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Speaker 1: So how about that cold open? Huh?

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Speaker 2: Oh fuck, we need a cold open.

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Speaker 1: Huh.

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Speaker 3: Let's see complaining about technology not so good? That's not appealing.

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Speaker 3: Did anyone else this was going to be my underrated

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Speaker 3: But I'll figure something else out unless this is just

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Speaker 3: bad what I'm about to say. I got twenty five

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Speaker 3: pounds of hatch chilis on Saturday. I went to a

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Speaker 3: Gelson's in Lakanyata at eight am, which.

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Speaker 1: Was asking if anybody else did that. Anyone else was

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Speaker 1: going to be head chilies.

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Speaker 3: My underrated was going to be waking up way too

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Speaker 3: early to get twenty five pounds of peppers and then

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Speaker 3: inadvertently pepper spraying myself and my dog by carrying around

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Speaker 3: It was actually fifty pounds.

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Speaker 2: I picked up twenty five pounds. He needs so many chilies?

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Speaker 3: I don't obviously, it's just hatch chili season.

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Speaker 2: It's chili season, and.

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Speaker 1: That's chili season.

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Speaker 2: Baby.

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Speaker 1: It's like Andrew's equivalent of Game seven.

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Speaker 3: It's a little hard to explain, but basically I bought

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Speaker 3: a box of hatch chilies, you know, a full fucking

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Speaker 3: farmer's box or whatever, and twenty five pounds divided up

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Speaker 3: amongst three people.

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Speaker 2: They roast them in the parking lot.

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Speaker 3: And then I turned into like a little little Walter

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Speaker 3: White with gloves and like my like kitchen scale portioning

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Speaker 3: out like bagging up chili's for my connect or for

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Speaker 3: my fucking clients.

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Speaker 1: And then you inadvertently like had a like the equivalent

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Speaker 1: of a meth den explosion.

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Speaker 2: Well, no, that was on the way back. Like basically,

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Speaker 2: I had fifty pounds of chili the windows down and

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Speaker 2: chili's went yeah everywhere.

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Speaker 3: But no, I like left them in the trunk and

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Speaker 3: I went and got breakfast and left my dog in there.

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Speaker 3: And she came back and was just like sneezing like

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Speaker 3: a motherfucker. Because in retrospect it makes sense and it

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Speaker 3: was only like two minutes.

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Speaker 2: She's fine.

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Speaker 1: But which ones are the Hatch Chilies. I'm like not

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Speaker 1: a food person, so like, are are they the ones

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Speaker 1: responsible for?

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Speaker 3: Uh No, it's a lot of it's it's like a

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Speaker 3: regional I think New Mexico.

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Speaker 2: It's in Hatch, New Mexico.

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Speaker 3: Yeah, and they're just like sort of there's a short season.

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Speaker 3: The harvest season is like August basically, and I think

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Speaker 3: they're sort of expanding a new Mexican cultural thing. I

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Speaker 3: think in New Mexico every grocery store has a guy

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Speaker 3: or maybe four guys. Always guys, it seems like, who

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Speaker 3: have like a big drum roaster that looks like the

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Speaker 3: kind of thing that you pull like the powerball out

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Speaker 3: of the roaster is made of mesh. It's like a

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Speaker 3: big mesh cylinder and it's just turning over a fire

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Speaker 3: with chili's in it, and they just kind of like

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Speaker 3: roast them.

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Speaker 2: And oh, kind of like what you would like do

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Speaker 2: nuts in or something.

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Speaker 1: Yeah, exactly like chestnuts roasting on an open fire.

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Speaker 3: Yeah, but it's like sort of like, you know, four

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Speaker 3: Mega Dudes. They have a real maga dude vibe. They're

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Speaker 3: probably fine, but maybe not so.

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Speaker 2: Their company is called four Megade four Mega Dudes.

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Speaker 1: Roasted up Chili for Mega Dudes, Buy Mega Dudes. Yeah,

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Speaker 1: it's fubu for white peoplea mega Mega. Hello the Internet,

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Speaker 1: and welcome to this week trend edition of Dirt Alies

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Speaker 1: Like Guys. Yeah yeah ah, and it's Prection Heart Radios podcast.

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Speaker 4: We take a deep dive into America shared consciousness and

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Speaker 4: this is the episode where we tell you what was

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Speaker 4: trending over the weekend, and my name is Jack O'Brien,

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Speaker 4: and over there sitting in for mister Miles Gray is

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Speaker 4: the wonderful one of our favorite guests, one of our

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Speaker 4: favorite co hosts.

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Speaker 1: It's Andrews from the Oza phrase the podcast, and.

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Speaker 2: I am TV.

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Speaker 3: I'm so fucked up, man, I'm just like chilied out

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Speaker 3: as everyone heard.

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Speaker 2: My life is just in shambles right now.

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Speaker 1: So fucked up on a Monday morning. Must be the chilies. Andrew,

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Speaker 1: it's great to great to have you back. You of

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Speaker 1: course put the T in TV as a TV writer producer.

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Speaker 1: It's I don't know, man, I am coming off of

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Speaker 1: a red eye. I'm on two hours of sleep. I'm

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Speaker 1: down to shore, came to my parents place down to shore,

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Speaker 1: and it's uh, it's not all. It's not all working

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Speaker 1: up top.

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Speaker 3: So just I just realized I recognize Jack summer summer

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Speaker 3: recording setup, right right, Yeah, this is yeah, you're yeah,

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Speaker 3: everyone's everyone's on summer break. Everyone's on a little what

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Speaker 3: are we at like eighteen percent capacity? I'll explain why

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Speaker 3: I'm on low sleep.

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Speaker 1: Those damn hatch chilies.

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Speaker 2: It's not the damn hatch chilies I had.

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Speaker 3: I had kum quats at midnight, and then I couldn't

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Speaker 3: sleep till two a m.

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Speaker 1: Wait, really is this gonna is this tie into your underrated.

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Speaker 2: No, this is this is just this is just in

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Speaker 2: my brain.

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Speaker 3: I have a new new Andrew weakness unlocked midnight snack

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Speaker 3: cum quats. Apparently just those those are going to keep

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Speaker 3: you up because the tart I did not realize. Yeah

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Speaker 3: that the astringency just just messed me up. Anyway, We're

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Speaker 3: fucking doing great, right, Jack.

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Speaker 2: You took a red eye voluntarily?

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Speaker 1: I did. I had like there were like children's things

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Speaker 1: that like they needed to be at, you know, my kids,

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Speaker 1: and so we and then I needed to be here

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Speaker 1: for this recording, and so we did a red eye

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Speaker 1: and it was airlines. I don't know, maybe be leep

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Speaker 1: that out where the seats don't move backwards at all,

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Speaker 1: like not at all, like not even it's ninety degrees

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Speaker 1: and like both both temperature but also like the angle

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Speaker 1: of the seat is just straight up and down.

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Speaker 3: Yeah, you're like you're like needing, just like you need

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Speaker 3: to sleep like a marine, like right, you're supposed to

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Speaker 3: just be like.

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Speaker 1: Yeah, I was. I was assembling and disassembling a gun

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Speaker 1: in my sleep as I was sitting there. That's you

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Speaker 1: gotta stay sharp. But yeah, I had put entirely too

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Speaker 1: much trust in a neck pillow that I had read

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Speaker 1: works and neck pillows. Uh, maybe I'll get into that.

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Speaker 1: Am I overrated because I don't really have an overrated

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Speaker 1: But this is of course the episode where we tell

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Speaker 1: you what was trending over the weekend. We also let

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Speaker 1: you get to know us a little bit better by

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Speaker 1: giving some underrated and overrateds from our lives. We, of course,

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Speaker 1: in our regular episodes, let the guests tell us what

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Speaker 1: they think is overrated and underrated. Andrew, is there something

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Speaker 1: that you think is underrated?

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Speaker 3: Here's a pivot from my original underrated, but it's still

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Speaker 3: still from the same grocery store visit.

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Speaker 2: Which is.

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Speaker 3: Keeping that or at least trying to keep that, like

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Speaker 3: crazy ass basil that isn't really supposed to live alive

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Speaker 3: from the grocery store.

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Speaker 1: Oh, the one that comes in a little potted plant.

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Speaker 3: Yeah, the little potted plant, which I just on a

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Speaker 3: whim was like, you shall live.

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Speaker 2: I bought one.

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Speaker 3: I made pesto the other day, and I was like,

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Speaker 3: I'm going to.

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Speaker 2: Try to keep you alive.

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Speaker 3: Wow, I'm I'm like a real you know, role got

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Speaker 3: a black thumb right here. I never keep a plant alive,

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Speaker 3: but I have gotten so much like baby's first gardening equipment,

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Speaker 3: like in the last week.

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Speaker 1: An actual fisher tool.

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Speaker 3: Yeah, I mean genuinely, they might as well be. It's

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Speaker 3: also because it's very small. I'm literally keeping I repotted

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Speaker 3: the basil plant, or the several basil plants.

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Speaker 2: I guess that's the first lesson.

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Speaker 3: Anyway, I don't know what I'm doing, and I'm making

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Speaker 3: a ton of mistakes, but I think I'm just gonna

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Speaker 3: attempt to keep this MFR alive for no. I guess

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Speaker 3: it was because the pesto was pretty good.

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Speaker 1: Yeah, hell yeah, I don't.

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Speaker 2: I don't want to do.

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Speaker 3: Pesto anyway, Yeah, keep that thing alive. All the advice

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Speaker 3: on the internet is that the container it comes in

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Speaker 3: is greatly insufficient.

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Speaker 2: You need like so much more.

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Speaker 1: It's small, so much it's a tea spoon of soil

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Speaker 1: that they.

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Speaker 3: So it's basically like the factory farming of cultivating basil.

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Speaker 3: It's like, you know, if if it were in any

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Speaker 3: way sentient, it would be like borderline cruelty conditions.

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Speaker 1: Right, yeah, it's one of those things. Like it's when

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Speaker 1: I when I was research and getting a turtle for

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Speaker 1: my seven year old and like my friends who have turtles, Okay,

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Speaker 1: I have a friend who has turtles. I was trying

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Speaker 1: to trying to flex, like I got many friends with turtles.

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Speaker 3: My friend wholes is a little rat that knows njitsu.

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Speaker 1: That's he keeps many of them.

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Speaker 2: Yeah. He was like, yeah, you just like use that.

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Speaker 1: Uh you know, you use a normal sized fish aquarium

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Speaker 1: and the turtles happy. Then you do research and it's

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Speaker 1: like you need to convert one of the rooms in

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Speaker 1: your house to have enough room for the turtle. If

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Speaker 1: you if you want to have a turtle, like you

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Speaker 1: need a full on you need to start digging up

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Speaker 1: your backyard if you want a turtle to live comfortably.

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Speaker 1: Like that sounds like what's going like Brian briand the

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Speaker 1: editor was like, you gotta repot those immediately in the

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Speaker 1: actual ground. Uh yeah, and you gotta have at least

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Speaker 1: an acre man for the basil to be to really

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Speaker 1: not expand it's not even close.

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Speaker 3: Wait why turtles, I will say, I mean because turtles

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Speaker 3: and parrots are famously like treated as like cheap maybe

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Speaker 3: nearly disposable pets, but under the right conditions can live

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Speaker 3: for like fucking forever.

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Speaker 2: Yeah.

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Speaker 1: Yeah, they're lifely they will outlive you. It's so it's.

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Speaker 3: Like it's almost like by design, those like grocery store

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Speaker 3: turtles are meant to be murdered, right, whatever happens, their

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Speaker 3: life is going to be way shorter than if people

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Speaker 3: weren't around.

