaker 1: don't want to be part of the future. Dog, Like,

952
00:51:35,320 --> 00:51:37,760
Speaker 1: I'm sick of this, dude. You can be poor, be poor,

953
00:51:38,080 --> 00:51:39,880
Speaker 1: you can be a brokie. Dude.

954
00:51:41,560 --> 00:51:44,720
Speaker 2: I in my fever dream, I did ask the meta

955
00:51:44,760 --> 00:51:49,560
Speaker 2: AI how I could short AI stocks, and you would

956
00:51:49,600 --> 00:51:50,680
Speaker 2: be surprised.

957
00:51:50,200 --> 00:51:51,919
Speaker 1: At how little it wants to tell me that.

958
00:51:52,440 --> 00:51:56,560
Speaker 3: Yeah, really, especially in depth answers on everything except that, Yeah,

959
00:51:57,160 --> 00:51:59,120
Speaker 3: I don't know.

960
00:51:58,520 --> 00:52:02,680
Speaker 2: I mean there's there's a Oh, financial advice is really

961
00:52:02,719 --> 00:52:06,759
Speaker 2: tricky for a method.

962
00:52:07,000 --> 00:52:09,240
Speaker 1: I can tell you which stocks you want to triple

963
00:52:09,360 --> 00:52:12,680
Speaker 1: down on on AI right now if you were interested

964
00:52:12,719 --> 00:52:16,640
Speaker 1: in that information. But yeah, this was like what the

965
00:52:16,680 --> 00:52:20,239
Speaker 1: sequence of events was basically like that job's report.

966
00:52:21,040 --> 00:52:26,120
Speaker 3: Report followed by investors selling off a lot of tech

967
00:52:26,160 --> 00:52:31,239
Speaker 3: stocks and the quote once hot artificial intelligence trade tech

968
00:52:31,400 --> 00:52:34,480
Speaker 3: shares were among the worst performers Monday.

969
00:52:34,600 --> 00:52:35,919
Speaker 1: So yesterday I do.

970
00:52:36,040 --> 00:52:39,600
Speaker 2: Like how this, I mean, surely this has to be

971
00:52:39,640 --> 00:52:42,520
Speaker 2: among the quickest that like a tech trend has you know,

972
00:52:42,560 --> 00:52:45,120
Speaker 2: because I feel like it took like even like the

973
00:52:45,160 --> 00:52:50,200
Speaker 2: bullshit ones like cryptocurrency, like a while for like like

974
00:52:50,280 --> 00:52:52,839
Speaker 2: people to kind of just generally realize this is because

975
00:52:52,840 --> 00:52:58,880
Speaker 2: this is like still within the like mainstream publicity window,

976
00:52:59,280 --> 00:53:03,279
Speaker 2: Like there are AI ads running during the Olympics for like,

977
00:53:03,360 --> 00:53:06,359
Speaker 2: it's like like the AI yeah and so so like

978
00:53:06,600 --> 00:53:08,840
Speaker 2: this is like the I guess the rug or the

979
00:53:09,160 --> 00:53:12,799
Speaker 2: fucking scales have been lifted during the mainstream PR push,

980
00:53:12,840 --> 00:53:14,680
Speaker 2: which seems much faster than usual.

981
00:53:15,280 --> 00:53:17,240
Speaker 1: Yeah, you said they were able to get that campaign

982
00:53:17,400 --> 00:53:19,120
Speaker 1: done and then.

983
00:53:19,160 --> 00:53:21,279
Speaker 2: You could have a coin based Super Bowl ad and

984
00:53:21,440 --> 00:53:25,520
Speaker 2: most people still don't realize it's a scam, right, Whereas

985
00:53:25,600 --> 00:53:28,480
Speaker 2: like I think most people realize AI is a scam

986
00:53:30,040 --> 00:53:33,319
Speaker 2: while they're pushing it. I don't know, maybe not, do

987
00:53:33,400 --> 00:53:36,680
Speaker 2: they not? I mean Wall Street, I don't like no

988
00:53:36,760 --> 00:53:39,440
Speaker 2: one was like no one was like making money shorting

989
00:53:39,520 --> 00:53:43,680
Speaker 2: coin base during the coin based Super Bowl ad, right right,

990
00:53:44,239 --> 00:53:46,240
Speaker 2: which is what is happening with AI.

