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Speaker 1: Hello the Internet, and welcome to this episode of the

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Speaker 1: Weekly Zeitgeist. These are some of our favorite segments from

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Speaker 1: this week, all edited together into one NonStop infotainment laugh stravaganza.

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Speaker 1: Uh yeah, So, without further ado, here is the Weekly Zeitgeist.

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Speaker 2: We have one of our favorite guests of all time,

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Speaker 2: one of the listener's favorite guests of all time.

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Speaker 1: This is a man who you.

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Speaker 2: Know, has has really inspired the imaginations of people across

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Speaker 2: the country with his YouTube searches and his niche interests

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Speaker 2: and obviously his love of cold brew. The Poetry Window

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Speaker 2: will consider that shit open because we are welcoming in

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Speaker 2: our third set our guest today, mister Chris Craft.

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Speaker 3: What's up, Chris, Welcome to the Chris Kroft and Daily Zeitgeist. Okay,

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Speaker 3: such a lovely place, Such a lovely place. I've been

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Speaker 3: through the desert on a horse named Chris crofton it.

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Speaker 3: Good to be out of the guyst love that ne

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Speaker 3: those Aka is brought to you by me.

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Speaker 2: Am Wow what the Eagles and then Neil Young.

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Speaker 3: I know, two champions, two champions of the white community.

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Speaker 4: Time.

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Speaker 1: I did not know that.

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Speaker 2: I didn't know what he did. I didn't know I

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Speaker 2: thought Horse with No Name was a Neil Young song,

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Speaker 2: but it's not. It's some dude who is trying to

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Speaker 2: be like Neil I guess what what what dudes?

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Speaker 3: Yeah, some dudes trying to sound like Neil Young America.

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Speaker 2: And then you recognize obviously part of Jaques's AKA not

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Speaker 2: Like Us, because, like I was quizzing you before, you

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Speaker 2: you have intersected with the Kendrick Lamar beef ending track.

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Speaker 1: Not Like Us.

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Speaker 3: Yeah, and I cannot I can't believe. I'm just I'm

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Speaker 3: just humble as a white man to know about it.

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Speaker 1: Yeah, well, you know, and I and I.

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Speaker 3: And it's just a testament to how far that has

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Speaker 3: gone that a man my age of my complexion knows

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Speaker 3: about this fucking beef you, Chris, was.

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Speaker 2: It like a thing where you're like, what what? Why

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Speaker 2: is everybody keep talking about miners and stuff? The fuck

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Speaker 2: is going on? And then you just kind of naturally

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Speaker 2: figured it out or on YouTube enough that you figured

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Speaker 2: it out.

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Speaker 3: I just fucking I'm online, yeah, way too much. And

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Speaker 3: so I know about a little bit about Drake being

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Speaker 3: accused of that sort of stuff, and then I but

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Speaker 3: I didn't know, I mean obviously, and I know Kendrick

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Speaker 3: Lamar a little bit, but so I don't exactly know.

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Speaker 2: I don't know that. I mean, I know I've heard

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Speaker 2: the song.

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Speaker 1: I've heard the song.

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Speaker 3: I've heard the song. I just don't know exactly what

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Speaker 3: is going on or Yeah that's fine. But I think

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Speaker 3: that as far as I can tell, it was a

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Speaker 3: big win for Kendrick yeah yeah, yeah.

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Speaker 2: And a big loss for Drake. Yeah yeah, but I

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Speaker 2: think he'll be back because Drake's like Avengers movies for

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Speaker 2: the music indust like they need they need him to

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Speaker 2: generate hits. But we'll see. The brand is pretty fucked

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Speaker 2: up though at the moment. So I mean I say this,

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Speaker 2: like Drake is gonna sell records, yeah, yeah.

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Speaker 5: When he makes a song, and especially if it's a hit,

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Speaker 5: it's gonna give radio play.

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Speaker 1: Yeah.

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Speaker 2: But he's getting the rap community, Yeah, that nigga is.

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Speaker 1: Yeah he is.

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Speaker 2: People will like forever.

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Speaker 1: Yeah.

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Speaker 2: People are not looking at him. He will be viewed

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Speaker 2: as like a right to like hip hop.

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Speaker 1: Fans right for sure.

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Speaker 2: For sure, capital age Drake fans, capitol age hip hop fans. Exactly.

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Speaker 1: What is something from your search histories that's revealing about

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Speaker 1: who you are? Alex? You want to kick us off.

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Speaker 6: Oh gosh, okay, I don't. The thing is, I don't

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Speaker 6: think so I use Duck duckgo, and so it doesn't

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Speaker 6: actually keep the search history. And if I actually look

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Speaker 6: at my Google history, it's actually going to be really shameful.

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Speaker 6: It's gonna be me like searching my own name to

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Speaker 6: see it, like if people are like ship talking to

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Speaker 6: me online.

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Speaker 1: Now, this isn't just how we tell if someone's honest,

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Speaker 1: is if they actually give that answer, we're like, okay.

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Speaker 6: So yeah, you actually search yourself.

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Speaker 1: Yeah.

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Speaker 6: Actually, but I think the last thing I actually searched

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Speaker 6: was like queer barbers in the Bay Area because I

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Speaker 6: haven't had a haircut in like a year, and I

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Speaker 6: think I need to turm up or get you know,

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Speaker 6: air out the the sides of my head for pride months.

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Speaker 6: So that's yeah, that's elastic. I searched, what.

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Speaker 1: Are you going? You're going full shaved on the sides.

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Speaker 6: Or maybe trim it a little bit and up the

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Speaker 6: back and bring out the curls a little bit.

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Speaker 1: So okay, love it? Yeah on board.

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Speaker 2: I wish I could a few more days in look.

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Speaker 6: In July like discounts, Like it's like like after Valentine's Day,

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Speaker 6: do I get an undercut at fifty percent off?

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Speaker 1: Now?

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Speaker 2: Right?

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Speaker 1: Exactly? Emily, how about you? What's something from your search history?

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Speaker 7: So forgive the poor pronunciation of this and the rest

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Speaker 7: of the story because Spanish is not one of my languages.

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Speaker 7: But champurrado, Oh yeah, it's something I search instress.

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Speaker 2: Yeah.

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Speaker 7: So I was in Mexico City for a conference last

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Speaker 7: week and at one of the coffee breaks, they had

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Speaker 7: coffee and decaf coffee, and then they had.

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Speaker 1: And the Spanish pronunt give us about that?

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Speaker 2: What do you see when you see that?

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Speaker 1: Way? Champarado Mexican hot chocolate.

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Speaker 2: So yeah, you're literally Google results.

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Speaker 7: So the the labels all had like translations into English,

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Speaker 7: and so it was champurado with o hack and chocolatem Like, yeah,

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Speaker 7: I got that. What's choppado? And so I look it

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Speaker 7: up because I want to know what I'm consuming before

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Speaker 7: I consume it. And it's basically a corn flour based

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Speaker 7: thick drink like chocolate corn soup.

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Speaker 2: It was amazing, chocolate corn soup.

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Speaker 1: You had me until chocolate corn soup.

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Speaker 6: The corn is just a thickening yeah, chocolate drink.

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Speaker 7: Yeah, slight corn flavor, like think corn tortilla, not on

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Speaker 7: the cob.

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Speaker 2: Yeah yeah, oh yeah, yeah, yeah that sounds amazing.

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Speaker 8: Yeah, it was really good.

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Speaker 1: I love some corn flakes in a chocolate bar. Yeah,

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Speaker 1: corn chocolate. There you go, Yeah, you got it.

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Speaker 2: You gotta arrive in your own way back with the

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Speaker 2: corn chocolate.

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Speaker 7: It was really good and just awesome that it was there.

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Speaker 7: Like you know, the the coffee breaks had like the

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Speaker 7: Mexican sweetbreads and stuff like that, but otherwise it was

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Speaker 7: pretty standard like coffee break stuff, and all of a sudden,

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Speaker 7: there's this wonderful mystery drink.

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Speaker 8: The big urns was it was lovely.

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Speaker 1: That sounds great. What is something you think is underrated? Emily?

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Speaker 8: I think Seattle's weather is underrated.

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Speaker 7: Oh yeah, everyone makes fun of our weather and like,

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Speaker 7: you know, fine believe that we don't need lots of

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Speaker 7: people coming here. And it's true it gets dark in

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Speaker 7: the winter, but like almost any day, you can be

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Speaker 7: outside and you are not in physical danger because you

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Speaker 7: are outside.

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Speaker 6: I guess that's that's I mean, if you're going for yeah,

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Speaker 6: that's interesting, but I mean it's I mean, the winters

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Speaker 6: are just so punishing though it's so gray.

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Speaker 8: It's dark.

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Speaker 2: But the weather it's dark, it looks it looks like shit,

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Speaker 2: but experientially not bad for you. I mean, I yeah,

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Speaker 2: I know, it's when does like it doesn't get all gloomy.

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Speaker 2: I imagine in the summer, right, you have wonderful blue

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Speaker 2: skies and you can enjoy.

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Speaker 8: The gorgeous fire season aside.

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Speaker 7: But yeah, from sort of mid October to early January,

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Speaker 7: it can be pretty like it's gray and so like

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Speaker 7: when the sun is technically above the horizon, it's.

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Speaker 8: A little hard to tell.

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Speaker 7: Yeah, right, right, So, but you know, compared to like Chicago,

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Speaker 7: where you have maybe four liverable weeks a year.

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Speaker 8: Between the too hot and the two cold.

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Speaker 6: Wow, wow, I'll do that. Because my thing was going

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Speaker 6: to be because I was just there and I was

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Speaker 6: going to say, my answer was going to be, Chicago

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Speaker 6: is the best American city. I stand on this, like one, no,

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Speaker 6: absolutely not true. No, I'll even deal. I'll even deal

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Speaker 6: with I'll deal with the winter. I mean if I okay,

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Speaker 6: I'll be honest, if I didn't, you know, if the

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Speaker 6: weather in Chicago, if if I could bring Bay Area

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Speaker 6: weather to Chicago, I would live in Chicago. I mean

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Speaker 6: there's other reasons, but I mean it's it's look, the vibes,

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Speaker 6: immaculate street festivals, the neighborhoods. It's the one place that's

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Speaker 6: probably the food. It's still comparatively affordable compared to the coasts.

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Speaker 6: Radical history, you know, just you know, some of the

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Speaker 6: best politics. Yeah, you know, I would say they.

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Speaker 1: Shot an issue there, the Fugitive, Oh.

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Speaker 6: I did, that's a deep cut. Yeah, I mean they

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Speaker 6: I think they've shot a lot of Batman movies there,

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Speaker 6: because you know, the iconic kind of lower Whacker Drive

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Speaker 6: and they call it with them and it's yeah, yeah,

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Speaker 6: that's pretty great city.

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Speaker 7: Crappy weather, right, if you're gonna dump on weather somehere.

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Speaker 7: Everyone makes fun of Seattle's weather.

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Speaker 6: Honestly, Emily, this is a hot take. I'd rather take

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Speaker 6: Chicago's weather than Seattle's weather. I can't I can't do gray.

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Speaker 6: I can do I.

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Speaker 1: Feel like I'm on crossfire.

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Speaker 6: I can fridge it, I cannot do gray. It's supers Well.

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Speaker 7: This is why I say, like, don't move to Seattle

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Speaker 7: if you can't handle our weather. Like the people who

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Speaker 7: move here and then complain about the weather, what.

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Speaker 1: Do you expect all of this?

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Speaker 2: What they say is true about it being great, like

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Speaker 2: I'd expect you to be that gray, right. I think

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Speaker 2: people talk about it like that.

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Speaker 1: What is something you think is overrated?

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Speaker 2: I'm going Tesla right now, y'all. I just know you

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Speaker 2: guys driving I finally.

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Speaker 9: Like rode in a Tesla and really paid attention to

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Speaker 9: the feeling of riding in a Tesla car. And I

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Speaker 9: gotta say, if you're gonna pay that much money for

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Speaker 9: a car, it's got to not feel like a weird

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Speaker 9: golf cart that doesn't have any smooth ride.

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Speaker 1: It sucks.

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Speaker 2: It's a it is not a smooth ride. I would

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Speaker 2: much rather be in like a nineteen ninety eight like

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Speaker 2: buickless Saber if I if I want to smooth ride,

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Speaker 2: But those cars suck to ride in. I'm saying. I

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Speaker 2: remember the first time I got in one, I was

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Speaker 2: so underwhelmed, like it was weird. I had built up

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Speaker 2: Tesla's in my mind like fucking crazy, and I remember

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Speaker 2: someone I knew it was like partner, drove one and

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Speaker 2: like picked us up to go somewhere. And first I

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Speaker 2: fucking embarrassed myself because I know the fucking door handle word. Yeah,

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Speaker 2: you can't get it. I was like, I was like

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Speaker 2: rubbing it. The guys gotta push it and then it

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Speaker 2: comes out. I was like, all right, and then I

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Speaker 2: was immediately I can fuck this door handle. And then

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Speaker 2: I got in and then like everything kind of felt

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Speaker 2: like like not substantial, Like when I pulled the door thing,

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Speaker 2: I was like, is this like just PVC pipe? Like

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Speaker 2: the field and synthetic leather. Everything feels very just yeah,

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Speaker 2: not substantial, transitory something like they're just you feel like, yes,

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Speaker 2: it's fast, and if it goes too fast, the car

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Speaker 2: might just kind of like fall apart around you, like

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Speaker 2: a cybertruck where the paneling will just turn it like

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Speaker 2: into an air fin and bend backwards on it. And

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Speaker 2: I feel like we don't even need to talk about those.