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Speaker 1: Right, Yeah, yeah, exactly. Also another note from Brian the editor.

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Speaker 1: He went to see the Gun movie over the weekend

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Speaker 1: and ran the editor. You may have heard him on

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Speaker 1: the podcast before listeners. It's not one who gives glowing

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Speaker 1: recommendations very often.

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Speaker 2: Yeah, this was glowing.

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Speaker 3: I have yet to see it, and I still know

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Speaker 3: it's a glowing wreck. And also, I mean this is

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Speaker 3: just you know, holding the tin cop out for you know,

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Speaker 3: all the people in the entertainment industry, but specifically the comedy.

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Speaker 2: Somebody, Yes, comedy is really really tow. So yeah, we

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Speaker 2: really need it.

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Speaker 3: If everyone listening could go see this movie, I don't know,

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Speaker 3: seven or eight times I have no personal stake in.

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Speaker 1: This, but I have none either. The only reason I

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Speaker 1: didn't I actually had a window where I could have

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Speaker 1: seen a movie over the weekend. Immediately felt guilty about

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Speaker 1: having that windows.

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Speaker 3: Like, what, yeah, you need to be doing something in time,

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Speaker 3: but I standing up to exactly.

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Speaker 1: But I didn't go see Naked Gun because I'm going

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Speaker 1: to see it this week with my mom and I

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Speaker 1: The hardest I remember laughed at a movie was like

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Speaker 1: when I was eight or so and saw Naked Gun

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Speaker 1: with my mom and was and so I was like, Mom,

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Speaker 1: we got to hit this Naked Gun.

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Speaker 3: Yeah, this is going. Okay, here's a question. Here's an

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Speaker 3: actual question. I haven't even looked at the rating. I

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Speaker 3: assume it's PG thirteen. I just rem is it our okay?

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Speaker 1: I think because all the reviews are like this is

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Speaker 1: the you know, biggest or all the all the.

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Speaker 2: But even though checking on.

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Speaker 1: The Baffo box office report and they're saying that this

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Speaker 1: is it is doing well for an R rated comedy.

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Speaker 3: Even but even the like original from the seventies, eighties, eighties,

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Speaker 3: I guess eighties, Yeah, ladies seems wildly inappropriate for kids,

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Speaker 3: if I recall, I think it was our all, so yeah, okay.

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Speaker 1: I just you know it was it was a different

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Speaker 1: time people were Yeah, that one was PG thirteen.

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Speaker 2: It was, but it was from the era, right airplane.

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Speaker 3: I remember seeing the non TV cut version and I

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Speaker 3: was like, oh, there's just random boobs in this. This

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Speaker 3: is like a real like you know, it's it's from

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Speaker 3: the seventies. It's from the era where you could plausibly

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Speaker 3: have Playboy for the articles and also for the boobies.

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Speaker 1: That's right, Yeah, exactly. They just slid them in there

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Speaker 1: wherever they could, wherever they could get them. Also, middle

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Speaker 1: aged white men were in charge of everything.

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Speaker 2: So yeah, there, thank god that's changed exactly.

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Speaker 1: We're gonna want to see those I think in this

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Speaker 1: otherwise children's comedy.

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Speaker 3: Like a silly a silly movie that's mostly pun based jokes,

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Speaker 3: but also yeah, like just enough nuded to give you.

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Speaker 1: That's right, that's right, like right off the bat too.

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Speaker 1: I think there's just like, uh, a topless woman in

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Speaker 1: the opening credits. Anyways, my underrated animals on trampolines might

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Speaker 1: have something to do with my sleep deprivation. I've there's

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Speaker 1: no shortage of clips of animals on trampolines. I've long

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Speaker 1: been a fan of bears in hammocks. Are you familiar

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Speaker 1: with these? Have you seen the bears in hammocks.

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Speaker 3: I mean, I don't know if there's an iconic one,

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Speaker 3: but I feel like i've seen it in general.

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Speaker 1: Yeah, there's I've seen some pretty iconic ones like that.

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Speaker 1: Just really like in terms of like that they're actual

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Speaker 1: physical comedy performances because they're really expressing the weirdness of

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Speaker 1: being in a hammock for the first time, like the

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Speaker 1: sort of like mixture of like comfort and chilling while

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Speaker 1: also feeling incredibly unsteady.

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Speaker 2: Yeah, it's like.

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Speaker 3: A type of precariousness that I think hammock has to be,

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Speaker 3: like just like the perfect personality test, Like if you

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Speaker 3: are able to, like, the amount you are able to

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Speaker 3: relax in a hammock is exactly how relaxed you are

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Speaker 3: as a human being.

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Speaker 1: Right, Because it's like Catherine you saying, all animals on

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Speaker 1: trampolines are AI.

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Speaker 3: Just the ones that are going around the internet right now,

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Speaker 3: the bunnies and the bear are AI. Unfortunately, Oh no, no,

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Speaker 3: I've been I've been had.

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Speaker 2: Wait the raccoons also, oh yuess that makes sense.

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Speaker 1: All right, Well, then I'm going with bears on hammocks

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Speaker 1: because those pre date AI and they fucking roll. I

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Speaker 1: highly recommend.

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Speaker 3: Can I just say that should be I guess it's

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Speaker 3: like I was gonna say, that should be the centerpiece

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Speaker 3: of every hammock company's advertising campaign.

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Speaker 2: Is this thing can hold a fucking bear.

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Speaker 1: Right, except when it doesn't.

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Speaker 2: The fact that even can kind of hold a bear?

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Speaker 2: How much is a bear? Way right? Six times the

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Speaker 2: biggest human five?

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Speaker 1: They really got my ass with us with these raccoons

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Speaker 1: on trampolines. All right, so we're going bears. We're going

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Speaker 1: bears and hammocks. Sorry, fuck fuck the raccoons on trampolines

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Speaker 1: for all the for all the listeners.

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Speaker 2: I did see just the pilot light go out in

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Speaker 2: Jack's eyes a.

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Speaker 3: Little bit just now, and it was already it was

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Speaker 3: already on dim.

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Speaker 1: That's right. It's this is going to be a tough

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Speaker 1: next thirty minutes. Guys, after learning that those are those

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Speaker 1: are AI, guess.

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Speaker 2: That makes sense, Listen. I mean to the extent that

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Speaker 2: you want to be hardened, even though the like.

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Speaker 3: Drawing of it is, you know, getting good enough that

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Speaker 3: it can be you know, it could fool a human

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Speaker 3: being on two hours sleep. The important thing really is that,

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Speaker 3: like I you know, because it's like as as AI's

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Speaker 3: like output fidelity is improving. You know, We're starting to

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Speaker 3: get to the point where it's like, oh, ken, this

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Speaker 3: do creative work. And I think the thing that is

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Speaker 3: helpful is that it can like repeat things, but it's

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Speaker 3: not generating new things. And thankfully the people who are

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Speaker 3: really into it have no actual like imagination. So I

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Speaker 3: guess for what it's worth different animals being on trampolines

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Speaker 3: is sort of the extent of where this is going

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Speaker 3: to go.

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Speaker 1: Yeah, it turns out that this is just an easy

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Speaker 1: thing to fake with AI. I was like, man, suddenly

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Speaker 1: everybody's shown all these videos raccoons have discovered. I was like,

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Speaker 1: they must have gotten the word out in the forest.

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Speaker 3: It's the value of like an original thing. So listen,

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Speaker 3: the next weird animal thing you'll be delighted by because

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Speaker 3: exactly will be a novelty to it, and then it

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Speaker 3: will be copied by AI.

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Speaker 1: It makes the bears and Hammock videos all the more

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Speaker 1: wonderful in retrospect, and said, now someone's gonna be like

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Speaker 1: those are also AI, bro, but those have been around

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Speaker 1: for like over a decade. Uh So this is my

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Speaker 1: pitch based on animals getting to enjoy the stuff that

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Speaker 1: we do is I feel like we need to have

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Speaker 1: a day where wild animals get to take over a

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Speaker 1: water park because I'm absolutely certain that bears would enjoy

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Speaker 1: the shit out of a water slide.

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Speaker 3: You know, this is like your villain origin story, like

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Speaker 3: you're here to wipe out humanity because you just want

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Speaker 3: to see what it would look.

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Speaker 2: Like if a bear got on a water slide.

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Speaker 1: Because yeah, that's well, because they do break in and

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Speaker 1: like enjoy people's pools, like you know, take a bath

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Speaker 1: during the summer. Take store.

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Speaker 3: I've ever told a story on the on the pod.

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Speaker 3: There was one time, and this was this was sort

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Speaker 3: of like many years ago now, but it was in

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Speaker 3: the like, you know, the city of Detroit really the

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Speaker 3: the sort of like industrial blight and the you know,

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Speaker 3: sort of contraction of the city was really setting in.

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Speaker 3: But I'm from Michigan. I went to a high school

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Speaker 3: friend's wedding and basically the the ceremony and the reception

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Speaker 3: were i don't know, pab four or five blocks apart

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Speaker 3: through like a you know, just a sort of like

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Speaker 3: at the time relatively abandoned part of downtown Detroit.

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Speaker 1: The barbarian Yeah, exactly.

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Speaker 3: Yeah, it was well, it was weirder than that because

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Speaker 3: we were walking and little little drunk. We're in our

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Speaker 3: I don't know, twenties probably, and walking down the street.

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Speaker 3: On one side of the road is a now abandoned bank.

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Speaker 3: It looks like I think, I think it was like

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Speaker 3: a bank, you know, it felt like a like a

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Speaker 3: city bank that had just been given up on. And

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Speaker 3: I saw a deer in a cubicle and it looked

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Speaker 3: me right in the eyes and it's I think I

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Speaker 3: will never forget.

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Speaker 2: And it was crazy, and.

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Speaker 3: It was just like, yeah, even in the fucking apocalypse,

364
00:20:05,600 --> 00:20:07,600
Speaker 3: sometimes you still got to like check it to your

365
00:20:07,640 --> 00:20:09,520
Speaker 3: shitty clock it at your shitty day job.

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Speaker 1: Was he sitting at a desk being like doing a

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Speaker 1: dumb typing gesture like, oh look at me.

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Speaker 3: It did say you you're you're a mindless drone, but

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Speaker 3: this is you.

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Speaker 1: Yeah, uh, Andrew, What is something you think is overrated?

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Speaker 3: Oh my god, I was gonna say, actually, overrated speaking

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00:20:37,400 --> 00:20:42,000
Speaker 3: of naked gun is not watching comedies in the theater.

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Speaker 3: It's really fun to laugh with a bunch of people.

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00:20:45,320 --> 00:20:48,480
Speaker 3: This is why, for instance, I hate comedy clubs, but

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Speaker 3: I know people love comedy.

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Speaker 2: Clubs, just you know, if you'll go to a comedy club.

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00:20:53,680 --> 00:20:57,040
Speaker 3: To watch a stand up do a routine you've probably

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00:20:57,040 --> 00:20:59,760
Speaker 3: already seen, or some crowd work that it's like, you know,

379
00:21:00,680 --> 00:21:03,320
Speaker 3: you're there because it's fun to be in a crowd

380
00:21:03,320 --> 00:21:07,639
Speaker 3: of people laughing and yeah the new Naked gone. But

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00:21:07,720 --> 00:21:10,960
Speaker 3: in general, just like comedies, there's comedies out there. It's

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00:21:10,960 --> 00:21:13,000
Speaker 3: fun to be in a theater. Go go do that

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00:21:13,119 --> 00:21:16,520
Speaker 3: because I know you would all rather watch it on

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00:21:16,520 --> 00:21:17,040
Speaker 3: on your.