991
00:53:47,040 --> 00:53:48,560
Speaker 1: Yeah, it just it just seems faster.

992
00:53:48,680 --> 00:53:50,360
Speaker 2: I'm not saying I wasn't going to get there, but

993
00:53:50,400 --> 00:53:53,160
Speaker 2: I was like, uh, is it just are we just

994
00:53:53,360 --> 00:53:57,040
Speaker 2: realizing it now? Is AI more half baked as a

995
00:53:57,080 --> 00:54:02,200
Speaker 2: grift than like cryptocurrency was, It's like weird. It's weird

996
00:54:02,200 --> 00:54:04,080
Speaker 2: that they're they're like figured it out so soon.

997
00:54:05,160 --> 00:54:07,600
Speaker 1: Yeah, it's just yeah. I mean, I think the one

998
00:54:07,680 --> 00:54:09,520
Speaker 1: of the big ones was when like one of the

999
00:54:10,000 --> 00:54:13,160
Speaker 1: like researchers, like Goldman Sachs was like, y'all, this thing

1000
00:54:13,360 --> 00:54:17,359
Speaker 1: ain't fucking doing shit. They're like, it's this like too

1001
00:54:17,440 --> 00:54:21,080
Speaker 1: much spend, too little benefit. And I think when once

1002
00:54:21,160 --> 00:54:23,959
Speaker 1: like Wall Street's like, bro, we're used to fucking making

1003
00:54:24,000 --> 00:54:26,560
Speaker 1: money here, Like we like giving you money because you

1004
00:54:26,600 --> 00:54:28,680
Speaker 1: think we're gonna get something back. But the whole, the

1005
00:54:28,680 --> 00:54:31,319
Speaker 1: whole name of the game here is shareholder value and

1006
00:54:31,640 --> 00:54:34,279
Speaker 1: we don't see the value right now. Yeah, but I

1007
00:54:34,320 --> 00:54:37,120
Speaker 1: mean that's the thing is like does this trigger like

1008
00:54:37,120 --> 00:54:40,480
Speaker 1: a much larger recession is what's kind of freaky. Like

1009
00:54:40,520 --> 00:54:46,640
Speaker 1: the market correction around AI obviously can have cascading effects, but.

1010
00:54:46,719 --> 00:54:52,200
Speaker 3: Yeah, I my bet would be that it won't be

1011
00:54:52,320 --> 00:54:55,839
Speaker 3: the level of like complete evaporation that we saw with

1012
00:54:55,920 --> 00:55:00,720
Speaker 3: like Bitcoin and all all of the all the crypt markets,

1013
00:55:00,760 --> 00:55:06,040
Speaker 3: because like AI is like that with magic tricks where

1014
00:55:06,200 --> 00:55:10,120
Speaker 3: like it can answer a question where you're like it's back.

1015
00:55:09,920 --> 00:55:12,120
Speaker 1: It's back online, it's it's smart.

1016
00:55:12,239 --> 00:55:17,160
Speaker 3: You know, like there there are things that AI because

1017
00:55:17,200 --> 00:55:22,240
Speaker 3: AI is basically just a marketing phrase for like things

1018
00:55:22,280 --> 00:55:26,239
Speaker 3: computers do good. Yeah, yeah, yeah, like any algorithmic thing

1019
00:55:26,320 --> 00:55:29,439
Speaker 3: that a computer does well. Like I feel like they're

1020
00:55:29,520 --> 00:55:31,640
Speaker 3: going to be able to keep coming back to this

1021
00:55:31,800 --> 00:55:34,560
Speaker 3: well and it will be profitable.