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Speaker 2: It's just like, you know, if you drive a cyber truck.

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Speaker 2: That's my honestly, that's like my favorite new Like I

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Speaker 2: was talking about this on the show the other day.

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Speaker 2: My new favorite like form of schodenfreude is watching the

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Speaker 2: people with their cyber trucks, Like, I can't the fuck

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Speaker 2: is wrong with my car. My steering wheel looks like

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Speaker 2: a little batmobile thing, Like my insurance company won't ensure it.

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Speaker 9: I think it's actually fully worthwhile to like. My new

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Speaker 9: thing with cyber trucks is if I see one, I

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Speaker 9: actually turn and point and laugh and see and see

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Speaker 9: if I can ever get the person to like be like.

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Speaker 1: Man, man, we're leaving at Yeah, well yeah it is

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Speaker 1: pretty cool. Uh wow, why are you convulsing?

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Speaker 2: I don't know, but yeah, I don't know.

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Speaker 9: And that you know, obviously the elon thing is is

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Speaker 9: is hard to swallow, and I did. There was a

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Speaker 9: time when I was like, yeah, ev is so cool.

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Speaker 9: And now I'm just like, give us a train. Somebody,

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Speaker 9: give us a train. Somebody give us a fast, cool

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Speaker 9: train like they have in Europe or Japan or whatever,

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Speaker 9: so that I can go somewhere and not have to drive,

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Speaker 9: and also not have my car drive me. I don't

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Speaker 9: really want that to you.

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Speaker 1: I see your train, and I raise you a train

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Speaker 1: tunnel that you can drive in Sea Jack.

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Speaker 2: This is what we don't want.

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Speaker 1: In this house. We believe the Boring Company is the future.

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Speaker 1: And I'm just picture.

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Speaker 9: I'm just picturing your yard with all of your missile signs.

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Speaker 9: It's so many signs.

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Speaker 1: So many signs, and that guy keeps shooting lost in yeah,

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Speaker 1: what what's something you think is underrate it?

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Speaker 2: I don't know.

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Speaker 5: Can I ask my daughter?

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Speaker 2: She don't give the Yeah, okay, she don't give from

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Speaker 2: I won't even okay.

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Speaker 5: Give me something that's overrated? Kids?

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Speaker 1: Kids? Okay, what's underrate abortions? There you go.

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Speaker 2: A juxtaposition, underrated abortions overrated. Oh wow, that's it's your

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Speaker 2: daughter comedian. She came.

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Speaker 1: She was quick with that, she she.

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Speaker 3: Writes on the show.

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Speaker 5: I tell her the time, she need to be a

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Speaker 5: damn comedian.

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Speaker 1: But she's okay, yeah, right when I.

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Speaker 5: Can, I can, let me tell you what I say.

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Speaker 5: They overrated because you don't get the tax break you

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Speaker 5: used to get for yes, could I mean?

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Speaker 1: No?

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Speaker 5: What's his name? Trump changed that? Yeah, we don't get

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Speaker 5: those great tax breaks. Tax break you used to get

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Speaker 5: for being poor. You don't get them anymore. So they

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Speaker 5: saw overrated. You used to get her income createy. It

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Speaker 5: takes a lot to get income creaty. Y'all father the

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Speaker 5: hell I'm talking about? Because you didn't need me a

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Speaker 5: tax break. So, but they used to give you a

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Speaker 5: ship ton of money per child. They don't do that anymore. Right, So, Yeah,

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Speaker 5: I used to tell my kids back in the day,

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Speaker 5: I said, when you're eighteen, I don't get income text

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Speaker 5: return for you, So that means we're done.

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Speaker 2: Right, you have no monetary value? No, I mean yeah,

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Speaker 2: I just have my first child. And I was like,

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Speaker 2: I can't wait to see my taxes. And I was

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Speaker 2: I was like, what it's like I didn't even have Yeah.

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Speaker 2: I was like, this was the reality I was promised.

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Speaker 2: But no, no, no, you said it was purely an investment.

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Speaker 2: You said, exactly, it.

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Speaker 5: Was a on an investment, as you put it in

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Speaker 5: the Mama.

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Speaker 1: That was it very business like. He's very business.

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Speaker 5: And the crazy part is you don't know how they

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Speaker 5: go turn out.

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Speaker 2: Right now, I got a few right here.

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Speaker 5: I have a few kids right here, and be like

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Speaker 5: I kept you.

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Speaker 1: Yeah, but you gotta love him the same. You gotta

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Speaker 1: love him the same. Yeah.

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Speaker 5: I love a lot of my kids. But let me

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Speaker 5: say this as a parent, because I have a lot

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Speaker 5: of parents the same life. Everybody got a favorite kid.

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Speaker 5: Now that doesn't mean that doesn't mean that my mother,

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Speaker 5: father don't love everybody. I have a favorite kid. I

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Speaker 5: love the rest of y'all, but this is right here,

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Speaker 5: it's my favorite.

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Speaker 1: And do you tell them though, yeah, you two my favorite. Wait,

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Speaker 1: why why is you bug your favorite?

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Speaker 5: I think I think because it was my last week,

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Speaker 5: it was ten to two. He's just so sweet. He's

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Speaker 5: my baby. He's twenty eight three. But like that, when

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Speaker 5: I'm just talking.

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Speaker 10: To I'm curious.

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Speaker 2: Yah See, I'm contemplating another child, maybe down the road.

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Speaker 2: And that's my fear is that I wouldn't immediately like

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Speaker 2: start comparing them, be like, oh man, this one ain't

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Speaker 2: ship compared to the other one. Not like in an

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Speaker 2: aggressive way, but that just merely by having multiple kids,

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Speaker 2: you have the ability to sort of compare and contrast

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Speaker 2: and like, and then from there you are kind of like, yeah,

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Speaker 2: maybe I like the other one better, or maybe I

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Speaker 2: like this one better.

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Speaker 5: Everybody like one man, everybody. But you get fake as

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Speaker 5: parents to say, oh my god, I love all of

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Speaker 5: my kids.

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Speaker 2: You don't.

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Speaker 5: One of them is probably smoke, dope. You can't tell

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Speaker 5: me if you got a cracky kid that's your favorite,

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Speaker 5: ain't gonna say you don't love him, and you ain't

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Speaker 5: gonna do anything you can to get him out, dope,

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Speaker 5: But that's your fucking head.

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Speaker 1: Yeah, well, people will tell truth.

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Speaker 5: My oldest used to be my hand and she's straightening up.

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Speaker 3: Now.

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Speaker 1: Yeah, I have two kids, but they're like really close

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Speaker 1: in age, so it's like back and forth and sometimes

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Speaker 1: I can't tell them apart, but they're yeah, back and

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Speaker 1: forth between who the favorite is? Yeah, back and forth

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Speaker 1: between who the favorite is there. But the younger one really,

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Speaker 1: you know, he's he's still a lot sweet, like sweet

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Speaker 1: most of the time, whereas the old rhymes starting to

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Speaker 1: he knows what rolling his eyes means now and that's

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Speaker 1: never forget your first time. You're like, what the fuck

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Speaker 1: is that? All right, let's uh, let's take a quick

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Speaker 1: break and we'll come back and we'll talk about all

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Speaker 1: that cocaine that the President of the United States is

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Speaker 1: going to be snorting tonight. We'll be right back and

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Speaker 1: we're back and all right. So Donald Trump seems worried

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Speaker 1: about the debate that he agreed to after like saying

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Speaker 1: he wasn't gonna do a debate. He yeah, he said

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Speaker 1: he was going to do it.

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Speaker 2: What happened, I Mean, the whole thing was, these were

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Speaker 2: rules you agreed to. It's like they're gonna cut the

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Speaker 2: mics off when you're not talking. They're like, yeah, you

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Speaker 2: agreed to.

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Speaker 1: That yeah, things as you agreed to them.

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Speaker 2: They can't I can't bring up I can only have

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Speaker 2: a water and paper and pen up there. I can't

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Speaker 2: bring my big old books up there and other crap.

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Speaker 2: They're like, yeah, yeah, you can't have any of that.

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Speaker 2: But yeah, It's like, over the last couple of weeks

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Speaker 2: we've heard increased speculation from the right about how Joe

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Speaker 2: Biden is going to be higher than method Man and

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Speaker 2: Red Man on the Blackout album and on Thursday, this

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Speaker 2: is gonna happen on Thursday night when they're scheduled to

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Speaker 2: debate for the first time since obviously twenty twenty. The excuses, though,

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Speaker 2: really began trickling in over the last week, as like,

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Speaker 2: you know, Trump surrogates and people going out there doing

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Speaker 2: the news shows, and Trump himself began like lamenting again

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Speaker 2: the rules he agreed to or how mean Jake Tapper

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Speaker 2: has been to him in the past. And Trump has

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Speaker 2: good reason, I think, to not be excited about sharing

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Speaker 2: the stage because you know, not that poll numbers really matter,

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Speaker 2: but every time he debates a Democrat, like in twenty

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Speaker 2: sixteen and twenty twenty, his numbers dip. When people were

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Speaker 2: just like that juxtapositions a little odd for some people,

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Speaker 2: like this person spoken complete sentences and this guy was

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Speaker 2: stalking a woman around the stage. And while again, pulling

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Speaker 2: is the whole game. It's just like the last time

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Speaker 2: twenty twenty, that first debate with Biden and Trump, it

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Speaker 2: just wasn't a good look because Trump came off looking

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Speaker 2: like a fucking freak next to Joe Biden, who was

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Speaker 2: merely just an old man, like standing on.

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Speaker 1: Stage like he did when there were like primary debates

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Speaker 1: for the Democratic primary, like in twenty twenty. Biden was

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Speaker 1: not like good, No, he's not like a good debater, No, no, yeah, no.

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Speaker 9: My question is like who comes to these debates going like,

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Speaker 9: you know what, I've I've stayed pretty independent up to

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Speaker 9: this point, and I'm just going to see what these

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Speaker 9: two gentlemen.

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Speaker 1: Have to say.

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Speaker 9: Man, Trump guys all about it, and I'm just going

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00:20:06,119 --> 00:20:09,320
Speaker 9: to base it on the issues. You reacht their websites

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Speaker 9: and they seem like they have some they have some differences.

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Speaker 2: I'm a Democrat, but I don't know, man, I just didn't.

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Speaker 2: I didn't really see Trump do his thing like that before.

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Speaker 2: I'm kind of into it now.

401
00:20:19,080 --> 00:20:22,320
Speaker 9: I'm kind of I'm kind of interested in now, and

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00:20:22,359 --> 00:20:24,800
Speaker 9: I think it is like disingenuous, right, Like, I mean,

403
00:20:24,840 --> 00:20:26,520
Speaker 9: do you know people that are like, I don't know, man,

404
00:20:26,560 --> 00:20:28,240
Speaker 9: I just don't know if I can hold my nose

405
00:20:28,280 --> 00:20:30,600
Speaker 9: and vote for Biden. And it's like this, this is

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Speaker 9: a conversation where I'm immediately like, Okay, well then I

407
00:20:34,480 --> 00:20:36,040
Speaker 9: just don't. I don't have a lot of patience for it.

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Speaker 1: You know.

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Speaker 9: It's like it's like turning that thing into like a

410
00:20:39,680 --> 00:20:42,240
Speaker 9: single issue vote where you're you know, I'm like, whatever

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00:20:42,359 --> 00:20:45,200
Speaker 9: your issue with Biden is, I beg you to show

412
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Speaker 9: me like a version that you find a better version

413
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Speaker 9: of your beliefs than Trump.

414
00:20:51,280 --> 00:20:51,480
Speaker 1: Yeah.

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00:20:51,520 --> 00:20:54,000
Speaker 2: I mean, it's just so hard because you're like, especially

416
00:20:54,040 --> 00:20:56,320
Speaker 2: with the Biden stuff, so many people are contending with

417
00:20:56,400 --> 00:20:58,600
Speaker 2: the anger of how the two party system just like

418
00:20:58,760 --> 00:21:01,439
Speaker 2: forces you to be like, obviously I don't want Trump

419
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Speaker 2: to be president totally, but Biden is completely unresponsive to

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Speaker 2: anything that like matters, and what the fuck is this?

421
00:21:08,160 --> 00:21:11,119
Speaker 2: But again, by both parties just trade off being the

422
00:21:11,160 --> 00:21:13,119
Speaker 2: bad guy so then the other one can raise funds

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00:21:13,160 --> 00:21:14,879
Speaker 2: and then you know, they do the Merrick around. But

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00:21:15,160 --> 00:21:19,359
Speaker 2: it's clear Trump is still fucking hooked on doing freestyle jazz,

425
00:21:19,440 --> 00:21:23,520
Speaker 2: talking up there, just flowing on some stream of consciousness, consciousness,

426
00:21:23,600 --> 00:21:26,960
Speaker 2: word association shit. And on Saturday in Philadelphia, I don't

427
00:21:26,960 --> 00:21:29,000
Speaker 2: know if you saw that like epic rant he had

428
00:21:29,080 --> 00:21:31,560
Speaker 2: about water and the sinks and shit like that. A

429
00:21:31,600 --> 00:21:34,880
Speaker 2: lot of like he's he's definitely in his water phase

430
00:21:35,040 --> 00:21:38,240
Speaker 2: between like the batteries and the boats and the sharks

431
00:21:38,640 --> 00:21:42,119
Speaker 2: and like dishwashers, which he also period. Yeah, this is

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Speaker 2: this is water era.