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Speaker 1: Netflix is yeah, go do it yea, or get a

386
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Speaker 1: leak and then charge people money to come do it

387
00:21:26,119 --> 00:21:28,880
Speaker 1: watch it in your little No, actually we can't real Yeah, yeah,

388
00:21:28,880 --> 00:21:29,960
Speaker 1: that's probably not good.

389
00:21:30,520 --> 00:21:33,840
Speaker 3: Just just like you know, get on your neighborhood, hang

390
00:21:33,920 --> 00:21:37,320
Speaker 3: up a white sheet and a projector and get all

391
00:21:37,359 --> 00:21:40,240
Speaker 3: the kids. I do like, I like a Gorilla cinema,

392
00:21:40,359 --> 00:21:42,840
Speaker 3: but it has to be like Little Rascal style.

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00:21:43,000 --> 00:21:47,920
Speaker 1: Yeah the best, you know. Uh huh, all right, my overrated.

394
00:21:49,560 --> 00:21:52,639
Speaker 1: Let's see, I've got a bunch of like ones that

395
00:21:52,720 --> 00:21:57,000
Speaker 1: don't really make sense that I came up with this morning,

396
00:21:57,160 --> 00:22:00,560
Speaker 1: like what you have at the all right, an that

397
00:22:00,600 --> 00:22:04,400
Speaker 1: don't mean shit? Yeah yeah, well all right, I don't

398
00:22:04,440 --> 00:22:07,040
Speaker 1: even know if this is an acronym also, but my

399
00:22:07,160 --> 00:22:10,919
Speaker 1: son is into military history. Don't ask me how he

400
00:22:11,000 --> 00:22:14,520
Speaker 1: got there. He's just like real into uh.

401
00:22:14,359 --> 00:22:18,600
Speaker 3: You know, I mean listen, I like the Oceans has

402
00:22:18,680 --> 00:22:25,679
Speaker 3: like a like three generations ago red flag, like exactly.

403
00:22:25,960 --> 00:22:29,800
Speaker 1: Right, like this red flag runs deep.

404
00:22:30,240 --> 00:22:31,240
Speaker 2: Yeah. Oh oh.

405
00:22:31,480 --> 00:22:35,119
Speaker 3: The guy that really loves, you know, really loving military

406
00:22:35,160 --> 00:22:37,959
Speaker 3: history is like one step away from like, do you

407
00:22:37,960 --> 00:22:41,600
Speaker 3: want to see my Nazi plate? But yeah, that's good.

408
00:22:41,600 --> 00:22:44,440
Speaker 3: It's so it's so charmingly quaint. It's like what someone

409
00:22:44,480 --> 00:22:45,760
Speaker 3: from the fifties would have.

410
00:22:46,119 --> 00:22:49,160
Speaker 1: I know, It's it's weird. It's like he went from

411
00:22:49,480 --> 00:22:52,000
Speaker 1: Star Wars into like real wars.

412
00:22:52,400 --> 00:22:55,040
Speaker 2: But he's nice.

413
00:22:55,400 --> 00:22:59,119
Speaker 1: He's nice about like he knows the Nazis the bad guys,

414
00:23:01,560 --> 00:23:03,760
Speaker 1: but he did. He had been asking me what D

415
00:23:03,920 --> 00:23:06,920
Speaker 1: Day stood for, and I was just telling him Demolition

416
00:23:07,040 --> 00:23:09,040
Speaker 1: Day for a long time because.

417
00:23:08,800 --> 00:23:10,000
Speaker 2: I didn't want to look it up.

418
00:23:10,840 --> 00:23:13,639
Speaker 1: And then I found out it stands for the D

419
00:23:13,840 --> 00:23:22,880
Speaker 1: stands for day, So it's day day. That's so numb.

420
00:23:25,400 --> 00:23:28,840
Speaker 3: Wait, so like as in like this is the day right,

421
00:23:29,040 --> 00:23:30,440
Speaker 3: like we're doing it.

422
00:23:30,720 --> 00:23:31,720
Speaker 2: Oh my god.

423
00:23:32,119 --> 00:23:34,399
Speaker 1: It's like a long standing military thing to use D

424
00:23:34,680 --> 00:23:37,040
Speaker 1: for like the big day. But they just called it

425
00:23:37,240 --> 00:23:41,199
Speaker 1: D Day because they're they're bad at naming stuff. I

426
00:23:41,200 --> 00:23:44,240
Speaker 1: guess that's very not an acronym.

427
00:23:44,280 --> 00:23:44,879
Speaker 2: It's just uh.

428
00:23:45,880 --> 00:23:50,320
Speaker 1: Brian healthfully pointed out that's not an acronym, it's initialisms.

429
00:23:50,880 --> 00:23:52,560
Speaker 2: Do you know what what isn't?

430
00:23:52,920 --> 00:23:53,119
Speaker 1: Do you?

431
00:23:53,320 --> 00:23:56,439
Speaker 2: I mean, this is this is our wheelhouse. I feel

432
00:23:57,600 --> 00:23:58,160
Speaker 2: the like.

433
00:23:58,280 --> 00:24:01,600
Speaker 3: I guess it was like not uh strictly a hip

434
00:24:01,640 --> 00:24:06,160
Speaker 3: hop thing, but you know, creating acronyms in the nineties.

435
00:24:06,520 --> 00:24:09,840
Speaker 3: I mean, I guess Junior Mafia is my favorite example.

436
00:24:09,880 --> 00:24:13,360
Speaker 3: I'm trying to It's like masters at finding something.

437
00:24:15,640 --> 00:24:17,800
Speaker 2: Oh where they like make one up.

438
00:24:18,000 --> 00:24:20,920
Speaker 1: You can tell that they've like backed into it somehow.

439
00:24:21,240 --> 00:24:24,119
Speaker 2: Yeah, yeah, I remember that. Yes, weird.

440
00:24:24,600 --> 00:24:28,960
Speaker 1: There's this song that closed Jesus Liquid Swords album Basic

441
00:24:29,000 --> 00:24:33,359
Speaker 1: Instructions Before Leaving Earth. Yeah, or I mean, which was

442
00:24:33,440 --> 00:24:38,040
Speaker 1: the less successful sequel to uh Cream Casuals Everything Around Me,

443
00:24:38,920 --> 00:24:40,720
Speaker 1: which that that one works pretty well?

444
00:24:41,480 --> 00:24:44,000
Speaker 2: I did. I came up with one. Oh, wait here

445
00:24:44,000 --> 00:24:44,199
Speaker 2: we go.

446
00:24:44,280 --> 00:24:50,160
Speaker 3: Junior Mafia was masters at finding intelligent attitudes. It's all

447
00:24:50,280 --> 00:24:55,359
Speaker 3: just very like middle school teacher, like exactly.

448
00:24:55,600 --> 00:24:59,320
Speaker 1: That sounds like something they said was what it stood

449
00:24:59,359 --> 00:25:01,200
Speaker 1: for too middle school teacher.

450
00:25:01,440 --> 00:25:03,960
Speaker 2: Yeah, yeah, exactly what do you what do you mean?

451
00:25:04,040 --> 00:25:07,920
Speaker 1: We shouldn't call ourselves junior Mafia. It stands for uh

452
00:25:08,320 --> 00:25:11,240
Speaker 1: masters at finding positive attitudes.

453
00:25:13,040 --> 00:25:18,240
Speaker 3: I really liked like off the cream Era. For some reason,

454
00:25:18,280 --> 00:25:22,720
Speaker 3: my friends and I came up with milk m I

455
00:25:22,920 --> 00:25:25,360
Speaker 3: LC money is like cash.

456
00:25:26,200 --> 00:25:29,320
Speaker 1: Money is like cash. You guys were dead right about that.

457
00:25:29,760 --> 00:25:33,000
Speaker 3: Yeah, actually we were as wrong as possible. Cash is

458
00:25:33,160 --> 00:25:36,560
Speaker 3: cash is nothing. WU Tag and us were wrong about

459
00:25:36,600 --> 00:25:39,160
Speaker 3: what cash is. We of course should have been talking

460
00:25:39,160 --> 00:25:41,520
Speaker 3: about cryptocurrency credit.

461
00:25:41,600 --> 00:25:42,440
Speaker 2: Yeah, even better.

462
00:25:44,040 --> 00:25:49,960
Speaker 1: It's actually a pretty easy transition. Also, I also have

463
00:25:50,080 --> 00:25:54,240
Speaker 1: another day Day, which I think that is Ryan asked

464
00:25:54,520 --> 00:25:57,480
Speaker 1: if that's Day Day from Friday too, which I think

465
00:25:57,480 --> 00:26:00,560
Speaker 1: there might have been a character named Day Day. I

466
00:26:00,920 --> 00:26:03,760
Speaker 1: was also like, I think this is the inverse of

467
00:26:03,800 --> 00:26:08,120
Speaker 1: being fooled by AI. Because there's a Smurf ad in

468
00:26:08,359 --> 00:26:12,679
Speaker 1: Los Angeles. One of the they have like these ads

469
00:26:12,720 --> 00:26:15,760
Speaker 1: that were like, you know, smurfet and like with a

470
00:26:15,880 --> 00:26:19,320
Speaker 1: Quippi line, and one of them was the Nerdy Smurf

471
00:26:19,320 --> 00:26:22,760
Speaker 1: and it said talk nerdy to me. But somebody had

472
00:26:22,800 --> 00:26:25,480
Speaker 1: altered the poster to make it look like it looked

473
00:26:25,560 --> 00:26:29,600
Speaker 1: like the Nerdy Smurf was covered in cum and like

474
00:26:29,720 --> 00:26:32,720
Speaker 1: his white hat was just a glob of com and

475
00:26:33,440 --> 00:26:37,880
Speaker 1: I thought that was real ad and I was like, well,

476
00:26:37,880 --> 00:26:39,679
Speaker 1: that's a weird one. I should bring that up on

477
00:26:39,800 --> 00:26:43,120
Speaker 1: Overrated and then uh yeah, I looked it up and

478
00:26:43,480 --> 00:26:46,000
Speaker 1: he does not in the original ad appear to be

479
00:26:46,040 --> 00:26:46,640
Speaker 1: covered in come.

480
00:26:47,000 --> 00:26:49,520
Speaker 2: This is listen. This is why street art.

481
00:26:49,480 --> 00:26:55,639
Speaker 1: Rules exactly got my ass. Hopefully didn't use AI, but

482
00:26:56,080 --> 00:27:01,119
Speaker 1: maybe they did. Impossible to say, all, right, back on

483
00:27:01,160 --> 00:27:05,840
Speaker 1: the rails, we can start the show. Well, exactly, We'll

484
00:27:05,840 --> 00:27:18,560
Speaker 1: be right back and we're back. We've got big news

485
00:27:18,600 --> 00:27:23,000
Speaker 1: to report. The super producer Catherine has located a TikTok

486
00:27:23,000 --> 00:27:27,480
Speaker 1: that appears to be an actual coyote discovering an actual trampoline.

487
00:27:28,040 --> 00:27:31,479
Speaker 1: Uh doesn't. It doesn't follow the same pattern that all

488
00:27:31,480 --> 00:27:34,160
Speaker 1: the other ones do where they immediately like know exactly

489
00:27:34,160 --> 00:27:37,160
Speaker 1: how to jump on it. But it does have that

490
00:27:38,119 --> 00:27:41,560
Speaker 1: like I was saying with the bear and the hammock.

