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Speaker 1: Hello the Internet, and welcome to Season two eighty eight,

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Speaker 1: Episode three.

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Speaker 2: It's just the production of Iyheart Radio.

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Speaker 1: And it's a podcast where we take a deep dive

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Speaker 1: into American share consciousness. And it's Wednesday, May twenty fourth,

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Speaker 1: twenty twenty three. Oh man, it's Brother's Day. Shout out

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Speaker 1: others the siblings out there, and you know, and also

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Speaker 1: to my fellow African Americans. I'll also say his Brother's Day. Also, yeah,

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Speaker 1: why not? I mean, I don't know what the spirit

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Speaker 1: of it. The photograph seems to be two Caucasian kids,

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Speaker 1: so I'm guessing it's just for the sibling brother from

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Speaker 1: but I would.

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Speaker 2: Expand that to the larger spiritual sense.

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Speaker 1: Also Aviation Maintenance Technician Day, National Wyoming Day, National Scavenger

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Speaker 1: Hunt Day, National escargold Day, we we Emergency Medical Services,

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Speaker 1: Emergency Medical Services for Children Day.

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Speaker 2: Is it saying like you're advocating for them, like, yeah.

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Speaker 1: They should also be able to access emergency medical and

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Speaker 1: National Yucatan Shrimp Day.

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Speaker 2: That sounds like some it like elteredo make up. You

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Speaker 2: ever had s cargas s cargas.

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Speaker 3: Oh hell, you're talking about the many similarities between the

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Speaker 3: French accent and the Philly accent, the two most sophisticated

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Speaker 3: accents on the planet. My name is Jack O'Brien aka

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Speaker 3: hold on to your butt to everyone, buddy, you're a podcaster,

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Speaker 3: smart man on the mic, talking about the news with

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Speaker 3: Oh you're insight repping the daily zeitgeist every single day,

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Speaker 3: y'all go and I'll.

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Speaker 2: Bring the truth, make get your own way singing. Miles

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Speaker 2: and Jack.

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Speaker 1: They're the hosts on the show, keeping us informed, making

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Speaker 1: sure we all know yeah, and then there's like they

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Speaker 1: have like a second part of the chorus. So this

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Speaker 1: is an AKA brought to you by chet GPT spot said.

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Speaker 1: Due to AKA writers strike, akas are now app source

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Speaker 1: to chat GPTCA.

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Speaker 2: This thing sucks. It's real exactly, it's.

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Speaker 3: Real long and like it doesn't seem to know what

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Speaker 3: the what the words to we will rock you are.

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Speaker 2: So we're gonna spend the show.

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Speaker 3: We're gonna spend a lot of this episode talking about

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Speaker 3: why you should be.

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Speaker 2: Scared of AI.

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Speaker 3: But I just wanted to open up with you know,

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Speaker 3: a reason to laugh and point at chat gpt because

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Speaker 3: it's long. Man, there's a good Miles, you're a funny man,

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Speaker 3: quick with the jokes, adding laughter to the serious topics.

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Speaker 3: Spoke and Jack your the brains bringing knowledge to the game.

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Speaker 2: Together your duo. That'll make your name. Okay, make my name. Yeah,

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Speaker 2: we'll make all all of our names. We'll make our name.

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Speaker 1: I'm thrilled to be joined as always by my co

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Speaker 1: hosts famously of that a ka, I just did it's

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Speaker 1: mister Miles, Yes, aka all do Dave come to the

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Speaker 1: of the road, Lebron look so it's unnatural, the mortality.

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Speaker 2: I don't like this. Shout out to Christy Amagucci Mane

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Speaker 2: sweat room. But time we knew it was. I said

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Speaker 2: it in the thread After that, I was like, it's room.

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Speaker 1: But time get out the suction vehicles whatever, it's time

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Speaker 1: to sweep clean up the floor. As the Lakers went

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Speaker 1: out to the Nuggets, I mean, I can't feel bad

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Speaker 1: about it. They're the month they were just they were

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Speaker 1: so much better. I can't so good man, they were

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Speaker 1: so good. I can't really freaking good. That Yokich guy real,

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Speaker 1: he that good man? Hey, Yoker, give me, give Joker,

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Speaker 1: give Antony a shot at the ball. I like Joel,

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Speaker 1: but yeah, Joel, but that Joker is really good. My

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Speaker 1: little house Keeping up Top.

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Speaker 3: For the first time in the history of the show,

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Speaker 3: we are trying a new publication schedule for the summer.

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Speaker 3: We're going to scale back to a mere eight episodes

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Speaker 3: a week, and we're gonna try some new episode formats

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Speaker 3: out because we want to so for for you the listeners.

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Speaker 3: The difference will be one episode on Friday, one episode

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Speaker 3: on Monday, and then Tuesday mornings episode. We'll be trying

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Speaker 3: some different stuff out. We'll be doing like interview questions,

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Speaker 3: like interview episodes with like experts. We might even like

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Speaker 3: do some mail bag episodes where we get to hear

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Speaker 3: from you, guys, from We will do so with.

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Speaker 2: That in mind.

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Speaker 3: By the way, yes, first prompt to you guys, we

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Speaker 3: want to hear about your jobs. You know, our guests

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Speaker 3: tend to be people who work in media like us,

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Speaker 3: so we want to hear, like something interesting about your job.

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Speaker 3: What's the craziest thing that ever happened to you on

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Speaker 3: your job, What's something most people misunderstand about your job.

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Speaker 3: You can hit us at Daily Zeitgeist, on Twitter or

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Speaker 3: in the discord let us know.

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Speaker 2: We just want to hear from you.

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Speaker 3: Yeah, we can keep it anonymous or not whatever y'all

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Speaker 3: want to do.

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Speaker 1: Yeah, DMS are open or DM me you are, Yeah,

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Speaker 1: DMS wide open.

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Speaker 3: Please we're hearing from you. Sick sick motherfuckers. Hey and Miles,

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Speaker 3: speaking of sick, disgusting mother motherfuckers, were true. Joined in

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Speaker 3: our third seat by a brilliant comedian writer actor whose

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Speaker 3: stand up album The Blake Album Stuffed Boy and Live

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Speaker 3: from Pandemic debuted at number one on iTunes. His album

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Speaker 3: Twelve Years of Voicemails from Todd Glass to Blake Wexler

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Speaker 3: charted on Billboard. Please Welcome. He is the hilarious chaotic.

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Speaker 3: He's riding a recumbent bicycle in short shorts and his

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Speaker 3: plumpers are plake.

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Speaker 4: What this is, Blake Wexler A K A. I crave

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Speaker 4: to zeit in Zike the Blake of Dawn, Come to

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Speaker 4: Miles because Jackie will be gone.

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Speaker 2: Crave to zeit.

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Speaker 4: It was craved to zeit by Beanie Siagull Eye Cherry,

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Speaker 4: which is a mand I just made up, but no

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Speaker 4: I wrote.

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Speaker 2: That Beie A.

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Speaker 1: I wow, that was a real Philly mashup. You you

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Speaker 1: went with the broad Street bully. Beanie Siagull is eagle

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Speaker 1: eye Cherry from Philadelphia.

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Speaker 4: No, but there is eagle in its name, and there's

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Speaker 4: a street called Cherry Street, so Cherry Temple's uniforms.

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Speaker 2: Yes. Oh my god, Egle, you know where eye Cherry

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Speaker 2: is from? Beyond Sweden? Sweden? I don't know Cherry was Swedish.

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Speaker 2: My bump is on the Swedish It does sound like

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Speaker 2: a name that was mistranslated. So yeah, yeah, I can

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Speaker 2: see it.

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Speaker 1: Oh my god, this all makes sense. His half brother

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Speaker 1: is in Nina Cherry Oh Buffalo Stance. Remember Nina Cherry

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Speaker 1: Buffalo Stance. Yeah, that's his brother. Wow, the cherries are I.

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Speaker 5: Didn't realize the all Cherries are out. Oh wait, so

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Speaker 5: then he moved.

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Speaker 2: He's born in Sweden. But then when by the.

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Speaker 1: Way, we talked about we're doing the new format, this

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Speaker 1: is one of them where Miles just oh my god,

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Speaker 1: this is ch it's my family history of the myself

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Speaker 1: an expert on Cherry for Me, interview expert on uh

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Speaker 1: Eugli Cherry, Blake Wexler.

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Speaker 3: Yes, last time I spoke to you was on an

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Speaker 3: episode of our sister podcast, Miles and Jack Got Mad Boostie.

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Speaker 2: We were talking about the seventy six ers.

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Speaker 3: Yeah, we were talking about how we weren't going to

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Speaker 3: be fooled even though things were looking good for them

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Speaker 3: at that point. I think it was maybe like the

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Speaker 3: peak of optimism for the seventy six ers, Like it

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Speaker 3: was yes, yeah, people were like, I don't know, these

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Speaker 3: sick like they're putting it together and you and I were.

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Speaker 5: Like, shh yeah, right, what are we dumb?

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Speaker 2: Yeah they're idiot.

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Speaker 5: Yeah they're gonna drop it. But look at the momentum

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Speaker 5: they're carrying into the post. This looks fantastic.

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Speaker 1: Like, I know the eventual crash is going to be hilarious,

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Speaker 1: is what is all I see? When I see momentum,

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Speaker 1: I'm like, I just hope they're wearing protective gear because

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Speaker 1: it's going to be a catastrophic crash. Sure enough, it

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Speaker 1: was super frustrating they lost to a Celtics team that,

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Speaker 1: as we're seeing, was beatable. The Celtics are getting in

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Speaker 1: the process that you know, they're down three to zero

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Speaker 1: as of this recording to the Miami Heat.

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Speaker 2: Yeah it could be broom time.

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Speaker 3: Yeah, But I do think this series has made me

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Speaker 3: feel better about the Celtics loss, because, first of all,

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Speaker 3: second round losses is where we belong as Philadelphia seventy

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Speaker 3: six ers.

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Speaker 2: And also we would have gotten rinsed by the heat.