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Speaker 1: And that's something that like young children go like age

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Speaker 1: like three, you go through your water period where like

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00:21:50,160 --> 00:21:51,800
Speaker 1: water is the coolest thing in the world, and you

436
00:21:51,840 --> 00:21:55,160
Speaker 1: have like your little water tables and you kept playing.

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00:21:54,880 --> 00:21:58,720
Speaker 2: With water exactly. He's in his water period at the moment.

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Speaker 2: And if you this is a lot of you were

439
00:22:00,240 --> 00:22:01,680
Speaker 2: talking about this, but just to give you a taste,

440
00:22:01,960 --> 00:22:04,280
Speaker 2: like this is how the guy is talking when he's

441
00:22:04,359 --> 00:22:08,000
Speaker 2: just talking. So try and like imagine this on a

442
00:22:08,040 --> 00:22:08,800
Speaker 2: debate stage.

443
00:22:09,080 --> 00:22:11,119
Speaker 4: No water in your faucets, you ever, try buying a

444
00:22:11,160 --> 00:22:13,480
Speaker 4: new home and you turn on to have restrictors in there.

445
00:22:13,760 --> 00:22:16,040
Speaker 4: You want to wash your hair or you want to

446
00:22:16,160 --> 00:22:18,280
Speaker 4: wash your hands, you turn on the water and it

447
00:22:18,280 --> 00:22:21,920
Speaker 4: goes drip drip the soap. You can't get it off

448
00:22:21,960 --> 00:22:24,720
Speaker 4: your hat, so you keep it running for about ten times.

449
00:22:24,720 --> 00:22:27,160
Speaker 1: We'll get you try. The worst is your hair.

450
00:22:28,080 --> 00:22:30,800
Speaker 11: I have this beautiful, luxuriant.

451
00:22:30,160 --> 00:22:35,280
Speaker 1: Hair, luxuriant, and I put stuff on. I put it

452
00:22:35,320 --> 00:22:36,240
Speaker 1: in hair.

453
00:22:36,440 --> 00:22:38,840
Speaker 4: I like lots of lather because I like it to

454
00:22:38,840 --> 00:22:42,720
Speaker 4: come out extremely dry, because it seems to be slightly

455
00:22:42,760 --> 00:22:43,600
Speaker 4: thicker that way.

456
00:22:44,200 --> 00:22:49,119
Speaker 2: What lack anyway, So this is like a lot of

457
00:22:49,119 --> 00:22:52,600
Speaker 2: people like he's rambling, he's talking. He's trying to talk

458
00:22:52,640 --> 00:22:55,560
Speaker 2: about like water restrictors and shower heads, like this is

459
00:22:55,600 --> 00:22:58,639
Speaker 2: the thing he's talked about before. But again he starts

460
00:22:58,680 --> 00:23:01,600
Speaker 2: off trying to make some point about like what about

461
00:23:01,600 --> 00:23:04,680
Speaker 2: our water, and turns into I like my hair real dry,

462
00:23:04,840 --> 00:23:09,760
Speaker 2: take care that. He's just just freestyle man. He's riffs.

463
00:23:09,800 --> 00:23:10,879
Speaker 2: He's the king of rifts.

464
00:23:11,119 --> 00:23:13,760
Speaker 1: And think about how our toilets can't choke down his

465
00:23:13,960 --> 00:23:17,200
Speaker 1: giant shits. It can't be far behind. It's just a prediction.

466
00:23:17,280 --> 00:23:19,720
Speaker 2: Oh yeah, yeah, yeah, we're talking soon, we got to

467
00:23:19,760 --> 00:23:22,280
Speaker 2: be talking toilets. Like again, we're in the water phase,

468
00:23:22,480 --> 00:23:26,840
Speaker 2: so yeah, something with something, something aquatic will turn up.

469
00:23:27,160 --> 00:23:30,679
Speaker 2: But prior to that performance of ranting, he had an

470
00:23:30,720 --> 00:23:33,960
Speaker 2: interview with some right wing blogger and said that they're like, uh,

471
00:23:34,080 --> 00:23:35,600
Speaker 2: you got this. The guy was asking, like, you got

472
00:23:35,600 --> 00:23:37,760
Speaker 2: this debate coming up? It's pretty intense, and he's like, yeah,

473
00:23:37,760 --> 00:23:40,639
Speaker 2: I'm getting pretty Just listen. This is what Trump was saying,

474
00:23:40,960 --> 00:23:44,080
Speaker 2: like how he's fucking preparing for the debate.

475
00:23:43,960 --> 00:23:47,240
Speaker 1: Being interviewed by a guy completely bald with a beard.

476
00:23:47,600 --> 00:23:49,800
Speaker 1: Just in case that wasn't that's probably clear. I probably

477
00:23:49,800 --> 00:23:50,360
Speaker 1: don't need to.

478
00:23:50,600 --> 00:23:52,639
Speaker 2: Yeah, what a right wing blogger, dude looks like.

479
00:23:52,760 --> 00:23:55,679
Speaker 12: Yeah, Joe Biden at Camp David, as you and I

480
00:23:55,760 --> 00:23:59,600
Speaker 12: stand here, your debate is Thursday with him, no audience,

481
00:24:00,040 --> 00:24:03,280
Speaker 12: CNN controls the mics, Dana Bash, Jake Tapper.

482
00:24:03,920 --> 00:24:05,440
Speaker 2: How do you feel about that matchup?

483
00:24:05,600 --> 00:24:09,359
Speaker 11: Well, it's probably one on three and I've been doing

484
00:24:09,359 --> 00:24:11,639
Speaker 11: this for a long time. Though. We'll handle that. And

485
00:24:11,680 --> 00:24:14,639
Speaker 11: people say, how are you preparing. I'm preparing by taking

486
00:24:14,720 --> 00:24:17,919
Speaker 11: questions from you and others, if you think about it, so,

487
00:24:18,400 --> 00:24:21,840
Speaker 11: but I'm prepared by dealing with you. You tell her

488
00:24:21,880 --> 00:24:22,280
Speaker 11: than all.

489
00:24:22,200 --> 00:24:24,399
Speaker 12: Of them that, Well, it is a real pleasure to

490
00:24:24,400 --> 00:24:25,600
Speaker 12: be here, sir. I know you've got a lot of

491
00:24:25,640 --> 00:24:28,119
Speaker 12: fans waiting, so it's welcome. You to town, sir, and

492
00:24:28,119 --> 00:24:29,040
Speaker 12: thank you so much for your time.

493
00:24:29,040 --> 00:24:30,760
Speaker 11: You've been a great friend. Thank you very much, Chris

494
00:24:30,760 --> 00:24:31,320
Speaker 11: appreciate it.

495
00:24:31,320 --> 00:24:33,080
Speaker 1: Thank you, thank you much for that.

496
00:24:33,119 --> 00:24:35,760
Speaker 2: Guy has like tears in his eye. Yeah. I always

497
00:24:35,840 --> 00:24:39,200
Speaker 2: love name Gath. Yeah he got his name right.

498
00:24:39,359 --> 00:24:43,639
Speaker 1: But yeah, Chris, Yeah, I'm my best friend in this world.

499
00:24:44,119 --> 00:24:45,920
Speaker 2: Now, this is where you know, I think most people

500
00:24:45,960 --> 00:24:49,480
Speaker 2: become very skeptical because if if you go off the

501
00:24:49,560 --> 00:24:51,480
Speaker 2: answer of what he said is debate prep was like,

502
00:24:51,720 --> 00:24:54,320
Speaker 2: it's like talking to people like you. I'm taking questions,

503
00:24:54,320 --> 00:24:56,720
Speaker 2: so in a way, that's preparing, isn't it. And that

504
00:24:56,880 --> 00:24:59,760
Speaker 2: sounds like you're not preparing at all because you're not

505
00:24:59,800 --> 00:25:02,159
Speaker 2: going to go on the debate stage. And if you

506
00:25:02,400 --> 00:25:05,359
Speaker 2: are going on the debate stage, and your version of

507
00:25:05,400 --> 00:25:09,840
Speaker 2: preparing is just like completely whiffing on softballs from sycophants,

508
00:25:10,359 --> 00:25:13,040
Speaker 2: and that's your preparation for the debate of your life.

509
00:25:13,480 --> 00:25:16,399
Speaker 2: I'm again this will be it'll just be straight up

510
00:25:16,480 --> 00:25:18,919
Speaker 2: chaos because he's obviously gonna be getting a ton of

511
00:25:19,040 --> 00:25:23,560
Speaker 2: questions about all his bullshit, like felonies to January sixth,

512
00:25:23,840 --> 00:25:26,679
Speaker 2: asking about Egene Carroll where he may fucking owe her,

513
00:25:26,880 --> 00:25:29,040
Speaker 2: like millions of dollars again by opening his mouth to

514
00:25:29,160 --> 00:25:33,159
Speaker 2: Rico charge his fucking classified documents and talking to a

515
00:25:33,200 --> 00:25:35,320
Speaker 2: guy who would never be like I mean, like you know,

516
00:25:35,480 --> 00:25:39,560
Speaker 2: like did you really why did you have those classified documents?

517
00:25:39,320 --> 00:25:42,879
Speaker 2: That's not gone out. He's not preparing it anyway. This

518
00:25:43,000 --> 00:25:45,120
Speaker 2: is why I think it's going That's why I think

519
00:25:45,160 --> 00:25:48,720
Speaker 2: now we see that there's this like reason that's emerging

520
00:25:48,760 --> 00:25:51,200
Speaker 2: from the right, which is coming from a lot of people,

521
00:25:51,240 --> 00:25:54,800
Speaker 2: including Trump, which is Joe Biden is on fucking crank

522
00:25:55,359 --> 00:25:57,800
Speaker 2: and there's no way he can debate a guy who's

523
00:25:57,840 --> 00:26:00,439
Speaker 2: on fucking speed, even though Trump, Joe.

524
00:26:00,320 --> 00:26:03,400
Speaker 9: Biden is on speed. I mean, this is my prayers

525
00:26:03,480 --> 00:26:05,760
Speaker 9: that Joe Biden does some speed before this debate, because

526
00:26:05,800 --> 00:26:07,959
Speaker 9: I mean, like, as long as we get Joe Biden

527
00:26:08,240 --> 00:26:11,560
Speaker 9: like talking fast and walking quick, I think we win

528
00:26:11,640 --> 00:26:12,040
Speaker 9: this thing.

529
00:26:12,480 --> 00:26:15,080
Speaker 2: And like, hey, have me that computer monitor, Jack and

530
00:26:15,119 --> 00:26:18,600
Speaker 2: a screwdriver. Yeah, see what's going on Porto on I'm

531
00:26:18,680 --> 00:26:22,600
Speaker 2: kind of fucking amped, man. We put Joe Biden, we

532
00:26:22,640 --> 00:26:24,880
Speaker 2: put Joe Biden on some meth, and he will win

533
00:26:24,960 --> 00:26:27,399
Speaker 2: the debate and he'll steal a bunch of copper piping

534
00:26:27,440 --> 00:26:30,600
Speaker 2: out of the I.

535
00:26:30,560 --> 00:26:33,360
Speaker 1: Took the bike apart and I took the spokes out

536
00:26:33,400 --> 00:26:35,640
Speaker 1: of the wheels, but he used to put the spokes back.

537
00:26:37,160 --> 00:26:40,760
Speaker 9: Look, jack anybody can do this. Yeah, it is weird

538
00:26:40,800 --> 00:26:42,840
Speaker 9: to how Trump always stages it like I mean even

539
00:26:42,840 --> 00:26:44,760
Speaker 9: that clip was like a wrestling it like it did

540
00:26:44,800 --> 00:26:46,840
Speaker 9: feel like, well, it's three on one this weekend.

541
00:26:47,760 --> 00:26:50,040
Speaker 1: They were standing in front of a giant American flag,

542
00:26:50,160 --> 00:26:51,640
Speaker 1: like doing the standing interviews.

543
00:26:51,720 --> 00:26:55,399
Speaker 2: Yeah, camera and something, take the mic and go direct

544
00:26:55,400 --> 00:27:02,640
Speaker 2: a camera. But yeah, Ronnie Jackson aka fucking doctor feel Good,

545
00:27:02,640 --> 00:27:05,240
Speaker 2: the old White House doctor who has had everybody pilled

546
00:27:05,320 --> 00:27:06,119
Speaker 2: up in.

547
00:27:06,160 --> 00:27:09,800
Speaker 1: Both administrations by the way, also yeah, the Obama administration. Yeah.

