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Speaker 1: I was undecided heading in last night.

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Speaker 2: Yeah, but you are a lot of questions when they

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Speaker 2: interviewed the undecided afterwards, and they're just like, mm hmmm, yeah,

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Speaker 2: I just saw a bunch of lies on both sides.

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Speaker 1: I don't I don't really know.

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Speaker 3: Yeah, I mean, part of it feels like if you

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Speaker 3: can somehow credibly say you're undecided, you will simply get

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Speaker 3: to be in the New York Times every weekend. Yeah, exactly.

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Speaker 1: You are a media resource. You are like the greatest

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Speaker 1: jewel the media could possibly stumble.

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Speaker 3: Across, scientific anomaly essentially. Yeah. Yeah.

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Speaker 1: I was once on a jury for like a wrongful

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Speaker 1: death lawsuit, and we just had this one person who

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Speaker 1: was holding out for no no reason, like no, just

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Speaker 1: like other then. I just don't know, you know, I.

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Speaker 3: Was gonna say it's basically poly shore Jerry Duty. Like

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Speaker 3: the reason they're undecided is because they like what they

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Speaker 3: get from being undecided.

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Speaker 1: Yeah yeah, yeah, they like the attention.

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Speaker 3: Yeah, how else are.

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Speaker 1: They going to get it?

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Speaker 3: Howlse are they going to get like like allegedly the

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Speaker 3: smartest people, the smartest people they deal with every day

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Speaker 3: to be like, I really want to know what's in

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Speaker 3: your what makes you tick.

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Speaker 1: I think we should just rebrand the Daily Zeikeeist as

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Speaker 1: like the last undecided podcasts, like where we haven't figured it.

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Speaker 3: Out, Watch the numbers, go through the roof.

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Speaker 1: Watch out, but it's all New York Times reporters. Hell

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Speaker 1: in the.

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Speaker 2: Weed.

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Speaker 1: We need you on our network somehow publish your podcast

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Speaker 1: in the print edition.

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Speaker 3: We just need to. We'll just yeah, we'll just have

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Speaker 3: chat GPT Trent's. We'll just tip it out, no editing needed.

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Speaker 1: Hello the Internet, and welcome to season three point fifty five,

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Speaker 1: episode four of Daily sa Guy Stay production. Yes, it

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Speaker 1: production of iHeartRadio. Also, we are America's only undecided podcast.

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Speaker 1: We still don't know who we're voting for either way.

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Speaker 1: It could go either way. You know you convince me,

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Speaker 1: CNN interviewer. This is a podcast where we take a

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Speaker 1: deep dive into America's share consciousness. And it is Thursday,

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Speaker 1: September twelfth, twenty twenty four. My name is Jack O'Brien

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Speaker 1: aka Soup. It's ministrony soup, So come on and cook

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Speaker 1: me soup, drink it through a straw. Soup that is

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Speaker 1: courtesy of Halcyon Salad. On the discord, it was meant

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Speaker 1: to be a help the Beatles ak, but you know

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Speaker 1: who can sing? Really? I'm thrilled to be joined in

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Speaker 1: our second seat by one of the very faces on Mountain, Zeitemore,

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Speaker 1: a hilarious and brilliant producer, TV writer. You know him

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Speaker 1: from the US this racist podcast.

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Speaker 3: It's Andrew t Aka this time.

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Speaker 4: Every time to that guys calling, I put down my

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Speaker 4: phone and say, I need to find us song bad people, No,

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Speaker 4: so I can make it ak a since solo.

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Speaker 1: He that was nice.

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Speaker 3: Well, finally finally had the time to come.

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Speaker 1: Up with something that was an Andrew t ridge.

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Speaker 3: I mean barely. Yeah, I was scrambling.

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Speaker 1: That was great, Andrew, how are you doing?