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Speaker 3: It would have somehow been worse than this, like what's

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Speaker 3: happening to Boston more embarrassing. So thank you Boston for

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Speaker 3: taking this one for us, and thank you James harden

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Speaker 3: enjoyl Embiid for quitting at the end of game seven

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Speaker 3: to just be being like they saw the matrix, they

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Speaker 3: were like, this would yeah, so.

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Speaker 2: It was good timing.

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Speaker 4: I wish they just saw it so we could have

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Speaker 4: just been swept and not had to deal with any

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Speaker 4: of it. You're playoffs just it would have been even better. Yeah,

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Speaker 4: it would have been the time that we could have saved.

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Speaker 4: And this team is so frustrating and so just completely

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Speaker 4: void of joy. Yeah, I don't even get any there's

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Speaker 4: no I told you. So that makes you feel good

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Speaker 4: because it's like, yeah, we saw it coming. It's like

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Speaker 4: predicting a car accident. It's like, why don't people are

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Speaker 4: in the streets, And I don't feel good about it.

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Speaker 1: I wish you listened to It was nice that they

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Speaker 1: chose the crowning failure that destroyed all of our hearts

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Speaker 1: and minds.

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Speaker 3: They saved it for Mother's Day, so that was kind.

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Speaker 3: That was full of them.

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Speaker 2: Anyways, Blake, glad we had our Philly Sports Minute up top.

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Speaker 4: A new sponsored segment, Yes, sponsored by a Tasty cake.

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Speaker 1: That's by Pat Steaks, by Pat's King of Steak since

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Speaker 1: nineteen thirty, folks, Cash, We're going to get to know

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Speaker 1: you a little bit better in a moment. First, we're

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Speaker 1: going to tell our listeners a couple of things we're

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Speaker 1: talking about that's gonna.

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Speaker 2: Be a AI full episode.

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Speaker 3: An AI generated image of an explosion at the Pentagon,

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Speaker 3: spook to the markets. AI can now read your brain

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Speaker 3: waves and turn it into video, and being used to

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Speaker 3: track mass movements and their weapons to detector's stink.

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Speaker 2: So it's just all very confusing.

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Speaker 3: There's a lot of information coming at us in general,

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Speaker 3: so we'll just talk about the difficulty of sorting through

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Speaker 3: it all.

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Speaker 1: All of that plenty more. But first, Blake, we like

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Speaker 1: to ask our guest, what is something from.

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Speaker 2: Your search history?

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Speaker 4: Search history a cheap gift. My wife's birthday is this weekend.

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Speaker 4: I know the actual is of Randy Johnson kills bird

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Speaker 4: was what it was because I think a cup maybe

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Speaker 4: it was over the weekend, Randy Johnson, by the famous

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Speaker 4: I'm picking up on Jack's Q that I was talking

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Speaker 4: about obscure Philadelphia sports stuff, and Jack had to keep

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Speaker 4: jumping and explaining what the fuck I was talking about.

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Speaker 2: So I'll just do it myself.

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Speaker 4: Randy Johnson was a FA A Hall of Fame pitcher,

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Speaker 4: and this seems mathematically impossible, but one time he was

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Speaker 4: pitching and he threw the pitch to the plate and

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Speaker 4: then a bird flew in the path of the pitch

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Speaker 4: and the bird, I don't know what happened to it.

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Speaker 2: I don't know if it survived. It was below it explode.

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Speaker 5: Yes, yeah, totally like there, it was decimated.

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Speaker 3: A bird is flying and then there is just a

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Speaker 3: cloud of feathers and that's it.

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Speaker 4: It was stunning. And then it happened again. Over the weekend.

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Speaker 4: We're in warm ups. There was a pitcher for the

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Speaker 4: same team that Randy Johnson played for, the Arizona Diamondbacks,

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Speaker 4: was warming up in his pitch. By the way, this

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Speaker 4: is all accident. These people aren't burderers, right.

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Speaker 3: And they blood thirsty motherfuckers, these fucking fat There was

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Speaker 3: foul play, but I do need to go.

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Speaker 2: But no, they hit. I probably should be believing it.

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Speaker 4: I'm feeling faint, but yeah, another pitcher hit a bird

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Speaker 4: with a pitch over the week over the weekend, over

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Speaker 4: the weekend, same kind of image.

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Speaker 2: It was not as cool.

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Speaker 4: I know, you love that that just that bird exploding

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Speaker 4: sort of thing. Jack, you don't like to get your

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Speaker 4: your but no, this shot, it wasn't nearly but he

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Speaker 4: was out in the outfield, he was warming up, so

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Speaker 4: you know, yeah, he didn't put much behind it, but

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Speaker 4: it did kill the bird.

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Speaker 1: Also in the Cleveland Guardians the Cleveland Guardians White Sox game,

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Speaker 1: a bird also got fucked up by like a ground

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Speaker 1: ball like this over over the weekend pitch. I mean, yeah,

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Speaker 1: Randy Johnson, known as the Big Unit. You can imagine

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Speaker 1: what kind of heat was coming off that arm. Now

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Speaker 1: he's just a you know, he's just like a hobby photographer.

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Speaker 3: Yeah, he seemed like he would like just disappear into

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Speaker 3: the desert once.

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Speaker 1: He goes to like he takes like sports photos, like

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Speaker 1: you see him in the background of like football games

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Speaker 1: and ship like on the field like a telephoto lens.

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Speaker 5: He's like, I'm just really into photography. He gets the

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Speaker 5: best shots because he's six eleven, right.

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Speaker 2: He's out here.

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Speaker 1: He's he just loved he just loves looking at things

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Speaker 1: through the lens. You know, he's taking like concert photos anyway.

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Speaker 1: It just it's always fun to see he's like. I

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Speaker 1: think I can think he got into it in college

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Speaker 1: or something. And that's I was just like, and I

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Speaker 1: just kept it going after that.

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Speaker 2: That's cool.

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Speaker 5: What is something you think is overrated?

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Speaker 4: Blake overrated coffee while eating out at a restaurant, And

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Speaker 4: I've never for a variety of reasons. And some of it,

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Speaker 4: like some of our listeners might and I do share

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Speaker 4: these listeners with You might have a weak stomach, and

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Speaker 4: it's it's an away game, so you're dealing with a

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Speaker 4: public restroom. And also if you're specific about how your

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Speaker 4: coffee's made. I find out that the sweeteners can often

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Speaker 4: be very limited at a place at like UV, you

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Speaker 4: still see the sweet and low that that pink travesty,

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Speaker 4: and then there's equal where I don't know, nothing's less

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Speaker 4: equal than when you drink that shit and splenda, which

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Speaker 4: is like a nightmare as well. So I also have

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Speaker 4: found that depending on what breakfast place you go to,

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Speaker 4: I like a ratio in my coffee of sweetener milk

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Speaker 4: to coffee, and then these these sickos will come over

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Speaker 4: and then just dump more coffee into your thing, which

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Speaker 4: ruins all do the math. Yeah, and then at night

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Speaker 4: not a big espresso person after after dinner.

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Speaker 2: Yeah yeah, let me cut you off there. What's something

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Speaker 2: you think it's underrated?

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Speaker 6: Along?

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Speaker 2: Enough of that? Yeah, yeah, we're on that. Where the

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Speaker 2: fuck that came from?

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Speaker 4: I don't know the last time I broke it to

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Speaker 4: a sweat so quickly that you burned me that badly?

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Speaker 2: Just that I'm soaked.

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Speaker 4: It's like sixty one degrees if I plated completely dredged.

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Speaker 2: His limpic has been hijacked. Gun.

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Speaker 4: If it was possible to take out my legs from

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Speaker 4: underneath me, you just did it.

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Speaker 2: And it's not possible usually because your legs pump stuck

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Speaker 2: to the ground.

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Speaker 1: Wait, so you are you really that incensed when the

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Speaker 1: balance is thrown off?

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Speaker 2: Like, yeah, you can't do more.

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Speaker 1: If you if you're a half and half person, if

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Speaker 1: you're like a sugar person that you need to get

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Speaker 1: the balance right, and they just come in.

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Speaker 2: It's very presumptuous.

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Speaker 5: It's a different drink entirely.

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Speaker 1: What is that fault here you're saying, so that's the

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Speaker 1: worker's fault. I'm just trying to see where you're at.

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Speaker 4: They suck, right if you can even find one. No

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Speaker 4: one wants to work anymore these businesses. No one's there.

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Speaker 4: You serve self service kiosks are everywhere, so yeah, it's and.

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Speaker 1: Then you write the zero out on the tip thing

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Speaker 1: zero if I pay a nation point.

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Speaker 2: Yeah, ask Joe Brandon, is what you write on there?

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Speaker 2: I have switched to black coffee. Uh, and it is.

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Speaker 3: Yeah, it's just better all around other than and it

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Speaker 3: tastes like shit, what is something you think is underrated?

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Speaker 5: Underrated?

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Speaker 4: Yeah, I hope this meets your standards.

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Speaker 5: It's taking a break in the middle of the day.

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Speaker 2: Huh.

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Speaker 5: We're taking a break in the middle of the day.

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Speaker 4: Where if you have a non traditional office hour job,

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Speaker 4: like you don't have like nine to five in the office.

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Speaker 4: I've found that working remotely, it's it can be hard

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Speaker 4: to take like if you're a workaholic, it's hard to

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Speaker 4: take breaks, like there's no oh, everybody left the office.

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Speaker 4: I should probably go now so you can work super late.

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Speaker 4: So with a non traditional schedule, I think it's good

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Speaker 4: and rejuvenating, like middle of the day, read a book

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Speaker 4: for forty five minutes, put on like a forty an

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Speaker 4: episode like of a drama for forty five minutes, and

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Speaker 4: then get back to work and it kind of just

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Speaker 4: wipes your whole. Brian said, game changing.

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Speaker 2: I love it.

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Speaker 4: Yeah, it completely changes the makeup of your day because

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Speaker 4: you still need that rest. It's just like it doesn't

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Speaker 4: necessarily have to be at the end.

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Speaker 5: Of your day something else. Yeah, yeah, exactly.

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Speaker 2: That's like.