548
00:27:09,840 --> 00:27:12,840
Speaker 2: He submitted a letter as a congressman said quote, I

549
00:27:12,960 --> 00:27:15,439
Speaker 2: demand this is to Joe Biden. I demand that you

550
00:27:15,520 --> 00:27:18,320
Speaker 2: submit to a clinically validated drug test in order to

551
00:27:18,359 --> 00:27:21,520
Speaker 2: reassure the American people that you are mentally fit to

552
00:27:21,600 --> 00:27:26,120
Speaker 2: serve as president and not relying on performance enhancing drugs

553
00:27:26,160 --> 00:27:28,720
Speaker 2: to help you with your debate performance command.

554
00:27:29,760 --> 00:27:31,000
Speaker 1: There is either Queen of England.

555
00:27:31,359 --> 00:27:34,680
Speaker 2: Yeah no, And again like you're saying, Zach, like this

556
00:27:34,760 --> 00:27:36,919
Speaker 2: is this is a guy whose time in the and

557
00:27:36,960 --> 00:27:42,800
Speaker 2: the White House was described as quote a wash in speed. Yes, yeah,

558
00:27:42,840 --> 00:27:45,800
Speaker 2: like everything they said. Apparently this was staffer's popping pills

559
00:27:45,800 --> 00:27:48,000
Speaker 2: and washing them down with alcohol. In large part to

560
00:27:48,080 --> 00:27:51,600
Speaker 2: Jackson's leadership as chief medical Advisor, common poll requests included

561
00:27:51,680 --> 00:27:56,000
Speaker 2: medafinil adderall, fentanyl, morphine, and ketamine, according to a Pentagon

562
00:27:56,040 --> 00:27:58,679
Speaker 2: report release in January, but other unlisted drugs such as

563
00:27:58,720 --> 00:28:00,840
Speaker 2: xanax were equally easy to come by from the White

564
00:28:00,840 --> 00:28:01,720
Speaker 2: House Medical Unit.

565
00:28:02,280 --> 00:28:07,240
Speaker 1: It really takes a step up at a fentodelmorphia. It's like, yeah, wait,

566
00:28:07,280 --> 00:28:09,680
Speaker 1: what for what the the other ones are like, Yeah,

567
00:28:09,680 --> 00:28:12,439
Speaker 1: that's what I expect. The what the White House running on?

568
00:28:12,560 --> 00:28:21,320
Speaker 1: Like like fent like those anymore? Right? Yeah, it's interesting.

569
00:28:21,320 --> 00:28:23,639
Speaker 1: I mean we always talk about how his instinct is

570
00:28:23,680 --> 00:28:26,080
Speaker 1: always to accuse the other people of doing the thing

571
00:28:26,200 --> 00:28:29,879
Speaker 1: he's doing. And he seems so high when he's up

572
00:28:29,880 --> 00:28:33,280
Speaker 1: on the stage, like just the way he's just rambling

573
00:28:33,320 --> 00:28:35,680
Speaker 1: from one thing to the other and just talking about

574
00:28:35,720 --> 00:28:39,280
Speaker 1: how luxuriant his hair is. Like it feels feels like

575
00:28:39,320 --> 00:28:40,920
Speaker 1: he's on like ecstasy or something.

576
00:28:41,000 --> 00:28:43,040
Speaker 2: Yeah, and it just feels like a guy who knows,

577
00:28:43,080 --> 00:28:45,000
Speaker 2: like I gotta get I guess I gotta talk for

578
00:28:45,080 --> 00:28:48,600
Speaker 2: an hour straight. So what I'm just gonna talk about

579
00:28:48,600 --> 00:28:49,480
Speaker 2: whatever the fuck I want.

580
00:28:49,680 --> 00:28:51,920
Speaker 1: Like, you know, he doesn't have to, but he has

581
00:28:52,040 --> 00:28:56,440
Speaker 1: to exactly the only thing that fills the the sucking void.

582
00:28:57,000 --> 00:28:59,400
Speaker 2: A good campaign ad for Joe Biden should just be

583
00:28:59,480 --> 00:29:05,960
Speaker 2: taking Trump transcripts and having someone read back transcripts of

584
00:29:05,960 --> 00:29:09,760
Speaker 2: Trump to potential voters and being like, so, just a

585
00:29:09,840 --> 00:29:14,160
Speaker 2: quick thing when you hear Trump say, you know, because

586
00:29:14,200 --> 00:29:16,240
Speaker 2: if there's a star in the crowd, you know, their

587
00:29:16,280 --> 00:29:18,920
Speaker 2: cameras on my head, the back of the whole time, cameras.

588
00:29:18,960 --> 00:29:21,040
Speaker 2: They're the best. Think about the seats, this is a

589
00:29:21,040 --> 00:29:23,960
Speaker 2: beautiful crowd, and how we're going to get the water,

590
00:29:23,960 --> 00:29:25,160
Speaker 2: and then just be like, so what do you think

591
00:29:25,160 --> 00:29:28,520
Speaker 2: about that? What do you mean? Just and just get

592
00:29:28,560 --> 00:29:31,120
Speaker 2: the reaction. That should be the whole thing.

593
00:29:31,560 --> 00:29:34,200
Speaker 1: I think it's great. I think it's I think it's awesome.

594
00:29:34,240 --> 00:29:36,000
Speaker 2: Manh huh. And what was he saying?

595
00:29:36,720 --> 00:29:39,160
Speaker 1: I don't know, man, I fucking love cameras. Man, They're

596
00:29:39,200 --> 00:29:42,560
Speaker 1: like magic. Just don't get them wet or near a magnet,

597
00:29:42,600 --> 00:29:42,760
Speaker 1: you know.

598
00:29:42,800 --> 00:29:45,520
Speaker 2: Yeah, yeah, sharkle by that camera.

599
00:29:45,760 --> 00:29:50,680
Speaker 1: I do think though, like this is this happens every

600
00:29:50,720 --> 00:29:54,280
Speaker 1: debate where especially the Republicans. This seems to be like

601
00:29:54,480 --> 00:29:58,560
Speaker 1: a piece of accepted wisdom among Republicans, So you really

602
00:29:58,680 --> 00:30:05,840
Speaker 1: need to aggressively attack expectations and that that does tend

603
00:30:06,000 --> 00:30:08,000
Speaker 1: to work. Like That's why I'm just like, is the

604
00:30:08,000 --> 00:30:12,280
Speaker 1: mainstream media just like falling for the same bullshit every

605
00:30:12,360 --> 00:30:14,760
Speaker 1: like that they fall for every time, where like Trump's like,

606
00:30:15,040 --> 00:30:17,400
Speaker 1: Biden's one of the great debaters of our time, and

607
00:30:17,880 --> 00:30:20,840
Speaker 1: he he killed Like at the time, I guess he said,

608
00:30:20,960 --> 00:30:24,240
Speaker 1: like remember when Biden debated Paul Ryan, and everyone was like,

609
00:30:24,280 --> 00:30:29,280
Speaker 1: Biden's gonna get fucking killed, and then Biden like did fine,

610
00:30:29,560 --> 00:30:32,880
Speaker 1: held his own against Paul Ryan, which in retrospect not

611
00:30:33,040 --> 00:30:37,200
Speaker 1: that impressive. Paul Ryan's a fucking dipshit, but that like

612
00:30:37,400 --> 00:30:40,120
Speaker 1: he did better than expectations. So now Trump's like, this

613
00:30:40,200 --> 00:30:41,960
Speaker 1: guy's one of the great debaters of all time and

614
00:30:42,000 --> 00:30:44,560
Speaker 1: he's gonna be so he's gonna be flying on peds

615
00:30:44,680 --> 00:30:47,000
Speaker 1: up there and then he's gonna show up and like

616
00:30:47,400 --> 00:30:50,680
Speaker 1: have the expectations set where he wants them. So I'm

617
00:30:50,720 --> 00:30:53,040
Speaker 1: a little I'm a little like, I don't know, he'll

618
00:30:53,040 --> 00:30:54,880
Speaker 1: probably show up, like it would be such a bad

619
00:30:54,880 --> 00:30:56,680
Speaker 1: look for him, not to show up like I don't

620
00:30:56,680 --> 00:30:59,719
Speaker 1: know he's giving you know, like because this is an

621
00:30:59,800 --> 00:31:01,560
Speaker 1: of these but he's gonna be on drugs.

622
00:31:01,760 --> 00:31:03,720
Speaker 2: I couldn't debate somebody and then he can just be

623
00:31:03,800 --> 00:31:05,560
Speaker 2: like I'm not talking to that speed freak.

624
00:31:05,960 --> 00:31:06,520
Speaker 1: Yeah he won't.

625
00:31:06,560 --> 00:31:08,200
Speaker 2: He won't take a drug test, and I'm not gonna

626
00:31:08,200 --> 00:31:09,920
Speaker 2: play like you know, it would.

627
00:31:09,720 --> 00:31:12,440
Speaker 1: Be a real bad look. It was like, I hope

628
00:31:12,440 --> 00:31:15,600
Speaker 1: he doesn't show up because that seems like a terrible look.

629
00:31:15,720 --> 00:31:18,160
Speaker 2: Is there even? Like because even in this version, right,

630
00:31:18,200 --> 00:31:20,440
Speaker 2: even if he shows up and completely ships the bed

631
00:31:20,600 --> 00:31:26,040
Speaker 2: figuratively or literally, yeah, no one's gonna gonna whatever, it's

632
00:31:26,080 --> 00:31:28,120
Speaker 2: gonna be like whatever, you know what I mean, Like,

633
00:31:28,400 --> 00:31:31,440
Speaker 2: no one's It's so it's hard to know because he's

634
00:31:31,440 --> 00:31:33,880
Speaker 2: tryab to be like I'm not losing anybody, you know

635
00:31:33,920 --> 00:31:35,280
Speaker 2: what I mean, So like what do I have to

636
00:31:35,320 --> 00:31:37,680
Speaker 2: lose if I don't even go up there? But again,

637
00:31:37,800 --> 00:31:39,960
Speaker 2: I know he wants to start wind milling about the

638
00:31:40,000 --> 00:31:44,760
Speaker 2: fucking like like immigrants or killing people angle, and that's

639
00:31:44,800 --> 00:31:47,200
Speaker 2: gonna be a moment for him to sort of, you know,

640
00:31:47,400 --> 00:31:50,080
Speaker 2: try and press Biden on something like that. But I

641
00:31:50,080 --> 00:31:51,960
Speaker 2: don't know. At the end of the day based on

642
00:31:52,080 --> 00:31:54,920
Speaker 2: like how I don't know, just he just seems very

643
00:31:55,160 --> 00:31:58,000
Speaker 2: like he's just not into it. But look, we don't

644
00:31:58,000 --> 00:31:58,480
Speaker 2: fucking know.

645
00:31:58,880 --> 00:32:04,600
Speaker 13: But I feel like maybe the debate polls are it

646
00:32:04,680 --> 00:32:07,000
Speaker 13: might be like like a thing with like the pepsi

647
00:32:07,200 --> 00:32:11,760
Speaker 13: taste tests, where you know, pepsi would win taste tests

648
00:32:11,880 --> 00:32:15,120
Speaker 13: when it was like a little sip of pepsi versus

649
00:32:15,120 --> 00:32:17,480
Speaker 13: a little sip of coke, but like you can't drink

650
00:32:17,520 --> 00:32:21,640
Speaker 13: a whole glass of pepsi without your teeth falling out feeling.

651
00:32:21,360 --> 00:32:25,000
Speaker 1: Like they're vibrating. Like I just feel like you're testing

652
00:32:25,000 --> 00:32:29,320
Speaker 1: for different things. And like the he always successfully like

653
00:32:29,440 --> 00:32:33,880
Speaker 1: makes it horribly ugly in any debate he's in, Like

654
00:32:33,960 --> 00:32:37,240
Speaker 1: I'd never leave the debate being like, well he just

655
00:32:37,280 --> 00:32:40,040
Speaker 1: got his ass kicked, you know, it's always so I

656
00:32:40,880 --> 00:32:44,200
Speaker 1: just feel like some of this is like people like

657
00:32:44,560 --> 00:32:46,840
Speaker 1: wishful thinking that he's not going to show up, that

658
00:32:46,880 --> 00:32:49,600
Speaker 1: he's going to show up and just like suck. I

659
00:32:49,600 --> 00:32:52,080
Speaker 1: don't like, I feel like it could go the other

660
00:32:52,200 --> 00:32:55,560
Speaker 1: direction pretty easily. Not that that like this is just

661
00:32:55,640 --> 00:32:59,120
Speaker 1: also me, like this is the same comportment I take

662
00:32:59,160 --> 00:33:02,080
Speaker 1: into my sports fandom, where I'm like we suck, We're

663
00:33:02,080 --> 00:33:06,680
Speaker 1: gonna lose forty points. But it does feel like, I

664
00:33:06,680 --> 00:33:11,240
Speaker 1: don't know, it could go badly for Biden. Oh, given

665
00:33:11,360 --> 00:33:13,760
Speaker 1: what we've seen of him speaking temporaneous.

666
00:33:13,880 --> 00:33:17,280
Speaker 2: Yeah, no mistake, they're both. No, I don't know who

667
00:33:17,320 --> 00:33:20,920
Speaker 2: a favorite is going into this, because just as easily

668
00:33:21,160 --> 00:33:23,240
Speaker 2: Trump can just suck all the fucking air out of

669
00:33:23,280 --> 00:33:26,640
Speaker 2: the room and just keep these like these same things.

