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Speaker 1: Like that one Chasing Status track. We're done with Don

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Speaker 1: dom In. You're like, yeah, that.

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Speaker 2: Songs about Jason States, stif Jason, That's all about Jason

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Speaker 2: State Bro Jason Steifem.

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Speaker 1: No Chasing States, It's Chase and stateis Bros. The most

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Speaker 1: powerful drumming.

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Speaker 2: By Jyson States like Jason Tatum, just like Jason Tightum,

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Speaker 2: Jason State thom Man, Jason Tum.

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Speaker 1: Irish chicked like Jason Titum. Whoa what that's that's like?

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Speaker 1: That could actually be a bar, that could be you

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Speaker 1: sell tick Yeah yeah, I sell ticks like Jay. But

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Speaker 1: you have to do a ticket mat Yeah yeah, yeah, yeah, yeah,

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Speaker 1: there it is there. It is all right to wash guys.

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Speaker 1: I'm in up with one bar. So we came up

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Speaker 1: with this one bar, and I think you're gonna want

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Speaker 1: to hear it all the pitch meeting about it. Yeah,

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Speaker 1: we come to UTA uninvited. Sorry, what's going on? I

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Speaker 1: thought we were talking about a podcast and none. No's

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Speaker 1: just shut the ship.

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Speaker 2: The fuck you're gonna want to hear this? Hello the Internet,

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Speaker 2: and welcome to season three, seventy eight, episode five of

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Speaker 2: Dirt at Least, I guys stay Production of iHeartRadio. This

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Speaker 2: is a podcast where we take a deep dive into

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Speaker 2: American shared consciousness. It is, of course, Friday, March seventh,

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Speaker 2: twenty twenty five, three seven two five niner.

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Speaker 1: Guess what, Jack, It's National Flapjack Day. All right, Hey, Jackie,

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Speaker 1: I remember I called that. I called a teacher flapjack

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Speaker 1: in high school and I got in trouble. Oh really,

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Speaker 1: They're like, hey, you got to get your knees out

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Speaker 1: of like enough blush. Try to go all right flapjack

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Speaker 1: And they're like, okay, you died detention and.

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Speaker 3: The teacher no, I grow up in high school musical Like,

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Speaker 3: I don't know.

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Speaker 1: I just thought that was a funny name to be disrespectful.

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Speaker 1: I mean, they knew I was trying to disrespect. That's

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Speaker 1: probably what I got onto.

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Speaker 4: What they did.

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Speaker 2: They have like a flat flat style. There's nothing to

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Speaker 2: do with anyway. Also, National totally sounds like something that

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Speaker 2: you swore at them, and then a TNT sensor came

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Speaker 2: in and changed it to flapjack.

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Speaker 1: Yeah, you know, it.

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Speaker 2: Sounds like sounds like what Bruce Willis calls Hons Gruber

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Speaker 2: in the TBS cut of Die Right right, Yeah, Hey

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Speaker 2: yip flapjack.

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Speaker 1: Yeah, I don't remember asking you a flap jack thing anyway. Yes.

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Speaker 1: Also National Hospitalist Day. What is a hospitalist? That sounds Brittish?

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Speaker 1: They manage patient care throughout their impatients staying okay, shout

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Speaker 1: out whatever you help you helping people out in all spill.

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Speaker 1: Also National Dress and Blue Day, National Speech and Education Day,

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Speaker 1: National Tartar Sau's Day, National Employee Appreciation Day, National Crown Roast,

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Speaker 1: the Fourthday, National Serial Day, National b Herd Day, and

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Speaker 1: the Anniversary of My Dad leaving my mom? Were here?

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Speaker 5: We here?

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Speaker 3: Yeah?

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Speaker 2: No, no call to your dad like we did season No.

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Speaker 1: Man, man, go back to that episode.

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Speaker 2: That's that's that's a classic.

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Speaker 3: Wait what happened?

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Speaker 2: Yeah? On the show, I think did you did you talk?

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Speaker 3: Are you in touch with your dad?

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Speaker 6: Yeah?

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Speaker 2: Yeah, yeah yeah I called him and because I think

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Speaker 2: someone was like, wow, you really wasn't, manh where.

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Speaker 1: Like you really remember that day? I'm like yeah, but

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Speaker 1: kind of fucked me up very vividly. And then I

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Speaker 1: called my dad on the show and I was like, hey,

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Speaker 1: you know today is and he's like what no, And

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Speaker 1: I was like, it's just like the anniversary of like

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Speaker 1: you know, and you mom split up with mom. And

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Speaker 1: he's like, oh, you're still thinking about that. He's like,

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Speaker 1: you're doing and then he got very little.

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Speaker 2: Good about it.

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Speaker 7: So good and since here didn't know he was on

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Speaker 7: the show, No, no, my god, and he was like

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Speaker 7: are you Then he was like, are you like you're

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Speaker 7: not a hat?

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Speaker 3: Really? Yeah?

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Speaker 1: You're yeah, yeah, anyway, I'm okay, I'm fine, You're fine.

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Speaker 1: You're doing sobbing.

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Speaker 3: You can't see it, but he saw. He's like, need

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Speaker 3: a bunch of cocaine, quitting right, welcome crying. Damn, he

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Speaker 3: is biting those knuckles real hard.

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Speaker 2: Single tear, rolling down frozen face. That's the only way

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Speaker 2: I cry, like a man. Uh huh, like Kyrie at

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Speaker 2: the free throw line. That's how you cry.

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Speaker 1: Damn.

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Speaker 2: Yeah, all right, my name is Jack O'Brien aka cyber Truck.

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Speaker 2: They in it gross, Watch me make them feel like hoes.

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Speaker 2: Watch me with my thumb deployed. I'll stupefied tech bros.

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Speaker 2: When I say boom. That's courtesy of Halcyon on the discord.

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Speaker 3: Don't that was sick? But you call me a home.

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Speaker 1: Oh yeah, you do drive a cyber truck. I forgot

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Speaker 1: about that. Cyber truck people who drive cyber trucks.

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Speaker 3: Oh okay, what if has anybody made a cyber I've

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Speaker 3: seen cyber trucks with like ads on the side. But

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Speaker 3: I'm wondering if anyone's like sleeping in their cyber truck,

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Speaker 3: that would be like insane, you.

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Speaker 1: Know, cyber truck slash bed and breakfast.

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Speaker 3: Yeah, they just roll out the back ramp.

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Speaker 2: Yeah. There are people do seem willing to just like

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Speaker 2: draw on those ships, like I've seen there's one in

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Speaker 2: my neighborhood that is like has like some sort of

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Speaker 2: like Sonic three like shadow reference like written on like

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Speaker 2: decaled onto it.

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Speaker 1: It's a it's like on the car.

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Speaker 3: Yeah, they have so many wraps on these cars. A

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Speaker 3: lot of them are like advertising. I'm like, to pay

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Speaker 3: for the car that you got.

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Speaker 1: You're basically making money. The cyberstrucks or cyber stuck subreddit,

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Speaker 1: it's like for all the cyber truck heaters. As one

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Speaker 1: person posts like, guys, I made a terrible mistake. I

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Speaker 1: put an egg shell sticker on my untreated stainless steel

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Speaker 1: plate I had, and it's very difficult to get off.

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Speaker 1: And I'm just saying, do not do that. If you

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Speaker 1: have stainless steel plates, it is very hard to get off.

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Speaker 1: Do not make people just do this ship all the

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Speaker 1: time just posting these.

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Speaker 4: Very do they do?

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Speaker 3: Cyber trucks have like cameras on the on the then

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Speaker 3: like how are people how do they? How do they do?

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Speaker 1: I feel like it's got to probably be like Ocean's eleven,

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Speaker 1: Like you probably need somebody to like use like some

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Speaker 1: kind of laser or infrared ship to like nullify the

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Speaker 1: camera and then you could pull up.

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Speaker 3: And then you know, or you just have like a

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Speaker 3: horde of people come up and then circle around it

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Speaker 3: and then disperse.

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Speaker 2: You know, yeah, yeah, yeah, British don cheetle comes up

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Speaker 2: and knocks out the power to the entire block just.

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Speaker 3: Trucks.

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Speaker 2: I mean the one time seeing people just come up

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Speaker 2: with a mask on and that's yeah, and then after

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Speaker 2: that they're then they're like they went out of frame?

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Speaker 1: Where'd they go? Is the cheese part of it?

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Speaker 2: Like we were talking about how people like to throw

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Speaker 2: cheese at cyber trucks. Is that just because people who

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Speaker 2: do that who are victory victor? Did you say does

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Speaker 2: that discolor? Like do we think that there's a discolor

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Speaker 2: race aspect to it's no way to know until Victor

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Speaker 2: comes through.

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Speaker 3: With I think people did this. I think people did

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Speaker 3: this to their babies on TikTok. Remember yeah the stop

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Speaker 3: crying people through. I mean sure, But also I think

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Speaker 3: they were just throwing cheese at their babies because it

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Speaker 3: was funny, made a fun yeah yeah.

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Speaker 1: Yeah, yeah yeah, but it was funny because everyone, But

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Speaker 1: it is funny. How that was Tree is like I

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Speaker 1: don't know why, but when you throw a slice of

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Speaker 1: American cheese on a baby's bald head while they're crying,

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Speaker 1: it like disrupts.

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Speaker 3: The the way.

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Speaker 1: They thought it had something to do with the cheese,

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Speaker 1: Like no, you put a weird cold sheet on their

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Speaker 1: head suddenly.

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Speaker 3: And like yeah, is this also like did you see

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Speaker 3: the tape thing where people will undo like a packing

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Speaker 3: tape and it makes a sound and then the baby

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Speaker 3: starts laughing. It's like a big baby joke. Yeah, Hendrick Lamar,

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Speaker 3: babies love it.

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Speaker 2: Yeah, it's a big baby joke to you.

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Speaker 3: Yeah, big baby joke.

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Speaker 1: We're having trouble in my house. My baby's all team

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Speaker 1: Drake right now. No, yeah, yeah, you're.

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Speaker 3: I raised you wrong. I'm so sorry.

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Speaker 1: He's just something's going on. I don't know if he's

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Speaker 1: overhearing the tiktoks I look at but he's just he's

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Speaker 1: calling me a Gerbert like a Toronto roadman. Fum, I'm

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Speaker 1: joy as always buy my home.

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Speaker 2: Number one, Gerbert, mister Miles great, He's a Gerbert bomb

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Speaker 2: straight from Brampton.

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Speaker 6: AKA, you can call me Gary Slime anytime. My wake

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Speaker 6: up screaming now for me. You can call me Gary

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Speaker 6: slyme anytime I miss huge.

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Speaker 1: Shout out to Snarkila for that look I said anytime.

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Speaker 1: Brian McKnight, aka's you came through. Also, Gerald Dean, I

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Speaker 1: also saw you come through that AKA, I sees you.

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Speaker 1: Thank you for that. Thank you. Yeah, I like that.

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Speaker 3: We're doing Brian McKnight in line with dads that leave

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Speaker 3: their families. You know what I mean. Oh my god,

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Speaker 3: Brian McKnight is a horrible father. He likes first family

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Speaker 3: who's like and now he's like in an inter I

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Speaker 3: think interracial or relationship with someone who's like lighter. It's

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Speaker 3: like colorism. He like renamed a child he had in

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Speaker 3: his new family like Brian McKnight Junior, even though he

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Speaker 3: already has one. Like he's fully like like I.

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Speaker 1: Already had like a daughter named like Briana or something too.

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Speaker 3: He's like George so egoistical and so mean to his

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Speaker 3: first family. He's like first he's like disowned them for

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Speaker 3: like it's crazy.

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Speaker 4: So he had one.

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Speaker 2: Family and then he started back at one with another family.

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Speaker 2: Oh my what I'm hearing?

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Speaker 6: And I always tell my daughter, you can't call me anything.

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Speaker 1: You cannot, you cannot.

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Speaker 2: Beautiful, beautifully done. Everybody involved, and especially the person from

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Speaker 2: the discord who came up with Gary Slime still one

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Speaker 2: of my favorites of all time.

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Speaker 1: Anagrams.

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Speaker 2: They're out here, Miles Worth thrilled to be joined in

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Speaker 2: our third seat by a hilarious stand up comedian, writer,

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Speaker 2: actor want to be British gangster improviser. You cant her

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Speaker 2: on stand up stages everywhere. Just go check her website

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Speaker 2: and at the monthly Facial Recognition comedy show which she

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Speaker 2: also produces. It's poll. Hi guys, Hi, Hi, Hi, Hey?

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Speaker 1: How are you great?

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Speaker 3: Mickey?

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Speaker 1: Oh boy? So glad to be? Does Mickey still make

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Speaker 1: for kids?

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Speaker 3: Like?

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Speaker 1: Are they familiar with Mickey?

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Speaker 3: Oh?

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Speaker 2: You don't know the Mickey Clubhouse, Mickey Mouse Clubhouse.

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Speaker 1: Oh is that a show.

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Speaker 2: Yeah, and the absolutely nailed Mickey's energy. He's always just like,

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Speaker 2: oh gee ya.

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Speaker 1: Energy, affable and.

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Speaker 3: Dumb, Oh my god, and Jack's coming for Mickey.

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Speaker 7: I'm just like this character, dumb ass piece of sh

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Speaker 7: fucking vermin, get a job, Mickey.

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Speaker 2: He's just like this weird, inoffensive, like uncle type person

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Speaker 2: who's just like, oh, I don't know, there's not a

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Speaker 2: lot there. I want more from you, Mickey.

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Speaker 3: You want I think you want bugs, Bunny. I think

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Speaker 3: you're looking for bugs, bunny energy.

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Speaker 2: Yeah, you're looking for bugs, Bunny. Why don't you get

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Speaker 2: the thug out of here?

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Speaker 4: Okay?

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Speaker 2: If they say to me at Disney when I show up, yeah, yeah,

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Speaker 2: I got Almer Fudd in one of those rifles with

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Speaker 2: the big blamer at the end of it, just a.

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Speaker 1: Double bit of yeah, impossibly like flared out double.

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Speaker 2: Barrel shotgun, flared shotgun.

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Speaker 1: Season.