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Speaker 3: I'm alive. I'm only working on off of secondary takes,

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Speaker 3: so I did not watch the debate. So we'll see,

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Speaker 3: we'll see the last second scratch, We'll see what it

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Speaker 3: feels better. But a brain, you know, yeah, just I'm

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Speaker 3: the first derivative of of knowledge here. But we'll see.

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Speaker 1: We'll all come. You are, you know, going off of

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Speaker 1: secondhand takes that have been digested and then baby birded

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Speaker 1: back into your mouth. I am an undecided voter who

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Speaker 1: crotched the debate very closely, and you know, I saw it.

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Speaker 1: Both sides had interesting things to say. But Andrew, we

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Speaker 1: are thrilled to be joined in our third seat by

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Speaker 1: a wildly talented Ethio American vocalist, songwriter and composer whose

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Speaker 1: album was named among the best albums of the year

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Speaker 1: by band Camp in the Sunday Times. She's performed on

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Speaker 1: stages all over the world and is the host of

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Speaker 1: the podcast Movement with mcleat, which just dropped season two,

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Speaker 1: So go check it out. Please welcome back to the show,

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Speaker 1: the brilliantly talented mcle.

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Speaker 5: Hey, Hey, y'all. Good to be with you.

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Speaker 1: Good to have you here. I am yeah.

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Speaker 5: I didn't have an a ka, but.

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Speaker 1: I know you've like sung twice. Like when you entered

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Speaker 1: the chat. You were like hey, and I was like, god, damn, God,

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Speaker 1: damn it. That was beautiful and I was like, oh man, soup,

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Speaker 1: I'm a yeah. Anyways, So that the two things that

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Speaker 1: we've discovered are we've been told by Alpha God Jesse

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Speaker 1: waters are unacceptable to somebody who is masculine, is eating

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Speaker 1: soup and consuming anything through a straw. Just in case

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Speaker 1: you guys are wondering why the context.

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Speaker 3: For my aka solids only no waters. For water, yes,

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Speaker 3: has to be in the form of a brick like salt.

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Speaker 3: Lick is the only thing that one can consume. You

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Speaker 3: have to eat like a horse or like a have

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Speaker 3: a bag of oats strapped to the front of your face.

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Speaker 5: Because but you also drink through a straw.

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Speaker 1: What's that?

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Speaker 5: Would you also drink the bag of oats through a straw?

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Speaker 1: Yeah? Or yeah, I guess horses eat straw, which is

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Speaker 1: unacceptable because.

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Speaker 3: Straw is not manly. So yeah, this is this is

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Speaker 3: the right wing news. We come here for I love,

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Speaker 3: and we just have.

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Speaker 1: To, like, you know, get down to the fine you know,

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Speaker 1: dissect what what is manly like? Is eating straw unmanly?

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Speaker 1: Or is it kind of tough? Because this is.

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Speaker 3: Why we need a white man around. I would never

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Speaker 3: know this.

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Speaker 1: That's what I'm here for, you guys. I do, of

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Speaker 1: course tape Jesse Waters is the high priest of white manness.

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Speaker 1: But yeah, I do have to give credit where due.

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Speaker 1: All right, McLean, we are going to get to know

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

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Speaker 1: going to tell the listeners a couple of the things

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Speaker 1: we're talking about, and we are still talking about that debate.

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Speaker 1: We're gonna dig into the real story of what's happening

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Speaker 1: in Springfield, Ohio. Of course we heard a version of

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Speaker 1: events from former President Donald Trump.

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Speaker 3: We'll find out both sides. Anything could happen.

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Speaker 1: It's I mean, we're going to look into it. You know,

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Speaker 1: we've got to take this man at his work and

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Speaker 1: so we're gonna we're gonna dig in to the journalism

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Speaker 1: around this story. And of course we have to look

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Speaker 1: at Kamala Harris's earrings the night of the debate. They

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Speaker 1: looked awfully familiar to those of us who still click

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Speaker 1: on those banner ads that say one thing doctors don't

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Speaker 1: want you to know. If you if you pay attention

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Speaker 1: to those sidebar and banner ads, then you probably recognize

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Speaker 1: those earrings, or maybe not because this is complete bullshit story.