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Speaker 1: That's like when I when I had like a job

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Speaker 1: where and I had my own office. Whenever I had lunch,

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Speaker 1: I'd lock the door and I would just watch like

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Speaker 1: Reddit video, like the Reddit video sub reddit for fucking

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Speaker 1: an hour, and it didn't refresh with it at all.

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Speaker 1: I was like, is it fucking five yet? Please keep

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Speaker 1: watching these lego stop motion recreations that are quite fantastic.

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Speaker 2: But yeah, but I get that.

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Speaker 1: Yeah, it's good to have something that's a little that

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Speaker 1: mentally switches gears rather than like putting your day on

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Speaker 1: pause and be like I'm holding all this mental bandwidth

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Speaker 1: just for a second and then right back into.

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Speaker 2: It something like taking a walk.

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Speaker 3: Not Yeah, that's kind of nice, just to some something

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Speaker 3: to get quick bike ride, get the blood plumping and hey, well,

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Speaker 3: speaking of taking a break, uh, we're we're gonna take

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Speaker 3: a quick one and we'll be right back with some news.

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Speaker 2: And we're back. And so.

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Speaker 3: Was written an article today about the godfather of AI,

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Speaker 3: who has been responsible for a lot of the more

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Speaker 3: impressive developments that chat, GPT and all these various headline

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Speaker 3: making applications were built.

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Speaker 2: On top of.

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Speaker 3: He has decided to leave Google because he wants to

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Speaker 3: be able to like ring the alarm louder. He thinks

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Speaker 3: we as a species are in trouble with this AI stuff,

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Speaker 3: and he kind of lays out a progression of the

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Speaker 3: problems he thinks we're going to see, and he said

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Speaker 3: his immediate concern is that the Internet will be flooded

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Speaker 3: with false photos, videos, and text and the average person

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Speaker 3: will not be able to know what is true anymore.

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Speaker 3: So that's his first immediate concern. And then like maybe.

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Speaker 1: Five minutes after I read that article, a news story

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Speaker 1: broke that the market's like all freaked out for like

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Speaker 1: five minutes because somebody generated an image of an explosion

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Speaker 1: at the Pentagon that was realistic enough that it freaked

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Speaker 1: people out and they started. There was like a sell off,

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Speaker 1: which some people are assuming was all by design, right

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Speaker 1: to like you create a little dip in the market,

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Speaker 1: then you buy and things go back to normal and

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Speaker 1: you make a little profit.

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Speaker 2: But I don't know.

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Speaker 1: Yeah, it's just it seems like everything is happening very fast.

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Speaker 1: The guy issues a warning like three weeks ago, and

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Speaker 1: then that thing like happens before the end of the

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Speaker 1: sentence that he's speaking warning us all.

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Speaker 2: And yeah, I don't know, I'm I've begun.

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Speaker 3: Super producer Brian sent along some documents about how you

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Speaker 3: know that this could be used to end the world

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Speaker 3: or end our species pretty quickly, and I found those troubling.

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Speaker 2: So I'm a fan of existence personally.

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Speaker 5: Yeah, it's every every fucking headline.

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Speaker 1: It's like it's either like oh that's kind of interesting

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Speaker 1: or it makes you want to ship your pants in fear,

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Speaker 1: like yes, yeah, or both, and you're like, oh, I

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Speaker 1: don't know how to make heads or tails of it,

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Speaker 1: because come on, those AI avatars.

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Speaker 5: I made a Pixar version of me and it was accurate,

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Speaker 5: thank you, so fun.

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Speaker 2: No, I didn't.

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Speaker 1: There was like an app that's like do you want

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Speaker 1: to give us forty dollars to do it. I'm like,

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Speaker 1: that makes that a weekly fucking not get away from me.

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Speaker 3: You want to give us forty dollars in your genetic sequencing? Okay,

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Speaker 3: now we're talking many pieces of biographical data.

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Speaker 2: Will it make it cuter if I give you my DNA?

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Speaker 2: Oh yeah, way cuter. We'll nail the aisles.

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Speaker 3: The thing that got the guy to quit was when

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Speaker 3: Microsoft being launched their chat GPT interface. That led Google

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Speaker 3: to like start disregarding all of his warnings and all

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Speaker 3: of like the AI people's warnings. Apparently Google had been

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Speaker 3: like kind of holding back and trying to be responsible

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Speaker 3: for the last year and then but the second that

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Speaker 3: like someone else was first to market with an AI product,

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Speaker 3: they were like, all right, well that's out the window.

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Speaker 3: And now it's just a race to like see who

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Speaker 3: can be the first and you know, make the biggest

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Speaker 3: splash with this shit. And so yeah, it just feels like,

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Speaker 3: barring some massive change from how the US government is

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Speaker 3: organized the profit motive, it will not allow us to

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Speaker 3: keep this shit bottled up.

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Speaker 2: It's going to cause.

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Speaker 3: Major problems sooner rather than later if they can't regulate it.

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Speaker 3: But we're in a world that seems like it has

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Speaker 3: decided it is beyond regulation when it comes to major

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Speaker 3: corporations finding ways to make money off of things.

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Speaker 4: And not to scare us even more, but it's it

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Speaker 4: is moving incredibly quickly. I think that's the wildest part

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Speaker 4: of it right now. But it's been around for a while,

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Speaker 4: just us as the public is becoming more aware of

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Speaker 4: it because chat GPT was kind of the first popular

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Speaker 4: consumer facing application of AI, where you know, banks have

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Speaker 4: been using this shit to like fight fraud for a

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Speaker 4: very long time, and now we're just becoming aware of it.

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Speaker 4: Where you see and the writers strike and and whatnot. Which,

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Speaker 4: by the way, I know you said you don't stand

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Speaker 4: with the WGA. Both of you said before the show.

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Speaker 4: My mind's not made up.

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Speaker 2: But well allow me. They're greedy. Yeah, it's just a

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Speaker 2: love of work, as we talked about.

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Speaker 1: Yet, Yeah, I love the guy, the genius behind this

431
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Speaker 1: HBO Max rebrand to Max, he just pointed out that

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Speaker 1: like the the w these writers need to show their love.

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Speaker 3: Of work and get back to work. That's the thing

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Speaker 3: that's going to say that, Yeah, if.

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Speaker 4: You don't want to write, then work somewhere else is

436
00:24:08,440 --> 00:24:11,240
Speaker 4: what is what I've been screaming at the top of

437
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Speaker 4: my lungs out of my car while driving by the

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Speaker 4: picket lines.

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Speaker 2: But no one's got under your head as you drive.

440
00:24:19,480 --> 00:24:21,720
Speaker 4: They just hear gun because the rest of it like,

441
00:24:21,760 --> 00:24:25,120
Speaker 4: no one hears no.

442
00:24:25,240 --> 00:24:29,840
Speaker 2: I was just sorry, I was siding with the business owners.

443
00:24:30,160 --> 00:24:33,439
Speaker 4: I'm just a very conservative fiscal ideologies.

444
00:24:33,520 --> 00:24:34,880
Speaker 5: I don't like guns flavored.

445
00:24:36,200 --> 00:24:38,399
Speaker 4: But yeah, and then I think also we're talking about,

446
00:24:38,440 --> 00:24:41,080
Speaker 4: you know, the race of these big tech companies, where

447
00:24:41,160 --> 00:24:44,400
Speaker 4: I was just reading something that all the big tech

448
00:24:44,440 --> 00:24:48,280
Speaker 4: companies have had a head start with this except Apple,

449
00:24:48,480 --> 00:24:52,160
Speaker 4: for some reason, I believe, is just starting its generative

450
00:24:52,160 --> 00:24:55,680
Speaker 4: AI department, which is kind of funny where you would

451
00:24:55,840 --> 00:24:59,000
Speaker 4: not expect that they would be behind on something like this, right.

452
00:24:58,840 --> 00:25:01,479
Speaker 3: And the person who decides did not to have a

453
00:25:01,680 --> 00:25:06,199
Speaker 3: generative AI department that was kind of competitive with the

454
00:25:06,320 --> 00:25:10,320
Speaker 3: other companies has been fired, I can guarantee you. Well, yeah, yeah,

455
00:25:10,320 --> 00:25:13,439
Speaker 3: that was not like a decision that was respected around

456
00:25:13,840 --> 00:25:17,240
Speaker 3: the massive corporation that is Apple, Like they're all.

457
00:25:17,000 --> 00:25:19,960
Speaker 2: Being like, what the fuck did we do? We're getting killed?

458
00:25:19,960 --> 00:25:22,840
Speaker 5: That thought different, Yeah, damn.

459
00:25:23,240 --> 00:25:25,480
Speaker 1: But I mean like, yeah, there's other like there's a

460
00:25:25,520 --> 00:25:29,280
Speaker 1: couple other developments, right, there's one that I saw that. Uh,

461
00:25:29,320 --> 00:25:32,440
Speaker 1: this is just the headline from VICE US Intelligence building

462
00:25:32,560 --> 00:25:36,240
Speaker 1: system to track mass movement of people around the world.

463
00:25:36,520 --> 00:25:38,760
Speaker 3: I mean, intelligence is a good thing. It's what I

464
00:25:39,119 --> 00:25:42,560
Speaker 3: were in a friend US, that's US US like my

465
00:25:42,680 --> 00:25:43,560
Speaker 3: legs right.

466
00:25:43,480 --> 00:25:46,639
Speaker 5: Yes, massive quick the BM I took this morning.

467
00:25:47,040 --> 00:25:49,160
Speaker 2: Yes, we're good. Movement is good.

468
00:25:49,200 --> 00:25:52,520
Speaker 3: It keeps it keeps the blood pumping exactly exactly.

469
00:25:52,720 --> 00:25:55,200
Speaker 1: But but the people that run you know that whose

470
00:25:55,240 --> 00:25:58,480
Speaker 1: business it is to run US intelligence or big spy

471
00:25:58,840 --> 00:26:02,200
Speaker 1: as we call it, have announced that this project will

472
00:26:02,200 --> 00:26:04,600
Speaker 1: be able to track like what they say, anomalists, mass

473
00:26:04,640 --> 00:26:08,400
Speaker 1: movement for our own safety. Obviously, it says quote in

474
00:26:08,440 --> 00:26:11,920
Speaker 1: an ever increasing amount of geospatial data is created every day.