1022
00:55:34,800 --> 00:55:37,680
Speaker 2: I think they're just going to lose the term they

1023
00:55:37,760 --> 00:55:43,040
Speaker 2: they the miss stuff was in the marketing of AI. Yeah,

1024
00:55:43,080 --> 00:55:45,440
Speaker 2: and I'm like, right, it's because it's nothing and it

1025
00:55:45,480 --> 00:55:46,240
Speaker 2: never will be ready.

1026
00:55:46,280 --> 00:55:49,840
Speaker 1: But it's not ready, not justparately yell and also, like

1027
00:55:49,920 --> 00:55:53,520
Speaker 1: I think everything it's also maybe they kind of benefited

1028
00:55:53,560 --> 00:55:56,759
Speaker 1: and got fucked over by what movies? How movies sort

1029
00:55:56,760 --> 00:56:00,640
Speaker 1: of inoculated us or we're like AI right. Haley Joel

1030
00:56:00,640 --> 00:56:04,120
Speaker 1: Osmond is a fake baby who goes around and talks

1031
00:56:04,160 --> 00:56:07,120
Speaker 1: with his robot Teddy Bear as they solve complex problems.

1032
00:56:07,280 --> 00:56:09,520
Speaker 1: Damn you know what I mean? Like there's show that

1033
00:56:09,600 --> 00:56:11,959
Speaker 1: movie was sad for me, like before.

1034
00:56:11,680 --> 00:56:15,399
Speaker 3: I had kids, I can't Oh yeah imagine watching that now,

1035
00:56:15,880 --> 00:56:17,640
Speaker 3: like oh my.

1036
00:56:17,120 --> 00:56:20,880
Speaker 1: God, like having kids. The age of Hailey Joe and

1037
00:56:20,960 --> 00:56:23,120
Speaker 1: I know when that my little robot is like at

1038
00:56:23,160 --> 00:56:25,759
Speaker 1: the bottom of the sea, like deactivated. I remember being like,

1039
00:56:26,000 --> 00:56:30,040
Speaker 1: what this sociopath would make a robot like that, Like

1040
00:56:30,360 --> 00:56:34,040
Speaker 1: if you actually made a robot that's shaped and acted

1041
00:56:34,080 --> 00:56:37,640
Speaker 1: like Hailey Joel Osmon, that would be a crime against humanity,

1042
00:56:38,560 --> 00:56:43,960
Speaker 1: like an actual crime against humanity doing that in real life. Yeah. Yeah,

1043
00:56:44,440 --> 00:56:48,800
Speaker 1: so fucked up by so stupid way for the future.

1044
00:56:49,280 --> 00:56:52,480
Speaker 3: So and I do want to caveat this by saying

1045
00:56:53,320 --> 00:56:56,520
Speaker 3: I don't know shit about the stock market. Uh, you know,

1046
00:56:56,719 --> 00:56:59,759
Speaker 3: it could be tomorrow. They're like, actually, none of that

1047
00:56:59,880 --> 00:57:02,520
Speaker 3: was like AI sell off. I don't know, it's just

1048
00:57:03,320 --> 00:57:05,600
Speaker 3: it does it is a reason to bring back up

1049
00:57:05,680 --> 00:57:09,080
Speaker 3: like it does feel like there is this bubble that

1050
00:57:09,200 --> 00:57:12,600
Speaker 3: is happening, and we at least know experts in AI

1051
00:57:12,800 --> 00:57:15,600
Speaker 3: say here's what it actually does. It's just an autocomplete,

1052
00:57:15,920 --> 00:57:19,160
Speaker 3: and the companies say it's this thing, so who knows.

1053
00:57:19,200 --> 00:57:22,880
Speaker 3: I'm not an expert. However, dat no fact. On Twitter,

1054
00:57:23,280 --> 00:57:25,760
Speaker 3: also new to the stock market but had had a

1055
00:57:25,760 --> 00:57:28,640
Speaker 3: good piece of analysis. They said, hello, I'm new to

1056
00:57:28,720 --> 00:57:29,560
Speaker 3: the stock market.