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Speaker 1: We might even get into Halloween costume ideas. Should we

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Speaker 1: talk about Halloween costume ideas?

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Speaker 3: It's the only news that matters.

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Speaker 1: Yeah, exactly, all of that, plenty more. But first mccleet,

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Speaker 1: we do like to ask our guest, what is something

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Speaker 1: from your search history that's revealing about who you are?

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Speaker 5: Okay, this is my nerd side all right, this is

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Speaker 5: a real left turn from our previous conversation yesterday.

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Speaker 1: We don't allow that as left. Unmanly. We were like

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Speaker 1: ups trucks up here. We only do right. We only

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Speaker 1: take yeah.

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Speaker 5: Only okay, okay, non sequitors. Cool right turns are left. Yes,

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Speaker 5: I was looking for h A. I was looking for

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Speaker 5: an audio program called the isotope Spectron.

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Speaker 1: Isotope spectron that just sounds smart as hell.

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Speaker 5: So I used to play this thing called a star guitar.

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Speaker 5: I got these star sounds from my friend at NASA,

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Speaker 5: and I would hybridize them with the sound of my

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Speaker 5: guitar live on stage. But it required this program that

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Speaker 5: they discontinued in twenty eleven, And every once in a while,

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Speaker 5: like every six months, I look up has it come back?

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Speaker 5: Because I can't actually find any program that will let

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Speaker 5: me play a star guitar anymore. So that that was

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Speaker 5: my yesterday.

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Speaker 1: So it takes the noise from stars, because that is

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Speaker 1: like an underrated thing that scientists do. They'll like point

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Speaker 1: a microphone of sorts at space and be like, this

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Speaker 1: is what Jupiter sounds like, and it just sounds It

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Speaker 1: sounds like a fucking horror movie. Up there. It's just

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Speaker 1: like Jupiter is being tortured. Somebody goes a lot of that.

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Speaker 5: Yeah, there's it's all three aliens. Yeah, yeah, you know,

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Speaker 5: but yeah, there's it's sonic like curve soonifications of stars,

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Speaker 5: and there's one in particular that has just like the

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Speaker 5: name of the star is just a lot of let

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Speaker 5: and numbers, like there's no actual name, but it's the

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Speaker 5: pulse is like this incredible rhythm. And so I would

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Speaker 5: and you could choose this program. Let you choose, like

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Speaker 5: I only want the highs from the star and I'll

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Speaker 5: take the mids and the lows from the guitar, and

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Speaker 5: you could totally like work with it. And it's gone.

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Speaker 5: It's gone, and every once in a while I look

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Speaker 5: forward to come back because it was really fun to

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Speaker 5: play the Star guitar.

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Speaker 1: It's zotpe, yeah, spectrum, yeah, sweetwater. It's telling us it's

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Speaker 1: out of stock.

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Speaker 5: That's all of stock.

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Speaker 1: That sucks.

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Speaker 5: But I never but yeah, I'm hanging on for a

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Speaker 5: resurgence one day.

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Speaker 1: That's cool. But you have like messed with it and

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Speaker 1: like played music with the Sound of Stars.

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Speaker 5: I used to tour with it. I used to play

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Speaker 5: it on tour and now I can't anymore. So that's nice.

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Speaker 3: Is music like that?

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Speaker 1: Like, are there like the way that computers like just

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Speaker 1: suddenly like any software from like twenty eleven, Like I

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Speaker 1: wouldn't be able to run on a modern No you.

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Speaker 5: Counter you count run it?

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Speaker 1: No, it would.

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Speaker 3: It feels like it would almost be like the best

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Speaker 3: bet is finding a working like twenty eleven computer.

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Speaker 5: Right, do you think that I haven't tried that?

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Speaker 3: Yeah?

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Speaker 5: I have one hundred person No, no.