475
00:26:11,960 --> 00:26:15,080
Speaker 1: This is like the lead programmer of the Haystack program.

476
00:26:15,320 --> 00:26:18,639
Speaker 1: With Haystack, we have the opportunity to leverage machine learning

477
00:26:18,640 --> 00:26:22,439
Speaker 1: and advances in artificial intelligence to understand mobility patterns with

478
00:26:22,520 --> 00:26:25,880
Speaker 1: exceptional clarity. The more robustly we can model normal movements,

479
00:26:26,040 --> 00:26:28,760
Speaker 1: the more sharply we can identify what is out of

480
00:26:28,800 --> 00:26:33,359
Speaker 1: the ordinary and foresee a possible emergency. And now that

481
00:26:33,400 --> 00:26:36,800
Speaker 1: are out of the ordinary, must be stopped exactly.

482
00:26:36,920 --> 00:26:38,840
Speaker 2: That that's called horse.

483
00:26:40,960 --> 00:26:43,400
Speaker 5: That picks off the things of the haystack, which is us.

484
00:26:44,960 --> 00:26:47,880
Speaker 1: So like other other researchers and companies that are participate

485
00:26:47,880 --> 00:26:50,040
Speaker 1: painting in this project said like with all the data

486
00:26:50,040 --> 00:26:52,159
Speaker 1: that's just like laying around as they see it, Like

487
00:26:52,200 --> 00:26:53,639
Speaker 1: they're like, we can make sense of a lot of

488
00:26:53,640 --> 00:26:55,280
Speaker 1: this stuff. They're like, we can figure out if someone

489
00:26:55,280 --> 00:26:58,040
Speaker 1: has suffered a traumatic brain injury just from like the

490
00:26:58,160 --> 00:27:01,000
Speaker 1: gyroscopic or like movement sense or in a like on

491
00:27:01,040 --> 00:27:03,480
Speaker 1: a mobile phone. Like they're like, yeah, we could figure

492
00:27:03,480 --> 00:27:05,840
Speaker 1: that out pretty I think, like pretty easily with about

493
00:27:05,880 --> 00:27:07,080
Speaker 1: ninety percent accuracy.

494
00:27:07,320 --> 00:27:10,080
Speaker 5: The theme of this like announcement was very like don't worry.

495
00:27:10,080 --> 00:27:14,080
Speaker 1: It's not some scary drag net to disrupt protests, right,

496
00:27:14,560 --> 00:27:17,720
Speaker 1: it could definitely be used to disrupt protests or at

497
00:27:17,800 --> 00:27:20,359
Speaker 1: least predict where they're going down, because they're saying, like

498
00:27:20,600 --> 00:27:23,439
Speaker 1: what if like you saw a certain group of people

499
00:27:23,560 --> 00:27:27,560
Speaker 1: suddenly like moving towards one area, or like they don't

500
00:27:27,640 --> 00:27:30,479
Speaker 1: use their phones, or like certain cars are not on

501
00:27:30,520 --> 00:27:33,000
Speaker 1: the there's like there's less traffic in a usual direction

502
00:27:33,080 --> 00:27:36,080
Speaker 1: than normal. We can begin to extrapolate where like the

503
00:27:36,119 --> 00:27:38,840
Speaker 1: flow of people might be moving in another direction, And.

504
00:27:39,040 --> 00:27:43,159
Speaker 3: What are they claiming if it's not to disrupt protests?

505
00:27:43,200 --> 00:27:49,720
Speaker 3: Like is it to terrorists? Terrorists? Do terrorists like work

506
00:27:49,800 --> 00:27:51,760
Speaker 3: in flash mobs? Like are they uh?

507
00:27:52,440 --> 00:27:54,800
Speaker 2: Do they like have it? It's like anything else?

508
00:27:54,880 --> 00:27:57,520
Speaker 1: Right, Like they're saying like in an event of an attack, right,

509
00:27:57,560 --> 00:28:00,719
Speaker 1: they could foresee like where like if infrastruate taken out,

510
00:28:00,760 --> 00:28:03,080
Speaker 1: where people most likely would go, and they could.

511
00:28:02,920 --> 00:28:05,879
Speaker 2: Foresee an emergency. It's like a lot of very vague stuff.

512
00:28:05,880 --> 00:28:07,720
Speaker 1: But I'm sort of like you're using all this cell

513
00:28:07,760 --> 00:28:10,400
Speaker 1: phone and other like they're saying they're tapped into smart devices,

514
00:28:10,400 --> 00:28:14,119
Speaker 1: cell phones, fucking like existing sensors that that are like

515
00:28:14,160 --> 00:28:16,480
Speaker 1: set up around cities, and they're like there's just so

516
00:28:16,560 --> 00:28:18,840
Speaker 1: much data no one's bothering to make sense of it.

517
00:28:18,960 --> 00:28:22,159
Speaker 1: And if we do, then really have an advantage on

518
00:28:22,440 --> 00:28:25,200
Speaker 1: you know, those non do gooders. And if you're like

519
00:28:25,240 --> 00:28:27,240
Speaker 1: one of these people was like I'm going off the grid,

520
00:28:27,440 --> 00:28:29,520
Speaker 1: Like this is bullshit. I just want you to hear

521
00:28:29,520 --> 00:28:32,520
Speaker 1: from the lead researcher even like his perspective on the

522
00:28:32,520 --> 00:28:35,520
Speaker 1: concept of going off the grid, because it's not good.

523
00:28:36,040 --> 00:28:39,000
Speaker 3: By the way, this is doctor Jack Cooper no longer

524
00:28:39,000 --> 00:28:41,960
Speaker 3: from DARPA because that is a scary word. This is

525
00:28:41,960 --> 00:28:43,280
Speaker 3: from IARPA.

526
00:28:43,640 --> 00:28:45,560
Speaker 5: Yeah, thank god.

527
00:28:45,640 --> 00:28:46,440
Speaker 2: Yeah, thank god.

528
00:28:46,560 --> 00:28:50,200
Speaker 1: It's IARPA, the Intelligence Advanced Research Projects Activity.

529
00:28:50,320 --> 00:28:51,120
Speaker 2: Yeah.

530
00:28:51,160 --> 00:28:55,560
Speaker 1: But here here's doctor Jacques Cooper on why you can't

531
00:28:55,600 --> 00:28:56,080
Speaker 1: run from this.

532
00:28:56,600 --> 00:29:01,080
Speaker 6: Thinking about it is with respect to private to see today,

533
00:29:01,200 --> 00:29:04,120
Speaker 6: you might think that privacy means going to let off

534
00:29:04,200 --> 00:29:06,800
Speaker 6: live off the grid in the middle of nowhere. That's

535
00:29:06,840 --> 00:29:11,800
Speaker 6: just not realistic in today's environment. Sensors are cheap. Everybody's

536
00:29:11,800 --> 00:29:13,920
Speaker 6: got one. There's there's no such thing as as living

537
00:29:13,960 --> 00:29:14,920
Speaker 6: off off the grid.

538
00:29:15,840 --> 00:29:17,240
Speaker 2: He says, it's so matter of fact.

539
00:29:17,280 --> 00:29:19,920
Speaker 1: It's like, sensors are cheap, Like we can, we can

540
00:29:19,960 --> 00:29:21,959
Speaker 1: figure shit out pretty quickly if we wanted to.

541
00:29:22,240 --> 00:29:25,600
Speaker 3: When he says sensors are chea, what is he saying there?

542
00:29:25,640 --> 00:29:28,760
Speaker 3: Probably he also appears to be like just on the

543
00:29:28,880 --> 00:29:30,880
Speaker 3: verge of falling asleep at all moments.

544
00:29:30,880 --> 00:29:35,200
Speaker 2: Oh yeah, he's and apart. Oh yeah. Emotionally, Yeah, he's been.

545
00:29:35,320 --> 00:29:35,720
Speaker 2: He's been.

546
00:29:35,800 --> 00:29:38,520
Speaker 1: He's on a cocktail of sedatives that the Defense Department

547
00:29:38,520 --> 00:29:41,000
Speaker 1: has given him in order to deliver this frightening news

548
00:29:41,240 --> 00:29:43,760
Speaker 1: in a monotone But I mean, I think it's just

549
00:29:43,800 --> 00:29:46,640
Speaker 1: all to say that, like it's like his sales pitched

550
00:29:46,720 --> 00:29:49,960
Speaker 1: version of you can you can gather data so easily

551
00:29:50,000 --> 00:29:53,600
Speaker 1: with how cheap the sensors are, like specific sensors that could.

552
00:29:53,440 --> 00:29:55,960
Speaker 5: Give you sort of certain geospatial data right now.

553
00:29:55,960 --> 00:29:58,680
Speaker 1: They're saying, like, we can't tell you exactly what we

554
00:29:58,720 --> 00:30:00,960
Speaker 1: can figure out to do now, but with all the

555
00:30:00,960 --> 00:30:03,600
Speaker 1: information we have, this is what they think will help

556
00:30:03,680 --> 00:30:05,760
Speaker 1: with especially through machine learning, as they feed it more

557
00:30:05,800 --> 00:30:09,280
Speaker 1: and more information about like certain movement patterns that like

558
00:30:09,440 --> 00:30:14,920
Speaker 1: anomalous ones will come out very quickly, So that's christ Also,

559
00:30:15,080 --> 00:30:19,200
Speaker 1: how about this AI can fucking read your mind potentially.

560
00:30:20,120 --> 00:30:23,120
Speaker 1: This is another headline from a group of researchers in

561
00:30:23,160 --> 00:30:26,080
Speaker 1: Hong Kong who are showing like really promising results and

562
00:30:26,200 --> 00:30:31,480
Speaker 1: using AI to essentially translate brain activity into understandable visuals

563
00:30:31,520 --> 00:30:35,240
Speaker 1: like use like just brain scanning combined with generative AI.