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Speaker 2: It's wonderful to have you here. We're gonna get to

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Speaker 2: know you a little bit better in a moment. First,

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Speaker 2: we're going to tell the listeners a couple of things

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Speaker 2: we're talking about. We are going to talk about the

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Speaker 2: continuing attempt to dodge any sort of accountability on the

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Speaker 2: right for money. That's right. We are in the it

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Speaker 2: Ain't my fault era era, the Second Drum administration. And

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Speaker 2: Elon's also could you stop saying like he's saying, uh,

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Speaker 2: it ain't my fault. Yeah, he's a big fan of

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Speaker 2: that song. He loves Shaka uh. We'll talk about the

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Speaker 2: latest legal trend, which is lawyers citing cases that were

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Speaker 2: hallucinated by AI.

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Speaker 1: Oh wait, this is people.

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Speaker 2: Sorry, I didn't know. I couldn't do that.

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Speaker 1: Are these people that are like lawyers are using AI

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Speaker 1: and it's giving them fake ass cases and then they

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Speaker 1: go into ir L corp to the judge.

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Speaker 2: The judges like these are not cases and lawyers have

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Speaker 2: some really good explanations that I can't wait.

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Speaker 1: First of all, there's no such team as the Spungongos.

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Speaker 3: It's like that's like chat GPT is my lawyer. I'm

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Speaker 3: fucked right.

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Speaker 2: We'll talk about Sesame Street union busting now point in

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Speaker 2: uh late capitalism, where Sesame Street is union busting.

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Speaker 1: Turned to Wall Street.

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Speaker 2: Huh okay, that's right, all of that plenty more, But first,

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Speaker 2: Paul Ganalan, we do like to ask our guests, what

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Speaker 2: is something from your search history that's revealing about who

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Speaker 2: you are.

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Speaker 3: Okay, so not to be like all about me, but

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Speaker 3: it is my birthday week. Whoa March for.

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Speaker 1: Birthday?

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Speaker 3: And I'm hella busy in an adult, so I can't

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Speaker 3: celebrate this week. But I'm really excited Jackies and I

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Speaker 3: are gonna go in the future at some point that

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Speaker 3: I won't tell everyone to the gentle barn. Have you guys?

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Speaker 1: Have you the Yeah? Yeah, is that like up in

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Speaker 1: Santa Clarita area.

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Speaker 3: Yeah, it's like a farm sanctuary. I follow all these

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Speaker 3: rescue farms and they're so sweet and they cuddle cows

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Speaker 3: and they like I feel like every animal is just

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Speaker 3: like some version of a puppy, Like like cows are

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Speaker 3: just like milk pluppies, you know, Turkeys are thingsgiving puppies,

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Speaker 3: you know, oh you them. No, I don't think I'm vegan. Okay,

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Speaker 3: now you consume giving puppy. But it's just like puppies

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Speaker 3: that we haven't like played with yet, you know what

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Speaker 3: I mean. Like they all have personalities, they all have

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Speaker 3: individual you know, like individual like emotions and things. You know,

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Speaker 3: they're just like they all got little things. And so

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Speaker 3: I really love like rescue farms because it's just like

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Speaker 3: kind of the misfit toys of animals that get to

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Speaker 3: all just like live and thrive. And so I'm really

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Speaker 3: excited to go. I'm gonna go to the gentle barn.

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Speaker 1: The guys child loves animals and like horses and donkeys

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Speaker 1: and shit like that. And everyone's like, you gotta take them,

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Speaker 1: you gotta take them to the gentle farm. And I'm like, yeah,

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Speaker 1: let me know how it goes. Let me know how.

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Speaker 3: Yeah, you can do like you can cuddle cows. And

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Speaker 3: there was this like viral video that went around a

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Speaker 3: while ago of this woman being like what does it?

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Speaker 3: It's like like when you when you get like so

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Speaker 3: like angry because it's something so cute, You're like, I

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Speaker 3: want to eat your cheek. Cute aggression. Yeah yeah, And

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Speaker 3: this woman was like, get it all out. You can

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Speaker 3: get it all out on cows. You can hug them

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Speaker 3: as hard as you want because they're so big you

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Speaker 3: could just like take out your cute And so I'm like,

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Speaker 3: oh my god, I'm gonna cuddle the cow so hard

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Speaker 3: it's gonna be.

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Speaker 1: Those cows are like tough guys, are they.

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Speaker 3: You're trying to fight a cow at the gentle bar.

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Speaker 1: You say, you saying the cow can handle it all.

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Speaker 3: At the gentle barn. Are you gonna come in with

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Speaker 3: that energy to the gentle bar?

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Speaker 1: Are you here to assault the animals?

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Speaker 3: No, they're going to post up.

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Speaker 4: I just want to know.

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Speaker 1: I just want to know. I just want to check

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Speaker 1: how the bill. But oh, that does make sense, because yeah,

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Speaker 1: those animals are so like I like when I think

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Speaker 1: they're huge the bait. My baby's like going up to

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Speaker 1: like a cat or something, but a big ass cow.

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Speaker 1: I'm like, bro, there's nothing you can do to me, Yeah, except.

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Speaker 4: Just want to This is coming from AI over you.

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Speaker 2: But I do remember writing about this back of the day,

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Speaker 2: that cows are responsible for an average of twenty two

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Speaker 2: human deaths each year.

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Speaker 3: Okay, but how many humans are responsible for how many

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Speaker 3: cow deaths?

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Speaker 2: Are you?

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Speaker 3: Are you trying to tone police and oppressed class in

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Speaker 3: how they revolt against their oppressor.

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Speaker 2: Yeah, I'm just saying watch them keeping.

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Speaker 1: That's crazy way Jack, You're saying that. So the cows

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Speaker 1: are claiming twenty two of our human lives a year?

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Speaker 1: Uh huh Okay, Well I'm just sliggling up the new

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Speaker 1: stats from twenty twenty three about thirty two point eight

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Speaker 1: million cattle were slidered in the United States.

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Speaker 5: Hold that cows, my gor I know this is not

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Speaker 5: the feeling coming into this.

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Speaker 3: I'm sorry about the milk puppies.

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Speaker 1: I thought we were doing the scoreboard thing. I'm too

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Speaker 1: caught up in sports.

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Speaker 3: Sorry, race wars with other species.

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Speaker 2: Victor asked a very good question.

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Speaker 3: Can you see that that clip of that little girl

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Speaker 3: who was like at a rescue farm thing and then

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Speaker 3: she was like, that's the cheeseburger.

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Speaker 2: Like that for a long time to connect the food

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Speaker 2: to the animal. They're like, no, it's not chickens, Like

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Speaker 2: that's chicken. That's the same same word, but it's for

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Speaker 2: different things, like that's the food chicken, and those are

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Speaker 2: like our cute our neighbor's cute chickens.

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Speaker 3: I think that's how much are in their brains.

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Speaker 2: The deaths are not based on that. They're not like

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Speaker 2: cheating this victor, but victors like I was just them

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Speaker 2: choking on steak or whatever, or like having heart attacks

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Speaker 2: from high and that. If we added that as well

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Speaker 2: as like their methane admissions, we would be that's it

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Speaker 2: from the.

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Speaker 1: Yeah, the cows are like we're playing the.

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Speaker 3: Long get taking the earth down with us.

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Speaker 2: This is like two like small smashings where they're just

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Speaker 2: like smashing people or like dumb people are like and

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Speaker 2: cow tipping and then it like just rolls over on

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Speaker 2: them and they're like dead. Now when people are like

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Speaker 2: a farm related incident, let's not tell the whole.

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Speaker 4: Truth of that one.

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Speaker 2: Sure, Paula the what's something? He thinks underrated?

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Speaker 3: If you guys haven't been watching Paradise on Hulu, Oh

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00:19:16,720 --> 00:19:20,800
Speaker 3: my god, Sterling K. Brown, James Marsden amazing?

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Speaker 1: Does James Marsden die immediately right away?

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Speaker 3: No comment?

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Speaker 2: Is he cut before he dies?

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Speaker 1: He cut before this?

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Speaker 3: James Marsden joint a proper This is a proper James

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Speaker 3: Marsden joint is it's I can't. I can't. It's too much.

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Speaker 3: You guys have to you would be so upset. Can

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Speaker 3: just watch it? It's it's Paradise. It has to do

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Speaker 3: with the fate of the world, and every episode has

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Speaker 3: like ten episodes.

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Speaker 2: Packed into what to do with the fate of You

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00:19:52,960 --> 00:19:53,400
Speaker 2: can't see.

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00:19:53,440 --> 00:19:55,600
Speaker 3: Victor says, don't spoil it. I can't wait.

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Speaker 1: What do you mean? Can I not even read like

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Speaker 1: a description?

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00:19:59,119 --> 00:20:03,000
Speaker 3: Don't read don't just watch it, go in unspoiled, son't

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00:20:03,040 --> 00:20:06,560
Speaker 3: read a description, don't talk to anyone, don't put up

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00:20:06,600 --> 00:20:09,880
Speaker 3: on Twitter. Just go watch it. It is so good

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00:20:09,960 --> 00:20:12,000
Speaker 3: and more has revealed every episode.

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00:20:14,880 --> 00:20:18,360
Speaker 2: Hey, Apple media assistant, turn on Paradise and then keep

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Speaker 2: your eyes closed until you start hearing music. Yeah, you're

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00:20:22,200 --> 00:20:24,680
Speaker 2: not allowed to even it is so good.

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Speaker 3: Like I feel like this is like the Severance team

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Speaker 3: will also be on this one, you know what I mean,

390
00:20:30,400 --> 00:20:32,320
Speaker 3: Like everyone who loves Severance. I feel like you guys

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00:20:32,359 --> 00:20:36,040
Speaker 3: will love Paradise. It's more like action packed because there's

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00:20:36,119 --> 00:20:39,080
Speaker 3: like more crazy shit happening every episode, but there's so

393
00:20:39,200 --> 00:20:41,440
Speaker 3: many twists and turns and it's just so well done,

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Speaker 3: and it also feels like it could actually happen, and

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Speaker 3: you're like, oh my god.

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Speaker 2: That's why I've been pitching that Severance should be recasting

397
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Speaker 2: their main roles with Steven Segal, cloud band Dan Sylvester

398
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Speaker 2: as hell, you know, give me out of here?

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Speaker 4: All right?

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00:21:03,920 --> 00:21:06,440
Speaker 2: Well, I don't know, I mean, I guess that's a

401
00:21:06,480 --> 00:21:08,000
Speaker 2: pretty intriguing endorsement.

402
00:21:08,800 --> 00:21:13,200
Speaker 1: Yeah, especially when this the producer started spamming the chat like, don't.

403
00:21:13,000 --> 00:21:13,600
Speaker 2: Even read.

404
00:21:15,400 --> 00:21:18,359
Speaker 3: Somebody much, right, it's just I feel like I don't know.

405
00:21:18,480 --> 00:21:20,280
Speaker 1: It's just now that I see the image, I'm like,

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00:21:20,359 --> 00:21:23,320
Speaker 1: oh wait, I am familiar that of what this show is.

407
00:21:23,840 --> 00:21:27,080
Speaker 1: But I've only seen the look at the image, don't

408
00:21:27,119 --> 00:21:27,879
Speaker 1: look at anything.

409
00:21:28,080 --> 00:21:29,440
Speaker 2: I know I what it is.

410
00:21:29,600 --> 00:21:32,159
Speaker 1: But the log line is very innocuous. I would say

411
00:21:32,160 --> 00:21:33,640
Speaker 1: it's not they ain't give it away much.

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00:21:33,840 --> 00:21:34,480
Speaker 3: It's so good.

413
00:21:35,000 --> 00:21:37,439
Speaker 1: Just a secret service team is tasked with Miles.

414
00:21:42,560 --> 00:21:44,040
Speaker 2: Now, I'm never gonna watch it.

415
00:21:44,600 --> 00:21:48,760
Speaker 3: Oh my god, you ruined everything, Miles. I do. So.

416
00:21:49,600 --> 00:21:51,479
Speaker 2: I feel like it's been a while. I might need

417
00:21:51,560 --> 00:21:56,160
Speaker 2: to retire my characterization of James Marsden as like somebody

418
00:21:56,200 --> 00:21:57,399
Speaker 2: who gets cucked in movies.

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00:21:57,640 --> 00:21:59,880
Speaker 1: I think that's like where his career started.

420
00:22:00,000 --> 00:22:04,440
Speaker 3: Okay, that's so true. Wait, but enchanted he got he

421
00:22:04,520 --> 00:22:05,000
Speaker 3: got cooked.

422
00:22:05,080 --> 00:22:06,280
Speaker 1: Yeah, yeah, he got cooked.

423
00:22:06,600 --> 00:22:08,240
Speaker 3: He got cocked in that and the Notebook.

424
00:22:09,280 --> 00:22:12,600
Speaker 2: Yeah, the Notebook. He got cooked, Like his character in

425
00:22:12,800 --> 00:22:15,960
Speaker 2: the X Men universe got cooked, right, Like that was

426
00:22:16,080 --> 00:22:16,879
Speaker 2: like sort of his.

427
00:22:17,040 --> 00:22:21,320
Speaker 3: You know, Psychlisnic He's getting cooked by I needed to

428
00:22:21,400 --> 00:22:24,760
Speaker 3: do a little work with the fan fiction to like

429
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Speaker 3: get it. There is tugging the fuck out of him.

430
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Speaker 1: Yeah.

431
00:22:29,960 --> 00:22:32,560
Speaker 3: No, I love Marston he's so good.

432
00:22:33,400 --> 00:22:35,680
Speaker 2: Yeah, yeah, he's great. I mean he was great in

433
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Speaker 2: a jury duty too.

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Speaker 4: Is that what that?

435
00:22:38,760 --> 00:22:39,520
Speaker 3: Yeah? I haven't seen that.

436
00:22:39,600 --> 00:22:40,720
Speaker 2: Though he's great.

437
00:22:41,440 --> 00:22:44,160
Speaker 1: What is something you think is overrated?

438
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Speaker 3: Okay, I haven't quite thought this out perfect, and I'm

439
00:22:48,040 --> 00:22:50,920
Speaker 3: just it's gonna be like a hot take, mostly because

440
00:22:50,920 --> 00:22:54,199
Speaker 3: I wanted to apply to me, but like hating nepotism normally,

441
00:22:54,800 --> 00:22:57,320
Speaker 3: I'm like, Okay, I hate nepotism, but like now I

442
00:22:57,440 --> 00:22:59,159
Speaker 3: want it to work for me. I'm like, let me

443
00:22:59,359 --> 00:23:03,760
Speaker 3: be an EPO. I mean, yeah, I would love you.