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Speaker 1: Ted that he thinks you didn't try that.

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Speaker 5: I totally try. I totally like I have. I have

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Speaker 5: tried it all. I've tried it all because it was

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Speaker 5: so cool, it was so special. But anyway, that was

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Speaker 5: my yesterday searching on the isotope spectron and then thinking,

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Speaker 5: oh hey, I think I'll talk about this on TDZ too.

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Speaker 3: There you go.

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Speaker 1: Yeah, and if anybody has the inside lane on an

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Speaker 1: isodope's spectrum, you know, hit mclet up. I don't know,

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Speaker 1: maybe we for some musicians. I know we have musicians

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Speaker 1: in our audience, so yeah, I just that makes me

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Speaker 1: wonder if all those people who like paid a bunch

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Speaker 1: of money to be like I got the mixing board

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Speaker 1: the thriller was made on, Like are they just they

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Speaker 1: pay the money and they're like, oh, there's no way

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Speaker 1: to use this or because that's not I think analog analogy.

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Speaker 3: Yeah, it's that like weird mid digital obsolescence. It's like

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Speaker 3: what do you do with all your jazz drives?

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Speaker 1: Like, yup?

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Speaker 3: Exactly?

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Speaker 5: That is exactly.

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Speaker 3: Yes, Yeah, you're like, right, anything old, you just plug

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Speaker 3: it in as long as there's still electricity or maybe

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Speaker 3: even not.

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Speaker 5: Yeah, you can solder it to fix it, but you

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Speaker 5: can't solder the icotate spectra.

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Speaker 1: Yeah, that's cool. I don't do much soldering myself, but

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Speaker 1: I'm working on it, Jesse. I'm working on it, Jesse Waters.

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Speaker 1: What is something you think is underrated?

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Speaker 5: I think opening acts are underrated. Like if you go

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Speaker 5: to a show and you're like, oh, I've got I'm

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Speaker 5: going to dinner with my friends and I'm going to

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Speaker 5: show up right when the headliner starts, and you're like

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Speaker 5: and and I think people just think of it as

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Speaker 5: like like kind of a fill you know, But if

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Speaker 5: you look at opening acts, like even if you looked

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Speaker 5: at a venues list of opening acts, you'd start to

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Speaker 5: get a sense of a local music scene and it's

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Speaker 5: a way like somebody put a lot of thought and

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Speaker 5: heart into what you might like as an audience member,

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Speaker 5: and it's like the it's like a way to get

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Speaker 5: a pulse on a local arts and music scene. So

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Speaker 5: I think opening acts are underrated and you should show

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Speaker 5: up when the show actually starts and check out that

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Speaker 5: band that's first, and you like might fall totally in

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Speaker 5: love with some new music.

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Speaker 1: Yeah, there's like legendary stories in my family. My older

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Speaker 1: sister went and saw a band and what the White

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Speaker 1: Stripes opened for them, like in this like tiny like

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Speaker 1: venue and she got to see the White Stripes before

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Speaker 1: like they were a thing.

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Speaker 5: And that's what I'm talking about.

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Speaker 1: My dad went to I forget what the main band was,

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Speaker 1: but he saw Billy Joel was the opening act, and

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Speaker 1: he's like and he had that place rocking man, Like he.

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Speaker 6: Still he loves to tell that story. But yeah, like

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Speaker 6: there's it's it's worth it to like try it out.

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Speaker 6: And yeah, that's not something that I had actually realized.

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Speaker 6: I guess I dumbly assumed that like they always traveled together,

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Speaker 6: like the opening act would like be on tour with them,

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Speaker 6: but it's actually they'll just like be like, this is

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Speaker 6: a cool LA band that we kind of fuck with.

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Speaker 6: They'll open for us while we're in LA, and then

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Speaker 6: you know, same with Saint Louis totally.