564
00:30:35,680 --> 00:30:37,840
Speaker 1: So what they were doing was like to show this,

565
00:30:38,280 --> 00:30:41,480
Speaker 1: they showed participants in this study video like all kinds

566
00:30:41,480 --> 00:30:44,520
Speaker 1: of different videos, like like a couple walking across a bridge,

567
00:30:44,600 --> 00:30:47,560
Speaker 1: or like a windy plane, or like a cat like

568
00:30:47,640 --> 00:30:49,480
Speaker 1: you know, just like walking in front of a camera.

569
00:30:49,880 --> 00:30:52,280
Speaker 1: And as these people watch the videos while they were

570
00:30:52,280 --> 00:30:54,120
Speaker 1: like hooked up to these like brain scanners. I think

571
00:30:54,160 --> 00:30:57,400
Speaker 1: it was like an MRI type device. They fucking transformed.

572
00:30:57,560 --> 00:31:00,240
Speaker 1: They just off the brain activity that the sensors were

573
00:31:00,280 --> 00:31:03,720
Speaker 1: picking up. They were able to translate it into somewhat

574
00:31:03,760 --> 00:31:07,520
Speaker 1: accurate video depictions of what was basically going on from

575
00:31:07,520 --> 00:31:09,840
Speaker 1: this brain activity. So like in one of the examples,

576
00:31:09,840 --> 00:31:13,160
Speaker 1: you see like this striped cat, and based on what

577
00:31:13,200 --> 00:31:17,040
Speaker 1: the generative AI did from the person's brain activity, was

578
00:31:17,120 --> 00:31:20,120
Speaker 1: putting a striped cat with like very similar coat up

579
00:31:20,160 --> 00:31:21,120
Speaker 1: as its version.

580
00:31:20,840 --> 00:31:22,480
Speaker 5: Of like and this is what the brain is seeing

581
00:31:22,560 --> 00:31:23,800
Speaker 5: right now, even though we can.

582
00:31:23,840 --> 00:31:26,680
Speaker 4: It's a cuter cat is the most concerning part, Like

583
00:31:26,720 --> 00:31:28,680
Speaker 4: they made the cat more adorable.

584
00:31:28,760 --> 00:31:29,520
Speaker 2: The eyes are bigger.

585
00:31:29,640 --> 00:31:31,960
Speaker 5: Yeah, aah yeah, it's a much cuter cat.

586
00:31:32,400 --> 00:31:32,720
Speaker 2: Yeah.

587
00:31:32,880 --> 00:31:36,720
Speaker 1: This whole like website has all kinds of like these examples,

588
00:31:37,000 --> 00:31:40,680
Speaker 1: and it's really wild to see like how like just

589
00:31:40,720 --> 00:31:43,120
Speaker 1: like a fish would show a fish, or like a

590
00:31:43,120 --> 00:31:45,280
Speaker 1: group of a crowd walking through a city would also

591
00:31:45,360 --> 00:31:47,719
Speaker 1: show a crowd walking in a city. It obviously had

592
00:31:47,760 --> 00:31:51,440
Speaker 1: its like wonky Ai kind of art like vibes to it.

593
00:31:51,640 --> 00:31:54,200
Speaker 1: But again, as even with the other studies, are saying

594
00:31:54,560 --> 00:31:57,480
Speaker 1: they're like the more we feed it, though, the machine

595
00:31:57,600 --> 00:32:00,840
Speaker 1: learns and it gets better and better every time. And

596
00:32:01,240 --> 00:32:04,080
Speaker 1: I'm just like, wow, I can already see how how

597
00:32:04,120 --> 00:32:06,640
Speaker 1: this could be used in police interrogations or some shit.

598
00:32:06,800 --> 00:32:08,680
Speaker 1: Like I don't even know how like that what the

599
00:32:08,720 --> 00:32:12,160
Speaker 1: applications are for this, because we're already seeing like these

600
00:32:12,160 --> 00:32:16,560
Speaker 1: parallel advancements like happening like alongside AI. And while they're

601
00:32:16,600 --> 00:32:20,640
Speaker 1: certainly like benign or beneficial applications, the possibility for.

602
00:32:20,640 --> 00:32:23,760
Speaker 5: Like an AI powered police state is starting to really.

603
00:32:23,600 --> 00:32:24,880
Speaker 2: Kind of come into focus too.

604
00:32:24,880 --> 00:32:28,240
Speaker 1: And you're like, oh, man, like what I don't I

605
00:32:28,240 --> 00:32:31,040
Speaker 1: don't see how this is used to like make our

606
00:32:31,360 --> 00:32:36,200
Speaker 1: cities less polluted or food cheaper. Like I just saying,

607
00:32:36,640 --> 00:32:39,880
Speaker 1: my brain is saying, oh man, they're gonna be so

608
00:32:39,960 --> 00:32:44,360
Speaker 1: good at getting bed guys. But yeah, you think about right, like,

609
00:32:44,520 --> 00:32:48,400
Speaker 1: because we're in this situation where inequality is just increasing

610
00:32:48,440 --> 00:32:51,360
Speaker 1: at this rate, and also like our ability to like

611
00:32:51,440 --> 00:32:55,920
Speaker 1: surveil and clamp down on people is also like increasing too,

612
00:32:56,080 --> 00:32:58,960
Speaker 1: and what like it just sounds like the option of

613
00:32:59,040 --> 00:33:02,800
Speaker 1: rather than solving these like existential issues like oh yeah, man,

614
00:33:02,800 --> 00:33:05,000
Speaker 1: we got we're gonna have all these like space tools

615
00:33:05,000 --> 00:33:08,680
Speaker 1: to keep these motherfuckers in check when shit really goes now. Uh,

616
00:33:08,720 --> 00:33:12,400
Speaker 1: and that's what I just I get very very scared.

617
00:33:13,840 --> 00:33:18,160
Speaker 3: Yeah, all right, let's take a quick break and we'll

618
00:33:18,160 --> 00:33:21,080
Speaker 3: come back and maybe keep talking about it.

619
00:33:21,200 --> 00:33:23,680
Speaker 2: Well, we'll come back and talk about some different stories.

620
00:33:23,680 --> 00:33:37,000
Speaker 3: We'll be right back, and we're back, and Marjorie Taylor

621
00:33:37,040 --> 00:33:40,600
Speaker 3: Green is in the news for a couple of reasons. Actually,

622
00:33:41,920 --> 00:33:45,640
Speaker 3: one she paid one hundred thousand dollars for Kevin McCarthy's garbage,

623
00:33:46,480 --> 00:33:49,400
Speaker 3: like a piece of chapstick he had used, which is like,

624
00:33:50,480 --> 00:33:55,000
Speaker 3: I don't know, it's it's like a weird work activity

625
00:33:55,360 --> 00:33:58,640
Speaker 3: where like we're gonna auction off our stuff to each other.

626
00:33:59,800 --> 00:34:03,960
Speaker 3: But wait, where like somebody's like one hundred thousand dollars

627
00:34:04,000 --> 00:34:05,600
Speaker 3: for the boss's chapstick.

628
00:34:07,720 --> 00:34:13,319
Speaker 2: She off her Nazi gold that's what you're hanging with. Yeah,

629
00:34:13,520 --> 00:34:16,160
Speaker 2: she's able to through PayPal.

630
00:34:16,640 --> 00:34:18,799
Speaker 4: I don't know if I wanted to associate that that

631
00:34:18,920 --> 00:34:23,799
Speaker 4: poor business with what we just said. That is, let

632
00:34:23,880 --> 00:34:27,120
Speaker 4: me ask you a quick question. So I'm like addicted

633
00:34:27,160 --> 00:34:30,239
Speaker 4: to chapstick. If you were to borrow, like so your

634
00:34:30,360 --> 00:34:33,680
Speaker 4: partner's chapstick, you wouldn't even think twice about it. You

635
00:34:33,719 --> 00:34:35,560
Speaker 4: would just put it right on right, like you wouldn't

636
00:34:35,600 --> 00:34:37,520
Speaker 4: like wipe it or anything. Or is that is that

637
00:34:38,040 --> 00:34:40,320
Speaker 4: like using their toothbrush? Do you know what I'm saying?

638
00:34:40,680 --> 00:34:43,120
Speaker 2: Yeah, I think I would. I think I would use it.

639
00:34:43,680 --> 00:34:48,040
Speaker 2: I would direct what do you mean? Like my podcast

640
00:34:48,120 --> 00:34:51,760
Speaker 2: Miles Miles is Yes, Jack.

641
00:34:51,680 --> 00:34:53,360
Speaker 1: And I do a thing where he goes, oops, I

642
00:34:53,440 --> 00:34:56,160
Speaker 1: got a little extra my ma and I go, oh,

643
00:34:56,360 --> 00:35:01,239
Speaker 1: bring those lips here and go, that's really cute thing,

644
00:35:01,400 --> 00:35:03,480
Speaker 1: you know what do The thing that bucks me up

645
00:35:03,560 --> 00:35:10,600
Speaker 1: is record to get the guests one. Yeah, when I've

646
00:35:10,640 --> 00:35:13,719
Speaker 1: seen like if someone like, I'll use other people's chapstick

647
00:35:13,719 --> 00:35:16,560
Speaker 1: because if it's fucking super like the airs dry and

648
00:35:16,600 --> 00:35:18,440
Speaker 1: my lips are fucked up, I'll be like yeah. But

649
00:35:18,480 --> 00:35:20,840
Speaker 1: then like when I see like the grime on the

650
00:35:20,920 --> 00:35:22,880
Speaker 1: ridges on the side, I kind of get a little

651
00:35:23,080 --> 00:35:24,799
Speaker 1: freaked out. Or I'll do the thing where I like

652
00:35:24,840 --> 00:35:26,400
Speaker 1: really rub it on the top of my hand, you

653
00:35:26,440 --> 00:35:27,960
Speaker 1: know what I mean, just to sterilize it, you know,

654
00:35:28,040 --> 00:35:28,319
Speaker 1: just to.

655
00:35:28,239 --> 00:35:33,320
Speaker 2: Give what I would. Yeah, another person's not even her majesty,

656
00:35:33,480 --> 00:35:36,200
Speaker 2: like just another I use her majesty's chaps. I'm saying

657
00:35:36,239 --> 00:35:37,200
Speaker 2: I'm if I'm using like.