444
00:23:03,880 --> 00:23:06,879
Speaker 3: Turn around at the Oscars, every other person is an EPO. Baby,

445
00:23:07,000 --> 00:23:09,800
Speaker 3: You're like, what how did this? And then all of

446
00:23:09,840 --> 00:23:13,960
Speaker 3: the people with like like long standing careers have like family.

447
00:23:14,040 --> 00:23:16,080
Speaker 3: In the end, You're just like, oh, this would be

448
00:23:16,160 --> 00:23:18,159
Speaker 3: so great if it was if it was me, or

449
00:23:18,600 --> 00:23:21,520
Speaker 3: if I used my engineering nepotism and just stayed in

450
00:23:21,600 --> 00:23:24,000
Speaker 3: that industry, I'd be so so much.

451
00:23:24,040 --> 00:23:28,359
Speaker 1: Further picked up. I know, Yeah, you gonna do it.

452
00:23:28,440 --> 00:23:31,240
Speaker 1: Blew my mind when like Harold Parano was like posting

453
00:23:31,280 --> 00:23:34,160
Speaker 1: on Instagram was like congratulations to my daughter's best friend

454
00:23:34,240 --> 00:23:38,360
Speaker 1: for winning Best Actress last night, and wait, really yeah,

455
00:23:38,400 --> 00:23:42,440
Speaker 1: and it's like Harold Parano's daughter, like Mikey Mass He's.

456
00:23:42,320 --> 00:23:47,880
Speaker 3: A Harold Parano is an icon first and lost Romeo

457
00:23:48,119 --> 00:23:51,520
Speaker 3: cross Asia.

458
00:23:52,359 --> 00:23:56,720
Speaker 1: Scratches. Yeah, yeah, yeah, but yeah he It was just

459
00:23:56,760 --> 00:23:58,520
Speaker 1: would to see that too, And it's like clearly like,

460
00:23:58,600 --> 00:24:01,159
Speaker 1: oh my god, so like everybody is it?

461
00:24:02,200 --> 00:24:04,880
Speaker 3: Is it like the reverse of like on TikTok when

462
00:24:04,880 --> 00:24:08,240
Speaker 3: people are like I did it, like the the twenty

463
00:24:08,320 --> 00:24:10,720
Speaker 3: three in me and my grandfather is a serial killer.

464
00:24:10,840 --> 00:24:12,840
Speaker 3: It's like the opposite, like the old people are outing

465
00:24:12,960 --> 00:24:15,000
Speaker 3: the young people for being related to them.

466
00:24:16,520 --> 00:24:18,760
Speaker 2: So how did Adrian and Brody come back from that

467
00:24:19,000 --> 00:24:23,280
Speaker 2: SNL thing where he did the rasta accent like he

468
00:24:23,560 --> 00:24:27,520
Speaker 2: married the person who's the expert on getting someone uncanceled?

469
00:24:28,960 --> 00:24:31,440
Speaker 3: Oh he did. Who's wait, who's his wife.

470
00:24:31,320 --> 00:24:33,720
Speaker 2: Is Harvey Weinstein's ex wife?

471
00:24:34,280 --> 00:24:38,760
Speaker 3: Oh my god, I total sense for him sexually assaulting

472
00:24:38,840 --> 00:24:42,440
Speaker 3: halle Berry and also working with Woody Allen and Roman Polanska.

473
00:24:43,040 --> 00:24:46,400
Speaker 3: Like Jesus Christ, I hate Adrian Barrody so much.

474
00:24:46,920 --> 00:24:50,720
Speaker 1: She's got a great, great judge of character, it seems yeah, I.

475
00:24:50,760 --> 00:24:53,480
Speaker 3: Mean she she's like, he has a terrible character And

476
00:24:53,600 --> 00:24:54,000
Speaker 3: I love.

477
00:24:53,960 --> 00:24:56,359
Speaker 1: That, and I love that and I love that about him.

478
00:24:56,800 --> 00:24:59,280
Speaker 1: When I saw him do that rasta mand bit, I

479
00:24:59,440 --> 00:25:03,720
Speaker 1: was heart eyes, Harvey, I'm gonna snitch on you because

480
00:25:03,720 --> 00:25:04,800
Speaker 1: I found my new husband.

481
00:25:05,960 --> 00:25:08,399
Speaker 2: All right, let's take a quick break and we'll be

482
00:25:08,760 --> 00:25:09,120
Speaker 2: right back.

483
00:25:19,640 --> 00:25:20,639
Speaker 1: And we're back.

484
00:25:22,960 --> 00:25:26,280
Speaker 2: And this is this is an era of no responsibility.

485
00:25:27,160 --> 00:25:31,080
Speaker 2: But like I I either because maybe there is some

486
00:25:31,640 --> 00:25:35,480
Speaker 2: you know, the remnants of some responsibility and consequences left

487
00:25:35,520 --> 00:25:37,760
Speaker 2: to the bottom of the ketchup bottle. Or maybe it's

488
00:25:37,880 --> 00:25:42,359
Speaker 2: just like a vestigial, you know, reflex of people being like.

489
00:25:42,480 --> 00:25:45,359
Speaker 1: Don't look at me, but I just I just started

490
00:25:45,440 --> 00:25:46,000
Speaker 1: working here.

491
00:25:46,480 --> 00:25:49,520
Speaker 2: But yeah, but Mega and Elon Musk both seemed to

492
00:25:49,600 --> 00:25:53,159
Speaker 2: be trying to be like, not us, was it?

493
00:25:53,280 --> 00:25:54,879
Speaker 1: It's not my fault? Why are you aead at me?

494
00:25:55,240 --> 00:25:57,919
Speaker 1: I think that, yeah, exactly?

495
00:25:58,760 --> 00:26:01,560
Speaker 3: Or Brodi, we need that accent.

496
00:26:01,840 --> 00:26:04,880
Speaker 2: A lot of people talking about how the twenty fourteen

497
00:26:05,520 --> 00:26:09,200
Speaker 2: Obama White House correspondence dinner was the start of all

498
00:26:09,280 --> 00:26:12,040
Speaker 2: this talking shit to Trump. I think it was the

499
00:26:12,119 --> 00:26:12,840
Speaker 2: Shaggy song.

500
00:26:13,240 --> 00:26:17,159
Speaker 1: I think it was money, you know what I mean.

501
00:26:18,400 --> 00:26:20,960
Speaker 1: But look, I think we're all well aware of the

502
00:26:21,000 --> 00:26:23,480
Speaker 1: awful shit that the regime is doing in regards to

503
00:26:23,680 --> 00:26:28,520
Speaker 1: basic rights and the economy, and well, it's clear to us,

504
00:26:28,920 --> 00:26:32,399
Speaker 1: the not shitty people of America, that these things are

505
00:26:32,520 --> 00:26:35,399
Speaker 1: bad and should not be tolerated. But like as MAGA

506
00:26:35,480 --> 00:26:38,760
Speaker 1: voters begin to openly question if all this bad shit

507
00:26:38,880 --> 00:26:41,879
Speaker 1: they are seeing Trump and Musk do is actually bad.

508
00:26:42,480 --> 00:26:45,320
Speaker 1: We see how like the media again does their job

509
00:26:45,480 --> 00:26:47,639
Speaker 1: on the right wing to train them into believing that

510
00:26:48,119 --> 00:26:51,119
Speaker 1: bad is not bad. That you're seeing this is actually

511
00:26:51,320 --> 00:26:55,240
Speaker 1: just pret good is what you're experiencing.

512
00:26:55,720 --> 00:26:59,639
Speaker 3: It's critical thinking back, is that so bad?

513
00:27:01,480 --> 00:27:04,800
Speaker 1: Almost almost so? For starters right. One of the big

514
00:27:04,920 --> 00:27:08,040
Speaker 1: questions I think from a lot of people in MAGO

515
00:27:08,119 --> 00:27:10,840
Speaker 1: world is like, well, who the fuck is responsible for

516
00:27:10,960 --> 00:27:13,560
Speaker 1: all this shit? I just lost my job?

517
00:27:13,920 --> 00:27:15,120
Speaker 2: Yeah, I lost my job.

518
00:27:15,720 --> 00:27:20,520
Speaker 1: Contracts are being canceled. The outlook on you know, agricultural

519
00:27:20,600 --> 00:27:24,440
Speaker 1: expert exports looks Memphis bleak, to put it lightly, And

520
00:27:25,000 --> 00:27:27,159
Speaker 1: a lot of the response after the State of the

521
00:27:27,280 --> 00:27:29,760
Speaker 1: Union was that Trump did fuck all to convince anyone

522
00:27:29,840 --> 00:27:32,480
Speaker 1: that things will get better in any measurable way, And

523
00:27:32,560 --> 00:27:34,240
Speaker 1: people are just left with just kind of like, what

524
00:27:34,680 --> 00:27:36,720
Speaker 1: the so what are we supposed to think here? Because

525
00:27:36,800 --> 00:27:39,200
Speaker 1: you did all this stuff and you promised us day

526
00:27:39,280 --> 00:27:42,600
Speaker 1: one price drops but it's not happening. So here's Larry

527
00:27:42,680 --> 00:27:45,359
Speaker 1: Kudlow on Fox Business telling people it's like, look, I

528
00:27:45,560 --> 00:27:49,960
Speaker 1: know all the indicators and data say things are bad, okay,

529
00:27:50,119 --> 00:27:52,480
Speaker 1: but it just trusts me. This is it's not bad.

530
00:27:52,560 --> 00:27:53,280
Speaker 1: It's pretty good.

531
00:27:53,800 --> 00:27:58,879
Speaker 8: The GDP now tracker from the Atlanta Fed is showing,

532
00:27:59,280 --> 00:28:02,000
Speaker 8: i mean, for the first quarter a minus two and

533
00:28:02,119 --> 00:28:05,120
Speaker 8: a half or minus two point eight percent, and we've

534
00:28:05,200 --> 00:28:08,200
Speaker 8: had lousy numbers on things like housing and business investment.

535
00:28:08,760 --> 00:28:10,800
Speaker 1: My generic point here.

536
00:28:10,800 --> 00:28:14,960
Speaker 8: With respect to affordability and the economy is we're gonna

537
00:28:15,000 --> 00:28:17,880
Speaker 8: have to suffer through some bad news. This is nothing

538
00:28:17,920 --> 00:28:20,760
Speaker 8: to do with Trump. Trump's programs not in yet. And

539
00:28:20,880 --> 00:28:23,000
Speaker 8: I got people on the left who are blaming Trump.

540
00:28:23,160 --> 00:28:25,920
Speaker 8: How can you blame Trump when he wasn't president when

541
00:28:26,000 --> 00:28:27,280
Speaker 8: these seeds were planted.

542
00:28:29,160 --> 00:28:32,160
Speaker 7: You couldn't be more right about that because we've only

543
00:28:32,280 --> 00:28:33,840
Speaker 7: been here thirty plus days.

544
00:28:34,240 --> 00:28:38,400
Speaker 2: You can't turn an entire economy around in thirty days.

545
00:28:38,480 --> 00:28:38,800
Speaker 6: You can't.

546
00:28:38,920 --> 00:28:41,440
Speaker 1: Actually you can, and you seemingly have in that you're

547
00:28:41,680 --> 00:28:43,760
Speaker 1: crashing it fucking very rapidly.

548
00:28:44,480 --> 00:28:47,040
Speaker 2: I mean, if you just like fire everyone and change

549
00:28:47,120 --> 00:28:48,840
Speaker 2: the rules, you kind of care.

550
00:28:49,360 --> 00:28:52,240
Speaker 3: Yeah, it's wait easier to tear things down than build

551
00:28:52,280 --> 00:28:54,400
Speaker 3: things up, which is why we are tearing things down.

552
00:28:54,800 --> 00:28:59,480
Speaker 3: You Yeah, man, this has change. It's not so much hope,

553
00:28:59,520 --> 00:29:00,680
Speaker 3: but we do have change.

554
00:29:00,960 --> 00:29:03,720
Speaker 1: It is change, I do say for the better, but

555
00:29:03,920 --> 00:29:06,920
Speaker 1: it is change. And I just love again how they

556
00:29:07,080 --> 00:29:11,280
Speaker 1: are so again because they have no like terra firma

557
00:29:11,360 --> 00:29:13,160
Speaker 1: they stand on when it comes to like anything that

558
00:29:13,240 --> 00:29:17,320
Speaker 1: they argue about. But sometime it's you know what I mean,

559
00:29:17,760 --> 00:29:22,560
Speaker 1: I'm not here, this is radio bi lingue. But you know,

560
00:29:22,680 --> 00:29:25,000
Speaker 1: like they used to do the thing where like the

561
00:29:25,200 --> 00:29:29,000
Speaker 1: Republicans inherit a great economy from a democratic presidency and

562
00:29:29,080 --> 00:29:31,400
Speaker 1: they go, look at what the economy is doing. But

563
00:29:31,520 --> 00:29:34,080
Speaker 1: in this instance, when they fuck it up, then they're like,

564
00:29:34,200 --> 00:29:36,080
Speaker 1: these seeds were planted long ago.

565
00:29:36,480 --> 00:29:39,120
Speaker 2: Is out of our hands. Man, we can't take credit

566
00:29:39,200 --> 00:29:39,400
Speaker 2: for this.

567
00:29:39,960 --> 00:29:43,920
Speaker 1: So very very I guess not not the most convincing

568
00:29:44,880 --> 00:29:47,600
Speaker 1: sort of argument, but this is kind of where they're headed.

569
00:29:48,000 --> 00:29:51,080
Speaker 2: And I feel like, yeah, this happens both sides, right,

570
00:29:51,440 --> 00:29:55,120
Speaker 2: like the economy, you know, bouncing back from a shit economy,

571
00:29:55,560 --> 00:29:58,520
Speaker 2: Like both sides are going to be like, well, you know,

572
00:29:59,000 --> 00:30:00,960
Speaker 2: give us a little bit of time. That makes sense.

573
00:30:01,200 --> 00:30:04,000
Speaker 2: The thing that really kind of feels like a shift

574
00:30:04,080 --> 00:30:10,800
Speaker 2: in tone for this administration is Elon Musk though, right, yeah, exactly, yeah.

575
00:30:10,920 --> 00:30:12,360
Speaker 3: Because he talks weird.