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Speaker 5: And sometimes you get people like though there'll be a

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Speaker 5: West Coast opening act or you'll go like sometimes it's paired,

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Speaker 5: but most of the time it's either local or regional,

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Speaker 5: and it's like it's always a good opportunity. So yeah,

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Speaker 5: underrated opening back opening band.

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Speaker 3: I know this is my most like these kids today

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Speaker 3: type opinion. But it's like, if you guys like live

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Speaker 3: performance so much, see the whole thing are you paying for?

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Speaker 1: Yeah?

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Speaker 3: What else?

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Speaker 5: A you're paying eight for?

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Speaker 3: Yeah?

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Speaker 1: You know, yeah, yeah, it's definitely worth it. And it's

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Speaker 1: like the the the energy is much more laid back

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Speaker 1: during the opening and sometimes you see like something that's

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Speaker 1: sort of embarrassing, but some people who aren't great but

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Speaker 1: usually not though usually they're pretty good, so rare.

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Speaker 3: That was the most realistic part of the movie Trap

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Speaker 3: was when the opening band was playing to like twenty

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Speaker 3: five people in a like you know, Taylor Swift style concert.

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Speaker 1: Who oh so you saw Trap?

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Speaker 3: Oh yeah, I saw Trap.

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Speaker 1: Good.

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Speaker 3: I really enjoyed it. While I think I have I

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Speaker 3: have some notes. I just had some thoughts.

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Speaker 1: Yeah, what mcley is something that you think is overrated?

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Speaker 5: Matching socks? Nobody sees them. You don't have to match

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Speaker 5: your socks. It's all good. Just pick any sock from

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Speaker 5: the sock drawer and go on with your day. I like,

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Speaker 5: I never match. I never matched my socks.

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Speaker 1: And how different are we talk? And like different colors,

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Speaker 1: just different different genres of socks. Like do you have

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Speaker 1: one dress sock one sweat sock?

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Speaker 3: Sure?

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Speaker 5: Yeah, but what you don't match is like or what

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Speaker 5: you like. Even if the colors and styles don't match,

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Speaker 5: the lengths have to match. So it'll annoy me if

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Speaker 5: it's like one really short sock one longer sock, so

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Speaker 5: I have to match the lengths. But that's it.

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Speaker 3: There's still something, there's still something you can't you're not

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Speaker 3: full chaos. Well, yeah, I.

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Speaker 1: Was thinking of like an ankle sock and like a

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Speaker 1: big dress sock. That might be that might be tough.

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Speaker 3: I did that the other day, and I gotta tell

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Speaker 3: you it may be so uncomfortable.

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Speaker 1: You did that.

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Speaker 3: Yeah, unmatched and genuinely because so many, such a high

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Speaker 3: percentage of my socks are just Costco socks. So it

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Speaker 3: was medium gray and light gray, and I was like,

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Speaker 3: everyone can tell everybody knows, everyone knows about the secret

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Speaker 3: shame happening under my shoe right now. So stupid. I

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Speaker 3: understand I'm deeply stupid in your correct but wow, it

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Speaker 3: really I could barely handle it the whole time, just

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Speaker 3: white knuckling it through my day, like no one's looking

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Speaker 3: at my ankles.

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Speaker 1: Right, So is your sock drawer like, are at the

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Speaker 1: laundry stage? Are you not even trying to match? Is

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Speaker 1: it just like a salad of different individual socks.

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Speaker 5: It's it's a it's a salad, it's a soupo pourri,

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Speaker 5: it's a it's all of the above. Yeah no, no,

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Speaker 5: there's nothing nothing, there's no effort goes into matching it.

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Speaker 3: It sounds liberating. Yeah, this really will shave. This is

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Speaker 3: a good like life hack. This will shave so much

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Speaker 3: time to save your grind.