491
00:27:41,760 --> 00:27:47,440
Speaker 1: It gives you you can witness them experiencing the weirdness

492
00:27:47,480 --> 00:27:50,000
Speaker 1: of a trampoline for the first time, where the what

493
00:27:50,080 --> 00:27:50,640
Speaker 1: the fuck.

494
00:27:51,160 --> 00:27:54,880
Speaker 3: You know what probably is like the best indicator of

495
00:27:54,960 --> 00:27:59,680
Speaker 3: like whether something like that is AI is like, especially

496
00:27:59,680 --> 00:28:03,680
Speaker 3: because AI is like needs to be like short and efficient,

497
00:28:03,680 --> 00:28:06,560
Speaker 3: and it's designed for probably TikToker intro.

498
00:28:07,040 --> 00:28:07,600
Speaker 2: It's there.

499
00:28:07,680 --> 00:28:10,720
Speaker 3: There's no like wobble ever, there's no.

500
00:28:10,760 --> 00:28:13,280
Speaker 2: Like false start. It's just like you go. It goes

501
00:28:13,320 --> 00:28:14,040
Speaker 2: in and does the.

502
00:28:14,000 --> 00:28:17,000
Speaker 1: Things in and they immediately know how to like see

503
00:28:17,040 --> 00:28:21,880
Speaker 1: each other and ship. Yeah, all right, we will put

504
00:28:21,920 --> 00:28:25,720
Speaker 1: the link to the actual animal on trampoline and one

505
00:28:25,760 --> 00:28:32,359
Speaker 1: of the actual bears on Hammocks in the show. Notes notes,

506
00:28:33,400 --> 00:28:36,919
Speaker 1: all right, onto the news, and this is important. This

507
00:28:37,000 --> 00:28:39,440
Speaker 1: is this is kind of like the episode we recorded

508
00:28:39,560 --> 00:28:42,960
Speaker 1: on January sixth, like as January six was still happening,

509
00:28:43,280 --> 00:28:46,480
Speaker 1: because it's still too early to tell. On the Sydney

510
00:28:46,480 --> 00:28:50,600
Speaker 1: Sweeney stuff, people are reposting clips from four years ago

511
00:28:50,640 --> 00:28:53,560
Speaker 1: of her crying and being like I didn't like, why

512
00:28:53,600 --> 00:28:56,000
Speaker 1: are you being mean to me? And being like she's

513
00:28:56,320 --> 00:29:00,400
Speaker 1: saying this about the Republican thing. What we do know

514
00:29:00,480 --> 00:29:05,280
Speaker 1: is that Donald Trump thinks that he has confirmation that

515
00:29:05,320 --> 00:29:10,640
Speaker 1: Sidney Sweeney is a registered Republican and is this is

516
00:29:12,000 --> 00:29:14,400
Speaker 1: I think this is like genuinely working, Like I think

517
00:29:14,440 --> 00:29:17,680
Speaker 1: this is going to be the thing that allows them

518
00:29:17,680 --> 00:29:20,920
Speaker 1: to move on from the Epstein thing. It's them being

519
00:29:21,000 --> 00:29:26,120
Speaker 1: like everybody hates Sidney Sweeney's boobs, we love them, and

520
00:29:26,880 --> 00:29:32,040
Speaker 1: you're too mean to Sidney Sweeney. It's amazing to me

521
00:29:32,200 --> 00:29:35,640
Speaker 1: that like on the front page of the Drug Report,

522
00:29:35,800 --> 00:29:39,960
Speaker 1: like five days later, it's still like a picture of

523
00:29:39,960 --> 00:29:43,560
Speaker 1: Sidney Sweeney being like mega hero. It's just like, yeah,

524
00:29:43,640 --> 00:29:45,480
Speaker 1: all right, Jesus Christ.

525
00:29:45,720 --> 00:29:50,920
Speaker 3: I guess I was like, I mean, she just listen,

526
00:29:51,120 --> 00:29:55,280
Speaker 3: maybe I'm just distrusting of all rich white people. She

527
00:29:55,440 --> 00:29:58,120
Speaker 3: just seemed like the most obvious Republican I've ever seen

528
00:29:58,160 --> 00:29:58,760
Speaker 3: in my life.

529
00:29:58,760 --> 00:30:00,160
Speaker 2: Am I fucking crazy?

530
00:30:00,600 --> 00:30:00,800
Speaker 1: Yeah?

531
00:30:00,840 --> 00:30:04,360
Speaker 2: I don't know. I don't know enough about her to

532
00:30:04,520 --> 00:30:05,840
Speaker 2: like get.

533
00:30:07,280 --> 00:30:12,040
Speaker 3: I guess yeah, yeah.

534
00:30:10,240 --> 00:30:11,960
Speaker 2: I guess she's from Florida Fair.

535
00:30:12,440 --> 00:30:12,720
Speaker 1: Yeah.

536
00:30:12,800 --> 00:30:15,040
Speaker 3: But I'm just like and it's like, oh, don't you

537
00:30:15,080 --> 00:30:17,680
Speaker 3: know I had a bunch of people in mega hats

538
00:30:17,680 --> 00:30:19,240
Speaker 3: at my party, Like, don't.

539
00:30:20,160 --> 00:30:21,160
Speaker 1: They were doing bits?

540
00:30:21,520 --> 00:30:21,720
Speaker 2: Yeah?

541
00:30:21,800 --> 00:30:27,240
Speaker 1: Yeah, exactly, the fake mega hat fake up. Anyways, We're

542
00:30:27,280 --> 00:30:31,240
Speaker 1: obviously going to be staying on top of this further

543
00:30:31,400 --> 00:30:35,840
Speaker 1: reporting as the story develops, whether she's a she's a

544
00:30:35,880 --> 00:30:37,680
Speaker 1: registered Republican.

545
00:30:37,200 --> 00:30:40,880
Speaker 2: Or not, and whether it's wrong to hate her I suppose, yeah.

546
00:30:40,760 --> 00:30:46,400
Speaker 1: Exactly what, and whether anybody hates her, yeah, or anybody,

547
00:30:46,560 --> 00:30:48,160
Speaker 1: just like, I don't know, man, it was like a

548
00:30:48,200 --> 00:30:53,440
Speaker 1: weirdly worded ad that people should have thought twice about,

549
00:30:53,680 --> 00:30:58,200
Speaker 1: and that it's like a great example of white obliviousness.

550
00:30:58,480 --> 00:31:01,800
Speaker 1: That's all. Yeah, yeah, we're not saying she should be arrested.

551
00:31:02,160 --> 00:31:07,640
Speaker 1: I don't think. No, yeah, not yet, but that wait

552
00:31:07,680 --> 00:31:11,640
Speaker 1: a second, Yeah, we'll see what her public apology looks like.

553
00:31:13,200 --> 00:31:15,680
Speaker 1: But yeah, I don't know. It's I mean, you are

554
00:31:15,720 --> 00:31:18,160
Speaker 1: the expert, Yo, Is this racist? Is that? Is that

555
00:31:18,240 --> 00:31:19,400
Speaker 1: ad racist?

556
00:31:19,520 --> 00:31:21,720
Speaker 2: Andrew t the Great Jeans One.

557
00:31:21,920 --> 00:31:27,280
Speaker 3: I mean, it's like I think that the thing is

558
00:31:27,400 --> 00:31:31,719
Speaker 3: it's like, no, but read like it's not like the

559
00:31:31,840 --> 00:31:33,080
Speaker 3: racist part of it.

560
00:31:33,080 --> 00:31:34,920
Speaker 2: It's the like, hey, read the room.

561
00:31:35,040 --> 00:31:39,480
Speaker 3: We're already in a white supremacist eugenics era, So it's

562
00:31:39,560 --> 00:31:42,760
Speaker 3: it's the thing that is racist is not having any

563
00:31:42,880 --> 00:31:48,880
Speaker 3: clue anywhere. People might be like, hey, not not the time.

564
00:31:49,440 --> 00:31:54,360
Speaker 1: Right, Yeah, all right, let's talk about nothing I like

565
00:31:54,440 --> 00:31:57,840
Speaker 1: more than kicking off a Monday morning episode on two hours. Yeah,

566
00:31:58,520 --> 00:32:03,840
Speaker 1: by tucking into a big isle of fluffy economic policy.

567
00:32:05,840 --> 00:32:10,880
Speaker 1: Usually conversations about the FED and jobs reports are above

568
00:32:11,040 --> 00:32:13,640
Speaker 1: my pay grade. I guess, like, just like I don't know,

569
00:32:13,760 --> 00:32:16,720
Speaker 1: too boring, Like it's just too boring. Yeah, I can't

570
00:32:16,800 --> 00:32:18,560
Speaker 1: the fed at a certain point, I'm just like, I

571
00:32:18,600 --> 00:32:21,960
Speaker 1: don't I don't care enough. And it feels like everybody

572
00:32:21,960 --> 00:32:25,480
Speaker 1: talking about this. It honestly feels like people talk about

573
00:32:25,480 --> 00:32:28,000
Speaker 1: the weather, where like it's there are just as many

574
00:32:28,040 --> 00:32:31,400
Speaker 1: people predicted or like sports, just as many people predicting

575
00:32:31,440 --> 00:32:34,840
Speaker 1: one thing as the other. Anyone who like appears to

576
00:32:34,880 --> 00:32:38,880
Speaker 1: be an expert will be wrong soon enough, Like it's

577
00:32:39,000 --> 00:32:43,400
Speaker 1: just people kind of being fooled by randomness for a living.

578
00:32:43,920 --> 00:32:49,280
Speaker 2: Yeah, but I think it's so like I mean, I guess.

579
00:32:49,280 --> 00:32:52,560
Speaker 3: The other thing, though, is this is where the insidious

580
00:32:52,680 --> 00:32:54,880
Speaker 3: shit that has ruined our society is hiding.

581
00:32:55,400 --> 00:32:56,200
Speaker 1: Yes, that's right.

582
00:32:56,200 --> 00:32:58,960
Speaker 3: I'm not saying that you specifically or any of us,

583
00:32:59,120 --> 00:33:02,240
Speaker 3: or I'll just go on a limb anyone on this zoom,

584
00:33:02,280 --> 00:33:06,880
Speaker 3: but right now should should know about this.

585
00:33:07,040 --> 00:33:08,560
Speaker 1: I'll see where you're going with us, let me see

586
00:33:08,560 --> 00:33:09,160
Speaker 1: where you're leading.

587
00:33:09,240 --> 00:33:11,520
Speaker 3: I'm just saying we're all too dumb to understand this,

588
00:33:11,560 --> 00:33:14,200
Speaker 3: which is fine, but this is where they hide the

589
00:33:14,280 --> 00:33:17,160
Speaker 3: injustice or the root of the injustice.

590
00:33:17,200 --> 00:33:19,320
Speaker 2: Obviously the injustice is just out there.

591
00:33:19,760 --> 00:33:23,000
Speaker 1: Yeah, it feels but I know enough to feel like

592
00:33:23,400 --> 00:33:28,240
Speaker 1: this whole thing seems stacked to like be have a

593
00:33:28,280 --> 00:33:34,640
Speaker 1: lot of like functions to like take money and redistribute

594
00:33:34,640 --> 00:33:38,040
Speaker 1: it upwards. And then that's all disguised in a bunch

595
00:33:38,120 --> 00:33:41,600
Speaker 1: of not randomness, in a bunch of complexity. Right, So

596
00:33:41,720 --> 00:33:44,280
Speaker 1: like by design, it's like they want us to be like,

597
00:33:44,400 --> 00:33:47,920
Speaker 1: you're too dumb. Yeah I am, Like I am kind

598
00:33:47,920 --> 00:33:51,200
Speaker 1: of too dumb, Like I'm not gonna, yeah, learn all

599
00:33:51,280 --> 00:33:51,680
Speaker 1: this shit.