658
00:35:37,160 --> 00:35:40,279
Speaker 3: A like just some friends friends, yeah, or some guy

659
00:35:40,800 --> 00:35:41,480
Speaker 3: you know interesting.

660
00:35:41,680 --> 00:35:43,920
Speaker 4: Yeah, if if I was to use one of yours,

661
00:35:43,960 --> 00:35:46,480
Speaker 4: and I think that you guys have clean mouths in that,

662
00:35:46,520 --> 00:35:49,719
Speaker 4: and I do mean that sincerely, I would push it

663
00:35:49,800 --> 00:35:52,880
Speaker 4: all the way up to like the side, and I

664
00:35:52,920 --> 00:35:56,040
Speaker 4: would take like a piece off the side of it

665
00:35:56,440 --> 00:35:58,200
Speaker 4: and then put it on as if it's like a

666
00:35:58,280 --> 00:35:59,720
Speaker 4: more viscous lip bomb.

667
00:36:00,040 --> 00:36:01,640
Speaker 2: Wow what I'm saying. I like that.

668
00:36:01,760 --> 00:36:05,400
Speaker 1: I also envision you twirling the chapstick to like like

669
00:36:05,480 --> 00:36:07,600
Speaker 1: dispense it all the way out and then like corn

670
00:36:07,680 --> 00:36:09,840
Speaker 1: on the cob and just roll your lips on the side.

671
00:36:09,880 --> 00:36:18,040
Speaker 2: Park But it hasn't touched them out. You flipped the chapsticks. Great,

672
00:36:18,080 --> 00:36:21,040
Speaker 2: there you go. I'm like, dude, you fucking I like it.

673
00:36:21,120 --> 00:36:23,480
Speaker 2: I like the taste. I interrupted.

674
00:36:24,520 --> 00:36:27,160
Speaker 1: But the thing about Margie Taylor Green that don't it's

675
00:36:27,239 --> 00:36:29,799
Speaker 1: it's a fucking shell game, like it has nothing. All

676
00:36:29,880 --> 00:36:34,000
Speaker 1: this is is her moving her funds into Kevin McCarthy's fund.

677
00:36:34,360 --> 00:36:36,759
Speaker 2: That's all it is. It's a fucking it's just a leak.

678
00:36:36,880 --> 00:36:40,560
Speaker 1: It's just a like an fec workaround to be like, hey,

679
00:36:40,560 --> 00:36:41,960
Speaker 1: you got a lot of cash. You want to send

680
00:36:42,040 --> 00:36:44,240
Speaker 1: some to my pac like to my pack or something,

681
00:36:44,600 --> 00:36:47,440
Speaker 1: and it's it's legal to move the money like that.

682
00:36:47,560 --> 00:36:50,400
Speaker 1: So all it was was facilitating one hundred thousand dollars

683
00:36:50,400 --> 00:36:55,000
Speaker 1: transfer of like her donor cash to McCarthy. Yeah, but hey,

684
00:36:55,320 --> 00:36:57,480
Speaker 1: she bought, but she won the chapstick. I love how

685
00:36:57,560 --> 00:36:59,880
Speaker 1: just so like empty where it's like, you know, it's

686
00:36:59,920 --> 00:37:02,120
Speaker 1: a we know that this isn't what we're doing, but anyway,

687
00:37:02,640 --> 00:37:03,520
Speaker 1: you won the chapstick.

688
00:37:03,600 --> 00:37:05,040
Speaker 2: Are the fucking one hundred thousand?

689
00:37:05,080 --> 00:37:08,000
Speaker 3: Like what do you got in your pocket right now? Okay,

690
00:37:08,520 --> 00:37:09,719
Speaker 3: well yeah we'll go with that.

691
00:37:09,920 --> 00:37:12,920
Speaker 1: Yeah, okay, folks, we have we have a parting receipt

692
00:37:13,000 --> 00:37:18,759
Speaker 1: from Dulles International Airport. Entry time four fifty, exit time

693
00:37:18,800 --> 00:37:20,680
Speaker 1: two days later, nineteen twenty.

694
00:37:22,080 --> 00:37:24,960
Speaker 2: Cheap. We're in. We're in, not cheap, not cheap.

695
00:37:25,200 --> 00:37:27,239
Speaker 1: But you were saying she's in the news for other

696
00:37:27,280 --> 00:37:29,919
Speaker 1: reasons too, because on top of this, and on top

697
00:37:29,960 --> 00:37:33,200
Speaker 1: of like the Craven brinksmanship about the debt ceiling, which

698
00:37:33,320 --> 00:37:35,400
Speaker 1: I mean, well we'll get to that probably tomorrow.

699
00:37:35,480 --> 00:37:37,960
Speaker 2: Talking, I feel like they're not that. I feel like

700
00:37:38,000 --> 00:37:42,799
Speaker 2: that's just like they're incentivized to not back yeas they want.

701
00:37:43,239 --> 00:37:48,240
Speaker 3: Their whole strategy for the upcoming election is they feel

702
00:37:48,239 --> 00:37:51,319
Speaker 3: like they could do better with a catastrophe and they're

703
00:37:51,360 --> 00:37:53,960
Speaker 3: just going to create the catastrophe and blame Biden.

704
00:37:54,320 --> 00:37:58,040
Speaker 1: They need, I mean, we like they need a recession

705
00:37:58,320 --> 00:38:01,160
Speaker 1: to try and campaign on into twenty twenty four. And

706
00:38:01,200 --> 00:38:03,920
Speaker 1: the thing with Joe Biden in them, they fucked up

707
00:38:04,000 --> 00:38:06,280
Speaker 1: so bad because they could have raised the debt ceiling

708
00:38:06,360 --> 00:38:10,000
Speaker 1: during the lame duck between the election and not like

709
00:38:10,000 --> 00:38:12,880
Speaker 1: the inauguration for the incoming like new members of Congress.

710
00:38:12,960 --> 00:38:16,200
Speaker 1: They had the votes to raise it without the fucking Republicans.

711
00:38:16,440 --> 00:38:18,200
Speaker 1: But you know what they were doing, because they think

712
00:38:18,200 --> 00:38:21,239
Speaker 1: there's some four D chess assholes, is they thought, oh,

713
00:38:21,320 --> 00:38:24,000
Speaker 1: you know, we'll do we'll chess pass the ball to

714
00:38:24,040 --> 00:38:26,120
Speaker 1: them when they have control of Congress and make that

715
00:38:26,440 --> 00:38:29,360
Speaker 1: their fucking problem to deal with and we can score

716
00:38:29,400 --> 00:38:31,640
Speaker 1: points off of it when they completely show how much

717
00:38:31,680 --> 00:38:33,920
Speaker 1: of you know, how out of sorts they are. But

718
00:38:34,040 --> 00:38:35,880
Speaker 1: at the end of the day, they're like, they basically

719
00:38:35,920 --> 00:38:38,960
Speaker 1: just gave them the fucking football to be like, yeah,

720
00:38:39,000 --> 00:38:41,520
Speaker 1: go ahead, man, spike it on our heads now because

721
00:38:41,640 --> 00:38:45,920
Speaker 1: Democrats have no leverage. But this has been happening since,

722
00:38:45,960 --> 00:38:48,239
Speaker 1: you know, since Obama in twenty eleven was one of

723
00:38:48,280 --> 00:38:53,000
Speaker 1: the great debt ceiling crusades sequestration.

724
00:38:52,360 --> 00:38:54,040
Speaker 2: If you remember that phrase from that time.

725
00:38:54,760 --> 00:38:56,920
Speaker 1: But like you know, Joe Biden knew better, and at

726
00:38:56,920 --> 00:38:58,640
Speaker 1: the time, a lot of Democrats were like, man, we

727
00:38:58,680 --> 00:39:01,680
Speaker 1: can't keep doing this thing when when they have control

728
00:39:01,680 --> 00:39:03,880
Speaker 1: of the House and we have the presidency or a

729
00:39:03,960 --> 00:39:06,640
Speaker 1: majority of our other chambers, that we allow them to

730
00:39:06,719 --> 00:39:08,920
Speaker 1: dictate what's going to go on with our economy.

731
00:39:09,200 --> 00:39:10,000
Speaker 2: And then they.

732
00:39:09,880 --> 00:39:14,239
Speaker 1: Completely missed the point again, so good luck to them

733
00:39:14,360 --> 00:39:18,959
Speaker 1: and all of us, because if the government defaults, that's

734
00:39:19,120 --> 00:39:22,400
Speaker 1: a lot of payments that stop going out, especially for

735
00:39:22,440 --> 00:39:26,920
Speaker 1: people like that get like social security, veterans benefits, many

736
00:39:27,080 --> 00:39:29,360
Speaker 1: many other things would be affected by it. But they'll

737
00:39:29,400 --> 00:39:31,960
Speaker 1: get their sweet, sweet fucking recession.

738
00:39:32,200 --> 00:39:35,200
Speaker 3: It's a normal system of government. Find a new angle,

739
00:39:35,239 --> 00:39:36,840
Speaker 3: mid Yeah, exactly.

740
00:39:37,040 --> 00:39:40,000
Speaker 1: Oh so what or maybe the Democrats can always use

741
00:39:40,000 --> 00:39:43,440
Speaker 1: that an excuse to like cut back on social programs

742
00:39:43,480 --> 00:39:46,680
Speaker 1: and bring in more austerity under the guise of what

743
00:39:46,760 --> 00:39:48,480
Speaker 1: we had to do we had to negotiate with them

744
00:39:48,520 --> 00:39:50,319
Speaker 1: over the debt limit. I mean, there's many ways to

745
00:39:50,360 --> 00:39:52,919
Speaker 1: look at how you know, many cynical ways to look

746
00:39:52,920 --> 00:39:55,719
Speaker 1: at what's going on. But Marjorie Taylor Green, right, she's

747
00:39:55,800 --> 00:39:59,399
Speaker 1: dating some dude named Brian Glenn who was like on

748
00:39:59,440 --> 00:40:02,160
Speaker 1: a more talk show or morning news show in Dallas,

749
00:40:02,400 --> 00:40:05,879
Speaker 1: and a clip surfaced of like him covering like some

750
00:40:05,960 --> 00:40:08,719
Speaker 1: performance that was like a drag thing going on, and

751
00:40:08,760 --> 00:40:10,360
Speaker 1: he himself is in drag.