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Speaker 1: Yeah. Yeah, I have socks that, like half of my

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Speaker 1: children's sock drawer is just like unmatched socks that I've

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Speaker 1: like kind of given up on. Those are so little though,

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Speaker 1: children's socks are so small, and they I'm pretty sure

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Speaker 1: they get sucked into. And I think I've said this

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Speaker 1: before on the show, and Miles was like, what are

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Speaker 1: you talking about? You sound crazy, man, this idea that

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Speaker 1: socks get like sucked into like an intake in the

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Speaker 1: washing machine. But then I've had people like kind of

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Speaker 1: back me up that that can happen. That like there's

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Speaker 1: especially small children's socks will just get inhaled by laundry machines.

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Speaker 5: There was this episode of the Family Guy that I saw,

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Speaker 5: probably like I don't know, twenty years ago, where the

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Speaker 5: back of the laundry machine is Narnia with like a

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Speaker 5: half on half you know, human creature taking single socks

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Speaker 5: into the abyss. And so anyway, with the children's socks

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Speaker 5: a the really little ones, I completely agree, Like there's

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Speaker 5: there's no way, like I buy socks like every other

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Speaker 5: week because where did they all go? So I don't

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Speaker 5: match them for myself, but I do match them for

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Speaker 5: my child.

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Speaker 1: Yeah, well you're I'm the opposite because I'm a narcissistant.

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Speaker 1: I don't care what they look like. My children are

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Speaker 1: very sensitive about their socks. They're like what they don't

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Speaker 1: First of all, they don't like to wear them if

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Speaker 1: they're even like if they've been wearing them for like

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Speaker 1: an hour and then come back in the house, they

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Speaker 1: want them off immediately because they're like, they're dirty. Now,

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Speaker 1: get them off of me. So very the sock regimen

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Speaker 1: has to be disciplined in my household with these Wow,

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Speaker 1: these little fuckers.

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Speaker 5: Now, how often have I said that? Under my back?

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Speaker 1: All right, let's take a quick break and we'll come back,

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Speaker 1: and we'll keep talking about the debate on Tuesday night?

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Speaker 1: Was that already Tuesday? All right, we'll be right back,

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Speaker 1: and we're back. And as we talked about yesterday's trending episode,

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Speaker 1: you know, one of the big moments from the debate

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Speaker 1: seems to have been Donald Trump claiming that Haitian immigrants

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Speaker 1: are eating dogs and cats in Springfield, which one viewer

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Speaker 1: noticed synced up perfectly with the Peanuts music. Another person

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Speaker 1: synced it with the Simpson's song about like the Brothel

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Speaker 1: episode where they're singing about Springfield and then the songs

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Speaker 1: like in Springfield, and then it cuts to him saying,

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Speaker 1: they're eating our dogs, they're eating our cats in Springfield.

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Speaker 1: It's the source of a lot of great content, but

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Speaker 1: does it hold up under journalistic scrutiny? What do you

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Speaker 1: guys think? What's your guests? He did see it on TV, god,

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Speaker 1: which he didn't also like, I just wish instead of

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Speaker 1: being like, we talked to the like this official because

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Speaker 1: like obviously the people who are willing to believe him

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Speaker 1: are not like the type of people who are going

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Speaker 1: to believe a county official. If you had just been like,

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Speaker 1: where'd you what? TV? What'd you see it?

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Speaker 3: Yeah?

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Speaker 1: Yeah, oh you mean Facebook? When you say TV, you

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Speaker 1: mean Facebook, and you mean the guy who said my

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Speaker 1: neighbor's daughter's friend. Is that what you mean?

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Speaker 3: I will just say this and I think this is

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Speaker 3: maybe my only or most enduring legacy of the dailies,

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Speaker 3: like Guys from the Top was this is a perfect

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Speaker 3: example of the Gish gallop, which is he says one

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Speaker 3: insane lie and I'm just you know, loosely looking at

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Speaker 3: the amount of research to come and it is like,

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Speaker 3: you know, easily twenty x the number of words to

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Speaker 3: unravel his lie. No more like maybe like fifty x.

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Speaker 3: Like it's just it takes so much longer to depunk

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Speaker 3: something insane and blatantly lie than just like him saying it.