1057
00:57:29,720 --> 00:57:31,160
Speaker 1: Is it a good? Is it good?

1058
00:57:31,320 --> 00:57:36,400
Speaker 3: When the Intel CEO starts praying and quoted Pat Gelsinger,

1059
00:57:36,920 --> 00:57:40,160
Speaker 3: who wrote, let your eyes look straight ahead, fix your

1060
00:57:40,200 --> 00:57:43,840
Speaker 3: gaze directly before you, give careful thought to the paths

1061
00:57:43,880 --> 00:57:46,840
Speaker 3: for your feet, and be steadfast in all your ways

1062
00:57:47,080 --> 00:57:51,200
Speaker 3: Proverbs twenty six Proverbs.

1063
00:57:50,600 --> 00:57:54,160
Speaker 1: For something, Bros.

1064
00:57:57,160 --> 00:58:00,520
Speaker 2: Yes, I mean, one thing that has been nice about

1065
00:58:00,520 --> 00:58:04,640
Speaker 2: Twitter is like revealing how many of these alleged like

1066
00:58:05,280 --> 00:58:08,960
Speaker 2: cold calculating titans of industry are just as superstitious as

1067
00:58:09,000 --> 00:58:09,800
Speaker 2: any other dummy.

1068
00:58:10,440 --> 00:58:13,360
Speaker 1: Yeah right, right, yeah, they're not seeing the matrix. It's

1069
00:58:13,360 --> 00:58:15,919
Speaker 1: also a bunch of him there. They just got lucky there.

1070
00:58:15,960 --> 00:58:17,240
Speaker 1: It's like survivor bias.

1071
00:58:17,400 --> 00:58:20,920
Speaker 2: Like they're their dumb idea worked out, and like thus

1072
00:58:20,960 --> 00:58:25,840
Speaker 2: all of their like fake magic works quote unquote Yeah.

1073
00:58:25,200 --> 00:58:27,200
Speaker 3: And they go through the same life cycle of they're

1074
00:58:27,200 --> 00:58:29,640
Speaker 3: a dumb idea works out just by random chance.

1075
00:58:29,880 --> 00:58:31,480
Speaker 1: They read iinrand.

1076
00:58:32,080 --> 00:58:36,480
Speaker 3: They convince themselves that they're like magical geniuses.

1077
00:58:36,400 --> 00:58:37,680
Speaker 1: And then reality kicks in.

1078
00:58:37,760 --> 00:58:40,560
Speaker 3: They're like and lo, I said, unto the Lord.

1079
00:58:40,440 --> 00:58:44,640
Speaker 2: Please don't let this bunch of money please good, we

1080
00:58:44,720 --> 00:58:45,200
Speaker 2: just need.

1081
00:58:45,160 --> 00:58:47,200
Speaker 1: Enough compute all of my houses.

1082
00:58:48,080 --> 00:58:51,240
Speaker 3: Yes, all right, Andrew T. What a pleasure having you

1083
00:58:51,280 --> 00:58:54,560
Speaker 3: as always on the daily Zeitgeist. Where can people find you,

1084
00:58:54,720 --> 00:58:55,640
Speaker 3: follow you all that.

1085
00:58:56,560 --> 00:58:59,080
Speaker 2: I still got my podcast you it was this racist

1086
00:58:59,280 --> 00:59:03,040
Speaker 2: We're doing and premium shows at suboptimal pads dot com

1087
00:59:03,080 --> 00:59:06,960
Speaker 2: just to plug we are doing a speaking of gambling

1088
00:59:07,120 --> 00:59:10,040
Speaker 2: a I'm playing ten cent poker versus my friend Jessica

1089
00:59:10,080 --> 00:59:13,320
Speaker 2: Goau playing scratcher tickets off one hundred dollars bank roll

1090
00:59:13,400 --> 00:59:18,400
Speaker 2: to see who can win more spoiler alert, I mean

1091
00:59:18,440 --> 00:59:24,160
Speaker 2: come on anyway, Yeah, that's where you can find me

1092
00:59:24,240 --> 00:59:25,200
Speaker 2: andrew t everywhere.