576
00:30:13,800 --> 00:30:18,680
Speaker 2: Tone off in that way, but also him now no

577
00:30:18,880 --> 00:30:22,240
Speaker 2: longer he went from a week like less than a

578
00:30:22,320 --> 00:30:25,920
Speaker 2: week ago, being like I'm the cutter to the degree

579
00:30:25,960 --> 00:30:29,440
Speaker 2: of having like a cartoonishly big chainsaw, to now being like,

580
00:30:30,080 --> 00:30:31,560
Speaker 2: could you actually not.

581
00:30:33,520 --> 00:30:37,120
Speaker 1: Basically so right now, like to your point, right, Musk

582
00:30:37,360 --> 00:30:39,520
Speaker 1: feels the heat from all the outrage over his a

583
00:30:39,640 --> 00:30:43,160
Speaker 1: curb stumping of the federal workforce. So now he's giving

584
00:30:43,240 --> 00:30:47,240
Speaker 1: Republicans their little propaganda talking points to say essentially that

585
00:30:47,600 --> 00:30:51,040
Speaker 1: Doge is not responsible for these cuts. This is from

586
00:30:51,080 --> 00:30:54,080
Speaker 1: CNN quote and his meeting with House Republicans. Musk also

587
00:30:54,160 --> 00:30:57,880
Speaker 1: attempted to distance himself from the widespread firings across the

588
00:30:58,040 --> 00:31:02,080
Speaker 1: federal government, which he blame aimed on federal department heads.

589
00:31:02,560 --> 00:31:05,200
Speaker 1: The GOP representative Derek Van Orden said that must hold

590
00:31:05,240 --> 00:31:07,560
Speaker 1: the group that recently announced plans to cut more than

591
00:31:07,600 --> 00:31:10,600
Speaker 1: seventy thousand jobs of the Department of Veteran Affairs quote

592
00:31:10,880 --> 00:31:14,520
Speaker 1: wasn't a Doge decision. The Wisconsin Republican added that Musk

593
00:31:14,560 --> 00:31:18,719
Speaker 1: told lawmakers that quote individual departments were involved in plans

594
00:31:18,760 --> 00:31:21,520
Speaker 1: to cut employees across the federal government, and that Doge

595
00:31:21,880 --> 00:31:26,400
Speaker 1: was making the quote assumption that department heads know who

596
00:31:26,600 --> 00:31:31,160
Speaker 1: is being quote unproductive and would quote reward the people

597
00:31:31,240 --> 00:31:38,080
Speaker 1: that are being productive. So a little bit more. Uh yeah,

598
00:31:38,360 --> 00:31:41,000
Speaker 1: he again he's the one finding the cuts to make.

599
00:31:41,080 --> 00:31:42,480
Speaker 1: But then he's liked, well, they're the ones that are

600
00:31:43,120 --> 00:31:44,080
Speaker 1: actually decided to do.

601
00:31:44,120 --> 00:31:47,600
Speaker 3: A fucking email to everyone being like, we're gonna cut you.

602
00:31:47,880 --> 00:31:51,640
Speaker 3: If you don't like, send us a five point five

603
00:31:52,120 --> 00:31:56,040
Speaker 3: what you did? Yeah, top five, top five everyone.

604
00:31:56,680 --> 00:31:58,560
Speaker 1: Let me let me see your top five. Okay, let

605
00:31:58,600 --> 00:32:01,040
Speaker 1: me see your top eight friends on nice. But yeah,

606
00:32:01,120 --> 00:32:04,600
Speaker 1: this is just the I think because of all of

607
00:32:05,040 --> 00:32:07,200
Speaker 1: like it's clear that this has been a huge hang

608
00:32:07,320 --> 00:32:10,600
Speaker 1: up for Republicans and they're all probably banging on the

609
00:32:10,720 --> 00:32:13,120
Speaker 1: fucking White House door and Musk stored be like, what

610
00:32:13,200 --> 00:32:14,840
Speaker 1: are we supposed to say, like, you guys are doing

611
00:32:14,880 --> 00:32:16,200
Speaker 1: this shit. I don't even know how to tell him.

612
00:32:16,560 --> 00:32:19,760
Speaker 1: Who's response Like Musk's response like say, it's like the

613
00:32:19,880 --> 00:32:23,040
Speaker 1: department heads. I'm surprised Musk didn't just do the most

614
00:32:23,160 --> 00:32:25,800
Speaker 1: Republican thing like, oh, it's the immigrants that are doing

615
00:32:25,880 --> 00:32:26,640
Speaker 1: the cuts.

616
00:32:27,520 --> 00:32:28,840
Speaker 4: Just say that.

617
00:32:29,120 --> 00:32:31,840
Speaker 1: I mean, that's sort of the underlying logic of all

618
00:32:31,880 --> 00:32:34,880
Speaker 1: of this is because we have everything is so bad

619
00:32:35,000 --> 00:32:39,280
Speaker 1: because of immigrants or government waste or whatever. But I mean,

620
00:32:39,320 --> 00:32:41,320
Speaker 1: I feel like the easiest place to spot waste is

621
00:32:41,360 --> 00:32:43,640
Speaker 1: in the fucking Pentagon's budget. Like that's where you hear

622
00:32:43,720 --> 00:32:47,320
Speaker 1: the most ludicrous, like nonsense spending you've ever heard of.

623
00:32:47,840 --> 00:32:49,920
Speaker 1: But yeah, this is this is where this is, this

624
00:32:50,120 --> 00:32:53,680
Speaker 1: is the direction they're going. And right now Musk is

625
00:32:53,680 --> 00:32:56,680
Speaker 1: also blabbing on Twitter like that. He's like like welcoming

626
00:32:56,760 --> 00:32:59,800
Speaker 1: a government shut down and doesn't care if the Republicans

627
00:32:59,840 --> 00:33:00,800
Speaker 1: can get their shit in.

628
00:33:00,840 --> 00:33:05,440
Speaker 3: Time, find all the services for like for the course,

629
00:33:05,880 --> 00:33:06,920
Speaker 3: looks how it doesn't work.

630
00:33:07,400 --> 00:33:10,160
Speaker 1: Yeah, because now he's like, we need to privatize Amtrak

631
00:33:10,360 --> 00:33:13,080
Speaker 1: and the post Office and like this is this is

632
00:33:13,120 --> 00:33:16,320
Speaker 1: like the most sort of bumbling way to get to this.

633
00:33:16,440 --> 00:33:20,440
Speaker 1: It's like, well, sabotage it. So then everyone's like privatize it.

634
00:33:20,600 --> 00:33:22,520
Speaker 1: But when you're doing it, when you're telegraphed, I mean,

635
00:33:22,640 --> 00:33:25,320
Speaker 1: I don't know it's not being telegraphed to everyone obviously,

636
00:33:25,440 --> 00:33:28,280
Speaker 1: but it seems pretty clear that that's kind of part

637
00:33:28,320 --> 00:33:29,680
Speaker 1: of the whole plan here.

638
00:33:29,960 --> 00:33:33,960
Speaker 2: Yeah, just privatize it, let him take over, and then

639
00:33:34,600 --> 00:33:37,160
Speaker 2: we're all we're all fucked because he's actually not good

640
00:33:37,160 --> 00:33:40,320
Speaker 2: at the things that everybody assumes he's really good at. No, no, no, no,

641
00:33:40,760 --> 00:33:43,920
Speaker 2: Like parenting, that is the one thing that I really

642
00:33:44,720 --> 00:33:47,280
Speaker 2: I mean, if he wasn't good at parenting, polity, why

643
00:33:47,280 --> 00:33:50,600
Speaker 2: would he have so many kids? Okay to checkmate, thank you.

644
00:33:51,000 --> 00:33:54,480
Speaker 3: Also, like there's some relation between Elon Musk having a

645
00:33:54,520 --> 00:33:58,760
Speaker 3: million kids and Trump's like new advertising being like Daddy's

646
00:33:58,800 --> 00:34:01,080
Speaker 3: home because he never usually is you know.

647
00:34:01,040 --> 00:34:07,360
Speaker 2: What I mean, actually make an ad about like daddy

648
00:34:07,520 --> 00:34:10,520
Speaker 2: being home that like my kids could watch so then

649
00:34:10,920 --> 00:34:12,120
Speaker 2: I don't know, like they.

650
00:34:12,640 --> 00:34:15,640
Speaker 1: I wonder's my dad? Who would give them like a

651
00:34:15,800 --> 00:34:18,680
Speaker 1: boyfriend his kids, a boyfriend pillow to hold at night,

652
00:34:18,719 --> 00:34:19,880
Speaker 1: to be like I'm not going to be there, but

653
00:34:19,960 --> 00:34:20,920
Speaker 1: here you can hold on.

654
00:34:21,120 --> 00:34:24,480
Speaker 3: No, because he uses to help support his kids, doesn't

655
00:34:24,480 --> 00:34:26,719
Speaker 3: he like not pay for his kid in Texas or

656
00:34:26,800 --> 00:34:29,200
Speaker 3: something because he can get away with not paying.

657
00:34:29,480 --> 00:34:32,560
Speaker 2: Well, like my dad didn't give me, you know, he

658
00:34:32,640 --> 00:34:36,040
Speaker 2: gave me like one diamond mind like you know, so

659
00:34:36,200 --> 00:34:40,920
Speaker 2: like and now look at me, so that's yeah, he's

660
00:34:41,680 --> 00:34:44,520
Speaker 2: he's a self made man. He's just passing that favor

661
00:34:44,560 --> 00:34:47,520
Speaker 2: along too. Oh wait, I'm sorry. He was born rich

662
00:34:47,600 --> 00:34:49,799
Speaker 2: and just bought a bunch of companies and is good

663
00:34:50,000 --> 00:34:53,520
Speaker 2: at I wish sting well from people.

664
00:34:54,360 --> 00:34:56,320
Speaker 3: I wish his kids were self made so they wouldn't

665
00:34:56,320 --> 00:34:57,880
Speaker 3: have to say they're made from him, you know what

666
00:34:57,920 --> 00:35:01,520
Speaker 3: I mean. Yeah, that is horrific.

667
00:35:02,000 --> 00:35:06,000
Speaker 2: Yeah, it was made with a turkey baster by his partners.

668
00:35:06,280 --> 00:35:09,759
Speaker 2: You know, in many ways, in many ways. All right,

669
00:35:10,280 --> 00:35:14,920
Speaker 2: latest trend, hot trend in in the legal world is

670
00:35:15,239 --> 00:35:20,560
Speaker 2: people using AI to generate briefs for cases that then

671
00:35:20,800 --> 00:35:23,960
Speaker 2: you know, they turn into the judge and the judge

672
00:35:24,040 --> 00:35:28,880
Speaker 2: there's a term for this now called hallucination sites c

673
00:35:29,080 --> 00:35:34,759
Speaker 2: i tes, and it's where they're citing case law from

674
00:35:34,880 --> 00:35:37,000
Speaker 2: cases that didn't actually happen.

675
00:35:38,320 --> 00:35:40,640
Speaker 3: Oh fuck, we're so cooked.

676
00:35:40,920 --> 00:35:41,400
Speaker 1: Yeah, I know.

677
00:35:41,560 --> 00:35:43,600
Speaker 2: We talked about this when we had Emily Bender and

678
00:35:43,680 --> 00:35:48,480
Speaker 2: Alex hannah On last year about like what large language

679
00:35:48,520 --> 00:35:53,680
Speaker 2: models are. They're basically auto correct or autocompletes that are

680
00:35:54,000 --> 00:35:57,880
Speaker 2: just doing whatever they can to give you the appearance

681
00:35:57,960 --> 00:36:00,400
Speaker 2: that they're thinking and like trick you into thinking that

682
00:36:00,520 --> 00:36:03,160
Speaker 2: they have like done this work for you.

683
00:36:03,400 --> 00:36:05,760
Speaker 3: And wait, that's also Trump and Musk. That's crazy.

684
00:36:06,000 --> 00:36:08,480
Speaker 2: Yeah, that's a lot of things. That's the entire Yeah,

685
00:36:08,640 --> 00:36:11,560
Speaker 2: it's the whole new ethos of like just good enough

686
00:36:12,000 --> 00:36:13,400
Speaker 2: to fool people.

687
00:36:13,600 --> 00:36:16,040
Speaker 3: And which is fine if you're using it to correct

688
00:36:16,040 --> 00:36:18,160
Speaker 3: for the wage theft at your job, that's right.

689
00:36:18,560 --> 00:36:24,160
Speaker 2: And so they lawyers are starting to use AI because

690
00:36:24,360 --> 00:36:28,279
Speaker 2: and I don't know, like we're all like everybody has

691
00:36:28,480 --> 00:36:30,919
Speaker 2: more and more work to do for less and less money,

692
00:36:31,120 --> 00:36:33,920
Speaker 2: like for other lawyers, CEOs.

693
00:36:34,880 --> 00:36:38,000
Speaker 3: I know doctors who are using it to complete their notes.

694
00:36:38,239 --> 00:36:41,600
Speaker 2: Right, like, yeah, everybody's getting fucking pinched.

695
00:36:41,640 --> 00:36:42,680
Speaker 1: Does that makes sense to me?

696
00:36:43,040 --> 00:36:47,800
Speaker 3: I think it can't save them time, like in terms

697
00:36:47,840 --> 00:36:51,080
Speaker 3: of like like because their notes are like they'll like input,

698
00:36:51,280 --> 00:36:54,200
Speaker 3: like they'll say it out loud or whatever and then

699
00:36:54,320 --> 00:36:56,080
Speaker 3: like have I don't know what they do, but like

700
00:36:56,480 --> 00:36:58,279
Speaker 3: they I think it helps. It's supposed to help with

701
00:36:58,320 --> 00:37:01,640
Speaker 3: the paperwork time. And I'm all for AI like helping

702
00:37:01,800 --> 00:37:04,000
Speaker 3: with this shit we don't want to do. But I'm

703
00:37:04,040 --> 00:37:07,359
Speaker 3: also like, I don't want people to get lazy at

704
00:37:07,400 --> 00:37:08,800
Speaker 3: the expense of our lives.

705
00:37:09,000 --> 00:37:11,600
Speaker 1: No, not partly, I don't know, let the AI diagnose

706
00:37:11,719 --> 00:37:13,960
Speaker 1: this just the AI. Look at the chart that Yeah,

707
00:37:13,960 --> 00:37:16,480
Speaker 1: that's where it gets dangers. Yeah, that guy probably gets

708
00:37:16,480 --> 00:37:19,800
Speaker 1: a little dangerous. So judges are now having to find lawyers.