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Speaker 1: Yes, this is an early Andrew T like early years

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Speaker 1: of The Daily Like Andrew explained that there is a

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Speaker 1: technique in debate like the like official you know.

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Speaker 3: Or I know, yeah, but yeah, yeah, just like a

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Speaker 3: thing that like creationists. I heard a pioneered by about

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Speaker 3: Dwayne Gish, just serial creationist liar. Yeah, and he just

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Speaker 3: say so many lies that you can't you bunk them

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Speaker 3: in an amount of time.

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Speaker 1: Yeah, Like even if your brain wasn't like overloaded just

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Speaker 1: being like what you know, like it would still to

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Speaker 1: you so long to just debunk the madness.

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Speaker 3: Yeah.

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Speaker 1: So anyways, we're about to do that. We're about to

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Speaker 1: take the time to debunk it. Yeah, because it does

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Speaker 1: tie into just the overall incorrect assumptions underlying the rights

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Speaker 1: a fear of immigration. And I feel like also the

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Speaker 1: mainstream media, like the mainstream media just like allows them

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Speaker 1: to have these assumptions like that there are too many

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Speaker 1: people immigrating to the United States and that that's a problem,

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Speaker 1: like they give they start from that point, And studies

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Speaker 1: have shown that immigrants actually boost the economy by sparking

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Speaker 1: innovation and driving up wages. There is actually, according to

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Speaker 1: a lot of like economic theorists, a big shortage of

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Speaker 1: immigration in the United States, like the United States isn't high.

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Speaker 1: Like on a per capital level, the United States does

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Speaker 1: not have a lot of immigration. And when you look

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Speaker 1: at decades and decades of research, immigration is the thing

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Speaker 1: that fuels the US, like in all the ways that

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Speaker 1: the right wing likes to brag about, you know, it

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Speaker 1: makes the economy go. And actually they are far less

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Speaker 1: likely to commit crime, and like all these things that

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Speaker 1: they claim to care about are actually drastically helped by immigration.

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Speaker 1: And there's yeah, what would be helped with more immigration.

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Speaker 3: But anyway, as not to give Republicans strategies, but like

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Speaker 3: using the sample size of my family, they are exactly

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Speaker 3: primed to be Republicans except for the racism, right, Like literally,

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Speaker 3: if you just were racist, you would have these fuckers

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Speaker 3: as a voter.

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Speaker 1: Fuck like Lee's system, we will vote for you.

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Speaker 3: It's so weird.

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Speaker 1: Sorry, can't do it. Not gonna happen.

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Speaker 5: But one of this is based on logic or facts.

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Speaker 5: It's all about like it's all about you know, appealing

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Speaker 5: to be to be able to escapego to population for

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Speaker 5: the problems that they're not going to solve. Right, So

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Speaker 5: you know, we have so much evidence that like Europe,

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Speaker 5: for example, is facing a precipitous population decline, and the

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Speaker 5: only reason the United States is not going to go

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Speaker 5: through that is because of immigration, and that has all

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Speaker 5: kinds of other impacts, but we do need immigration. So

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Speaker 5: you know, this is not about logic, It's not about

439
00:25:37,720 --> 00:25:40,879
Speaker 5: anything that makes any kind of sense at all.

440
00:25:41,160 --> 00:25:46,320
Speaker 1: Yeah, but it is worth explaining what the logic actually

441
00:25:46,359 --> 00:25:49,919
Speaker 1: behind it would be I cared about that. So it

442
00:25:50,040 --> 00:25:53,720
Speaker 1: is true that as many as twenty thousand Haitian immigrants

443
00:25:53,760 --> 00:25:57,400
Speaker 1: have moved to Springfield, a town of around sixty thousand,

444
00:25:57,760 --> 00:26:03,320
Speaker 1: and this was by design. The population of Springfield had

445
00:26:03,359 --> 00:26:07,040
Speaker 1: trunk over the past several decades as businesses bailed on