1093
00:59:25,200 --> 00:59:27,880
Speaker 1: I don't know. Amazing. Is there workI media you've been enjoying.

1094
00:59:28,320 --> 00:59:31,480
Speaker 2: Well, yeah again, So I've watched so much YouTube and

1095
00:59:31,560 --> 00:59:34,240
Speaker 2: I think my favorite one about this AI thing was

1096
00:59:34,320 --> 00:59:38,560
Speaker 2: by a YouTuber named Angela Collier, who is I would

1097
00:59:38,600 --> 00:59:42,560
Speaker 2: say medium famous. But the one I really liked that's

1098
00:59:42,600 --> 00:59:46,320
Speaker 2: relevant to today's topics was the malicious optimism of AI

1099
00:59:46,360 --> 00:59:50,439
Speaker 2: first companies. And she goes on this run about when

1100
00:59:50,480 --> 00:59:54,600
Speaker 2: the CEO of Zoom was like, we'll be able to

1101
00:59:54,680 --> 00:59:58,640
Speaker 2: have like basically like robot versions of yourself, do your

1102
00:59:58,720 --> 01:00:03,280
Speaker 2: Zoom meetings and do your entire job, and and this

1103
01:00:03,440 --> 01:00:06,120
Speaker 2: is good for you, the consumer, really good for you.

1104
01:00:06,240 --> 01:00:08,680
Speaker 2: It's very funny. Yeah, it's like oh yeah, yeah, Yeah,

1105
01:00:08,720 --> 01:00:11,680
Speaker 2: that's probably the pitch. His pitch was like, you can

1106
01:00:11,800 --> 01:00:15,160
Speaker 2: just send the like AI version of yourself to meetings

1107
01:00:15,160 --> 01:00:18,160
Speaker 2: and you can go to the beach, right, And it's like, yeah,

1108
01:00:18,200 --> 01:00:20,080
Speaker 2: that's definitely what your employer is good.

1109
01:00:21,160 --> 01:00:23,560
Speaker 1: That's definitely how No, No, we'll pay you you go

1110
01:00:23,640 --> 01:00:26,880
Speaker 1: to the beach. As long as your ghost software is

1111
01:00:27,200 --> 01:00:29,520
Speaker 1: this robot that we own that does your entire job,

1112
01:00:29,760 --> 01:00:31,560
Speaker 1: we will definitely continue paying you.

1113
01:00:32,280 --> 01:00:35,080
Speaker 2: But also, I'm way like to this, and I hope

1114
01:00:35,120 --> 01:00:37,720
Speaker 2: I haven't said this previously, but I've been. I'm listening

1115
01:00:37,880 --> 01:00:40,840
Speaker 2: NonStop pretty much. The I saw the TV Glows soundtrack.

1116
01:00:41,480 --> 01:00:44,400
Speaker 1: It's very good. Nice, I like it. I will just

1117
01:00:44,400 --> 01:00:46,080
Speaker 1: say I like the movie. I like this better than

1118
01:00:46,080 --> 01:00:48,280
Speaker 1: the movie. Wow, amazing.

1119
01:00:48,680 --> 01:00:51,840
Speaker 3: Check it out, folks, Miles Gray. Where can people find

1120
01:00:51,920 --> 01:00:54,240
Speaker 3: use their work media you've been enjoying? Uh?