600
00:33:51,880 --> 00:33:55,320
Speaker 2: I think it's well, right, what I mean though, it's

601
00:33:55,360 --> 00:33:55,840
Speaker 2: not that.

602
00:33:55,800 --> 00:33:59,840
Speaker 3: You like we're too dumb to understand the concept that

603
00:34:00,200 --> 00:34:01,720
Speaker 3: trap I believe.

604
00:34:01,680 --> 00:34:04,760
Speaker 2: As someone who doesn't really know, I suppose is.

605
00:34:04,640 --> 00:34:09,080
Speaker 3: That what you're saying is the complexity the like education

606
00:34:09,480 --> 00:34:12,439
Speaker 3: and the specific type of education to like cut through

607
00:34:12,440 --> 00:34:15,719
Speaker 3: the jargon and the complexity that they present this stuff with.

608
00:34:17,040 --> 00:34:19,400
Speaker 3: That's the part that we're quote too dumb to understand.

609
00:34:19,560 --> 00:34:21,959
Speaker 3: But I honest I would also argue that's the part

610
00:34:22,080 --> 00:34:23,120
Speaker 3: that is the trap.

611
00:34:23,520 --> 00:34:26,520
Speaker 1: Like, yeah, it's the trap, and it makes you everybody

612
00:34:26,560 --> 00:34:30,680
Speaker 1: who speaks that language suddenly like no longer lives on

613
00:34:30,719 --> 00:34:33,120
Speaker 1: the same planet as made it feels, you know.

614
00:34:33,080 --> 00:34:37,520
Speaker 3: But it's because they're describing like this is like pr right,

615
00:34:37,560 --> 00:34:44,320
Speaker 3: the jobs report doesn't really like like with the way

616
00:34:44,360 --> 00:34:47,200
Speaker 3: they use and present the statistics like the actual report, yes,

617
00:34:47,280 --> 00:34:50,480
Speaker 3: but with sorry, the actual numbers do mean something. The

618
00:34:50,560 --> 00:34:55,680
Speaker 3: report is weirdly and I'm sorry, I'll just like read

619
00:34:55,760 --> 00:34:59,040
Speaker 3: down a little bit in the document. I think Trump

620
00:34:59,080 --> 00:35:03,560
Speaker 3: isn't totally raw what he's like, the shit is propaganda, right.

621
00:35:03,920 --> 00:35:04,959
Speaker 2: His solution of.

622
00:35:04,960 --> 00:35:09,080
Speaker 3: It should be my propaganda is the But he's not

623
00:35:09,120 --> 00:35:11,640
Speaker 3: wrong that it's propaganda.

624
00:35:11,719 --> 00:35:15,319
Speaker 1: I think it's Yeah, I think it's them doing some

625
00:35:15,680 --> 00:35:20,440
Speaker 1: best guessing, maybe some convenient best guessing at the current moment,

626
00:35:21,920 --> 00:35:23,520
Speaker 1: but I do I don't know.

627
00:35:23,520 --> 00:35:26,360
Speaker 3: So well, so if I made the piece that feels

628
00:35:26,440 --> 00:35:30,200
Speaker 3: to me like it's propaganda, like any statistics, is that

629
00:35:30,320 --> 00:35:32,800
Speaker 3: like what you choose to emphasize and how you choose

630
00:35:32,800 --> 00:35:35,440
Speaker 3: to collate the you know, like group the bucket to

631
00:35:35,680 --> 00:35:39,359
Speaker 3: buckets together. Because I just remember, like near the end

632
00:35:39,400 --> 00:35:42,600
Speaker 3: of the Biden term, when you know, I don't know

633
00:35:42,640 --> 00:35:46,239
Speaker 3: the mechanism, you know, without being sinister, it doesn't have

634
00:35:46,320 --> 00:35:47,279
Speaker 3: to be a conspiracy.

635
00:35:47,360 --> 00:35:50,200
Speaker 2: But like they were doing things like.

636
00:35:52,000 --> 00:35:57,120
Speaker 3: Treating new Uber drivers as new corporations because technically, since

637
00:35:57,160 --> 00:36:00,640
Speaker 3: they are independent contractors, a new uber driver is technically

638
00:36:00,719 --> 00:36:03,680
Speaker 3: a new boss that has been formed and a new

639
00:36:03,719 --> 00:36:06,880
Speaker 3: employee that has been created for that business, and that

640
00:36:07,040 --> 00:36:07,960
Speaker 3: books are really.

641
00:36:07,880 --> 00:36:11,120
Speaker 1: Bulls constantly, yea, in all sorts of different ways to

642
00:36:11,160 --> 00:36:13,160
Speaker 1: make it seem like people are doing better than there.

643
00:36:13,320 --> 00:36:14,600
Speaker 1: That's absolutely for sure.

644
00:36:14,680 --> 00:36:15,000
Speaker 2: Yeah.

645
00:36:15,080 --> 00:36:18,920
Speaker 3: So, but electing to have that as a statistic, like

646
00:36:19,120 --> 00:36:22,560
Speaker 3: perhaps that was like you know, just like like it's

647
00:36:22,600 --> 00:36:25,640
Speaker 3: it's the old data model was insufficient to describe it.

648
00:36:25,920 --> 00:36:29,040
Speaker 3: But also on the face of it, if you're putting

649
00:36:29,040 --> 00:36:31,799
Speaker 3: this report out, you like you have to look at

650
00:36:31,800 --> 00:36:34,400
Speaker 3: that and be like, well, that's not a completely bullshit

651
00:36:34,440 --> 00:36:37,120
Speaker 3: measure of the economy and still put it out, which

652
00:36:37,160 --> 00:36:40,160
Speaker 3: is a you know, a propaganda an editorial choice, not

653
00:36:40,239 --> 00:36:41,120
Speaker 3: a math.

654
00:36:40,960 --> 00:36:42,640
Speaker 1: Choice, right, that's right.

655
00:36:42,760 --> 00:36:43,040
Speaker 2: Yeah.

656
00:36:43,040 --> 00:36:45,399
Speaker 1: So there's a ton of framing being done, a ton

657
00:36:45,440 --> 00:36:50,120
Speaker 1: of like context being supplied. So the big the reason

658
00:36:50,120 --> 00:36:54,280
Speaker 1: this is the story is Friday August first, the Bureau

659
00:36:54,320 --> 00:37:00,239
Speaker 1: of Labor Statistics re like redid the numbers for the

660
00:37:00,239 --> 00:37:03,280
Speaker 1: past like three months. Basically we're like, we got new

661
00:37:03,520 --> 00:37:07,120
Speaker 1: data and the numbers for the past three months are

662
00:37:07,320 --> 00:37:13,840
Speaker 1: worse than we thought they were. This is bad for Trump, Like,

663
00:37:13,920 --> 00:37:17,600
Speaker 1: this is kind of a tremendously inconvenient development for him,

664
00:37:18,000 --> 00:37:22,799
Speaker 1: because after the tariffs, everybody, all the economists were like,

665
00:37:22,880 --> 00:37:26,080
Speaker 1: this is probably gonna be bad for the economy and

666
00:37:26,239 --> 00:37:29,359
Speaker 1: for like jobs and like people who are like further down,

667
00:37:29,440 --> 00:37:30,840
Speaker 1: like the people who are going to feel this, or

668
00:37:30,920 --> 00:37:35,920
Speaker 1: like further down, you know, poor people who have like

669
00:37:36,719 --> 00:37:40,120
Speaker 1: you know, our our paycheck to paycheck. And then there

670
00:37:40,160 --> 00:37:45,600
Speaker 1: were a series of numbers released that were actually non disastrous,

671
00:37:46,239 --> 00:37:48,920
Speaker 1: and the New York Times and a bunch of people

672
00:37:49,480 --> 00:37:51,759
Speaker 1: were like, there was a New York Times article that

673
00:37:51,840 --> 00:37:54,439
Speaker 1: was like, the economy seems healthy. Were the warnings about

674
00:37:54,480 --> 00:37:57,719
Speaker 1: tariffs over blown? And like that's what that's been sort

675
00:37:57,760 --> 00:38:00,640
Speaker 1: of the consensus for the past three months. He did

676
00:38:00,680 --> 00:38:04,680
Speaker 1: the tariffs, these jobs reports and all the you know,

677
00:38:04,960 --> 00:38:07,239
Speaker 1: data and tea leaves that they read came in and

678
00:38:07,239 --> 00:38:12,400
Speaker 1: were like pretty normal and non disastrous, and they're like, Okay,

679
00:38:13,280 --> 00:38:16,920
Speaker 1: then maybe he knows what he's doing. Let's let Trump cook.

680
00:38:17,520 --> 00:38:20,960
Speaker 1: And then they were like, oh yeah, those initial tea

681
00:38:21,000 --> 00:38:25,359
Speaker 1: leaves were like guesses, and the numbers have actually now

682
00:38:25,440 --> 00:38:31,160
Speaker 1: come in and they're bad. Yeah, And it's kind of

683
00:38:31,200 --> 00:38:33,960
Speaker 1: in line with people who were like, this is going

684
00:38:34,000 --> 00:38:38,839
Speaker 1: to be a disaster because of the tariffs, and so

685
00:38:39,360 --> 00:38:43,640
Speaker 1: he was pissed. He was like, this is you know,

686
00:38:44,080 --> 00:38:46,359
Speaker 1: this person who made up this report is in the

687
00:38:46,440 --> 00:38:52,920
Speaker 1: tank for Biden. Harris and fired them. And so people

688
00:38:53,040 --> 00:38:58,480
Speaker 1: are now you know, nervous because they're like the you know,

689
00:38:59,200 --> 00:39:04,320
Speaker 1: we had the appearance of a objective, Like these people

690
00:39:04,360 --> 00:39:08,000
Speaker 1: were not political appointees who were collecting these numbers of

691
00:39:08,000 --> 00:39:12,480
Speaker 1: releasing them, right, They were you know, people who were

692
00:39:12,520 --> 00:39:16,360
Speaker 1: supposed to just be doing calling balls and strikes as

693
00:39:16,440 --> 00:39:18,320
Speaker 1: politicians love to talk about.

694
00:39:18,160 --> 00:39:19,440
Speaker 2: Whether they were doing that or not.

695
00:39:20,600 --> 00:39:24,680
Speaker 1: Like it it seems like before they were certainly keeping

696
00:39:24,680 --> 00:39:28,040
Speaker 1: in line with their like like we like you were

697
00:39:28,080 --> 00:39:33,120
Speaker 1: mentioning before, like under Biden, they released very positive numbers

698
00:39:33,600 --> 00:39:36,440
Speaker 1: and then like as the election approached they had to

699
00:39:36,480 --> 00:39:40,279
Speaker 1: be like ah, those numbers, you know, and like how

700
00:39:40,320 --> 00:39:42,480
Speaker 1: they pull them back. And now it seems like they're

701
00:39:42,480 --> 00:39:44,759
Speaker 1: doing the same thing again under Trump, where they're like

702
00:39:44,880 --> 00:39:50,480
Speaker 1: feel like the initial estimates are what the the existing

703
00:39:50,520 --> 00:39:54,280
Speaker 1: administration wants to hear, and then like once the actual

704
00:39:54,360 --> 00:39:56,640
Speaker 1: numbers come in, they have to revise them backward.