670
00:33:27,040 --> 00:33:29,680
Speaker 2: And then Biden's probably like I need a nap. Who knows,

671
00:33:29,800 --> 00:33:31,200
Speaker 2: like what the fuck's gonna happen.

672
00:33:30,960 --> 00:33:33,440
Speaker 1: But anything asks for an actual nap.

673
00:33:34,120 --> 00:33:39,120
Speaker 2: Oh god, Okay, he's like time out, man, time out?

674
00:33:38,720 --> 00:33:39,960
Speaker 1: Can we get like it?

675
00:33:40,040 --> 00:33:41,400
Speaker 2: I need a nap and a caramel?

676
00:33:41,920 --> 00:33:42,120
Speaker 1: Yeah?

677
00:33:42,240 --> 00:33:46,240
Speaker 9: Yeah, yeah, I mean maybe Biden, maybe Biden. Maybe the

678
00:33:46,240 --> 00:33:49,880
Speaker 9: best plan for Biden is uh yeah, let let Trump

679
00:33:49,920 --> 00:33:54,400
Speaker 9: talk more and and and also get on some performance

680
00:33:54,480 --> 00:33:57,400
Speaker 9: enhancing drugs that make him like super ripped. I mean, like,

681
00:33:57,600 --> 00:33:59,640
Speaker 9: can we get him on HGH at this point or

682
00:33:59,640 --> 00:34:02,520
Speaker 9: something so that yeah, looks like the best self?

683
00:34:03,000 --> 00:34:06,120
Speaker 2: How quickly can he look like a light heavyweight MMA

684
00:34:06,240 --> 00:34:06,960
Speaker 2: fighter physically?

685
00:34:07,000 --> 00:34:07,680
Speaker 1: Yeah?

686
00:34:07,720 --> 00:34:10,760
Speaker 2: And I'm sure I'm sure Joe Rogan's got some tips,

687
00:34:10,760 --> 00:34:13,480
Speaker 2: So like, let's let's get let's get him just shredded

688
00:34:13,520 --> 00:34:14,200
Speaker 2: for this one.

689
00:34:14,560 --> 00:34:17,640
Speaker 1: Get his organs to grow, and that would actually be

690
00:34:17,760 --> 00:34:21,120
Speaker 1: the one thing that Trump would respond to because as we.

691
00:34:21,120 --> 00:34:24,479
Speaker 2: Know, like he's terrified. Oh my god, guy.

692
00:34:24,440 --> 00:34:26,800
Speaker 1: Came out there with arms like Christmas hams.

693
00:34:27,040 --> 00:34:30,720
Speaker 2: He's wearing a smaller suit jacket, isn't he They're bulging

694
00:34:30,760 --> 00:34:33,520
Speaker 2: out of the sleeves. No, no, no, no, no, no not.

695
00:34:33,719 --> 00:34:35,719
Speaker 2: He looks like right out of Central Casting. Trump love

696
00:34:35,800 --> 00:34:39,080
Speaker 2: Central Casting. He does you get a super strong president.

697
00:34:39,160 --> 00:34:40,040
Speaker 2: Trump's gonna like it.

698
00:34:40,200 --> 00:34:43,160
Speaker 1: And these guys they have big muscles, maybe not so

699
00:34:43,239 --> 00:34:45,240
Speaker 1: much down here and here, but being up.

700
00:34:45,080 --> 00:34:47,840
Speaker 2: Here up here, Yeah, huge brains.

701
00:34:48,520 --> 00:34:51,160
Speaker 1: All right, let's uh, let's take a quick break. We'll

702
00:34:51,160 --> 00:34:53,560
Speaker 1: come back. We'll talk a little pop culture. We'll be

703
00:34:53,680 --> 00:35:07,960
Speaker 1: right back. And we're back. We're back, We're back. And

704
00:35:08,840 --> 00:35:11,520
Speaker 1: on your show, you've done some good stuff on just

705
00:35:11,760 --> 00:35:16,640
Speaker 1: the surveillance side of AI, which I mean that turns

706
00:35:16,640 --> 00:35:20,800
Speaker 1: out a lot of the technology that we initially thought

707
00:35:21,040 --> 00:35:26,000
Speaker 1: was promising was just eventually used for the purposes of

708
00:35:26,000 --> 00:35:29,400
Speaker 1: marketing and surveillance in the end, And it seems like

709
00:35:29,480 --> 00:35:32,680
Speaker 1: AI skipped all the promising stuff. And it's just like,

710
00:35:32,800 --> 00:35:35,920
Speaker 1: what if we just went right to the right, the

711
00:35:36,840 --> 00:35:38,920
Speaker 1: harming harming people.

712
00:35:39,360 --> 00:35:42,160
Speaker 6: Yeah, I will, I will say that kind of. I

713
00:35:42,160 --> 00:35:46,120
Speaker 6: mean you had mentioned that this term AI is kind

714
00:35:46,160 --> 00:35:49,480
Speaker 6: of being used, Lucy Goosey, and you know, I mean

715
00:35:49,800 --> 00:35:54,080
Speaker 6: we we AI is kind of synonymous with large language

716
00:35:54,120 --> 00:35:57,279
Speaker 6: models and image generators. But you know, things that have

717
00:35:57,400 --> 00:36:04,680
Speaker 6: been called AI also encompass things like biometrics, surveillance, like

718
00:36:04,680 --> 00:36:09,480
Speaker 6: like different different systems which use this technology called quoteunquote

719
00:36:09,480 --> 00:36:13,240
Speaker 6: machine learning and which is kind of this large scale

720
00:36:13,320 --> 00:36:14,840
Speaker 6: patterned recognition.

721
00:36:15,000 --> 00:36:16,319
Speaker 2: So a lot of.

722
00:36:16,320 --> 00:36:20,760
Speaker 6: It's being used, especially at the border, so doing things

723
00:36:20,840 --> 00:36:26,440
Speaker 6: like trying to detect verify identities by voices or by faces.

724
00:36:27,160 --> 00:36:29,080
Speaker 6: You probably see this if you've been in the airport.

725
00:36:29,160 --> 00:36:32,479
Speaker 6: The TSA has been using this and you can still

726
00:36:32,560 --> 00:36:36,359
Speaker 6: voluntarily opt out for now, but they're really incentivizing it.

727
00:36:36,680 --> 00:36:39,759
Speaker 6: I saw that TSA has this touchless thing now, which

728
00:36:39,800 --> 00:36:42,480
Speaker 6: is this facial recognition, so you don't have to present

729
00:36:42,520 --> 00:36:45,520
Speaker 6: your ID, you can just scan your face and go and.

730
00:36:46,800 --> 00:36:49,960
Speaker 7: Like don't do that, like take every option to opt out,

731
00:36:50,000 --> 00:36:52,120
Speaker 7: and that the fact that those signs are there saying

732
00:36:52,120 --> 00:36:57,040
Speaker 7: that this is optional, was it, Penny somebody actually Petty? Yeah, Yeah,

733
00:36:57,200 --> 00:36:59,080
Speaker 7: The only reason we had that science is because of

734
00:36:59,080 --> 00:37:01,680
Speaker 7: her activism saying like this has to be clear to

735
00:37:01,680 --> 00:37:03,879
Speaker 7: the travelers. That's actually optional and you can opt out,

736
00:37:04,160 --> 00:37:05,480
Speaker 7: so it's posted there that you.

737
00:37:05,400 --> 00:37:06,319
Speaker 8: Don't have to do this.

738
00:37:07,000 --> 00:37:09,720
Speaker 1: Yeah, all right, then I'm gonna feel you up. Sorry,

739
00:37:09,840 --> 00:37:10,960
Speaker 1: those are just the rules.

740
00:37:11,400 --> 00:37:14,319
Speaker 6: Yeah, it's just it's absolutely but I mean it gets,

741
00:37:14,440 --> 00:37:18,960
Speaker 6: it gets, you know, leveraged against people who fly to

742
00:37:19,040 --> 00:37:22,480
Speaker 6: a lesser degree. But I mean folks who are refugees

743
00:37:22,600 --> 00:37:26,480
Speaker 6: orsile's you know, I mean people on the move really

744
00:37:26,560 --> 00:37:30,200
Speaker 6: encounter to stuff incredibly violent ways. You know, they do

745
00:37:30,360 --> 00:37:34,719
Speaker 6: things like try to they take their blood and say that,

746
00:37:34,760 --> 00:37:38,359
Speaker 6: well we can we can associate your we're gonna you know,

747
00:37:38,480 --> 00:37:41,880
Speaker 6: sequence your genome and safe you're actually from the country

748
00:37:41,880 --> 00:37:44,680
Speaker 6: you say you're from, which is first, it's pseudoscience. I

749
00:37:44,719 --> 00:37:48,600
Speaker 6: mean basically all biologists have been like, you can't use

750
00:37:48,640 --> 00:37:53,080
Speaker 6: this and determine if someone is X, y Z, like nationality,

751
00:37:53,160 --> 00:37:57,920
Speaker 6: because nationalities are one political entities, they're not biological ones,

752
00:37:58,680 --> 00:38:01,719
Speaker 6: and so like we can sort of pinpoint you to

753
00:38:01,800 --> 00:38:05,560
Speaker 6: a region, but it says nothing to say of anything

754
00:38:05,560 --> 00:38:08,919
Speaker 6: about the political borders of a country. There's a great

755
00:38:08,960 --> 00:38:13,200
Speaker 6: book I started reading by Petro Molner, which is called

756
00:38:13,239 --> 00:38:16,480
Speaker 6: The Walls Have Eyes, which is about this kind of

757
00:38:17,239 --> 00:38:22,480
Speaker 6: intense surveillance state or intense surveillance architecture. You know, it's

758
00:38:22,520 --> 00:38:26,120
Speaker 6: being used in you know, typically in the border, the

759
00:38:26,239 --> 00:38:30,720
Speaker 6: US Mexico border, but also the you know, the various

760
00:38:30,719 --> 00:38:36,279
Speaker 6: points of entry in Europe where African migrants are fleeing to,

761
00:38:36,840 --> 00:38:40,000
Speaker 6: you know, fleeing you know, places like Sudan and Congo

762
00:38:40,360 --> 00:38:44,640
Speaker 6: and the Tagrai region of Ethiopia. So just like and

763
00:38:44,760 --> 00:38:46,719
Speaker 6: this is just some of the most violent kind of

764
00:38:46,760 --> 00:38:49,799
Speaker 6: stuff he in it you can imagine, and it's way

765
00:38:49,880 --> 00:38:53,080
Speaker 6: far away from you know, this kind of Oh, here's

766
00:38:53,200 --> 00:38:56,480
Speaker 6: like a fake little child, you know, or a Jesus

767
00:38:57,280 --> 00:39:00,560
Speaker 6: holding twelve thousand babies writing in America truck with the

768
00:39:00,600 --> 00:39:04,319
Speaker 6: American flag on it, you know, I mean, right, So

769
00:39:04,360 --> 00:39:07,280
Speaker 6: the reality is much more stark.

770
00:39:08,239 --> 00:39:10,920
Speaker 7: And you see that you see that one too many

771
00:39:11,640 --> 00:39:14,719
Speaker 7: image matching, so you get all these false arrests. So

772
00:39:14,760 --> 00:39:17,400
Speaker 7: people because the AI said that they matched the image

773
00:39:17,400 --> 00:39:20,239
Speaker 7: from the grain surveillance video. And it's one of these

774
00:39:20,239 --> 00:39:24,480
Speaker 7: things where it's bad if it works, because you have

775
00:39:24,560 --> 00:39:27,160
Speaker 7: this like increased surveillance power of the state and it's

776
00:39:27,160 --> 00:39:28,759
Speaker 7: bad if it doesn't work, because you get all these

777
00:39:28,840 --> 00:39:31,040
Speaker 7: false arrests, like it's it's just a bad idea, it's

778
00:39:31,080 --> 00:39:33,480
Speaker 7: just a don't and it's not just image stuff. So

779
00:39:34,040 --> 00:39:37,520
Speaker 7: we read a while back about a situation in Germany,

780
00:39:37,560 --> 00:39:41,719
Speaker 7: I think, where asylum seekers were being vetted as to

781
00:39:41,760 --> 00:39:45,440
Speaker 7: whether or not they spoke the right language using you know,

782
00:39:45,560 --> 00:39:47,000
Speaker 7: so one of the things you can do with pattern

783
00:39:47,040 --> 00:39:51,960
Speaker 7: matching is okay, language identification, that this string what languages

784
00:39:52,000 --> 00:39:54,480
Speaker 7: that come from. But it was being done based on

785
00:39:54,640 --> 00:39:57,960
Speaker 7: completely inadequate data sets by people who don't speak to languages,

786
00:39:57,960 --> 00:40:00,560
Speaker 7: who are not in a position to actually vet the

787
00:40:00,560 --> 00:40:02,439
Speaker 7: output of the machine. And so you have these folks

788
00:40:02,440 --> 00:40:04,799
Speaker 7: who are in the worst imaginable situation, like you don't

789
00:40:05,120 --> 00:40:09,560
Speaker 7: go seeking asylum on a lark, right the other people.

790
00:40:10,160 --> 00:40:10,960
Speaker 2: I broke at home?

791
00:40:11,160 --> 00:40:11,399
Speaker 1: Yeah?