752
00:40:10,680 --> 00:40:11,359
Speaker 2: And if you know.

753
00:40:11,400 --> 00:40:17,200
Speaker 1: About the you know, GOP's current just violently homophobic, transphobic agenda.

754
00:40:17,200 --> 00:40:19,359
Speaker 2: You're like, oh, really, this is this is who you're with.

755
00:40:19,600 --> 00:40:22,880
Speaker 1: So this is just a clip of her boyfriend doing

756
00:40:22,920 --> 00:40:26,200
Speaker 1: this news thing, like in drag and just having you know,

757
00:40:26,920 --> 00:40:29,719
Speaker 1: a fun time with these other performers that this news

758
00:40:29,719 --> 00:40:30,480
Speaker 1: story he was covering.

759
00:40:31,239 --> 00:40:32,920
Speaker 2: I could tell you I don't think I think a

760
00:40:32,920 --> 00:40:33,719
Speaker 2: lot of people up now.

761
00:40:33,760 --> 00:40:36,080
Speaker 1: They're all hashtag I am up of course, come and

762
00:40:36,160 --> 00:40:36,839
Speaker 1: check it out here.

763
00:40:37,160 --> 00:40:38,280
Speaker 2: So I saw on Saturday.

764
00:40:38,480 --> 00:40:41,240
Speaker 1: I'm kicking these shoes off, but make keep the panty

765
00:40:41,280 --> 00:40:42,480
Speaker 1: hose on this steel kind of good.

766
00:40:42,800 --> 00:40:44,960
Speaker 2: Actually I'm not gonna I promiser.

767
00:40:45,200 --> 00:40:47,040
Speaker 1: Okay, Well, I don't know if I'd believe that part.

768
00:40:47,120 --> 00:40:48,560
Speaker 1: He probably should pee in them.

769
00:40:48,880 --> 00:40:51,799
Speaker 4: Pee in them like that sounds like someone who's about

770
00:40:51,800 --> 00:40:52,680
Speaker 4: to piss and some panties.

771
00:40:52,719 --> 00:40:53,960
Speaker 2: Oh yeah, like what like?

772
00:40:54,000 --> 00:40:56,120
Speaker 1: And also like, who no one asked you that? Why

773
00:40:56,200 --> 00:40:58,719
Speaker 1: is that a thought? I'm not gonna pee in your

774
00:40:58,719 --> 00:40:59,440
Speaker 1: panty hose?

775
00:41:00,120 --> 00:41:01,360
Speaker 5: I'm sorry?

776
00:41:03,480 --> 00:41:05,239
Speaker 2: Hey, can I borrow your hat? And don't worry? I'm

777
00:41:05,239 --> 00:41:06,759
Speaker 2: not gonna pee in your hat?

778
00:41:07,160 --> 00:41:10,399
Speaker 4: Like what my catheter didn't fit in the pantos they're

779
00:41:10,400 --> 00:41:11,200
Speaker 4: really tight.

780
00:41:11,000 --> 00:41:12,400
Speaker 2: So it's a.

781
00:41:13,440 --> 00:41:16,000
Speaker 1: Patriot Takes on Twitter was like, wow, like, look at

782
00:41:16,000 --> 00:41:19,960
Speaker 1: this energy, and obviously the Republicans don't they believe in nothing,

783
00:41:20,440 --> 00:41:23,359
Speaker 1: so say there's no way you can shame them on

784
00:41:23,400 --> 00:41:27,799
Speaker 1: their fucking I don't give her. Actually, her response to

785
00:41:27,840 --> 00:41:29,880
Speaker 1: this tweet is so funny and it's so of like

786
00:41:29,920 --> 00:41:33,240
Speaker 1: a person who's like knows, like how how much.

787
00:41:33,120 --> 00:41:37,000
Speaker 2: Of a bad look? She tweeted quote, I'm literally lolling.

788
00:41:37,360 --> 00:41:39,600
Speaker 1: Brian Glenn dressed in drag for morning news in Dallas

789
00:41:39,680 --> 00:41:42,520
Speaker 1: years ago, reporting on an upcoming local theater production, and

790
00:41:42,560 --> 00:41:45,880
Speaker 1: the morons over at Patriot Takes thinks this is an attack.

791
00:41:46,239 --> 00:41:49,920
Speaker 1: Brian loves to throw back and is reposting laughing crime oji.

792
00:41:49,960 --> 00:41:54,759
Speaker 1: The left is so stupid. Hmm okay, sure they're just

793
00:41:54,800 --> 00:41:59,040
Speaker 1: showing you again. How how Uh, how wobbly everyone is

794
00:41:59,120 --> 00:42:02,120
Speaker 1: with their outrage jure, especially especially on the right.

795
00:42:02,160 --> 00:42:05,680
Speaker 3: But yeah, I'm literally literally lulling right now.

796
00:42:06,040 --> 00:42:09,480
Speaker 5: They're so stupid. Oh my god, you guys are so stupid.

797
00:42:12,280 --> 00:42:14,080
Speaker 1: I'm sure, and I'm sure like many and like the

798
00:42:14,200 --> 00:42:21,080
Speaker 1: replies are like it was for a news thing, like what.

799
00:42:19,040 --> 00:42:20,920
Speaker 2: What so drag?

800
00:42:21,080 --> 00:42:24,360
Speaker 1: Like, I'm curious because if anyone in drag is a groomer,

801
00:42:25,080 --> 00:42:27,399
Speaker 1: that there are there is a context in which being

802
00:42:27,400 --> 00:42:31,080
Speaker 1: in drag you're not a groomer. And if so, please

803
00:42:31,120 --> 00:42:33,279
Speaker 1: explain the nuance, because prior to this, I did not

804
00:42:33,400 --> 00:42:38,040
Speaker 1: know there was any nuance from a normal red blooded

805
00:42:38,040 --> 00:42:42,560
Speaker 1: American wearing pantyhose and promising you that I'm not gonna

806
00:42:42,560 --> 00:42:43,200
Speaker 1: piss them.

807
00:42:43,480 --> 00:42:46,160
Speaker 4: Yeah, and I promise I did not have a good time.

808
00:42:46,280 --> 00:42:48,799
Speaker 4: I promise I did not have a good It's like,

809
00:42:49,320 --> 00:42:51,480
Speaker 4: just have a good time. Drag shows are some of

810
00:42:51,480 --> 00:42:54,160
Speaker 4: the most fun things that you can go to allow

811
00:42:54,239 --> 00:42:55,759
Speaker 4: yourself to have a good time.

812
00:42:57,800 --> 00:43:02,200
Speaker 3: It's different from a local news reporter dressing and drag

813
00:43:02,719 --> 00:43:06,759
Speaker 3: and some somebody else, uh dressing, dressing and drag.

814
00:43:07,280 --> 00:43:07,400
Speaker 1: Uh.

815
00:43:07,719 --> 00:43:11,440
Speaker 3: The local news obviously has a car out when it

816
00:43:11,480 --> 00:43:14,440
Speaker 3: comes to uh what what but when they dress and

817
00:43:14,480 --> 00:43:15,319
Speaker 3: drag and have fun.

818
00:43:15,880 --> 00:43:18,800
Speaker 1: Yes, exactly, oh someone like and one of them replies,

819
00:43:18,800 --> 00:43:20,640
Speaker 1: someone goes, there's a difference between.

820
00:43:20,320 --> 00:43:23,120
Speaker 5: A costume and a drag show, is there?

821
00:43:24,000 --> 00:43:25,400
Speaker 2: I mean? Interesting?

822
00:43:25,480 --> 00:43:29,480
Speaker 1: Please go on though, mister punisher skull emoji with the

823
00:43:29,560 --> 00:43:30,440
Speaker 1: red American flag.

824
00:43:30,640 --> 00:43:37,520
Speaker 3: They just have this like very detailed, Like it's wrong,

825
00:43:37,680 --> 00:43:39,160
Speaker 3: but it is very thought out.

826
00:43:39,719 --> 00:43:44,280
Speaker 2: The fucking float chart is like detailed. Yeah.

827
00:43:44,320 --> 00:43:48,800
Speaker 3: Well, Blake, Blake Wexler the pleasure having it, Blake, Blake Wexler.

828
00:43:49,040 --> 00:43:51,960
Speaker 2: Where can people find you? Follow you all that good stuff?

829
00:43:52,000 --> 00:43:55,600
Speaker 4: People can find me? Follow me at Blake Wexler on

830
00:43:55,960 --> 00:44:01,440
Speaker 4: all social media. I'm gonna be in Bristol, Tennessee at

831
00:44:01,480 --> 00:44:05,439
Speaker 4: a comedy club called the Blue Ridge Comedy Club June

832
00:44:05,520 --> 00:44:06,960
Speaker 4: second to June third.

833
00:44:07,760 --> 00:44:10,360
Speaker 2: And also I talked about.

834
00:44:10,200 --> 00:44:12,720
Speaker 4: That bike ride that I did earlier called the Eagles

835
00:44:12,760 --> 00:44:16,520
Speaker 4: Autism Challenge. Donations are still open on that for like

836
00:44:16,600 --> 00:44:18,560
Speaker 4: until the end of the week. So there's a link

837
00:44:18,600 --> 00:44:20,160
Speaker 4: in my bio if you want to support. It's an

838
00:44:20,160 --> 00:44:24,439
Speaker 4: amazing cause, autism awareness, autism research. Those links are on

839
00:44:24,480 --> 00:44:27,000
Speaker 4: my social media as as well. And thanks to everyone

840
00:44:27,040 --> 00:44:28,400
Speaker 4: who already already donated.

841
00:44:28,880 --> 00:44:33,359
Speaker 2: Oh amazing and there raised six million. You said those

842
00:44:33,400 --> 00:44:33,960
Speaker 2: six million.