705
00:39:56,920 --> 00:39:57,120
Speaker 2: Right.

706
00:39:57,440 --> 00:40:00,000
Speaker 3: I think the thing that Trump doesn't seem to understand

707
00:40:00,120 --> 00:40:02,600
Speaker 3: because why wood he is a fucking moron. Is like,

708
00:40:05,400 --> 00:40:08,440
Speaker 3: this is a bipartisan effort. But I just think he

709
00:40:08,480 --> 00:40:12,480
Speaker 3: doesn't understand that, like, given that the two parties are

710
00:40:12,560 --> 00:40:15,319
Speaker 3: the Democrats, a center right party, and the Republicans a

711
00:40:15,360 --> 00:40:20,560
Speaker 3: far right party, this is not like, this is not

712
00:40:20,640 --> 00:40:24,080
Speaker 3: like politically motivated in the way that he thinks it's

713
00:40:24,120 --> 00:40:27,080
Speaker 3: not politically motivated to make the more right wing party

714
00:40:27,480 --> 00:40:31,560
Speaker 3: seem look bad. They always overestimate, not always, but they

715
00:40:31,560 --> 00:40:36,160
Speaker 3: mostly overestimate the jobs report because their politics is really

716
00:40:36,239 --> 00:40:39,160
Speaker 3: just about making the market seem good. So the big

717
00:40:39,200 --> 00:40:43,520
Speaker 3: headline is always things are this higher number, and then the.

718
00:40:43,400 --> 00:40:47,200
Speaker 2: Revisions downwards are always quiet.

719
00:40:46,640 --> 00:40:49,400
Speaker 3: Like because it's just about perception, Like they don't actually

720
00:40:49,480 --> 00:40:52,120
Speaker 3: care about how many people have fucking jobs. They just

721
00:40:52,160 --> 00:40:55,080
Speaker 3: want a better headline given the data set. So it's

722
00:40:55,120 --> 00:40:58,040
Speaker 3: just like the data set with all of its estimates

723
00:40:58,160 --> 00:41:01,600
Speaker 3: is always high rolled, Like the headline is always high rolled,

724
00:41:02,080 --> 00:41:05,360
Speaker 3: and then the low roll comes in because that's closer

725
00:41:05,360 --> 00:41:06,000
Speaker 3: to reality.

726
00:41:06,960 --> 00:41:10,839
Speaker 1: Yeah, that seems like how they're already doing it. And

727
00:41:10,960 --> 00:41:14,640
Speaker 1: he's just like, you know, he could have just like

728
00:41:15,080 --> 00:41:18,680
Speaker 1: kind of quietly let there be some you know, troubling

729
00:41:18,719 --> 00:41:22,840
Speaker 1: headlines in the Economist in the Atlantic and instead he

730
00:41:23,520 --> 00:41:26,920
Speaker 1: fired the person and like drew attention to it. So

731
00:41:26,920 --> 00:41:29,600
Speaker 1: it might just be like part of this new thing

732
00:41:29,640 --> 00:41:34,319
Speaker 1: where he's you know, bad at crisis management, as we

733
00:41:34,360 --> 00:41:37,080
Speaker 1: saw with the Epstein case, Like he might be losing

734
00:41:37,080 --> 00:41:40,400
Speaker 1: his touch on that front. So yeah, there's gonna be

735
00:41:40,440 --> 00:41:42,920
Speaker 1: a lot of like confident people claiming they know what's

736
00:41:42,960 --> 00:41:46,160
Speaker 1: going to happen next. Nobody knows shit. Does seem like

737
00:41:46,920 --> 00:41:50,680
Speaker 1: literally everybody who is saying taris are going to not

738
00:41:50,920 --> 00:41:55,800
Speaker 1: work and hurt people seem to be like the numbers

739
00:41:55,840 --> 00:41:58,319
Speaker 1: that are coming in seem to be like leading in

740
00:41:58,360 --> 00:41:58,840
Speaker 1: that direction.

741
00:41:59,160 --> 00:42:01,759
Speaker 3: Yeah, it's just really like a matter of like all

742
00:42:01,800 --> 00:42:04,120
Speaker 3: he cares about and understands is the headlines.

743
00:42:04,360 --> 00:42:07,680
Speaker 2: So like, I mean, listen, I know that.

744
00:42:07,800 --> 00:42:11,680
Speaker 3: Like weirdly, one of my stances has been like obviously

745
00:42:11,760 --> 00:42:14,640
Speaker 3: this is bad, but like we're not really thinking about

746
00:42:14,760 --> 00:42:18,839
Speaker 3: how little or like like things were so bad under

747
00:42:18,880 --> 00:42:23,480
Speaker 3: Biden and like like on any of these measures that

748
00:42:23,520 --> 00:42:27,920
Speaker 3: were like really screaming about like you know, fucking Trump

749
00:42:28,000 --> 00:42:32,359
Speaker 3: being a monster on like Biden and Obama deported a

750
00:42:32,440 --> 00:42:37,120
Speaker 3: ton of fucking people. Yes, so like, yeah, this is bad,

751
00:42:37,600 --> 00:42:43,000
Speaker 3: but we're really like hiding that it's not that much worse.

752
00:42:43,400 --> 00:42:45,520
Speaker 1: It's not that much different than what we already have.

753
00:42:45,760 --> 00:42:47,280
Speaker 2: It's the headlines are worse.

754
00:42:47,440 --> 00:42:50,000
Speaker 3: But like and so I think, like, like, if Trump

755
00:42:50,040 --> 00:42:52,560
Speaker 3: wants a political studge, that's going to high roll the

756
00:42:52,840 --> 00:42:56,319
Speaker 3: like labor statistics, and then you know, I guess what

757
00:42:56,520 --> 00:42:58,960
Speaker 3: maybe won't happen is the downward revision.

758
00:42:59,440 --> 00:43:04,560
Speaker 1: Guess what want? Yeah, yeah, I do think like this.

759
00:43:05,200 --> 00:43:08,440
Speaker 1: The thing about Trump is he does ship that's so

760
00:43:08,719 --> 00:43:12,000
Speaker 1: obviously wrong that it makes it easy for me to

761
00:43:12,200 --> 00:43:15,400
Speaker 1: understand as somebody who like, you know, my brain turns

762
00:43:15,400 --> 00:43:19,320
Speaker 1: off when I hear people talking about the FED revising

763
00:43:19,400 --> 00:43:23,000
Speaker 1: interest rates lower, like you know, like I now know

764
00:43:23,160 --> 00:43:27,120
Speaker 1: what that means because of bullshit like this where he's

765
00:43:27,280 --> 00:43:31,960
Speaker 1: just like he brings a kindergartener's understanding to all this

766
00:43:32,080 --> 00:43:37,040
Speaker 1: ship and it makes it possible for me, a person

767
00:43:37,080 --> 00:43:39,680
Speaker 1: with the mind of a kindergartener, to understand.

768
00:43:39,680 --> 00:43:42,319
Speaker 3: But that's the thing is, well, I I you know,

769
00:43:42,400 --> 00:43:44,360
Speaker 3: I think you're you're not giving yourself out of credit.

770
00:43:44,840 --> 00:43:47,000
Speaker 2: All is exactly.

771
00:43:47,080 --> 00:43:49,360
Speaker 3: All you have to have is the like the competency

772
00:43:49,440 --> 00:43:52,360
Speaker 3: of a second grader, and you're running laps around this

773
00:43:52,719 --> 00:43:53,600
Speaker 3: the fucking president.

774
00:43:53,880 --> 00:43:56,479
Speaker 2: Yeah, and that's kind of all it takes. That's right.

775
00:43:56,680 --> 00:43:59,520
Speaker 1: Let's uh, let's take a quick break. We'll come back

776
00:44:00,480 --> 00:44:03,520
Speaker 1: trying to find some bullshit to talk about that has

777
00:44:03,560 --> 00:44:05,319
Speaker 1: nothing to do with economics.

778
00:44:05,480 --> 00:44:20,160
Speaker 2: We'll be right back. God, and we're back, and.

779
00:44:21,000 --> 00:44:22,960
Speaker 1: I mean, we got a lot of shit to talk

780
00:44:23,000 --> 00:44:27,239
Speaker 1: about here. Trump did like he's mad at Charlemagne the

781
00:44:27,280 --> 00:44:28,600
Speaker 1: God right now?

782
00:44:28,880 --> 00:44:34,080
Speaker 2: Did you see that? So it would but oh my god, what.

783
00:44:35,040 --> 00:44:38,560
Speaker 1: Yeah, just his statement is so wild because Charlemage the

784
00:44:38,560 --> 00:44:42,239
Speaker 1: Gods like statements are so middle of the road, like

785
00:44:42,719 --> 00:44:46,520
Speaker 1: just the most like CNN level to I'm like, wait,

786
00:44:46,600 --> 00:44:48,960
Speaker 1: is Charlemagne the God like running for president because he's

787
00:44:49,040 --> 00:44:52,600
Speaker 1: like I wouldn't give his presidency a good rating simply

788
00:44:52,640 --> 00:44:55,280
Speaker 1: because the least of us are still being impacted the worst.

789
00:44:55,400 --> 00:44:58,480
Speaker 2: It's like very It's like, yeah, I feel like most.

790
00:44:59,400 --> 00:45:01,400
Speaker 2: But he's also like I would.

791
00:45:01,239 --> 00:45:05,279
Speaker 3: Say, running for president as like a fucking Republican, Like

792
00:45:05,400 --> 00:45:07,840
Speaker 3: it's not strong at.

793
00:45:07,680 --> 00:45:10,359
Speaker 1: All, not strongly where yeah exactly, Like it's like Eric

794
00:45:10,400 --> 00:45:13,799
Speaker 1: Adams type shit, like anything that takes away medicaid from

795
00:45:13,880 --> 00:45:16,719
Speaker 1: people and will put people in a worse financial situation

796
00:45:16,920 --> 00:45:23,440
Speaker 1: than they were previously. I'm not for damn, and Trump

797
00:45:23,840 --> 00:45:29,440
Speaker 1: got very mad, like he's just he can't take you know, well, I.

798
00:45:29,440 --> 00:45:30,680
Speaker 2: Mean I think that's the thing.

799
00:45:30,680 --> 00:45:33,680
Speaker 3: It's like, no matter how like centrist or right wing,

800
00:45:33,719 --> 00:45:36,000
Speaker 3: the black person, he's like, Trump is a type of

801
00:45:36,080 --> 00:45:39,080
Speaker 3: racist that any criticism from a black person, he can't

802
00:45:39,400 --> 00:45:42,800
Speaker 3: can't do it. He cannot brook. Yeah, he cannot truck

803
00:45:42,840 --> 00:45:43,400
Speaker 3: with calls.

804
00:45:43,520 --> 00:45:47,160
Speaker 1: So he responded calling Charlotte they got a low IQ individual.