792
00:40:11,760 --> 00:40:17,319
Speaker 7: Yeah, And then they're getting denied because some algorithms said, oh,

793
00:40:17,360 --> 00:40:19,280
Speaker 7: you don't speak the language from the place you claim

794
00:40:19,320 --> 00:40:22,440
Speaker 7: to be coming from where the person your accent is

795
00:40:22,440 --> 00:40:24,560
Speaker 7: wrong or your variety is wrong or whatever. And the

796
00:40:24,600 --> 00:40:27,879
Speaker 7: person who's who's run this computer system has no way

797
00:40:27,920 --> 00:40:30,000
Speaker 7: of actually checking its output, but they believe it, and

798
00:40:30,000 --> 00:40:31,720
Speaker 7: then they get these asylum seekers turned away.

799
00:40:31,960 --> 00:40:34,880
Speaker 2: Yeah, so how does that you know? With everything you said,

800
00:40:35,040 --> 00:40:39,279
Speaker 2: how should we feel that Open AI recently welcome to

801
00:40:39,360 --> 00:40:44,440
Speaker 2: their board? The eighteenth director of the NSA, Paul nakasone,

802
00:40:45,280 --> 00:40:49,319
Speaker 2: is that bad? Or what should we take from that one?

803
00:40:49,520 --> 00:40:51,799
Speaker 8: How should we feel not at all surprised?

804
00:40:51,920 --> 00:40:52,080
Speaker 1: Right?

805
00:40:52,120 --> 00:40:53,920
Speaker 7: How should we feel when open AI? It's like, okay,

806
00:40:53,960 --> 00:40:55,839
Speaker 7: bad is whatever the rest of that is is bad?

807
00:40:56,080 --> 00:40:56,279
Speaker 1: Yeah?

808
00:40:56,280 --> 00:40:58,280
Speaker 6: It seems bad, man, Yeah.

809
00:41:00,040 --> 00:41:02,360
Speaker 2: It seems like there's again we're talking like this technology

810
00:41:02,400 --> 00:41:05,200
Speaker 2: to mass surveillance pipeline, and who better than someone who

811
00:41:05,239 --> 00:41:08,440
Speaker 2: ran the fucking nssay, Like, and I know the way

812
00:41:08,480 --> 00:41:10,719
Speaker 2: it's being spun. It's like, you know, this is a

813
00:41:10,760 --> 00:41:13,560
Speaker 2: part of cyber command, like he inherently knows like how

814
00:41:13,640 --> 00:41:16,719
Speaker 2: what the what the guardbrails need to be in terms

815
00:41:16,760 --> 00:41:19,080
Speaker 2: of keeping us safe. But to me, it's just feel like, no,

816
00:41:19,200 --> 00:41:22,560
Speaker 2: you brought in a surveillance pro, not someone who understands

817
00:41:22,560 --> 00:41:25,680
Speaker 2: inherently like what this specific technology is, but more someone

818
00:41:25,680 --> 00:41:29,120
Speaker 2: who's like learns how to harness technology for this other

819
00:41:29,200 --> 00:41:29,960
Speaker 2: specific aid.

820
00:41:30,960 --> 00:41:34,560
Speaker 7: Yeah, so surveillance is not synonymous with safety like the

821
00:41:34,600 --> 00:41:36,920
Speaker 7: one the one kind of one use case for the

822
00:41:36,920 --> 00:41:39,760
Speaker 7: word surveillance that I think actually was pro public safety

823
00:41:40,160 --> 00:41:42,600
Speaker 7: is there's a study, the long term study in Seattle

824
00:41:42,600 --> 00:41:46,000
Speaker 7: called the Seattle Flu Study, and they are doing what

825
00:41:46,000 --> 00:41:48,880
Speaker 7: they call surveillance testing for flu viruses. So they get

826
00:41:48,960 --> 00:41:51,000
Speaker 7: volunteers to come in and get swabbed, and they are

827
00:41:51,080 --> 00:41:54,040
Speaker 7: keeping track of what viruses are circulating in our community. Right,

828
00:41:54,440 --> 00:41:57,080
Speaker 7: I'm all for surveilling the viruses, especially if you can

829
00:41:57,120 --> 00:41:57,920
Speaker 7: keep the people out of it.

830
00:41:58,080 --> 00:41:59,160
Speaker 1: Yeah, I would. I would.

831
00:41:59,280 --> 00:42:01,360
Speaker 6: I would add a cult to that just because I

832
00:42:01,360 --> 00:42:03,400
Speaker 6: think that I mean there's a lot of surveillance. I mean,

833
00:42:03,440 --> 00:42:05,480
Speaker 6: that's the kind of technology, that's the kind of terminology

834
00:42:05,520 --> 00:42:08,960
Speaker 6: they use of health surveillance to detect kind of virus

835
00:42:09,040 --> 00:42:11,759
Speaker 6: rates and whatnot. I would also add the wrinkle that

836
00:42:11,920 --> 00:42:15,200
Speaker 6: like a lot of those you know, organizations are really

837
00:42:15,200 --> 00:42:18,080
Speaker 6: trusted by distrusted by marginalized people, Like what are you.

838
00:42:18,120 --> 00:42:18,440
Speaker 2: Going to do?

839
00:42:18,440 --> 00:42:21,680
Speaker 6: What to be you know, like especially thinking like you know,

840
00:42:21,840 --> 00:42:25,279
Speaker 6: like lots of lots of transphoks and like especially like

841
00:42:26,040 --> 00:42:28,600
Speaker 6: under housed or unhoused transfers, and just like you're going

842
00:42:28,640 --> 00:42:30,160
Speaker 6: to do what you want this data fond me for

843
00:42:30,200 --> 00:42:30,840
Speaker 6: who you know?

844
00:42:31,160 --> 00:42:31,359
Speaker 1: Right?

845
00:42:31,680 --> 00:42:36,200
Speaker 7: So, yeah, understanding especially because because surveillance in general, like

846
00:42:36,280 --> 00:42:39,239
Speaker 7: is not a safety thing, right, It's not. It is

847
00:42:39,520 --> 00:42:43,200
Speaker 7: maybe a like safety for people within the walls of

848
00:42:43,200 --> 00:42:45,719
Speaker 7: the walled garden thing, but that's not safety, right. That's

849
00:42:47,200 --> 00:42:50,200
Speaker 7: the other thing about this is that what we call

850
00:42:50,280 --> 00:42:53,759
Speaker 7: AI these days is predicated on enormous data collection, right,

851
00:42:53,800 --> 00:42:55,680
Speaker 7: And so to one eccent, it's the sort of an

852
00:42:55,719 --> 00:42:58,840
Speaker 7: excuse to go about claiming access to all that data.

853
00:42:59,320 --> 00:43:01,239
Speaker 7: And once you have access all that data, you can

854
00:43:01,239 --> 00:43:02,799
Speaker 7: do things with it that have nothing to do with

855
00:43:02,840 --> 00:43:06,000
Speaker 7: the large language models. And so there's you know, this

856
00:43:06,080 --> 00:43:09,520
Speaker 7: is I think less typically less immediately like threatening to

857
00:43:09,600 --> 00:43:12,759
Speaker 7: life and limb than the applications that Alex was starting with.

858
00:43:12,800 --> 00:43:15,280
Speaker 7: But there's a lot of stuff where it's like, actually,

859
00:43:15,360 --> 00:43:18,240
Speaker 7: we would be better off without all that information about

860
00:43:18,320 --> 00:43:21,480
Speaker 7: us being out there. And there's an example that came

861
00:43:21,480 --> 00:43:23,120
Speaker 7: out recently. So did you see this thing about the

862
00:43:23,160 --> 00:43:27,799
Speaker 7: system called recall that came out with Windows eleven? So

863
00:43:28,239 --> 00:43:30,279
Speaker 7: this thing, oh god, this is such a mess. So

864
00:43:30,840 --> 00:43:32,640
Speaker 7: initially it was going to be by default turned on.

865
00:43:32,920 --> 00:43:35,560
Speaker 2: Oh yes, yeah, this is kind of like the Adobe

866
00:43:35,600 --> 00:43:36,359
Speaker 2: story too. Yeah.

867
00:43:36,640 --> 00:43:38,600
Speaker 7: Yeah, every five seconds, it takes a picture of your

868
00:43:38,640 --> 00:43:41,520
Speaker 7: screen and then you can use that to like using

869
00:43:41,560 --> 00:43:44,080
Speaker 7: AI search for stuff that you've sort of and their

870
00:43:44,120 --> 00:43:46,160
Speaker 7: example is something stupid. It's like, yeah, I saw a recipe,

871
00:43:46,160 --> 00:43:47,360
Speaker 7: but I don't remember where I saw it, so you

872
00:43:47,400 --> 00:43:49,120
Speaker 7: want to be able to search back through your activity

873
00:43:49,360 --> 00:43:52,520
Speaker 7: and like zero thought to what this means for people

874
00:43:52,560 --> 00:43:56,719
Speaker 7: who are victims of intimate partner violence, right that they

875
00:43:56,920 --> 00:44:00,520
Speaker 7: have this surveillance going on in their computer that eventually

876
00:44:00,560 --> 00:44:03,480
Speaker 7: it ended up being shipped as off by default because

877
00:44:03,480 --> 00:44:05,759
Speaker 7: the cybersecurity folks pushed back really hard.

878
00:44:06,040 --> 00:44:07,760
Speaker 8: And by folks, I don't mean the people at Microsoft.

879
00:44:07,760 --> 00:44:09,239
Speaker 7: I mean the people out in the world or saw

880
00:44:09,239 --> 00:44:12,960
Speaker 7: this coming. Yeah, but that's another example of like surveillance

881
00:44:13,000 --> 00:44:15,600
Speaker 7: in the name of AI that's supposed to be the

882
00:44:15,680 --> 00:44:18,200
Speaker 7: sort of, you know, helpful little thing for you, but

883
00:44:18,280 --> 00:44:20,040
Speaker 7: like no thought to what that means for people. And

884
00:44:20,040 --> 00:44:21,160
Speaker 7: it's like, yeah, we're just going to turn this on

885
00:44:21,200 --> 00:44:23,680
Speaker 7: by default because everybody wants this obviously, right.

886
00:44:24,000 --> 00:44:26,840
Speaker 2: It's like, no, I know how to look through my history.

887
00:44:27,040 --> 00:44:30,600
Speaker 2: Actually I've developed that skill. Yeah, I don't need you

888
00:44:30,680 --> 00:44:33,560
Speaker 2: to take snapshots of my desktop every three yeads.

889
00:44:33,719 --> 00:44:36,760
Speaker 1: But your show has covered so many kind of upsetting

890
00:44:36,760 --> 00:44:40,160
Speaker 1: ways that it doesn't seem like it's people implementing AI.

891
00:44:40,239 --> 00:44:43,400
Speaker 1: It's companies implementing AI in a lot of cases to

892
00:44:44,640 --> 00:44:47,879
Speaker 1: do jobs that it's not capable of doing. There there's

893
00:44:47,920 --> 00:44:53,440
Speaker 1: been incorrect obituaries. Grock, the Elon musk Won the Twitter

894
00:44:53,480 --> 00:44:57,160
Speaker 1: one made up fake headlines about Iran attacking Israel and

895
00:44:57,280 --> 00:44:59,920
Speaker 1: like public like put them out as like a made

896
00:45:00,040 --> 00:45:03,520
Speaker 1: your trending story. You have this great anecdote about a

897
00:45:03,560 --> 00:45:07,880
Speaker 1: Facebook chatbot AI like responding to someone had this like

898
00:45:08,000 --> 00:45:11,040
Speaker 1: very specific question they have like a gifted disabled child.

899
00:45:11,480 --> 00:45:14,440
Speaker 1: They were like, he, does anybody have experience with a

900
00:45:14,520 --> 00:45:18,799
Speaker 1: gifted disabled like two E child with like this specific

901
00:45:19,200 --> 00:45:24,160
Speaker 1: New York public school program. And the chatbot responds, yes,

902
00:45:24,200 --> 00:45:26,480
Speaker 1: I have experience with that, and just like made up

903
00:45:26,760 --> 00:45:29,760
Speaker 1: because they knew that's what that's what they wanted to hear.

904
00:45:30,000 --> 00:45:33,280
Speaker 1: And fortunately it was like clearly labeled as an AI chatbot.

905
00:45:33,360 --> 00:45:36,960
Speaker 1: So the person was like, what what the black mirror? Yeah,

906
00:45:38,239 --> 00:45:42,920
Speaker 1: but World Health Organization, you know, eating disorder institutions replacing

907
00:45:43,040 --> 00:45:49,120
Speaker 1: therapists with AI, Like you just have all these examples

908
00:45:49,560 --> 00:45:53,560
Speaker 1: of this going being used where it shouldn't be and

909
00:45:53,680 --> 00:45:59,120
Speaker 1: things going badly, and like that. There's a detail that

910
00:45:59,160 --> 00:46:01,400
Speaker 1: I think we talked about last time about dual lingo,

911
00:46:02,200 --> 00:46:08,400
Speaker 1: where the model where they let AI take over some

912
00:46:08,480 --> 00:46:10,960
Speaker 1: of the stuff that like human teachers and translators were

913
00:46:10,960 --> 00:46:15,359
Speaker 1: doing before. And you made the point that people who

914
00:46:15,400 --> 00:46:17,799
Speaker 1: are learning the language, who are beginners, are not in

915
00:46:17,840 --> 00:46:21,879
Speaker 1: a position to notice that the quality has dropped. Yeah,

916
00:46:21,960 --> 00:46:24,799
Speaker 1: And I feel like that's what we're seeing basically everywhere

917
00:46:24,880 --> 00:46:28,440
Speaker 1: now is just the Internet is so big, they're just

918
00:46:28,640 --> 00:46:32,880
Speaker 1: using it so many different places that it's hard to

919
00:46:33,000 --> 00:46:36,160
Speaker 1: catch them all, and then there's not an appetite to

920
00:46:37,320 --> 00:46:41,040
Speaker 1: report on all the ways it's fucking up, and so

921
00:46:41,760 --> 00:46:46,400
Speaker 1: it just everything is kind of getting slightly too drastically

922
00:46:46,480 --> 00:46:50,920
Speaker 1: shittier at once. Yeah, and I don't know what to

923
00:46:50,960 --> 00:46:51,800
Speaker 1: do with that.