709
00:37:19,920 --> 00:37:22,120
Speaker 1: And there's this one case that four o four media

710
00:37:22,680 --> 00:37:26,960
Speaker 1: called out that's pretty fun, where the lawyer explained that

711
00:37:27,120 --> 00:37:29,840
Speaker 1: he had used AI before to assist with legal matters

712
00:37:29,880 --> 00:37:32,760
Speaker 1: such as drafting agreements, and did not know that AI

713
00:37:32,960 --> 00:37:38,200
Speaker 1: was capable of generating fictitious cases and citations. These hallucination

714
00:37:38,560 --> 00:37:42,840
Speaker 1: sites included tex step excerpts, which appear to be credible.

715
00:37:43,239 --> 00:37:47,239
Speaker 1: He reported, he is since that this is wild. He

716
00:37:47,400 --> 00:37:51,480
Speaker 1: reported that he has since taken continuing legal education courses

717
00:37:51,560 --> 00:37:54,400
Speaker 1: on the topic of AI use and continues to use

718
00:37:54,480 --> 00:37:58,959
Speaker 1: AI products, which he has been assured will not produced.

719
00:37:59,000 --> 00:37:59,960
Speaker 1: What do you mean you finished?

720
00:38:00,239 --> 00:38:04,359
Speaker 3: What a disbar Okay, this is just the lawyer from

721
00:38:04,440 --> 00:38:08,399
Speaker 3: arrested Development all it is blah blah Barry. No, it's

722
00:38:08,520 --> 00:38:12,360
Speaker 3: the Winkler Yeah, yeah.

723
00:38:13,640 --> 00:38:16,760
Speaker 1: Henry Winkler Arson Arson investigator Henry Winkler.

724
00:38:17,239 --> 00:38:19,680
Speaker 2: The judging response was, it is abundantly clear that mister

725
00:38:19,800 --> 00:38:22,839
Speaker 2: Ramirez did not make the requisite reasonable inquiry into the law.

726
00:38:23,280 --> 00:38:26,680
Speaker 2: Had he expended even minimal effort to do so he

727
00:38:26,800 --> 00:38:30,719
Speaker 2: would have discovered that the AI generated cases cases do

728
00:38:30,880 --> 00:38:34,520
Speaker 2: not exist, that the AI generated excerts appeared valid to

729
00:38:34,600 --> 00:38:37,719
Speaker 2: mister Ramirez does not relieve him of his duty to

730
00:38:37,880 --> 00:38:42,120
Speaker 2: conduct a reasonable inquiry just because they looked good to you,

731
00:38:42,760 --> 00:38:45,359
Speaker 2: Like you think that's a good enough excuse and then

732
00:38:45,440 --> 00:38:47,120
Speaker 2: to be like and I'm going to keep on using

733
00:38:47,200 --> 00:38:49,719
Speaker 2: it like it just this this seems to be an

734
00:38:49,760 --> 00:38:52,360
Speaker 2: emerging problem with AI that wasn't. Like one of the

735
00:38:52,400 --> 00:38:55,600
Speaker 2: big concerns I had going in is that it seems

736
00:38:55,640 --> 00:38:58,880
Speaker 2: to be like more fun and it's I guess it

737
00:38:59,040 --> 00:39:03,080
Speaker 2: seems more liable to the people using it than it

738
00:39:03,200 --> 00:39:07,560
Speaker 2: actually is so liable for fucking around, right, It's like

739
00:39:07,719 --> 00:39:11,759
Speaker 2: trying to talk a drunk person out of driving, like

740
00:39:11,960 --> 00:39:16,920
Speaker 2: and they're just like, no, what I'm fucking amazing to

741
00:39:17,200 --> 00:39:20,080
Speaker 2: Like combining alcohol with like driving is actually like I'm

742
00:39:20,440 --> 00:39:24,000
Speaker 2: I'm a better driver. Yeah, actually drive slower because I

743
00:39:24,120 --> 00:39:25,799
Speaker 2: know I'm drunk and I don't want to get pulled over.

744
00:39:26,160 --> 00:39:29,880
Speaker 2: But like the promise of something that can make you

745
00:39:30,840 --> 00:39:35,279
Speaker 2: like look smart with minimal effort for like people in

746
00:39:35,960 --> 00:39:38,759
Speaker 2: like this world where we're all being stepped on by

747
00:39:38,800 --> 00:39:42,319
Speaker 2: like massive corporations is like just too good to be true,

748
00:39:42,520 --> 00:39:45,520
Speaker 2: and so people are just like drunk on the promise

749
00:39:45,600 --> 00:39:49,479
Speaker 2: of that and end up just doing the wildest shit.

750
00:39:49,920 --> 00:39:53,480
Speaker 2: And then even when they get caught, aren't willing to

751
00:39:53,520 --> 00:39:56,239
Speaker 2: be like I guess I shouldn't use AI. They're just

752
00:39:56,320 --> 00:39:58,440
Speaker 2: like I guess I'll just keep using AI.

753
00:39:59,040 --> 00:40:01,400
Speaker 3: This is also like tech. This is just like a

754
00:40:01,480 --> 00:40:04,879
Speaker 3: literacy in general. I think, like tech illiteracy, we never

755
00:40:05,120 --> 00:40:10,080
Speaker 3: have like the ethical implications ahead of the technology. It's

756
00:40:10,120 --> 00:40:13,560
Speaker 3: always behind, and I think like because of that, we're

757
00:40:13,600 --> 00:40:16,320
Speaker 3: eventually like not gonna have the tech development or the

758
00:40:16,360 --> 00:40:19,560
Speaker 3: medical development, or like the application of these solutions in

759
00:40:19,760 --> 00:40:23,040
Speaker 3: like the broad population, because we're just like too far

760
00:40:23,200 --> 00:40:26,640
Speaker 3: behind like educating people on this shit, right, Yeah, I.

761
00:40:26,680 --> 00:40:29,279
Speaker 1: Mean it's great. Doctor Ian Malcolm said, your scientists were

762
00:40:29,320 --> 00:40:31,919
Speaker 1: so preoccupied with whether or not they could they didn't

763
00:40:31,920 --> 00:40:33,400
Speaker 1: stop to think if they should.

764
00:40:34,239 --> 00:40:36,520
Speaker 3: Oh holy shit, that's like a real doctor, right I

765
00:40:36,600 --> 00:40:37,520
Speaker 3: read that online.

766
00:40:37,840 --> 00:40:42,399
Speaker 2: Yeah, that's a real doctor, my pediatrician.

767
00:40:45,320 --> 00:40:49,600
Speaker 1: Chaos theory. I mean, no, The thing with this is

768
00:40:50,040 --> 00:40:53,240
Speaker 1: I can't believe though too. I guess it's super frightening.

769
00:40:53,520 --> 00:40:56,759
Speaker 1: It also just reveals the level of intellect of people.

770
00:40:56,840 --> 00:40:59,440
Speaker 1: That just can get into a certain field. Like we're like, like,

771
00:41:00,040 --> 00:41:02,320
Speaker 1: I mean, not that I thought every single lawyer is

772
00:41:02,360 --> 00:41:05,279
Speaker 1: a genius, but that on some level there was like

773
00:41:05,480 --> 00:41:08,759
Speaker 1: the basics where we're like, okay, don't use AI to

774
00:41:08,920 --> 00:41:13,360
Speaker 1: cite the case of Beggars v. Choosers in a court document.

775
00:41:14,480 --> 00:41:17,759
Speaker 1: But god, that's what's I don't know. Like that's when

776
00:41:17,760 --> 00:41:20,240
Speaker 1: I'm like, wow, I don't think I knew.

777
00:41:20,120 --> 00:41:23,719
Speaker 3: That years ago when I first started stand up. These

778
00:41:23,719 --> 00:41:26,239
Speaker 3: are my friends, and people who listen to me may

779
00:41:26,560 --> 00:41:28,839
Speaker 3: know the podcast I was on, and so we're all cool.

780
00:41:29,000 --> 00:41:31,600
Speaker 3: But there was an episode of a podcast I did

781
00:41:31,719 --> 00:41:34,439
Speaker 3: with my friends and they were like, well, what makes

782
00:41:34,560 --> 00:41:38,359
Speaker 3: you more of a scientist than I am? And they shit,

783
00:41:38,640 --> 00:41:42,719
Speaker 3: weren't scientists. And I'm like I knew then that we

784
00:41:42,840 --> 00:41:47,399
Speaker 3: were cooked, Like because people people like will just be like, well,

785
00:41:47,520 --> 00:41:50,480
Speaker 3: I do my own research. And it's like YouTube videos,

786
00:41:50,560 --> 00:41:52,960
Speaker 3: you know what I mean, like googling and.

787
00:41:54,760 --> 00:41:55,120
Speaker 4: Speech.

788
00:41:55,360 --> 00:41:57,520
Speaker 3: I tutor with YouTube, but you can learn a lot,

789
00:41:57,600 --> 00:42:00,960
Speaker 3: but you do like have to know things. You know, Yeah, yeah,

790
00:42:02,000 --> 00:42:05,960
Speaker 3: tube videos are exactly But like for people to have

791
00:42:06,160 --> 00:42:09,160
Speaker 3: that arrogance of like, well, I'm also in this field

792
00:42:09,239 --> 00:42:10,480
Speaker 3: because I say I am.

793
00:42:10,880 --> 00:42:14,200
Speaker 2: It's actually like my scientific method is I can just

794
00:42:14,320 --> 00:42:16,720
Speaker 2: like I watch the YouTube videos at two x speed,

795
00:42:17,000 --> 00:42:19,480
Speaker 2: and then I watched the ones that have monetization turned

796
00:42:19,520 --> 00:42:21,920
Speaker 2: off because then they don't get broken up by ads,

797
00:42:22,000 --> 00:42:25,759
Speaker 2: and so therefore I learn five times as much. It

798
00:42:25,800 --> 00:42:28,279
Speaker 2: has nothing to do with the fact that the monetization

799
00:42:28,400 --> 00:42:34,000
Speaker 2: being turned off is because they tells information why the Holocaust.

800
00:42:34,680 --> 00:42:36,759
Speaker 2: But also I'm not gonna pay for YouTube premium bee thish,

801
00:42:36,800 --> 00:42:38,200
Speaker 2: it's a racket. I can't even figure out how to

802
00:42:38,239 --> 00:42:39,800
Speaker 2: put ad blockers and chrome, so fuck it.

803
00:42:40,680 --> 00:42:44,320
Speaker 1: Yeah. I it's funny when people turn like it feels

804
00:42:44,400 --> 00:42:46,799
Speaker 1: like that sort of attack of been like, well why

805
00:42:46,800 --> 00:42:48,480
Speaker 1: aren't I as scientists as if this is like a

806
00:42:48,560 --> 00:42:50,719
Speaker 1: philosophical quist question about what.

807
00:42:50,760 --> 00:42:53,239
Speaker 3: It's like, Well, you could have done it. You could

808
00:42:53,280 --> 00:42:55,840
Speaker 3: have just got everybody could who has I mean not

809
00:42:55,920 --> 00:42:58,600
Speaker 3: everybody because they need resources and shit, but like these

810
00:42:58,640 --> 00:43:01,000
Speaker 3: people definitely could have done had they wanted to.

811
00:43:01,680 --> 00:43:05,719
Speaker 2: Yeah, but I do think it's not necessarily I think

812
00:43:05,800 --> 00:43:11,400
Speaker 2: we're all dealing with the same shit of massive corporations.

813
00:43:11,760 --> 00:43:15,920
Speaker 2: Just everybody, like every bottom line is being shaved down

814
00:43:16,120 --> 00:43:20,439
Speaker 2: and down and down until one person's doing the work

815
00:43:20,520 --> 00:43:24,960
Speaker 2: of four people and then they're like, ho, this AI

816
00:43:25,160 --> 00:43:29,120
Speaker 2: thing is my a life saver, and they just don't

817
00:43:29,440 --> 00:43:32,360
Speaker 2: take the time to realize that it sucks. Shit, Like

818
00:43:32,440 --> 00:43:36,959
Speaker 2: I keep thinking about that Google that that Google super Bowl.

819
00:43:37,719 --> 00:43:40,319
Speaker 2: It was like an ad for Google AI. We've talked

820
00:43:40,320 --> 00:43:44,200
Speaker 2: about it a lot, where the Google AI is being

821
00:43:44,239 --> 00:43:48,160
Speaker 2: advertised with making the writing and research easy for a

822
00:43:48,280 --> 00:43:50,840
Speaker 2: cheese farmer who has to like put labels and like

823
00:43:50,960 --> 00:43:55,280
Speaker 2: marketing together for his cheese. And it had a blatantly

824
00:43:55,400 --> 00:43:58,480
Speaker 2: fake thing it said like Gouda is responsible for sixty

825
00:43:58,520 --> 00:44:01,759
Speaker 2: percent of the cheese consumption in the world, which is

826
00:44:01,840 --> 00:44:06,480
Speaker 2: like obviously false, and like just they just again like

827
00:44:06,640 --> 00:44:10,319
Speaker 2: it's the just not not wanting to admit that AI

828
00:44:10,520 --> 00:44:14,160
Speaker 2: sucks because it's like the answer to all these people's

829
00:44:14,520 --> 00:44:18,440
Speaker 2: prayers and it's like the answer to the stock markets prayers,

830
00:44:18,760 --> 00:44:22,040
Speaker 2: and so like I think that's the only way to explain,

831
00:44:22,120 --> 00:44:25,320
Speaker 2: Like how do you not fucking check your work? Like

832
00:44:25,480 --> 00:44:29,800
Speaker 2: the whole ad hinges on these facts being right, and

833
00:44:29,880 --> 00:44:31,360
Speaker 2: you don't check the.

834
00:44:31,520 --> 00:44:36,279
Speaker 1: Facts, you do it like yeah, I don't, Yeah, I don't,

835
00:44:36,400 --> 00:44:38,239
Speaker 1: I don't know. I think I'm okay, I have I

836
00:44:38,320 --> 00:44:42,040
Speaker 1: had an immigrant mom checking my ship bro there, Yeah,

837
00:44:42,520 --> 00:44:45,040
Speaker 1: you know what I mean, different different levels, different levels

838
00:44:45,080 --> 00:44:47,399
Speaker 1: to that. I'm also just but I'm like even thinking

839
00:44:47,400 --> 00:44:49,799
Speaker 1: about it, lawyer, right, That's different than like someone who's

840
00:44:49,880 --> 00:44:52,960
Speaker 1: having to like do all this like paperwork at a company, right,

841
00:44:53,080 --> 00:44:56,280
Speaker 1: Like an attorney, Like if you're a criminal defense attorney,

842
00:44:56,400 --> 00:44:59,720
Speaker 1: like this joker is you haven't you have? You're charging

843
00:45:00,200 --> 00:45:03,200
Speaker 1: fucking money, and the assumption is why am I paying

844
00:45:03,280 --> 00:45:07,480
Speaker 1: you nine one thousand dollars an hour whatever your hourly

845
00:45:07,640 --> 00:45:10,080
Speaker 1: rate is for you to fucking use AI? Like then

846
00:45:10,239 --> 00:45:11,640
Speaker 1: I just did. That's just in excuse me.