805
00:45:47,800 --> 00:45:51,839
Speaker 1: He seemed to really like it is weird because again,

806
00:45:51,960 --> 00:45:53,879
Speaker 1: this is the sort of ship that's being said all

807
00:45:53,960 --> 00:45:58,120
Speaker 1: day every day. I think the thing that really pissed

808
00:45:58,160 --> 00:46:01,440
Speaker 1: him off is the Charlemagne the God gets to be

809
00:46:01,480 --> 00:46:05,080
Speaker 1: called the god and like he is like because he

810
00:46:05,120 --> 00:46:10,320
Speaker 1: specifically like opens his things say calling Charlemagne racist les bag,

811
00:46:10,800 --> 00:46:13,719
Speaker 1: which I don't like. Race was not mentioned at all

812
00:46:13,760 --> 00:46:17,040
Speaker 1: in the in the interview, uh racist Lee's bag, Charlemagne

813
00:46:17,160 --> 00:46:19,279
Speaker 1: the God? Why is he allowed to use the word

814
00:46:19,320 --> 00:46:22,239
Speaker 1: god when describing himself? Can anyone imagine the up war

815
00:46:22,280 --> 00:46:25,239
Speaker 1: there would be if I use that nickname, just kind

816
00:46:25,280 --> 00:46:27,800
Speaker 1: of floating that out there. He's a little a Q

817
00:46:28,000 --> 00:46:37,480
Speaker 1: individual racist. But yeah, I think he's mad, like what

818
00:46:37,520 --> 00:46:40,440
Speaker 1: he wants to be able to call himself the god.

819
00:46:40,840 --> 00:46:45,440
Speaker 1: He also claims to have ended five wars, including avoiding

820
00:46:45,560 --> 00:46:50,360
Speaker 1: war between India and Pakistan, despite the fact that India

821
00:46:50,360 --> 00:46:54,440
Speaker 1: has then like what he it really like gave me

822
00:46:54,480 --> 00:46:57,840
Speaker 1: a clear image of him, just like watching news channels

823
00:46:58,000 --> 00:47:02,160
Speaker 1: and any good news he spread it for and genuinely

824
00:47:02,200 --> 00:47:07,520
Speaker 1: believes it's like a w for him because it's these headlines.

825
00:47:07,840 --> 00:47:11,320
Speaker 3: He didn't cause the bad thing to happen, so by inaction.

826
00:47:11,120 --> 00:47:15,000
Speaker 1: Right, by not getting involved somehow. Yeah, and then when

827
00:47:15,000 --> 00:47:18,799
Speaker 1: there's bad, just wrong idiots should have should have asked

828
00:47:18,840 --> 00:47:22,880
Speaker 1: me for help. Anyways. That's that's another Trump thing that

829
00:47:23,120 --> 00:47:24,560
Speaker 1: we don't need to do.

830
00:47:24,600 --> 00:47:27,560
Speaker 3: You think much too, do you think his truth social posts?

831
00:47:27,680 --> 00:47:31,799
Speaker 3: I mean, they're they're written in a way, you know, God,

832
00:47:31,880 --> 00:47:33,800
Speaker 3: Now I'm going to repeat a thing that I'm hoping

833
00:47:33,960 --> 00:47:36,640
Speaker 3: is not merely apocryphal, but you know, like those like

834
00:47:37,080 --> 00:47:41,719
Speaker 3: when Nigerian scam emails were a popular thing, like the

835
00:47:41,800 --> 00:47:43,239
Speaker 3: thing that I've read that now I'm just.

836
00:47:43,320 --> 00:47:44,920
Speaker 2: A rede a lot of money on those scams.

837
00:47:44,960 --> 00:47:49,320
Speaker 3: We're not wrong, Yeah, exactly, Well you freed all those princes.

838
00:47:50,000 --> 00:47:52,080
Speaker 2: Is like they're written in a.

839
00:47:52,000 --> 00:47:56,640
Speaker 3: Way that because they're they're they're like a mass you know,

840
00:47:57,040 --> 00:48:00,160
Speaker 3: essentially like a mass data collection, and they're really looking

841
00:48:00,480 --> 00:48:03,759
Speaker 3: for the true outliers and credulity and the people who

842
00:48:03,920 --> 00:48:07,200
Speaker 3: really do not understand what's going on. And so the

843
00:48:07,280 --> 00:48:11,200
Speaker 3: typos and all the weird phrasing actually is like, you know,

844
00:48:11,239 --> 00:48:13,480
Speaker 3: whether it was intentional or not, was like part of

845
00:48:13,520 --> 00:48:16,480
Speaker 3: the product because it like would filter out anyone that

846
00:48:16,520 --> 00:48:19,400
Speaker 3: would waste your time, anyone who like wasn't going to

847
00:48:19,440 --> 00:48:21,960
Speaker 3: send you money is not reading this.

848
00:48:22,080 --> 00:48:24,160
Speaker 2: And I do think the way these truth social.

849
00:48:23,920 --> 00:48:27,200
Speaker 3: Posts, it's like like they're literally written in a way

850
00:48:27,239 --> 00:48:30,040
Speaker 3: that is like idiotic. And I do not, by the way,

851
00:48:30,040 --> 00:48:31,759
Speaker 3: I think this is intentional. I just think this is

852
00:48:31,840 --> 00:48:34,839
Speaker 3: like a byproduct of what's happening. But like anyone who

853
00:48:34,880 --> 00:48:37,560
Speaker 3: buys it is stupid enough to think Trump is a

854
00:48:37,600 --> 00:48:38,640
Speaker 3: good president.

855
00:48:38,320 --> 00:48:42,080
Speaker 1: Right, And yeah, it's not like really it's genius. It's

856
00:48:42,160 --> 00:48:46,160
Speaker 1: just that's his accidentally being the right.

857
00:48:47,800 --> 00:48:51,160
Speaker 3: Time just filtered the right people, or I guess the

858
00:48:51,200 --> 00:48:54,319
Speaker 3: wrong people filtered the worst people into the bucket. But

859
00:48:54,360 --> 00:48:57,040
Speaker 3: it's like and and I guess our education system is

860
00:48:57,080 --> 00:49:00,880
Speaker 3: creating more of the worst people through its connection. But exactly,

861
00:49:01,320 --> 00:49:04,080
Speaker 3: it's just like it's it's it's the thing and and

862
00:49:04,120 --> 00:49:08,320
Speaker 3: the other thing that I think it did is like

863
00:49:08,320 --> 00:49:09,880
Speaker 3: like his like type of dumbness.

864
00:49:09,960 --> 00:49:12,400
Speaker 2: Specifically, I think.

865
00:49:13,680 --> 00:49:15,560
Speaker 3: The thing that it does really well, and I'm not

866
00:49:15,600 --> 00:49:19,880
Speaker 3: saying this is like intentional, is that it allows like

867
00:49:20,120 --> 00:49:24,439
Speaker 3: people like me, but even worse, like like Democrats, to think,

868
00:49:24,680 --> 00:49:28,560
Speaker 3: get a load of this moron is a sufficient political

869
00:49:28,600 --> 00:49:30,520
Speaker 3: message as opposed to like, you.

870
00:49:30,480 --> 00:49:32,600
Speaker 2: Know, helping anyone.

871
00:49:33,080 --> 00:49:35,880
Speaker 3: So like he was able to sort of and again

872
00:49:36,000 --> 00:49:38,600
Speaker 3: I don't think intentionally, but like the end result was

873
00:49:38,600 --> 00:49:41,560
Speaker 3: he sort of baited Democrats into thinking get a load

874
00:49:41,560 --> 00:49:42,000
Speaker 3: of this.

875
00:49:42,400 --> 00:49:45,720
Speaker 1: Dummy that he won the election.

876
00:49:46,080 --> 00:49:49,680
Speaker 2: Yeah, just like a necessary political strategy.

877
00:49:49,800 --> 00:49:54,160
Speaker 1: Yeah, yeah, it's weird. I don't chink over and over

878
00:49:54,360 --> 00:49:59,759
Speaker 1: every day as we wake up, like what the hey,

879
00:50:00,440 --> 00:50:05,360
Speaker 1: come on, can't believe I listen to this guy? Yeah,

880
00:50:05,480 --> 00:50:10,640
Speaker 1: it's a It is interesting, Like I wonder. I do wonder,

881
00:50:10,760 --> 00:50:14,000
Speaker 1: Like I don't think that Trump is like some genius

882
00:50:14,000 --> 00:50:17,440
Speaker 1: who like cracked the code by studying, like you know,

883
00:50:17,480 --> 00:50:20,680
Speaker 1: I think he's always written like a unhinged, you know,

884
00:50:20,760 --> 00:50:22,600
Speaker 1: narcissist who's just like yeah.

885
00:50:22,640 --> 00:50:23,480
Speaker 2: Has no filter.

886
00:50:24,000 --> 00:50:27,960
Speaker 1: But I do wonder at a certain point if they

887
00:50:27,960 --> 00:50:32,120
Speaker 1: are now studying and teaching this communication style and like

888
00:50:32,480 --> 00:50:38,560
Speaker 1: trump truth social posts and the you know Nigerian prints

889
00:50:38,640 --> 00:50:42,160
Speaker 1: emails are like the order texts that they're like, now

890
00:50:42,200 --> 00:50:45,640
Speaker 1: this is this is what you have to understand about

891
00:50:46,000 --> 00:50:50,560
Speaker 1: the rules of like punctuation when speaking in this language

892
00:50:50,680 --> 00:50:53,359
Speaker 1: to appeal to you know what I mean? Like JD

893
00:50:53,560 --> 00:50:57,120
Speaker 1: Vance is like studying this three hours a day.

894
00:50:57,400 --> 00:50:59,520
Speaker 3: I will say, for what it's worth, it does seem

895
00:50:59,600 --> 00:51:03,359
Speaker 3: more ore than science though, right because like like like,

896
00:51:03,480 --> 00:51:05,080
Speaker 3: how do you really dissect this?

897
00:51:06,520 --> 00:51:09,840
Speaker 1: It's just like, yeah, well I think grammar in general

898
00:51:09,920 --> 00:51:13,160
Speaker 1: is fucking stupid like that. Yeah, the any like English

899
00:51:13,160 --> 00:51:15,920
Speaker 1: classes I had where they were like just teaching you

900
00:51:16,040 --> 00:51:19,120
Speaker 1: the rules of speaking. It's like some of the ship

901
00:51:19,200 --> 00:51:21,120
Speaker 1: isn't even true most of the time.

902
00:51:21,080 --> 00:51:23,520
Speaker 2: And it doesn't communicate anything. Why I listen.

903
00:51:23,600 --> 00:51:25,919
Speaker 3: I fucking not love grammar, but I have a real

904
00:51:26,000 --> 00:51:29,040
Speaker 3: mind for it. So there's a part of relationship. These

905
00:51:29,080 --> 00:51:31,279
Speaker 3: are the rules, but it is like who cares I

906
00:51:31,960 --> 00:51:33,400
Speaker 3: at least understand.

907
00:51:33,200 --> 00:51:34,200
Speaker 2: Their stupid rules.

908
00:51:34,239 --> 00:51:37,360
Speaker 3: I just know them all the reason I would say,

909
00:51:37,440 --> 00:51:40,439
Speaker 3: I wonder. I think the trap once again for the JD.

910
00:51:40,560 --> 00:51:44,080
Speaker 3: Vances of the world is thinking that it is like

911
00:51:44,360 --> 00:51:46,560
Speaker 3: and it's the same mindset that apply applies to like

912
00:51:46,640 --> 00:51:49,919
Speaker 3: AI tech pros. They're the ones that think, oh, it's

913
00:51:49,960 --> 00:51:54,040
Speaker 3: the punctuation or it's the you know, the random capitalization.

914
00:51:54,160 --> 00:51:58,920
Speaker 3: It's not that, it's the like the higher order. Don't

915
00:51:58,920 --> 00:52:02,239
Speaker 3: give a fuckingness that is the thing that matters. And

916
00:52:02,320 --> 00:52:04,680
Speaker 3: I think, like, yeah, once again, it's like a trap

917
00:52:04,719 --> 00:52:06,960
Speaker 3: to devote, you know, for all the for all the

918
00:52:07,000 --> 00:52:10,400
Speaker 3: would be proto populist fascists out there. Don't waste your

919
00:52:10,400 --> 00:52:14,360
Speaker 3: time studying the actual syntax. Just get yourself an authentically

920
00:52:14,560 --> 00:52:16,040
Speaker 3: racist moron vibe.