924
00:46:52,560 --> 00:46:55,360
Speaker 6: I would say, yeah, well go ahead, Emily.

925
00:46:55,719 --> 00:46:57,000
Speaker 8: What you do with that do is you make fun

926
00:46:57,040 --> 00:46:57,200
Speaker 8: of it.

927
00:46:57,280 --> 00:46:59,719
Speaker 7: That's one of our things is ridiculous process to like,

928
00:47:00,120 --> 00:47:02,279
Speaker 7: you know, try to try to keep the mood up,

929
00:47:02,320 --> 00:47:03,440
Speaker 7: but also just show it for.

930
00:47:03,400 --> 00:47:04,759
Speaker 8: How ridiculous it is.

931
00:47:05,520 --> 00:47:07,600
Speaker 7: And then the other thing is to really seek out

932
00:47:07,880 --> 00:47:10,040
Speaker 7: the good journalism on this topic, because so much of

933
00:47:10,080 --> 00:47:13,080
Speaker 7: it is either fake journalism output by a large language

934
00:47:13,080 --> 00:47:17,880
Speaker 7: model these days, or journalists who are basically practicing access journalism,

935
00:47:17,920 --> 00:47:20,080
Speaker 7: who are doing the g's thing, who who are reproducing

936
00:47:20,080 --> 00:47:22,439
Speaker 7: press releases, and so finding the people who are doing

937
00:47:22,480 --> 00:47:24,640
Speaker 7: really good critical work and like supporting them, I think

938
00:47:24,680 --> 00:47:25,440
Speaker 7: is super important.

939
00:47:26,280 --> 00:47:27,759
Speaker 6: You we're going to say, well, I was, well, though,

940
00:47:27,800 --> 00:47:29,480
Speaker 6: you just teed me up really well, because I was

941
00:47:29,520 --> 00:47:31,719
Speaker 6: actually going to say, you know, some of the people

942
00:47:31,719 --> 00:47:33,640
Speaker 6: who are doing some of the best work on it

943
00:47:33,680 --> 00:47:36,840
Speaker 6: are like four or four Media, and you know, I

944
00:47:36,920 --> 00:47:38,560
Speaker 6: want to give a shout out to them because they're

945
00:47:38,600 --> 00:47:42,799
Speaker 6: you know, these folks are basically you know, they were

946
00:47:42,800 --> 00:47:48,120
Speaker 6: at Motherboard and Motherboard, you know, or the whole Vice

947
00:47:49,120 --> 00:47:54,560
Speaker 6: Empire was basically you know, Sunset, and so they laid

948
00:47:54,600 --> 00:47:57,120
Speaker 6: off a bunch of people. So they started this kind

949
00:47:57,120 --> 00:48:00,279
Speaker 6: of journalist owned and operated place, and you know that

950
00:48:00,320 --> 00:48:04,640
Speaker 6: focuses specifically on tech and AI and these folks have

951
00:48:04,800 --> 00:48:08,919
Speaker 6: been kind of in the game for so long they

952
00:48:09,120 --> 00:48:12,640
Speaker 6: know how to talk about this stuff without really having

953
00:48:12,960 --> 00:48:16,640
Speaker 6: this kind of being bowled over. You know, there's people

954
00:48:16,800 --> 00:48:20,879
Speaker 6: who play that access journalism, like like Kara Swisher who

955
00:48:20,960 --> 00:48:23,400
Speaker 6: like kind of poses herself as this person who was

956
00:48:23,560 --> 00:48:26,800
Speaker 6: very antagonistic, but like, you know, right.

957
00:48:26,640 --> 00:48:29,000
Speaker 2: Just like fawning over like AI people and.

958
00:48:29,280 --> 00:48:34,000
Speaker 6: Yeah, like all the time, Well I trusted Elon Musk

959
00:48:34,040 --> 00:48:35,680
Speaker 6: and tell I was like, well, why did you trust

960
00:48:35,719 --> 00:48:38,400
Speaker 6: this man in the first place? Like did you know

961
00:48:38,560 --> 00:48:43,839
Speaker 6: I was reading the Peter Thiel biography The Contrarian and

962
00:48:44,280 --> 00:48:46,960
Speaker 6: you know, and like it. It's a very it's a

963
00:48:47,040 --> 00:48:50,360
Speaker 6: very harrowing read. I mean it was fascinating, but it

964
00:48:50,440 --> 00:48:53,840
Speaker 6: was very harrowing. It wasn't an it was pretty like critical.

965
00:48:54,360 --> 00:48:58,560
Speaker 6: But like, you know, they discuss the PayPal days, you know,

966
00:48:58,600 --> 00:49:03,279
Speaker 6: twenty four years ago, when you know, Elon Musk was like, well,

967
00:49:03,360 --> 00:49:06,960
Speaker 6: I want to name PayPal to X and then and

968
00:49:07,000 --> 00:49:09,960
Speaker 6: then everybody was like, why the fuck would you do that?

969
00:49:10,080 --> 00:49:13,840
Speaker 6: People are already using people are using PayPal as a verb.

970
00:49:14,360 --> 00:49:17,280
Speaker 6: You know, that's effectively the same thing he did with Twitter,

971
00:49:17,400 --> 00:49:19,879
Speaker 6: Like people are talking about tweet as a verb. Why

972
00:49:19,880 --> 00:49:22,919
Speaker 6: would you say, you know, just it's been like an

973
00:49:22,960 --> 00:49:28,560
Speaker 6: absolutely vapid human being with no business sense. Anyways, journal

974
00:49:28,840 --> 00:49:31,640
Speaker 6: that was a very long way of saying Kara Switcher

975
00:49:31,760 --> 00:49:35,640
Speaker 6: sucks and then also saying that all was saying also

976
00:49:35,680 --> 00:49:38,239
Speaker 6: saying that there's lots of folks, there's a number of

977
00:49:38,280 --> 00:49:40,520
Speaker 6: folks doing great stuff. So I mean folks at four

978
00:49:40,560 --> 00:49:44,440
Speaker 6: or for Karen Howe who's independent, that had been at

979
00:49:44,440 --> 00:49:47,200
Speaker 6: The Atlantic and I T Tech Review and the Wall

980
00:49:47,200 --> 00:49:51,279
Speaker 6: Street Journal. Curry Johnson, who was at Wired is now

981
00:49:51,520 --> 00:49:53,960
Speaker 6: at cal Matters. There's a lot of people that really

982
00:49:54,480 --> 00:49:57,600
Speaker 6: report on AI from the perspective of like the people

983
00:49:57,640 --> 00:50:01,479
Speaker 6: who it's harming, rather than starting from well, this tool

984
00:50:01,560 --> 00:50:03,759
Speaker 6: can do X, Y and Z, right, you know, we

985
00:50:03,800 --> 00:50:07,120
Speaker 6: really should take these groups out there claims. But yeah,

986
00:50:07,120 --> 00:50:08,680
Speaker 6: I mean the larger part of it is, I mean,

987
00:50:08,719 --> 00:50:11,359
Speaker 6: there's just so much stuff out there, you know, and

988
00:50:11,400 --> 00:50:14,200
Speaker 6: it's it's so hard, and it is like whack them all.

989
00:50:14,480 --> 00:50:18,680
Speaker 6: And I mean we're we're not journalists by training. I mean,

990
00:50:18,680 --> 00:50:22,279
Speaker 6: we're sort of doing a journalistic thing right now. We're

991
00:50:24,160 --> 00:50:27,800
Speaker 6: I think we're I would not say we are journalists.

992
00:50:27,800 --> 00:50:29,960
Speaker 6: I always say we are doing a journalistic thing.

993
00:50:32,000 --> 00:50:33,160
Speaker 1: We're doing journalism.

994
00:50:33,480 --> 00:50:38,160
Speaker 6: We are not doing original reporting. But it is well,

995
00:50:38,239 --> 00:50:40,960
Speaker 6: and you know, I would you know, I'm not I

996
00:50:40,960 --> 00:50:43,000
Speaker 6: don't know, I'm not the I don't I don't know

997
00:50:43,040 --> 00:50:46,000
Speaker 6: who decides this is the court of journalism. But you know,

998
00:50:46,520 --> 00:50:49,520
Speaker 6: reporting in so far as looking at original papers and

999
00:50:49,560 --> 00:50:52,240
Speaker 6: effectively being like okay, This is marketing.

1000
00:50:52,280 --> 00:50:55,360
Speaker 1: This is why it's marketing. They're there, Yeah.

1001
00:50:55,680 --> 00:50:59,920
Speaker 6: Rather than you know, a wisbang c net article or

1002
00:51:00,080 --> 00:51:04,560
Speaker 6: or something that comes out of a content mill and

1003
00:51:04,680 --> 00:51:08,919
Speaker 6: says Google just published this tool that says you can

1004
00:51:09,040 --> 00:51:13,080
Speaker 6: you know, find eighteen million materials that are you know, complete,

1005
00:51:13,120 --> 00:51:15,799
Speaker 6: almost like okay, well let's look at those claims and

1006
00:51:16,080 --> 00:51:19,839
Speaker 6: upon what grounds those claims stand and and you know

1007
00:51:19,960 --> 00:51:22,440
Speaker 6: how that's that's a pretty pretty I.

1008
00:51:22,360 --> 00:51:25,040
Speaker 7: Think what we're doing is is first of all, sharing

1009
00:51:25,040 --> 00:51:27,760
Speaker 7: our expertise in our specific fields, but also like modeling

1010
00:51:27,760 --> 00:51:31,920
Speaker 7: for people how to be critical consumers of journalism, so

1011
00:51:32,880 --> 00:51:36,600
Speaker 7: journalism adjacent but yeah, definitely without training and journalism totally.

1012
00:51:36,640 --> 00:51:38,560
Speaker 1: Yeah, But I think we want to do we want

1013
00:51:38,600 --> 00:51:41,520
Speaker 1: to do the M and M articles. I mean, oh

1014
00:51:41,560 --> 00:51:45,680
Speaker 1: my gosh, there's this article that has like done our

1015
00:51:45,760 --> 00:51:49,720
Speaker 1: brains because it just has this series of sentences.

1016
00:51:49,440 --> 00:51:52,799
Speaker 2: That I don't know that because everything is degrading like journalism.

1017
00:51:52,880 --> 00:51:54,719
Speaker 2: You know, there's that story about like the Daily Mail

1018
00:51:54,840 --> 00:51:57,279
Speaker 2: was like Natalie Portman was hooked on cocaine when she

1019
00:51:57,400 --> 00:51:59,160
Speaker 2: was at Harvard. You're like, no, that was from that

1020
00:51:59,239 --> 00:52:02,040
Speaker 2: rap she did on SNL And it was like a

1021
00:52:02,160 --> 00:52:05,320
Speaker 2: bit but because this thing is street and then the

1022
00:52:05,400 --> 00:52:07,239
Speaker 2: Daily Mail had to be like at the end they

1023
00:52:07,280 --> 00:52:09,960
Speaker 2: corrected it. They're like, just she was not that was

1024
00:52:10,000 --> 00:52:13,080
Speaker 2: obviously satirical and that was due to human error, Like

1025
00:52:13,160 --> 00:52:15,719
Speaker 2: they really leaned into that, liken Ya. Of course, did

1026
00:52:15,719 --> 00:52:16,280
Speaker 2: I say by.

1027
00:52:16,160 --> 00:52:18,719
Speaker 7: The time that a fabricated quote of mine came out

1028
00:52:18,719 --> 00:52:20,400
Speaker 7: of one of these things and was printed as news.

1029
00:52:20,560 --> 00:52:21,000
Speaker 1: No? No.

1030
00:52:21,719 --> 00:52:24,400
Speaker 7: So I also, like alex so searched my own name

1031
00:52:24,440 --> 00:52:25,960
Speaker 7: because I talked to journalists or not that I like

1032
00:52:25,960 --> 00:52:28,360
Speaker 7: to see what's happening. And there was something in an

1033
00:52:28,360 --> 00:52:31,279
Speaker 7: outfit called Bihar Praba that attributed this quote to me,

1034
00:52:31,320 --> 00:52:33,920
Speaker 7: which was not something I'd ever said and not anybody

1035
00:52:33,960 --> 00:52:36,719
Speaker 7: I ever remember talking to. So I emailed the editor

1036
00:52:36,719 --> 00:52:39,640
Speaker 7: and I said, please take down this fabricated quote in

1037
00:52:39,640 --> 00:52:42,359
Speaker 7: print of retraction because I never said that, and they

1038
00:52:42,360 --> 00:52:45,000
Speaker 7: did so the article got updated, removed the thing attributed

1039
00:52:45,080 --> 00:52:46,880
Speaker 7: to me, and then there was a thing at the

1040
00:52:46,880 --> 00:52:49,759
Speaker 7: bottom saying we've attracted this. But what they didn't put publicly,

1041
00:52:49,840 --> 00:52:51,760
Speaker 7: but he told me over email is that the whole

1042
00:52:51,800 --> 00:52:53,000
Speaker 7: thing came out of Gemini.