1121
01:00:54,320 --> 01:00:57,640
Speaker 1: Yeah, you can find me on Twitter and Instagram at

1122
01:00:57,640 --> 01:01:00,600
Speaker 1: Miles of Gray. If you like basketball, like Jack and

1123
01:01:00,640 --> 01:01:02,280
Speaker 1: I do, you can catch us on Miles and Jack

1124
01:01:02,280 --> 01:01:05,720
Speaker 1: Got mad boost these uh. And if you like ninety

1125
01:01:05,760 --> 01:01:09,000
Speaker 1: day Fiance, catch me out on four twenty day Fiance.

1126
01:01:09,520 --> 01:01:14,480
Speaker 1: Very clever, Uh, some things I am like clever, very fun,

1127
01:01:14,720 --> 01:01:18,120
Speaker 1: very clever, very fun, Thank you very much. One is

1128
01:01:18,120 --> 01:01:21,840
Speaker 1: from at Matthew K. Begbie. It said rip Edgar Allan Poe,

1129
01:01:21,840 --> 01:01:24,520
Speaker 1: you would have loved watching a beloved children's author slowly

1130
01:01:24,600 --> 01:01:27,360
Speaker 1: driven to insanity by black mold inside the walls of

1131
01:01:27,400 --> 01:01:30,920
Speaker 1: her castle. That was on my short list as well.

1132
01:01:31,000 --> 01:01:34,280
Speaker 1: I want yeah, dude, They're just that picture is so

1133
01:01:34,720 --> 01:01:37,720
Speaker 1: wild like where I'm like, is she on some gray garden? Shit?

1134
01:01:38,000 --> 01:01:41,160
Speaker 1: Like she's so insulated where like she wouldn't even allow

1135
01:01:41,200 --> 01:01:45,800
Speaker 1: people like, JK, your walls do you just have? Like

1136
01:01:45,800 --> 01:01:49,440
Speaker 1: like the bathtub running all day every day for years?

1137
01:01:49,720 --> 01:01:53,680
Speaker 1: Is something happening? Are you are your mirrors like constantly

1138
01:01:53,680 --> 01:01:59,439
Speaker 1: steamed everywhere? Yeah? House and then okay but still okay.

1139
01:01:59,560 --> 01:02:02,680
Speaker 1: Underscore butt underscore still, tweeted Trump in nineteen ninety three.

1140
01:02:02,880 --> 01:02:05,760
Speaker 1: I'm very fond of Kermits. We get along well. We

1141
01:02:05,800 --> 01:02:07,760
Speaker 1: go to a lot of the same places and Marla

1142
01:02:07,880 --> 01:02:12,000
Speaker 1: she likes Miss Biggie, and they get along tremendously. Trump

1143
01:02:12,040 --> 01:02:16,000
Speaker 1: twenty twenty four. It's a pig wearing jewelry. It's disgusting.

1144
01:02:16,160 --> 01:02:19,479
Speaker 1: She hits him sometimes very hard, and he says, thank you, dear.

1145
01:02:24,560 --> 01:02:29,440
Speaker 3: Oh shit, you can find me on Twitter at Jack Underscore.

1146
01:02:29,480 --> 01:02:36,560
Speaker 3: O'Brien tweet I've been enjoying Mikayla At, Mikhailamas, Mikhail Lamas,

1147
01:02:36,640 --> 01:02:39,320
Speaker 3: Mickey Lamas, I don't know, m I kk I e

1148
01:02:39,600 --> 01:02:42,520
Speaker 3: l A. M As tweeted I'd be dead if I

1149
01:02:42,600 --> 01:02:45,000
Speaker 3: was in a quiet place because I'd be farting.

1150
01:02:45,840 --> 01:02:48,640
Speaker 1: Oh yeah, I saw that went too Yeah, hell yeah.