921
00:52:17,320 --> 00:52:18,560
Speaker 2: It's all authenticity.

922
00:52:19,040 --> 00:52:23,200
Speaker 1: It's all about the right mixture of Celsius and high noon.

923
00:52:24,280 --> 00:52:26,439
Speaker 2: By the way, this was all not talking about Trump.

924
00:52:26,520 --> 00:52:28,279
Speaker 1: Yeah, this is all how we were not going to

925
00:52:28,320 --> 00:52:30,839
Speaker 1: talk about that. This is great. The structure of this

926
00:52:30,920 --> 00:52:34,920
Speaker 1: is Sydney Sweeney Trump, Trump and then Celsiu's recall. So

927
00:52:35,120 --> 00:52:41,480
Speaker 1: just a nice American sandwich, So high noon. A company

928
00:52:41,520 --> 00:52:45,840
Speaker 1: that makes actually alcoholic vodka, Seltzers, has had to issue

929
00:52:45,880 --> 00:52:49,680
Speaker 1: a recall for two production lots of its drink after

930
00:52:49,840 --> 00:52:55,440
Speaker 1: some of its vodka Seltzers were mislabeled as Celsius. And

931
00:52:55,480 --> 00:52:58,520
Speaker 1: then like that's the little r in a circle astro

932
00:52:58,719 --> 00:53:05,160
Speaker 1: Vibe Energy Drink Comma Sparkling Blue Ras addition with a

933
00:53:05,239 --> 00:53:14,080
Speaker 1: silver top end quote according to the FDA, which I

934
00:53:14,120 --> 00:53:17,600
Speaker 1: feel like just amazing marketing here. Again, Like I don't

935
00:53:17,640 --> 00:53:22,279
Speaker 1: think this was intentional, but like the fact that every time, like.

936
00:53:22,320 --> 00:53:24,520
Speaker 2: Because they're very clearly, very proud.

937
00:53:24,280 --> 00:53:26,600
Speaker 1: Of the name that they've come up with for this

938
00:53:26,719 --> 00:53:30,040
Speaker 1: product that based on both a registered mark and a

939
00:53:30,320 --> 00:53:35,440
Speaker 1: trademark indication in a single drink title, would would indicate

940
00:53:35,440 --> 00:53:38,560
Speaker 1: that they're like, guys, we fucking did it. We nailed it,

941
00:53:40,000 --> 00:53:44,400
Speaker 1: And so now everybody just has to repeat over and

942
00:53:44,440 --> 00:53:50,160
Speaker 1: over again to beware when drinking your Celsius registered astro

943
00:53:50,280 --> 00:53:54,200
Speaker 1: Vibe trademark Energy Drink Sparkling Blue Ras addition with a

944
00:53:54,239 --> 00:54:00,000
Speaker 1: silver top. I feel like equally bad for you, probably

945
00:54:00,560 --> 00:54:01,719
Speaker 1: both of those drinks.

946
00:54:02,440 --> 00:54:05,840
Speaker 3: I mean, but it's also like that's so many words

947
00:54:06,120 --> 00:54:09,200
Speaker 3: that it is. If you like you got to see

948
00:54:09,280 --> 00:54:13,439
Speaker 3: energy drink in there, yes, Otherwise what is this?

949
00:54:14,080 --> 00:54:15,480
Speaker 2: Yes? What is this thing?

950
00:54:16,840 --> 00:54:19,680
Speaker 1: It's trying to an astro vibe. If I had to

951
00:54:19,719 --> 00:54:22,000
Speaker 1: describe it, I'd say it's like kind of giving an

952
00:54:22,040 --> 00:54:25,840
Speaker 1: astro vibe. I will say, this is like dangerous for

953
00:54:25,880 --> 00:54:30,000
Speaker 1: sober people, because sober people love love energy drinks, and

954
00:54:30,880 --> 00:54:35,080
Speaker 1: they just beware out there. This does feel like, on

955
00:54:35,160 --> 00:54:40,440
Speaker 1: the other hand, a Willy Wonka Golden Ticket style event

956
00:54:40,840 --> 00:54:47,600
Speaker 1: for like freshmen in high school. You know, just holy guys,

957
00:54:47,800 --> 00:54:50,400
Speaker 1: we could we could have a party this weekend if

958
00:54:50,400 --> 00:54:54,440
Speaker 1: we just get the right box of fucking Celsius astro

959
00:54:54,600 --> 00:54:57,719
Speaker 1: Vibe energy drinks with the silver top.

960
00:54:58,520 --> 00:55:01,120
Speaker 3: I know seven kom clad to put me down or

961
00:55:01,280 --> 00:55:06,080
Speaker 3: knock down. However, in the dirt, I'm just gonna say,

962
00:55:06,160 --> 00:55:11,319
Speaker 3: like that, Basel, I don't really drink anymore, but I

963
00:55:11,360 --> 00:55:14,480
Speaker 3: would much rather have a high noon than a Celsius

964
00:55:14,560 --> 00:55:18,480
Speaker 3: a registered trademark Aservibe trademark to drink sparkling blue presidition

965
00:55:18,560 --> 00:55:19,240
Speaker 3: with a silver.

966
00:55:19,280 --> 00:55:21,920
Speaker 2: That shit sounds like it would fucking kill me for real.

967
00:55:23,239 --> 00:55:26,480
Speaker 1: It truly sounds like it contains the thing that they

968
00:55:26,520 --> 00:55:29,239
Speaker 1: inject in your in your veins when they have to

969
00:55:29,280 --> 00:55:31,680
Speaker 1: like track them at the hospital, like track your blood

970
00:55:31,680 --> 00:55:32,719
Speaker 1: flow at the hospital.

971
00:55:33,600 --> 00:55:34,239
Speaker 2: Yeah.

972
00:55:34,360 --> 00:55:38,240
Speaker 3: Yeah, it changes the polarity of your white blood cells.

973
00:55:40,160 --> 00:55:42,879
Speaker 1: It's like now your veins are pumping towards your heart

974
00:55:43,000 --> 00:55:48,480
Speaker 1: and your your arteries are pumping back towards your yah

975
00:55:49,200 --> 00:55:49,560
Speaker 1: it is.

976
00:55:49,640 --> 00:55:52,759
Speaker 3: It is sort of like the Ouno reverse card of

977
00:55:52,800 --> 00:55:55,919
Speaker 3: blood flow within the human body.

978
00:55:56,480 --> 00:55:59,320
Speaker 1: My veins turned red and my arteries turned blue.

979
00:56:03,280 --> 00:56:04,800
Speaker 2: I mean, I listen.

980
00:56:04,880 --> 00:56:07,239
Speaker 3: I know I'm fucking old, and there's nothing worse than

981
00:56:07,239 --> 00:56:10,840
Speaker 3: an old man complaining about what these kids today are drinking.

982
00:56:10,840 --> 00:56:13,319
Speaker 2: But this is fucking crazy. Guys like.

983
00:56:14,760 --> 00:56:19,080
Speaker 3: You know, red Bull, the Red Bull, to Monster to

984
00:56:19,600 --> 00:56:23,840
Speaker 3: rock Star Pipeline. At least those are just normal words.

985
00:56:24,719 --> 00:56:25,720
Speaker 3: It's fucking nuts.

986
00:56:26,840 --> 00:56:30,400
Speaker 1: I mean, Celsius astrobo energy drink, sparkling d residition with

987
00:56:30,400 --> 00:56:31,680
Speaker 1: the silver top over top.

988
00:56:33,440 --> 00:56:35,759
Speaker 2: I have to hand it.

989
00:56:35,760 --> 00:56:38,360
Speaker 1: I mean, okay, so one, they're getting that name in

990
00:56:38,440 --> 00:56:41,120
Speaker 1: there multiple times with the product you can you call

991
00:56:41,320 --> 00:56:45,440
Speaker 1: full name. Also, what have we learned from the Labuobu

992
00:56:45,600 --> 00:56:51,560
Speaker 1: craze is that the mystery box unboxing thing is like

993
00:56:51,719 --> 00:56:52,760
Speaker 1: half the battle.

994
00:56:53,480 --> 00:56:54,200
Speaker 2: Oh my god.

995
00:56:54,560 --> 00:56:58,960
Speaker 1: Yes, this is like now giving people a reason to

996
00:56:59,560 --> 00:57:04,239
Speaker 1: have a mystery box on boxing of just being like

997
00:57:04,120 --> 00:57:06,680
Speaker 1: like am I vibrating off of my seat?

998
00:57:06,840 --> 00:57:08,200
Speaker 2: Or am I a little bit drunk?

999
00:57:08,960 --> 00:57:12,880
Speaker 3: We are far from just having a can that just

1000
00:57:12,880 --> 00:57:16,920
Speaker 3: says drink and it can contain anything from like liquid

1001
00:57:16,960 --> 00:57:21,080
Speaker 3: fentanyl to like fucking gravy.

1002
00:57:21,280 --> 00:57:23,040
Speaker 2: And it's just like who cares.

1003
00:57:23,200 --> 00:57:29,160
Speaker 1: Drink to die fast, drink to die slow. Yeah, it's

1004
00:57:29,200 --> 00:57:33,240
Speaker 1: just a chart of how fast it kills you to drink.

1005
00:57:35,920 --> 00:57:36,360
Speaker 2: All right.

1006
00:57:36,920 --> 00:57:41,800
Speaker 1: Anyways, be safe out there, people, that's gonna do it

1007
00:57:41,840 --> 00:57:46,240
Speaker 1: for this Monday morning, Monday after on the East Coast.

1008
00:57:46,840 --> 00:57:49,080
Speaker 2: We're going to be back tomorrow morning.

1009
00:57:48,720 --> 00:57:50,440
Speaker 1: With a whole last episode of the show.

1010
00:57:50,600 --> 00:57:53,240
Speaker 2: Yeah, if we live were not a guarantee.

1011
00:57:53,920 --> 00:57:57,960
Speaker 1: I've got I've got six of these high Noon Raspberry

1012
00:57:58,840 --> 00:58:01,760
Speaker 1: Edition with a silver caps that I'm about to shotgun

1013
00:58:01,920 --> 00:58:05,200
Speaker 1: and let God sort them out all all.

1014
00:58:05,000 --> 00:58:06,439
Speaker 2: From each production lot.

1015
00:58:06,520 --> 00:58:10,000
Speaker 3: We Jack has built himself sort of like a novelty

1016
00:58:10,120 --> 00:58:12,120
Speaker 3: revolver that's probably like.

1017
00:58:12,120 --> 00:58:14,600
Speaker 2: Two feet and he's just loaded up these cans.

1018
00:58:14,920 --> 00:58:23,320
Speaker 1: Yeah, it's like the quantum machine gun for drunkenness. All right,

1019
00:58:23,600 --> 00:58:24,560
Speaker 1: talk to you all tomorrow.

1020
00:58:24,720 --> 00:58:28,880
Speaker 2: Bye peace. The Daily Zeit Guist is executive produced by

1021
00:58:28,920 --> 00:58:31,840
Speaker 2: Catherine Law, co produced by bee Wayge.

1022
00:58:31,880 --> 00:58:35,760
Speaker 1: Co produced by Victor Wright, co written by j M McNabb,

1023
00:58:36,120 --> 00:58:38,760
Speaker 1: and edited and engineered by Brian Jefferies.