1043
00:52:53,840 --> 00:52:54,440
Speaker 1: Oh wow, and.

1044
00:52:54,680 --> 00:52:56,240
Speaker 8: They posted it as a news article.

1045
00:52:56,760 --> 00:52:59,279
Speaker 7: And you know, the only reason I discovered it was

1046
00:52:59,320 --> 00:53:01,520
Speaker 7: it was my own name, and like, I never said

1047
00:53:01,560 --> 00:53:01,879
Speaker 7: that thing.

1048
00:53:02,200 --> 00:53:05,439
Speaker 2: Well, I need your expertise here to decipher this food

1049
00:53:05,480 --> 00:53:08,920
Speaker 2: and wine article that was talking about how Eminem's was

1050
00:53:08,960 --> 00:53:13,720
Speaker 2: coming out with a pumpkin pie flavored eminem but very early.

1051
00:53:13,920 --> 00:53:16,600
Speaker 2: Normally pumpkin pie flavored things don't enter the market till

1052
00:53:16,600 --> 00:53:19,319
Speaker 2: around August, like around the fall comes. But eminem is.

1053
00:53:19,280 --> 00:53:21,640
Speaker 1: Why we were covering it, because we are journalists.

1054
00:53:21,840 --> 00:53:28,080
Speaker 2: Yes, we're like in May pumpkin spice already. No, but

1055
00:53:28,200 --> 00:53:31,040
Speaker 2: again they were saying, this is because apparently gen Z

1056
00:53:31,320 --> 00:53:34,880
Speaker 2: and millennial consumers are celebrating Halloween earlier. But this is

1057
00:53:34,920 --> 00:53:40,960
Speaker 2: this one section that completely wait wait, yeah, I don't

1058
00:53:41,000 --> 00:53:44,520
Speaker 2: know that's what they're saying. According to their analysis that Okay,

1059
00:53:44,800 --> 00:53:47,440
Speaker 2: we were that we apparent, So let me read this

1060
00:53:47,480 --> 00:53:50,399
Speaker 2: for you. Quote. The pre seasonal launch of the milk

1061
00:53:50,480 --> 00:53:53,320
Speaker 2: chocolate pumpkin pie Eminem's is a strategic move that taps

1062
00:53:53,320 --> 00:53:56,640
Speaker 2: into Mars' market research. This research indicates that gen Z

1063
00:53:56,760 --> 00:54:00,480
Speaker 2: and millennials plan to celebrate Halloween by dress, sing up,

1064
00:54:00,520 --> 00:54:04,480
Speaker 2: and planning for the holiday about six point eight weeks beforehand,

1065
00:54:04,719 --> 00:54:07,759
Speaker 2: well six point eight weeks from Memorial Day is the

1066
00:54:07,760 --> 00:54:10,319
Speaker 2: fourth of July, so you still have plenty of time

1067
00:54:10,360 --> 00:54:12,839
Speaker 2: to latch onto a pop culture trend and turn it

1068
00:54:12,840 --> 00:54:14,120
Speaker 2: into a creative costume.

1069
00:54:15,560 --> 00:54:20,160
Speaker 1: I don't that's it's right, It doesn't. It doesn't make

1070
00:54:20,200 --> 00:54:20,880
Speaker 1: any sense. I know.

1071
00:54:24,440 --> 00:54:27,560
Speaker 6: Yeah, I'm fixating on six point eight.

1072
00:54:29,520 --> 00:54:30,439
Speaker 2: What does that even mean?

1073
00:54:30,520 --> 00:54:33,120
Speaker 1: Fuck? Does that mean? And where did Memorial Day come from?

1074
00:54:33,160 --> 00:54:33,400
Speaker 5: And that?

1075
00:54:33,640 --> 00:54:36,200
Speaker 1: And what is six point eight weeks for Memorial Day?

1076
00:54:36,200 --> 00:54:39,000
Speaker 1: Because it's not any of the days that they said

1077
00:54:39,000 --> 00:54:39,399
Speaker 1: it was.

1078
00:54:39,719 --> 00:54:42,280
Speaker 8: They said July fourth, And also.

1079
00:54:42,320 --> 00:54:45,200
Speaker 2: Six point eight eight weeks isn't a real amount of time.

1080
00:54:45,239 --> 00:54:48,600
Speaker 2: That's forty seven point point six days? Yeah, what is

1081
00:54:48,960 --> 00:54:50,600
Speaker 2: what is even a six point eight week?

1082
00:54:51,120 --> 00:54:55,840
Speaker 7: So if this were real, it's possible that they surveyed

1083
00:54:55,880 --> 00:54:57,640
Speaker 7: a bunch of people and they said when do you

1084
00:54:57,640 --> 00:55:00,359
Speaker 7: start planning your Halloween costume? And those people gave dates

1085
00:55:00,400 --> 00:55:02,360
Speaker 7: and then they averaged that, and that's how you could.

1086
00:55:02,200 --> 00:55:02,920
Speaker 1: Get to get that.

1087
00:55:03,280 --> 00:55:05,440
Speaker 2: I get that that's fair, but also.

1088
00:55:05,480 --> 00:55:08,480
Speaker 7: It totally sounds like someone put into a large language

1089
00:55:08,480 --> 00:55:12,680
Speaker 7: model write an article about why millennials and gen z

1090
00:55:13,040 --> 00:55:15,240
Speaker 7: are planning their Halloween costumes earlier.

1091
00:55:15,440 --> 00:55:17,240
Speaker 8: Like it sounds like that.

1092
00:55:17,360 --> 00:55:19,480
Speaker 2: But also just so odd to say, well, six point

1093
00:55:19,560 --> 00:55:22,200
Speaker 2: eight weeks from Memorial Day is the fourth of July.

1094
00:55:22,400 --> 00:55:24,759
Speaker 2: This article didn't even come out like it came out

1095
00:55:24,800 --> 00:55:29,200
Speaker 2: after Memorial Day and yeah, fourth, It's just nothing made sense.

1096
00:55:29,239 --> 00:55:31,640
Speaker 2: And I was like, I don't fucking understand what they're

1097
00:55:31,640 --> 00:55:34,440
Speaker 2: doing to me right now. But again that's this is

1098
00:55:34,480 --> 00:55:35,880
Speaker 2: like the insidious part for me.

1099
00:55:35,960 --> 00:55:38,480
Speaker 8: But this appeared in Food and Wine.

1100
00:55:38,600 --> 00:55:40,640
Speaker 2: This is in Food and Wine magazine with a human

1101
00:55:41,480 --> 00:55:44,440
Speaker 2: like in the byline. And I actually DM this person

1102
00:55:44,520 --> 00:55:46,759
Speaker 2: on Instagram and I said, do you mind just clarifying

1103
00:55:46,800 --> 00:55:49,960
Speaker 2: this part? Like I'm a little bit confused, and I've

1104
00:55:50,200 --> 00:55:51,400
Speaker 2: I've gotten no response.

1105
00:55:51,440 --> 00:55:54,040
Speaker 6: I've got no I'm wondering if it's because I know that.

1106
00:55:54,239 --> 00:55:57,480
Speaker 6: I mean, there was some good coverage and futurism and

1107
00:55:57,520 --> 00:56:01,360
Speaker 6: they were talking about this company called ad Von Commerce

1108
00:56:02,080 --> 00:56:04,799
Speaker 6: and the way that basically this this this company has

1109
00:56:04,840 --> 00:56:10,440
Speaker 6: been basically making AI generated articles for a lot of

1110
00:56:10,480 --> 00:56:16,480
Speaker 6: different publications, usually on products like product placement. Right, And

1111
00:56:16,520 --> 00:56:18,880
Speaker 6: so it makes me think it's sort of like this

1112
00:56:19,120 --> 00:56:21,600
Speaker 6: Food and Wine, you know, may have been one of

1113
00:56:21,640 --> 00:56:24,640
Speaker 6: their for I forgot the article, but they had a

1114
00:56:24,640 --> 00:56:27,640
Speaker 6: bun they had like, you know, better homes and gardening

1115
00:56:27,719 --> 00:56:30,120
Speaker 6: and you know, kind of these legacy articles like that.

1116
00:56:30,200 --> 00:56:32,800
Speaker 6: So I don't know if it's something of that or

1117
00:56:32,880 --> 00:56:35,279
Speaker 6: this journalist kind of said, write me this thing, and

1118
00:56:35,360 --> 00:56:37,960
Speaker 6: I'm just going to drop it and then go with God,

1119
00:56:38,040 --> 00:56:38,239
Speaker 6: you know.

1120
00:56:39,400 --> 00:56:40,600
Speaker 1: Yeah.

1121
00:56:41,000 --> 00:56:41,239
Speaker 2: Yeah.

1122
00:56:41,440 --> 00:56:44,799
Speaker 1: My My other favorite example of is this headline I

1123
00:56:44,800 --> 00:56:47,680
Speaker 1: saw somewhere it's no big secret why Van Vaught isn't

1124
00:56:47,680 --> 00:56:50,719
Speaker 1: around anymore and with a picture of Vince Vaughan but

1125
00:56:50,800 --> 00:56:57,279
Speaker 1: they just like got his name completely wrong. It's no

1126
00:56:57,680 --> 00:57:01,400
Speaker 1: secret why Van he isn't around anymore.

1127
00:57:03,840 --> 00:57:07,560
Speaker 6: I'm like, you know, if I was just scrolling and

1128
00:57:07,600 --> 00:57:09,799
Speaker 6: I just and I'd say like, yeah, it was like,

1129
00:57:09,840 --> 00:57:12,400
Speaker 6: you know, I liked Van Vaught and the Intern and

1130
00:57:12,400 --> 00:57:15,879
Speaker 6: then yeah, but then I but and then I would

1131
00:57:15,880 --> 00:57:17,400
Speaker 6: have looked at it, and then I would have double taped.

1132
00:57:17,440 --> 00:57:20,440
Speaker 6: I'm like, wait, wait, wait did he co star with

1133
00:57:21,200 --> 00:57:22,960
Speaker 6: oh when Mick Wilson or something?

1134
00:57:23,160 --> 00:57:26,040
Speaker 2: Yeah, yeah, yeah, exact Russell Wilson was in that.

1135
00:57:26,880 --> 00:57:28,640
Speaker 7: I think it was the ad Week reporting that you're

1136
00:57:28,640 --> 00:57:30,240
Speaker 7: thinking of alex Futurism did a bunch of it, but

1137
00:57:30,280 --> 00:57:32,280
Speaker 7: then Adweek had the whole thing about Advon and I

1138
00:57:32,640 --> 00:57:33,520
Speaker 7: can't quite.

1139
00:57:33,360 --> 00:57:36,720
Speaker 6: No, no, no, it was it was future. It was futurism, yeah,

1140
00:57:36,760 --> 00:57:40,720
Speaker 6: because because Adweek had the thing on this program that

1141
00:57:40,840 --> 00:57:42,880
Speaker 6: Google was offering and it didn't have a name.

1142
00:57:43,000 --> 00:57:47,520
Speaker 8: Oh right, yeah was futurism. Yeah, but it totally sounds.

1143
00:57:47,240 --> 00:57:49,680
Speaker 1: Like what it is happening. Yeah, yeah, right, I.

1144
00:57:49,600 --> 00:57:51,680
Speaker 7: Thought you were going to talk about the surveillance by

1145
00:57:51,760 --> 00:57:54,840
Speaker 7: Eminem thing. You said, Eminem's So this was somewhere in Canada.

1146
00:57:55,840 --> 00:57:58,320
Speaker 7: There was an Eminem vending machine that was like taking

1147
00:57:58,360 --> 00:58:01,200
Speaker 7: pictures of the students while they were making their purchases.

1148
00:58:01,200 --> 00:58:03,320
Speaker 7: And I forget what the like a sensible purpose was,

1149
00:58:03,360 --> 00:58:06,080
Speaker 7: but the students found out and I got it removed.

1150
00:58:06,560 --> 00:58:11,240
Speaker 1: Wow, probably freaked out and made a big deal about it.

1151
00:58:11,280 --> 00:58:16,800
Speaker 1: All right, all right, that's gonna do it for this

1152
00:58:16,880 --> 00:58:21,560
Speaker 1: week's weekly Zeitgeist. Please like and review the show if

1153
00:58:21,760 --> 00:58:25,920
Speaker 1: you like. The show means the world to Miles. He

1154
00:58:25,920 --> 00:58:29,720
Speaker 1: he needs your validation, folks. I hope you're having a

1155
00:58:29,760 --> 00:59:06,400
Speaker 1: great weekend and I will talk to him Monday. By

1156
00:59:09,360 --> 00:59:09,480
Speaker 1: st