847
00:45:11,680 --> 00:45:14,120
Speaker 3: But I feel like that justification can be applied to

848
00:45:14,200 --> 00:45:16,759
Speaker 3: everyone because everyone can be like, well, I'm working really hard.

849
00:45:16,840 --> 00:45:18,360
Speaker 3: I may not be getting paid as much, but I

850
00:45:18,480 --> 00:45:20,040
Speaker 3: like don't have as much time I have my family

851
00:45:20,120 --> 00:45:23,080
Speaker 3: like whatever. It's like what Jack said. Like similarly with

852
00:45:23,280 --> 00:45:25,600
Speaker 3: like like doctors, they're like, well, if I don't have

853
00:45:25,680 --> 00:45:27,440
Speaker 3: to do the paperwork, then I can like see the

854
00:45:27,480 --> 00:45:28,879
Speaker 3: patient more, you know what I mean?

855
00:45:29,080 --> 00:45:31,760
Speaker 1: Sure, I guess there's levels, right because once, yeah, filing

856
00:45:31,880 --> 00:45:35,400
Speaker 1: a legal brief about a case where you're citing case

857
00:45:35,560 --> 00:45:38,800
Speaker 1: law to bolster your argument in court, if you're merely

858
00:45:38,920 --> 00:45:43,160
Speaker 1: using AI to like summarize your notes. That seems pretty harmless.

859
00:45:43,400 --> 00:45:45,880
Speaker 3: Yeah, I think we haven't been taught like where the

860
00:45:45,960 --> 00:45:46,400
Speaker 3: harm is.

861
00:45:46,800 --> 00:45:49,080
Speaker 2: And that's, by the way, because they're so good at

862
00:45:49,160 --> 00:45:53,480
Speaker 2: summarizing the texts and emails that I get so so

863
00:45:53,680 --> 00:45:54,080
Speaker 2: good at that.

864
00:45:54,560 --> 00:45:56,480
Speaker 1: That is funny on Apple. Now when you see some

865
00:45:56,560 --> 00:46:00,600
Speaker 1: of the messages, they're like, yeah, your friend believes so

866
00:46:01,000 --> 00:46:05,960
Speaker 1: so like Mavericks cooked. Also, dinner plans Tuesday. This is

867
00:46:06,040 --> 00:46:08,440
Speaker 1: not I said nothing. No one even said the Mavericks

868
00:46:08,480 --> 00:46:09,279
Speaker 1: were cooked in this text.

869
00:46:09,320 --> 00:46:11,520
Speaker 2: But but anyway, let's take a quick break.

870
00:46:11,719 --> 00:46:26,080
Speaker 4: We'll come back, and we're back. We're back, and we're back.

871
00:46:27,600 --> 00:46:30,640
Speaker 2: Over the break, we were talking about Moodang. Where I

872
00:46:30,760 --> 00:46:33,960
Speaker 2: was I was saying, wait, wait, what happened to Moudang.

873
00:46:34,120 --> 00:46:36,000
Speaker 2: Did Moudang get canceled?

874
00:46:36,640 --> 00:46:36,960
Speaker 6: She did.

875
00:46:37,200 --> 00:46:39,399
Speaker 3: She got canceled because she chose Trump as the winner

876
00:46:39,440 --> 00:46:40,200
Speaker 3: for the election.

877
00:46:40,760 --> 00:46:43,840
Speaker 1: From a predictive standpoint from endorsing.

878
00:46:45,960 --> 00:46:49,239
Speaker 3: I think, I think because she's a woman, she's a girl.

879
00:46:49,880 --> 00:46:52,480
Speaker 3: I think people it's misogyny on the internet.

880
00:46:53,480 --> 00:46:56,440
Speaker 2: I mean, I also thought Trump was going to win, Like.

881
00:46:56,760 --> 00:46:59,160
Speaker 3: Yeah, I mean I didn't want it to happen.

882
00:46:59,360 --> 00:47:05,400
Speaker 2: No, yeah, exactly, that, but Moodang because it's a woman

883
00:47:05,719 --> 00:47:08,560
Speaker 2: who's hot on the internet and there's so many haters

884
00:47:08,600 --> 00:47:11,839
Speaker 2: out there trying to take her down. Oh, actually okay,

885
00:47:12,400 --> 00:47:15,400
Speaker 2: to producer Victor, actually am I allowed to reveal this?

886
00:47:15,560 --> 00:47:18,080
Speaker 2: He Victor had a friend who worked with her, and

887
00:47:18,560 --> 00:47:19,880
Speaker 2: she's apparently problematic.

888
00:47:19,920 --> 00:47:22,919
Speaker 1: As finn I can not believe that weld on. Victor

889
00:47:22,960 --> 00:47:27,000
Speaker 1: said that Moodang hides hold on. Moodang bites toothpicks in

890
00:47:27,160 --> 00:47:29,799
Speaker 1: her pen to see if the zoo keepers are.

891
00:47:31,120 --> 00:47:34,400
Speaker 2: Clean up with the sufficient attention to detail.

892
00:47:34,640 --> 00:47:36,640
Speaker 1: God, God, I thought that was only Ellen.

893
00:47:36,719 --> 00:47:39,200
Speaker 2: You don't find that fucking toothpick, You're fired.

894
00:47:39,280 --> 00:47:41,359
Speaker 1: You're fired, fire, I'm.

895
00:47:41,320 --> 00:47:45,600
Speaker 3: Mood You can't cancel Moodang for not being kind. She

896
00:47:45,800 --> 00:47:53,000
Speaker 3: bites people and that's her whole thing, her whole problematic.

897
00:47:54,040 --> 00:47:54,320
Speaker 4: Ship.

898
00:47:54,680 --> 00:47:59,440
Speaker 2: All well, break back. So Moodang, though, is being uncanceled.

899
00:47:59,480 --> 00:48:02,719
Speaker 2: We feel like or is it is the lifespan for

900
00:48:03,840 --> 00:48:07,560
Speaker 2: like the cuteness of a baby hippo like short and

901
00:48:07,680 --> 00:48:08,480
Speaker 2: it's it's like.

902
00:48:08,640 --> 00:48:11,279
Speaker 3: I'm sorry, I call the Cincinnati.

903
00:48:12,280 --> 00:48:13,480
Speaker 4: And it's like Michael Jackson.

904
00:48:13,320 --> 00:48:15,440
Speaker 2: Hitting puberty where they're just like get this kid out

905
00:48:15,480 --> 00:48:17,239
Speaker 2: of here, Like chemically castraight thing.

906
00:48:17,520 --> 00:48:22,080
Speaker 3: Yeah, Michael Jackson famously not popular after hitting Pewery.

907
00:48:24,480 --> 00:48:26,760
Speaker 2: I'm not familiar with the latter part of his career,

908
00:48:27,960 --> 00:48:28,120
Speaker 2: just a.

909
00:48:28,120 --> 00:48:28,880
Speaker 4: Big fan of it.

910
00:48:29,480 --> 00:48:31,200
Speaker 1: He kept that voice high anyway.

911
00:48:32,280 --> 00:48:34,200
Speaker 3: You're like, he had that one hit wonder.

912
00:48:36,560 --> 00:48:39,799
Speaker 1: Knocked the Beatles off the charts. Wait, were you asking something?

913
00:48:40,160 --> 00:48:43,600
Speaker 2: I was I was asking are there other, uh like

914
00:48:43,719 --> 00:48:47,359
Speaker 2: hippos that have taken Moodang's place or we're no, it's

915
00:48:47,480 --> 00:48:48,040
Speaker 2: just other.

916
00:48:47,960 --> 00:48:52,280
Speaker 3: Baby animals like the baby Tapier taper Tapeier in Oregon.

917
00:48:53,360 --> 00:48:56,480
Speaker 3: There's like another baby. There was like a there's baby

918
00:48:56,560 --> 00:48:58,680
Speaker 3: something I don't know, but there. It's all the zoos

919
00:48:58,719 --> 00:49:02,120
Speaker 3: have been capitalizing ons presence, but also mooting took over

920
00:49:02,239 --> 00:49:07,000
Speaker 3: the Cincinnati Zoo, Pygmy hippos, Fritz and family, you know,

921
00:49:07,280 --> 00:49:11,160
Speaker 3: so like it's a cycle, you know, if you're following

922
00:49:11,320 --> 00:49:14,400
Speaker 3: the zoo accounts like I am, you know.

923
00:49:14,840 --> 00:49:18,160
Speaker 2: On that zoom on that zoo beat, on that zoom beat.

924
00:49:18,800 --> 00:49:23,080
Speaker 2: All right, zoo be quick, zoom beat, zoo beat, zoo

925
00:49:23,880 --> 00:49:24,520
Speaker 2: that's good.

926
00:49:26,719 --> 00:49:27,879
Speaker 4: Zoo by to be well.

927
00:49:27,920 --> 00:49:30,360
Speaker 2: I forget how that song goes. Anyways, Sasame Street, is

928
00:49:30,400 --> 00:49:31,840
Speaker 2: you in busting now? Anyways?

929
00:49:32,239 --> 00:49:33,800
Speaker 1: All right next question, you're on it. Wait, what do

930
00:49:33,880 --> 00:49:37,120
Speaker 1: you mean, because the who's organized, the who's who's getting organized.

931
00:49:37,440 --> 00:49:40,960
Speaker 2: The writers of puppeteers are already in unions, but there's

932
00:49:41,000 --> 00:49:42,200
Speaker 2: a new union.

933
00:49:42,200 --> 00:49:45,839
Speaker 3: That you mean, what do you mean puppeteers?

934
00:49:46,920 --> 00:49:49,279
Speaker 2: Okay, so this is gonna be a tough one.

935
00:49:49,280 --> 00:49:51,520
Speaker 1: I told you, Miles, I told you, this is uh

936
00:49:53,440 --> 00:49:54,800
Speaker 1: moodang videos.

937
00:49:54,880 --> 00:50:00,520
Speaker 2: Okay, cool, he's gonna be watching that while so, yeah,

938
00:50:00,600 --> 00:50:04,920
Speaker 2: keep watching that. The nonprofit that produces Sesame Street announced

939
00:50:04,920 --> 00:50:08,800
Speaker 2: that they're unionizing. Debut and new union logo that's basically

940
00:50:08,880 --> 00:50:12,120
Speaker 2: just a muppet version of the Predator handshake me the

941
00:50:12,840 --> 00:50:13,759
Speaker 2: son of a Bitch.

942
00:50:14,040 --> 00:50:20,600
Speaker 1: Yeah, grasping hand in hand, rest in peace, Carl Weathers.

943
00:50:21,000 --> 00:50:24,320
Speaker 2: But so the writers, Carl Weathers, I know.

944
00:50:26,440 --> 00:50:26,640
Speaker 1: Yeah.

945
00:50:27,600 --> 00:50:32,840
Speaker 2: Uh, this new union doesn't cover writers, and it covers

946
00:50:33,600 --> 00:50:42,160
Speaker 2: workers such a puppet puppet ears. Yes, people, they're floppy

947
00:50:42,200 --> 00:50:42,600
Speaker 2: and cute.

948
00:50:42,920 --> 00:50:43,040
Speaker 3: Uh.

949
00:50:44,040 --> 00:50:48,000
Speaker 2: So this covers workers such as the early childhood education experts,

950
00:50:48,120 --> 00:50:53,160
Speaker 2: fundraisers for facilities, staff producers, and pair of legals and

951
00:50:54,080 --> 00:50:58,279
Speaker 2: they the other scaffolding for for all the scraffolding, all

952
00:50:58,320 --> 00:51:00,880
Speaker 2: the invisible scaffolding that have is behind the scenes that

953
00:51:00,960 --> 00:51:05,160
Speaker 2: makes it as good like stand out from other children

954
00:51:05,280 --> 00:51:09,920
Speaker 2: childhood education early childhood education specialists. It is the whole

955
00:51:10,080 --> 00:51:14,399
Speaker 2: reason that I was like, okay, like Sesame Street, yeah,

956
00:51:14,680 --> 00:51:16,960
Speaker 2: like that's it. There's so much shit, Like there's so

957
00:51:17,080 --> 00:51:21,960
Speaker 2: much the world of early childhood entertainment is like evolves

958
00:51:22,080 --> 00:51:26,279
Speaker 2: incredibly fast these days. There's like some scary shit about

959
00:51:26,400 --> 00:51:29,759
Speaker 2: Like okay, so these ones are actually really bad for

960
00:51:29,880 --> 00:51:32,640
Speaker 2: your kids' brains because it's just like flashing lights and

961
00:51:32,719 --> 00:51:35,640
Speaker 2: shit and like it's trying to it's just like not good.

962
00:51:35,760 --> 00:51:39,000
Speaker 2: And so the fact that like Sesame Street has from

963
00:51:39,120 --> 00:51:43,200
Speaker 2: day one been like we consulted first Step, we consulted

964
00:51:43,320 --> 00:51:48,920
Speaker 2: early childhood educators and like had them shape how we

965
00:51:49,160 --> 00:51:51,880
Speaker 2: like present information to children is like one of the

966
00:51:51,960 --> 00:51:55,920
Speaker 2: coolest things about Sesame Street. So they are finally being like, uh,

967
00:51:56,280 --> 00:51:59,279
Speaker 2: we feel like we should like be protected. And one

968
00:51:59,400 --> 00:52:02,920
Speaker 2: day after was announced that they were unionizing, Sesame Workshop

969
00:52:03,000 --> 00:52:06,799
Speaker 2: announced that they would be significantly downsizing, laying off more

970
00:52:06,840 --> 00:52:10,080
Speaker 2: than two hundred employees, which is I mean.

971
00:52:10,760 --> 00:52:13,040
Speaker 3: This all is Elon Musk in charge of that too.