843
00:44:34,440 --> 00:44:37,680
Speaker 4: Yeah, they raised like six point two million, and I

844
00:44:37,719 --> 00:44:38,680
Speaker 4: think it's still.

845
00:44:38,640 --> 00:44:41,799
Speaker 2: Three point one per plumper perp. Yeah. I think I

846
00:44:41,840 --> 00:44:43,120
Speaker 2: saw the local.

847
00:44:43,640 --> 00:44:46,840
Speaker 1: Yeah, they're like local man willing to raise shorts to

848
00:44:47,000 --> 00:44:48,759
Speaker 1: show plumpers for It's.

849
00:44:48,520 --> 00:44:50,640
Speaker 4: A shame because they had my photo and they had

850
00:44:50,680 --> 00:44:53,680
Speaker 4: to blur out my legs, which also obscured the amount

851
00:44:53,680 --> 00:44:55,839
Speaker 4: of money that was that I wrote on each leg,

852
00:44:56,680 --> 00:44:57,640
Speaker 4: which is a bummer.

853
00:44:57,680 --> 00:45:00,319
Speaker 5: But you know it's local. Like the FCC. They won't

854
00:45:00,360 --> 00:45:01,360
Speaker 5: let my plumbers be.

855
00:45:04,760 --> 00:45:07,160
Speaker 2: Is there a work media you've been enjoying.

856
00:45:07,400 --> 00:45:11,560
Speaker 4: Yes, at the Todd Glass show on TikTok. Very funny

857
00:45:11,600 --> 00:45:14,640
Speaker 4: comedian Todd Glass who left me voicemails for twelve years

858
00:45:14,840 --> 00:45:17,400
Speaker 4: and I released that as an album. He is so

859
00:45:17,800 --> 00:45:21,680
Speaker 4: funny on TikTok where he does these videos where he

860
00:45:21,719 --> 00:45:24,880
Speaker 4: pretends to be an uber driver and the people in

861
00:45:24,920 --> 00:45:27,520
Speaker 4: the car are like, no, it's Todd, but they're in

862
00:45:27,560 --> 00:45:30,520
Speaker 4: the back of the car. And he posted one recently

863
00:45:30,600 --> 00:45:33,880
Speaker 4: where he just goes, he pulls over and he goes,

864
00:45:34,040 --> 00:45:36,080
Speaker 4: I can't drive when there's cars behind me.

865
00:45:36,160 --> 00:45:37,720
Speaker 2: I always think it might be a cop.

866
00:45:38,040 --> 00:45:40,080
Speaker 4: And then the guy in the back seat goes, you

867
00:45:40,080 --> 00:45:43,040
Speaker 4: can't drive with cars behind you, and he goes, yeah,

868
00:45:44,000 --> 00:45:46,759
Speaker 4: he has like a big series of those that's really

869
00:45:46,760 --> 00:45:49,919
Speaker 4: really funny. So if you want just good clean laughs, Yeah,

870
00:45:49,960 --> 00:45:53,319
Speaker 4: at the Todd Glass Show on on TikTok and Instagram.

871
00:45:52,880 --> 00:45:56,480
Speaker 3: Amazing miles. Where can people find you? Is there a

872
00:45:56,520 --> 00:45:58,080
Speaker 3: work media you've been enjoying?

873
00:45:58,280 --> 00:46:03,360
Speaker 1: You can find me on at Twitter, Instagram, at Miles

874
00:46:03,400 --> 00:46:04,880
Speaker 1: of Gray.

875
00:46:04,480 --> 00:46:11,040
Speaker 2: What at Twitter? Twitter? At Twitter? I don't know, folks,

876
00:46:11,239 --> 00:46:12,040
Speaker 2: what do you What are you gonna do? What are

877
00:46:12,040 --> 00:46:13,600
Speaker 2: you gonna do? What are gonna do? At Miles of

878
00:46:13,600 --> 00:46:14,480
Speaker 2: Gray where they got them?

879
00:46:14,719 --> 00:46:19,719
Speaker 1: Check Jack and I out on our basketball podcasties but

880
00:46:19,920 --> 00:46:23,880
Speaker 1: soon to be fun because the finals are coming slowly, slowly, slowly.

881
00:46:24,040 --> 00:46:26,759
Speaker 1: And also, if you like ninety fiance, check Sofia Alexandra

882
00:46:26,880 --> 00:46:29,880
Speaker 1: out on our ninety day podcast called four to twenty

883
00:46:29,960 --> 00:46:30,839
Speaker 1: Day Fiance.

884
00:46:32,120 --> 00:46:33,680
Speaker 2: Let's see Do I like a tweet?

885
00:46:34,200 --> 00:46:34,399
Speaker 4: Oh?

886
00:46:34,520 --> 00:46:37,360
Speaker 2: This was just this was just kind of stupid. Uh.

887
00:46:37,360 --> 00:46:39,799
Speaker 1: This is from the hype. It's just said her, Oh

888
00:46:39,800 --> 00:46:42,560
Speaker 1: my god, your dick is so small. Shrink Ray Company CEO.

889
00:46:42,760 --> 00:46:48,640
Speaker 1: Hell yeah, fans, there it is. Tweet I've been enjoying

890
00:46:48,760 --> 00:46:53,360
Speaker 1: is from Brody Goopta, who tweeted the new HBO Max

891
00:46:53,920 --> 00:47:01,240
Speaker 1: Max sorry advertisement that introducing Max the one to watch

892
00:47:01,280 --> 00:47:04,120
Speaker 1: for HBO max dot com.

893
00:47:04,520 --> 00:47:07,480
Speaker 2: And uh, she tweeted, drop.

894
00:47:07,280 --> 00:47:11,920
Speaker 3: The the face just book then ad it's the site

895
00:47:12,000 --> 00:47:13,200
Speaker 3: that is the Facebook.

896
00:47:13,440 --> 00:47:24,120
Speaker 2: It's clean drop just basically what they've done here.

897
00:47:24,719 --> 00:47:27,080
Speaker 3: You can find me on Twitter at Jack Underscore O'Brien.

898
00:47:27,160 --> 00:47:29,600
Speaker 3: You can find us on Twitter at daily Zeitgeist. We're

899
00:47:29,640 --> 00:47:32,919
Speaker 3: at the Daily Zeikeeist on Instagram. We have a Facebook fan.

900
00:47:32,840 --> 00:47:35,399
Speaker 1: Page and website Daily zeikeist dot com where we post

901
00:47:35,520 --> 00:47:40,359
Speaker 1: our episodes and our footnotes, the information that we talked

902
00:47:40,360 --> 00:47:41,920
Speaker 1: about today's episode, as well as a.

903
00:47:41,880 --> 00:47:45,560
Speaker 2: Song that we think you might enjoy. Miles, what song

904
00:47:45,600 --> 00:47:46,920
Speaker 2: do we think people might enjoy? Oh?

905
00:47:47,080 --> 00:47:50,880
Speaker 1: I'm still on this college acapella thing and it's fucking

906
00:47:50,920 --> 00:47:54,440
Speaker 1: me up because I there's just something about like college

907
00:47:54,440 --> 00:47:57,840
Speaker 1: acapella groups doing like like more mainstream songs.

908
00:47:57,840 --> 00:47:59,879
Speaker 2: It just kills me. I just love hearing it because

909
00:47:59,920 --> 00:48:02,040
Speaker 2: it just so just so fun. Uh.

910
00:48:02,080 --> 00:48:06,279
Speaker 1: And this one is from we Be Jazz in I'm

911
00:48:06,320 --> 00:48:08,240
Speaker 1: not sure what school they're from.

912
00:48:08,400 --> 00:48:10,600
Speaker 2: Let me make sure because.

913
00:48:14,640 --> 00:48:17,719
Speaker 1: That's where any any guesses where We Be Jazz in

914
00:48:18,000 --> 00:48:25,600
Speaker 1: is from E y U University, Winter's College in Toronto,

915
00:48:26,280 --> 00:48:29,439
Speaker 1: I believe is where they're from. Anyway, they did a

916
00:48:29,600 --> 00:48:34,400
Speaker 1: fantastic cover of Stevie Dan's Steve This is Stevie Dan.

917
00:48:35,760 --> 00:48:36,399
Speaker 2: To mash up.

918
00:48:38,160 --> 00:48:41,360
Speaker 1: Of Stevie Dan's pog You're gonna love it of Peg

919
00:48:41,440 --> 00:48:43,440
Speaker 1: by Steely Dan and it's just like it.

920
00:48:43,719 --> 00:48:44,800
Speaker 2: I don't know. They're like, it's funny.

921
00:48:44,800 --> 00:48:47,799
Speaker 1: Whenever I listen to acapella music, I picture people smiling

922
00:48:47,960 --> 00:48:50,480
Speaker 1: ear to ear like I've never you can never picture

923
00:48:50,480 --> 00:48:51,879
Speaker 1: someone like being like.

924
00:48:51,880 --> 00:48:53,920
Speaker 2: Action faced singing like acapella.

925
00:48:53,920 --> 00:48:58,359
Speaker 1: So I was like, like in my one's wide eyed

926
00:48:58,440 --> 00:48:59,360
Speaker 1: and that's what this gives you.

927
00:48:59,400 --> 00:49:02,600
Speaker 2: So this is we be Jazzy with PEG. Yeah, all right,

928
00:49:02,719 --> 00:49:04,759
Speaker 2: we'll link off to that in the footnote. Daily Zaike

929
00:49:04,760 --> 00:49:07,520
Speaker 2: as a production of iHeartRadio. For more podcasts from iHeart.

930
00:49:07,320 --> 00:49:09,799
Speaker 1: Radio, visit the iHeartRadio app, Apple podcast, or wherever you

931
00:49:09,800 --> 00:49:12,040
Speaker 1: listen to your favorite shows. That's going to do it for

932
00:49:12,120 --> 00:49:14,720
Speaker 1: us this morning, back this afternoon to tell you what's trending,

933
00:49:15,239 --> 00:49:22,759
Speaker 1: and we'll talk to y'all then bye bye