1151
01:02:48,720 --> 01:02:52,120
Speaker 3: You can find me on Twitter at Jack Underscore. Obrian

1152
01:02:52,120 --> 01:02:54,480
Speaker 3: you can find us on Twitter at Daily Zeitgeist. We're

1153
01:02:54,480 --> 01:02:57,240
Speaker 3: at the Daily Zeitgeist on Instagram. We have Facebook fan

1154
01:02:57,280 --> 01:02:59,520
Speaker 3: page and a website daily zeikeist dot com, where we

1155
01:02:59,600 --> 01:03:02,760
Speaker 3: post our episodes and our foot Nope, no, we link

1156
01:03:02,800 --> 01:03:05,280
Speaker 3: off to the information that we talked about in today's episode.

1157
01:03:05,520 --> 01:03:06,520
Speaker 1: What is a song that we.

1158
01:03:06,480 --> 01:03:09,640
Speaker 3: Think you might enjoy? Hey, Miles, Yeah, is there a

1159
01:03:09,680 --> 01:03:10,959
Speaker 3: song that you think people might enjoy?

1160
01:03:11,640 --> 01:03:15,240
Speaker 1: Yes, I think they're gonna like this track by Jordana

1161
01:03:15,320 --> 01:03:18,640
Speaker 1: and TV Girl called Better in the Dark. It's got

1162
01:03:18,680 --> 01:03:22,160
Speaker 1: like it's got this like breath, the sort of you know,

1163
01:03:22,360 --> 01:03:25,560
Speaker 1: DIY pop kind of vocals, but the production feels like

1164
01:03:25,640 --> 01:03:28,640
Speaker 1: if like a rap like boombap sample. Producer was like,

1165
01:03:28,680 --> 01:03:31,400
Speaker 1: I need to kind of make like an accessible indie

1166
01:03:31,440 --> 01:03:33,920
Speaker 1: pop track. This is what they would come up with.

1167
01:03:34,160 --> 01:03:36,200
Speaker 1: So I like the production on it because it's like

1168
01:03:36,280 --> 01:03:39,400
Speaker 1: sample heavy, but the vocals are completely different than you know,

1169
01:03:39,560 --> 01:03:42,200
Speaker 1: most sample based productions like this, I feel kind of

1170
01:03:42,200 --> 01:03:45,320
Speaker 1: more straightforward. So yeah. Better in the Dark by Jordana.

1171
01:03:45,080 --> 01:03:48,160
Speaker 3: TV bro Oh shit, the I saw the glow of

1172
01:03:48,160 --> 01:03:51,440
Speaker 3: the TV is from alex G. That's like one of

1173
01:03:51,520 --> 01:03:53,200
Speaker 3: my favorite musicians.

1174
01:03:53,200 --> 01:03:54,880
Speaker 1: Shut up to alex G.

1175
01:03:55,320 --> 01:04:00,520
Speaker 3: And yeah, did this filmmaker do the We're All Going

1176
01:04:00,520 --> 01:04:01,240
Speaker 3: to the World's Fair?

1177
01:04:01,960 --> 01:04:03,880
Speaker 1: Yes? Yeah, okay, I have not.

1178
01:04:03,920 --> 01:04:08,320
Speaker 3: Seen Yeah, but he did the soundtrack for that as well. Cool,

1179
01:04:08,800 --> 01:04:13,160
Speaker 3: all right, I'm very slow with my Google fingers. Anyways,

1180
01:04:13,160 --> 01:04:16,600
Speaker 3: we will link off to that song in the footnotes.

1181
01:04:17,040 --> 01:04:19,960
Speaker 3: Daily Zeitgeist is a production of iHeartRadio. For more podcasts

1182
01:04:19,960 --> 01:04:22,840
Speaker 3: from my heart Radio, visit the iHeartRadio app, Apple Podcasts

1183
01:04:23,040 --> 01:04:25,200
Speaker 3: wherever find podcasts or give it away for free. That

1184
01:04:25,320 --> 01:04:27,320
Speaker 3: is going to do it for us this morning, back

1185
01:04:27,360 --> 01:04:30,000
Speaker 3: this afternoon to tell you what is trending, and we'll

1186
01:04:30,000 --> 01:04:31,640
Speaker 3: talk to you all then Bye bye.