972
00:52:13,400 --> 00:52:15,520
Speaker 2: It just it feels like he's in charge of everything.

973
00:52:15,680 --> 00:52:19,719
Speaker 2: It feels like, I mean, the entertainment industry has you know,

974
00:52:19,880 --> 00:52:23,360
Speaker 2: been slowly taken over by finance people, and it's resulted

975
00:52:23,520 --> 00:52:29,759
Speaker 2: in budget slashing, specifically of things that aren't overtly like

976
00:52:30,040 --> 00:52:31,400
Speaker 2: present on screen.

977
00:52:31,920 --> 00:52:32,120
Speaker 3: You know.

978
00:52:32,680 --> 00:52:37,280
Speaker 2: So they're like writers' rooms turn into like a handful

979
00:52:37,320 --> 00:52:42,120
Speaker 2: of writers being like put on onerous contracts and like

980
00:52:42,600 --> 00:52:45,640
Speaker 2: in this case, the early childhood education experts, like you

981
00:52:45,719 --> 00:52:49,040
Speaker 2: won't miss those the first episode after they're gone, the

982
00:52:49,719 --> 00:52:52,600
Speaker 2: monsters and puppets, and you know, they like all the

983
00:52:52,719 --> 00:52:55,480
Speaker 2: writers will still be writing the same cute shit, but

984
00:52:55,680 --> 00:53:01,040
Speaker 2: like long term, like same way that like you know, content,

985
00:53:01,520 --> 00:53:04,560
Speaker 2: you can just like tell there's like something missing a

986
00:53:04,640 --> 00:53:07,400
Speaker 2: little bit. Like they're they're just like not quite to

987
00:53:07,560 --> 00:53:11,840
Speaker 2: the level of what you're used to from years past.

988
00:53:12,160 --> 00:53:16,400
Speaker 3: As they're bringing back black sitcoms, where are all the

989
00:53:16,520 --> 00:53:18,600
Speaker 3: black sitcoms other than that I love Abbit?

990
00:53:18,960 --> 00:53:20,759
Speaker 1: Yeah you mean woke sitcom? I don't know. I mean

991
00:53:20,800 --> 00:53:26,320
Speaker 1: that's the whole fucking there's bring back production. Yes, you know,

992
00:53:26,560 --> 00:53:29,640
Speaker 1: they go from likes.

993
00:53:28,680 --> 00:53:31,440
Speaker 2: They go from a writer's room of like eight people

994
00:53:31,719 --> 00:53:35,160
Speaker 2: who are hanging out like having fun, to like writers

995
00:53:35,600 --> 00:53:38,680
Speaker 2: google docs of four people, you know, and it's like

996
00:53:39,400 --> 00:53:42,680
Speaker 2: so there's like you could the jokes like feel similar

997
00:53:43,120 --> 00:53:46,800
Speaker 2: similarly like good and in places, and so you're not

998
00:53:47,080 --> 00:53:50,840
Speaker 2: totally noticing it, but it's just the overall quality like

999
00:53:51,040 --> 00:53:52,560
Speaker 2: it I feel. I just feel like this is the

1000
00:53:52,560 --> 00:53:55,920
Speaker 2: overall direction of the entire economy, Like you hide the

1001
00:53:56,200 --> 00:54:00,600
Speaker 2: poorly paid employees like that, like Amazon's and higher trick

1002
00:54:00,960 --> 00:54:04,160
Speaker 2: is like okay, so like you know, if you pay

1003
00:54:04,680 --> 00:54:07,399
Speaker 2: the people who work at the store like absolute shit

1004
00:54:07,640 --> 00:54:11,520
Speaker 2: and like you know, charge them money for taking bathroom breaks,

1005
00:54:12,440 --> 00:54:15,080
Speaker 2: people are going to notice that that that is going

1006
00:54:15,160 --> 00:54:17,880
Speaker 2: to show up in like the customer experience at the store.

1007
00:54:18,160 --> 00:54:20,680
Speaker 2: But if you never interact with those people, if the

1008
00:54:20,719 --> 00:54:23,680
Speaker 2: customer never interacts with those people, then we can just

1009
00:54:23,800 --> 00:54:27,040
Speaker 2: treat them like absolute shit and like just shave it

1010
00:54:27,239 --> 00:54:31,160
Speaker 2: down and down and down, so the quality suffers, but

1011
00:54:31,320 --> 00:54:34,880
Speaker 2: not in a way that's like immediately and overtly obvious

1012
00:54:34,960 --> 00:54:36,840
Speaker 2: to people, you know what I mean, And like that

1013
00:54:37,120 --> 00:54:40,560
Speaker 2: just seems to be the entire trick of how everything

1014
00:54:40,680 --> 00:54:43,280
Speaker 2: functions now, is like how do we just keep shaving

1015
00:54:43,400 --> 00:54:47,440
Speaker 2: it down further and further to worse and worse products

1016
00:54:47,560 --> 00:54:51,520
Speaker 2: that people can't like immediately see, oh this sucks shit,

1017
00:54:51,680 --> 00:54:55,000
Speaker 2: like right away on day one, but it eventually hits

1018
00:54:55,040 --> 00:54:55,359
Speaker 2: that point.

1019
00:54:55,400 --> 00:54:58,560
Speaker 3: It eventually with quantity over quality. They're doing like these

1020
00:54:58,600 --> 00:55:02,520
Speaker 3: things called verticals now in LA where it's like it's

1021
00:55:02,760 --> 00:55:05,520
Speaker 3: it's basically like what can I watch on my phone

1022
00:55:05,640 --> 00:55:09,000
Speaker 3: to take like the movie experience away from the user.

1023
00:55:09,480 --> 00:55:14,080
Speaker 3: And it's also like the like the worst freakin' like

1024
00:55:14,280 --> 00:55:17,799
Speaker 3: scripts and soapy things, and I will work on them

1025
00:55:17,920 --> 00:55:21,480
Speaker 3: if you hire me. And it is like it's like

1026
00:55:21,640 --> 00:55:24,600
Speaker 3: it's almost like AI slot, but it's like they're using

1027
00:55:24,760 --> 00:55:30,279
Speaker 3: like it's weird. They're using like Chinese AI, translating it

1028
00:55:30,600 --> 00:55:33,880
Speaker 3: and then translating it again and then making it consumable

1029
00:55:33,960 --> 00:55:38,160
Speaker 3: for American market. It's like the process is so convoluted, right,

1030
00:55:38,239 --> 00:55:40,680
Speaker 3: and it's so unnecessary when you could just get like

1031
00:55:40,840 --> 00:55:42,520
Speaker 3: a couple writers in a room together.

1032
00:55:42,880 --> 00:55:44,400
Speaker 1: I didn't know that's what you're talking. I know, I

1033
00:55:44,520 --> 00:55:46,520
Speaker 1: know some people who have been writing those.

1034
00:55:47,280 --> 00:55:50,080
Speaker 3: I had to write on like like a version of

1035
00:55:50,520 --> 00:55:52,640
Speaker 3: that for a few months, and then they laid me

1036
00:55:52,719 --> 00:55:55,360
Speaker 3: off and cut like cut my access to the slack

1037
00:55:55,719 --> 00:55:59,040
Speaker 3: and it was awful, Like the material that they got

1038
00:55:59,080 --> 00:56:01,480
Speaker 3: from the AI was terrible, And I didn't know that's

1039
00:56:01,520 --> 00:56:03,360
Speaker 3: what I was signing up for When I first started,

1040
00:56:03,640 --> 00:56:09,560
Speaker 3: it was crazy, Yeah, and what is the ultimate product

1041
00:56:09,920 --> 00:56:14,480
Speaker 3: that just like weird trashy like soapy and I love

1042
00:56:14,600 --> 00:56:17,120
Speaker 3: trash we know my love for reality TV, but like

1043
00:56:17,280 --> 00:56:20,640
Speaker 3: weird trashy soapy like podcasts or verticals like things that

1044
00:56:20,680 --> 00:56:23,000
Speaker 3: you can watch on your phone, like ten minute episodes,

1045
00:56:23,320 --> 00:56:25,439
Speaker 3: And it's like you could have that but like better

1046
00:56:25,560 --> 00:56:28,399
Speaker 3: quality if you just used human beings in a way

1047
00:56:28,480 --> 00:56:32,200
Speaker 3: that like right allowed them to do art. You know.

1048
00:56:32,360 --> 00:56:35,520
Speaker 1: Yeah, it's like basically getting into that like TikTok scroll Habit,

1049
00:56:35,560 --> 00:56:37,920
Speaker 1: but with like chunked out shows.

1050
00:56:37,880 --> 00:56:43,400
Speaker 3: With the worst content, like with really like inhuman content.

1051
00:56:44,760 --> 00:56:46,800
Speaker 1: Do people actually like watch that shit like that?

1052
00:56:47,520 --> 00:56:50,040
Speaker 3: Apparently it's like stuff to put up on.

1053
00:56:50,640 --> 00:56:53,160
Speaker 1: And people are getting hired. I mean, like I like

1054
00:56:53,280 --> 00:56:55,959
Speaker 1: we've said it's the bubble, but yeah, we know people

1055
00:56:56,040 --> 00:56:58,400
Speaker 1: that have been hired to work on that stuff because

1056
00:56:58,400 --> 00:56:59,960
Speaker 1: that seems to be like some of the only place.

1057
00:57:00,200 --> 00:57:02,680
Speaker 3: But that's great, Yeah, that's where the money is and

1058
00:57:02,800 --> 00:57:06,440
Speaker 3: we would much rather make like wonderful media, but we

1059
00:57:06,600 --> 00:57:09,760
Speaker 3: can't because like everybody from the top down is investing

1060
00:57:09,840 --> 00:57:12,200
Speaker 3: in this trash right do dooo?

1061
00:57:13,760 --> 00:57:20,560
Speaker 2: What if we invested in Dodoo. Okay, go on, Okay,

1062
00:57:21,040 --> 00:57:26,240
Speaker 2: I'm intrigued. Yeah, it's it's it's wild. I don't know,

1063
00:57:26,600 --> 00:57:29,560
Speaker 2: Like I'm sure eventually there will be a point in

1064
00:57:29,680 --> 00:57:34,440
Speaker 2: the future, like after you know, independent media starts like

1065
00:57:34,720 --> 00:57:39,120
Speaker 2: just dominating in terms of the quality of content, that

1066
00:57:39,680 --> 00:57:42,640
Speaker 2: these massive industries will start being like all right, I

1067
00:57:42,680 --> 00:57:43,840
Speaker 2: guess we fucking we.

1068
00:57:43,960 --> 00:57:47,640
Speaker 1: Need we need to make things people again, people have

1069
00:57:47,760 --> 00:57:50,320
Speaker 1: to abandon that product first for any of that, for

1070
00:57:50,480 --> 00:57:53,360
Speaker 1: that to make sense to like these massive studios and stuff.

1071
00:57:53,400 --> 00:57:56,400
Speaker 1: I mean, like we like you're saying, like there's the

1072
00:57:56,680 --> 00:57:59,120
Speaker 1: independent film has helped chip away at that or like

1073
00:57:59,160 --> 00:58:02,600
Speaker 1: these smaller budget sort of indie capital I indie kind

1074
00:58:02,640 --> 00:58:03,080
Speaker 1: of films.

1075
00:58:03,520 --> 00:58:06,880
Speaker 2: Yeah, but yeah, people doom Indie.

1076
00:58:08,760 --> 00:58:12,200
Speaker 3: Last like Indian people like yeah, yeah, yeah, yeah, yeah exactly.

1077
00:58:12,560 --> 00:58:13,320
Speaker 1: I'm sorry, I met.

1078
00:58:14,160 --> 00:58:16,240
Speaker 2: Sorry you really.

1079
00:58:17,720 --> 00:58:24,520
Speaker 3: Indy Hindi, you know, like those indie stars Khan.

1080
00:58:28,440 --> 00:58:33,560
Speaker 2: Alright, it's been so wonderful having you guest on this

1081
00:58:34,080 --> 00:58:41,600
Speaker 2: indie podcast. Yeah, what if we gave it more of

1082
00:58:41,640 --> 00:58:42,760
Speaker 2: an indie vibe?

1083
00:58:45,560 --> 00:58:46,600
Speaker 1: We had sizz.

1084
00:58:51,280 --> 00:58:53,240
Speaker 3: Wait she did dress up like a Hindoo.

1085
00:58:56,240 --> 00:58:59,160
Speaker 2: Where can people find you? Follow you? All that good stuff.

1086
00:58:59,640 --> 00:59:02,720
Speaker 3: I'm Paulavi Ganalan p A L l A v I

1087
00:59:02,960 --> 00:59:04,920
Speaker 3: g U n A l A n except on Blue Sky.

1088
00:59:04,960 --> 00:59:08,200
Speaker 3: I'm just Pulav because I got it and you can

1089
00:59:08,280 --> 00:59:11,720
Speaker 3: find me. This weekend in San Francisco, I'm headlining the Function,

1090
00:59:12,080 --> 00:59:16,240
Speaker 3: the only black owned comedy club in the Bay Area. Hella,

1091
00:59:16,640 --> 00:59:19,000
Speaker 3: it's a it's hell of funny. The people who run it.

1092
00:59:19,800 --> 00:59:21,480
Speaker 1: Wow, are you with that bay You said?

1093
00:59:21,560 --> 00:59:21,720
Speaker 4: Hell?

1094
00:59:21,800 --> 00:59:22,960
Speaker 1: Okay, Okay, I hear.

1095
00:59:23,800 --> 00:59:25,160
Speaker 3: That's the name of their that was the name of

1096
00:59:25,240 --> 00:59:27,640
Speaker 3: the brand before they they did that, and so that's

1097
00:59:27,680 --> 00:59:31,680
Speaker 3: why head Yeah. Yeah, but also they are hell of funny.

1098
00:59:31,800 --> 00:59:34,280
Speaker 3: But I'm gonna be there doing like four shows this weekend.

1099
00:59:34,440 --> 00:59:37,480
Speaker 3: It's going to be super fun. We have facial recognition

1100
00:59:37,640 --> 00:59:41,840
Speaker 3: at the Comedy Store. Our show is on March twenty

1101
00:59:42,040 --> 00:59:45,840
Speaker 3: first at ten pm in the Belly Room and then

1102
00:59:46,120 --> 00:59:50,240
Speaker 3: also just find me everywhere. I'm really excited to just

1103
00:59:50,320 --> 00:59:52,240
Speaker 3: do stand up a lot now and.

1104
00:59:52,280 --> 00:59:55,200
Speaker 1: Then are you practicing your thistle dance? My?

1105
00:59:55,360 --> 00:59:55,960
Speaker 3: What dance?

1106
00:59:56,280 --> 00:59:58,960
Speaker 1: Okay? We got some work to do the bird any

1107
00:59:59,040 --> 00:59:59,760
Speaker 1: Bay Area dance?

1108
00:59:59,840 --> 01:00:00,640
Speaker 3: The Oh?

1109
01:00:00,880 --> 01:00:01,240
Speaker 4: Gotcha?

1110
01:00:01,560 --> 01:00:06,920
Speaker 3: Sorry I didn't, I'm not wait can you just do

1111
01:00:07,040 --> 01:00:09,760
Speaker 3: that again for my entertainment? You're doing the bird in

1112
01:00:09,800 --> 01:00:16,600
Speaker 3: a close Oh shit, Oh my god, I'm more of

1113
01:00:16,640 --> 01:00:20,080
Speaker 3: the Gwen Stefani bay a person in that. I just

1114
01:00:20,200 --> 01:00:26,840
Speaker 3: used the word hella a lot. I don't know. I

1115
01:00:27,080 --> 01:00:28,720
Speaker 3: just exploit it for my own uses.

1116
01:00:30,240 --> 01:00:30,600
Speaker 1: Amazing.

1117
01:00:30,720 --> 01:00:33,080
Speaker 2: Is there a work of media that you've been enjoying?

1118
01:00:33,520 --> 01:00:36,920
Speaker 3: Okay, yeah, there's two things. One The New York Post

1119
01:00:37,840 --> 01:00:44,160
Speaker 3: just just tweeted the downfall of my Enemy. Olympic breakdancer

1120
01:00:44,240 --> 01:00:47,000
Speaker 3: Reagan's brother, Brendan Gunn, has been charged one hundred k

1121
01:00:47,280 --> 01:00:49,640
Speaker 3: in crypto fraud. And I knew it. I knew she

1122
01:00:49,760 --> 01:00:54,400
Speaker 3: was surrounded by bad people. Wow, fucking Nemesis. You guys

1123
01:00:54,840 --> 01:00:58,800
Speaker 3: were so excited about her. I hater Okay, you guys

1124
01:00:58,800 --> 01:00:59,040
Speaker 3: are so.

1125
01:01:01,480 --> 01:01:01,800
Speaker 1: I don't know.

1126
01:01:01,920 --> 01:01:03,760
Speaker 3: You guys were really into Mega, and I was like,

1127
01:01:03,840 --> 01:01:09,400
Speaker 3: this bitch is my enemy, my my human enemy. Because

1128
01:01:09,880 --> 01:01:14,000
Speaker 3: she was a good dancer, Oh my god, And thought

1129
01:01:14,440 --> 01:01:15,840
Speaker 3: politics you love for dancing.

1130
01:01:16,080 --> 01:01:18,080
Speaker 1: I thought it was just so unfair what they were

1131
01:01:18,160 --> 01:01:20,680
Speaker 1: saying to her people trying to gate keep break dancing.

1132
01:01:20,800 --> 01:01:22,000
Speaker 1: It just felt really gross.

1133
01:01:21,960 --> 01:01:25,240
Speaker 3: To me, Like, Okay, well, I think she's terrible and

1134
01:01:25,360 --> 01:01:28,160
Speaker 3: I hope she never dances again. And I think I

1135
01:01:28,280 --> 01:01:30,240
Speaker 3: just want to I want to gate keep it from her.

1136
01:01:30,600 --> 01:01:32,040
Speaker 1: I just want to live in a world where I

1137
01:01:32,120 --> 01:01:35,280
Speaker 1: can be a fucking neurobiologist and she can be an

1138
01:01:35,360 --> 01:01:37,600
Speaker 1: Olympic breakdancer. Okay, and we're not trying to.

1139
01:01:37,720 --> 01:01:42,360
Speaker 3: Give Yeah you can. You can just ai neurobiology or whatever. Anyways,

1140
01:01:42,560 --> 01:01:46,240
Speaker 3: she's awful. Her brother's terrible. I'm so happy for their downfall.

1141
01:01:46,720 --> 01:01:51,000
Speaker 3: And then I also saw a tweet by science Girl

1142
01:01:51,080 --> 01:01:53,640
Speaker 3: at Guns n' Roses Girl three, and it's a video

1143
01:01:53,760 --> 01:01:58,120
Speaker 3: of an Alaskan bear correcting a fallen roadside cone. He's

1144
01:01:58,200 --> 01:02:00,760
Speaker 3: just like he's like walking along this road and he

1145
01:02:00,880 --> 01:02:04,640
Speaker 3: turns it up right. And then at all to zach

1146
01:02:05,520 --> 01:02:07,680
Speaker 3: h A L L t O O Z A C

1147
01:02:07,840 --> 01:02:09,440
Speaker 3: it was like, yeah, they got to do it themselves

1148
01:02:09,520 --> 01:02:11,280
Speaker 3: now since Trump fired like ninety.

1149
01:02:11,280 --> 01:02:12,440
Speaker 1: Of the national parks.

1150
01:02:14,920 --> 01:02:21,240
Speaker 2: There, that's right, wonderful, well again, wonderful having you miles

1151
01:02:21,400 --> 01:02:22,360
Speaker 2: Where can people find you?

1152
01:02:22,600 --> 01:02:22,760
Speaker 5: Is the.

1153
01:02:25,360 --> 01:02:30,800
Speaker 1: Yes? Damn, I'm trying to find this fucking tweet, but anyway, yes,

1154
01:02:30,880 --> 01:02:32,840
Speaker 1: it says you can find me at miles of Gray

1155
01:02:32,880 --> 01:02:35,720
Speaker 1: wherever they got at symbols. You might even find Jack

1156
01:02:35,760 --> 01:02:37,919
Speaker 1: and I are roaming the streets of Austin Texas this week,

1157
01:02:38,600 --> 01:02:42,120
Speaker 1: so potentially as we gear up for the podcast awards,

1158
01:02:44,880 --> 01:02:48,840
Speaker 1: go oh and and and Cowboy boots will heel so thick,

1159
01:02:48,880 --> 01:02:50,840
Speaker 1: I'm going to twist my ankle just trying to walk

1160
01:02:50,880 --> 01:02:53,560
Speaker 1: in them. But yeah, find us there, find Jacket on

1161
01:02:53,640 --> 01:02:57,040
Speaker 1: the Basketball podcast, Miles and jackot Mad Boosties. You can

1162
01:02:57,080 --> 01:03:01,200
Speaker 1: also find us, you know out here just also talking

1163
01:03:01,200 --> 01:03:03,280
Speaker 1: about ninety day Fiance. And by say us, I mean

1164
01:03:03,880 --> 01:03:06,720
Speaker 1: Sophia Alexander and I on four twenty Day Fiance talking

1165
01:03:06,800 --> 01:03:10,440
Speaker 1: ninety day Fiance. A tweet I like is from so

1166
01:03:11,080 --> 01:03:13,760
Speaker 1: New York Basketball Like is like a Twitter account that

1167
01:03:14,080 --> 01:03:16,000
Speaker 1: takes pictures of people at the Knicks games and stuff

1168
01:03:16,000 --> 01:03:18,400
Speaker 1: like that. And there's a picture of John Tuturo like

1169
01:03:18,560 --> 01:03:22,240
Speaker 1: old John Tuturo like courtside and it says irving court side.

1170
01:03:22,720 --> 01:03:25,040
Speaker 1: And then someone quote tweeted that and said, this is

1171
01:03:25,080 --> 01:03:27,280
Speaker 1: what jk Rowling would name a black character.

1172
01:03:27,760 --> 01:03:29,600
Speaker 2: I saw that too, court side.

1173
01:03:33,000 --> 01:03:35,520
Speaker 3: There are so many this is what jk Rowling like.

1174
01:03:35,600 --> 01:03:44,400
Speaker 3: There was this black actress named Ebony Obsidian or what Yeah, yeah.

1175
01:03:47,040 --> 01:03:48,440
Speaker 1: That's so good. Uh.

1176
01:03:49,400 --> 01:03:52,800
Speaker 2: You can find me on Twitter at Jack underscorel Brian

1177
01:03:52,920 --> 01:03:55,760
Speaker 2: on Blue Sky. Jack ob the number one tweet I've

1178
01:03:55,800 --> 01:04:00,439
Speaker 2: been enjoying. Noah Garfinkle tweeted, You've been hit by You've

1179
01:04:00,480 --> 01:04:03,240
Speaker 2: been struck by a gray super rue.

1180
01:04:05,440 --> 01:04:08,200
Speaker 3: And that maybe, like that is such a zeitgeist ask tweet.

1181
01:04:08,440 --> 01:04:12,040
Speaker 3: That's such an intro ass tweet. You just love change

1182
01:04:12,080 --> 01:04:13,520
Speaker 3: the lyrics that a.

1183
01:04:13,560 --> 01:04:16,000
Speaker 1: Little bit, just a little please come forward to that

1184
01:04:16,400 --> 01:04:18,000
Speaker 1: that count of syllables.

1185
01:04:18,040 --> 01:04:20,040
Speaker 3: Please, I'm gonna I'm gonna bait you. I'm gonna start

1186
01:04:20,080 --> 01:04:22,520
Speaker 3: tweeting like lyric puns and then I'm going to see

1187
01:04:22,520 --> 01:04:23,320
Speaker 3: if you bring it up.

1188
01:04:23,200 --> 01:04:25,600
Speaker 2: On just one way slightly off.

1189
01:04:26,280 --> 01:04:26,800
Speaker 6: You got it?

1190
01:04:28,800 --> 01:04:29,360
Speaker 2: I love it.

1191
01:04:30,040 --> 01:04:30,200
Speaker 3: Uh.

1192
01:04:30,640 --> 01:04:33,760
Speaker 2: You can find us on Twitter at Daily Zeitgeist and

1193
01:04:33,920 --> 01:04:35,840
Speaker 2: on Blue Skuy at daily Zeus. We're at the Daily

1194
01:04:35,960 --> 01:04:38,440
Speaker 2: Zeitgeist on Instagram. We have a Facebook fan page and

1195
01:04:38,520 --> 01:04:41,800
Speaker 2: a website dailyzeiguis dot com. Or post our episodes and

1196
01:04:41,960 --> 01:04:44,640
Speaker 2: our foot notes where we look off to the information

1197
01:04:44,640 --> 01:04:47,680
Speaker 2: that we talked about in today's episode. We also do

1198
01:04:47,840 --> 01:04:50,120
Speaker 2: that in the show description. Wherever you listen to this,

1199
01:04:50,480 --> 01:04:53,680
Speaker 2: just click on the episode and look at the show

1200
01:04:53,760 --> 01:04:57,560
Speaker 2: description and you will find the footnotes where we link

1201
01:04:57,600 --> 01:04:59,840
Speaker 2: off to the information that we talked about in today's episode.

1202
01:04:59,880 --> 01:05:04,120
Speaker 2: The tweets that we enjoyed. We also link off to

1203
01:05:04,280 --> 01:05:08,760
Speaker 2: a song that we think you might enjoy. Say, Miles,

1204
01:05:09,680 --> 01:05:11,880
Speaker 2: is there a song that you think that people might enjoy?

1205
01:05:12,400 --> 01:05:17,160
Speaker 1: Yeah, well, Roy Ayers passed away recently, and he is

1206
01:05:17,240 --> 01:05:22,760
Speaker 1: an iconic sort of jazz fusion, jazz funk composer, fucking

1207
01:05:23,320 --> 01:05:28,439
Speaker 1: the vibes player and by that I mean vibraphonist, and yeah,

1208
01:05:28,480 --> 01:05:30,840
Speaker 1: I think I think people get familiar with the music.

1209
01:05:30,880 --> 01:05:32,840
Speaker 1: His body work is fantastic. I think most people know

1210
01:05:32,960 --> 01:05:36,960
Speaker 1: him from Everybody Loves the Sunshine. Is that his iconic track.

1211
01:05:37,240 --> 01:05:39,919
Speaker 1: But we'll go out on a cover of that track,

1212
01:05:40,000 --> 01:05:43,960
Speaker 1: Everybody Loves the Sunshine by the Brazilian artist Sue George.

1213
01:05:44,920 --> 01:05:48,400
Speaker 1: So this is sou George's version of Everybody Loves the Sunshine.

1214
01:05:48,400 --> 01:05:51,600
Speaker 1: Everybody Loves the Sunshine. Check it out. Feels good because

1215
01:05:51,600 --> 01:05:53,960
Speaker 1: we all do love the sunshine and folks get brown

1216
01:05:54,040 --> 01:05:57,680
Speaker 1: in the sunshine. Yeah, all right, we will look after that,

1217
01:05:57,960 --> 01:05:58,280
Speaker 1: all right.

1218
01:05:58,880 --> 01:06:02,280
Speaker 2: In the footnotes, guys, is a production of iHeartRadio for

1219
01:06:02,360 --> 01:06:04,480
Speaker 2: more podcast from my Heart Radio is the iHeartRadio ap

1220
01:06:04,480 --> 01:06:07,360
Speaker 2: Apple podcast or wherever fine podcasts or give it away

1221
01:06:07,400 --> 01:06:09,760
Speaker 2: for free. That's going to do it for us this week,

1222
01:06:10,240 --> 01:06:13,800
Speaker 2: we did it, another one in the books, swish. We're

1223
01:06:13,960 --> 01:06:18,320
Speaker 2: back on Monday morning to tell you or I guess

1224
01:06:18,560 --> 01:06:21,240
Speaker 2: Monday late afternoon to tell you, guys, what was trending

1225
01:06:21,320 --> 01:06:23,960
Speaker 2: over the weekend was trending on Monday morning. And we

1226
01:06:24,160 --> 01:06:27,880
Speaker 2: have a radist hits from this week's episode that drops

1227
01:06:27,920 --> 01:06:29,520
Speaker 2: tomorrow the weekly Zyite guys, so you.

1228
01:06:29,760 --> 01:06:31,240
Speaker 4: Go check that out as well.

1229
01:06:31,520 --> 01:06:36,640
Speaker 2: Until then, bye bye, bye bye, goodbye my friends, goodbye

1230
01:06:36,720 --> 01:06:37,200
Speaker 2: my sweet