446
00:26:07,600 --> 00:26:12,160
Speaker 1: the industry in Springfield, which I believe was car manufacturing

447
00:26:12,200 --> 00:26:15,040
Speaker 1: in the nineteen sixties. In nineteen sixty, the population was

448
00:26:15,080 --> 00:26:19,560
Speaker 1: eighty thousand. So the influx of Haitian immigrants restored the

449
00:26:19,600 --> 00:26:23,920
Speaker 1: population to what it was over sixty years ago. And

450
00:26:24,520 --> 00:26:27,000
Speaker 1: by the way, that quote that I was reading earlier,

451
00:26:27,040 --> 00:26:30,439
Speaker 1: immigrants boost the economy by sparking innovation and driving up

452
00:26:30,560 --> 00:26:33,439
Speaker 1: wages and all that stuff. If you're wondering what left

453
00:26:33,680 --> 00:26:37,480
Speaker 1: wing rag pointed that out, that's from the George W.

454
00:26:37,560 --> 00:26:43,120
Speaker 1: Bush Institute. So yeah, but in twenty fourteen, city officials

455
00:26:43,560 --> 00:26:47,199
Speaker 1: enacted a plan to woo businesses back to Springfield. It worked,

456
00:26:47,560 --> 00:26:50,760
Speaker 1: but they didn't have enough workers to fill all the

457
00:26:50,760 --> 00:26:55,800
Speaker 1: new jobs, and so they, you know, Haitian immigrants started

458
00:26:56,080 --> 00:26:59,840
Speaker 1: moving to Springfield. They were in the country legally, they

459
00:26:59,840 --> 00:27:01,840
Speaker 1: moved to the town in order to fill the gaping

460
00:27:01,920 --> 00:27:05,760
Speaker 1: labor shortage. There's about eight thousand new jobs were created

461
00:27:05,800 --> 00:27:09,720
Speaker 1: by twenty twenty and they have only increased since then.

462
00:27:10,119 --> 00:27:13,479
Speaker 1: But there just weren't enough workers to fill them. And

463
00:27:13,520 --> 00:27:17,600
Speaker 1: then a bunch of Haitian immigrants moved to town and

464
00:27:18,560 --> 00:27:23,440
Speaker 1: it has worked out incredibly well. That there was a

465
00:27:23,480 --> 00:27:28,399
Speaker 1: car accident that happened, an isolated car accident where a

466
00:27:28,680 --> 00:27:32,359
Speaker 1: Haitian immigrant with a foreign license not valid in Ohio

467
00:27:32,560 --> 00:27:35,760
Speaker 1: grammed into a school bus, killing an eleven year old boy.

468
00:27:36,280 --> 00:27:41,880
Speaker 1: And that is the thing that you know, sparked this

469
00:27:42,440 --> 00:27:46,840
Speaker 1: resident like Springfield backlash, where residents basically use the event

470
00:27:46,920 --> 00:27:51,000
Speaker 1: as a springboard for you know, racist vitriol and false

471
00:27:51,040 --> 00:27:55,800
Speaker 1: allegations and you know, people like want One quote from

472
00:27:55,840 --> 00:28:00,760
Speaker 1: the time at a chamber meeting was, how do you

473
00:28:00,800 --> 00:28:05,280
Speaker 1: know we aren't getting criminals, rapists and of course like

474
00:28:05,320 --> 00:28:09,760
Speaker 1: that that echoes Trump perfectly, you know, and the.

475
00:28:09,760 --> 00:28:12,840
Speaker 3: Way trying to find it. But also the kid's parents

476
00:28:13,080 --> 00:28:18,120
Speaker 3: have come out and said, like, stop using this as like, yeah,

477
00:28:18,160 --> 00:28:21,400
Speaker 3: against Haitians, So I stop using this incident, this tragedy.

478
00:28:21,840 --> 00:28:25,360
Speaker 1: Yes, the father of the son, I think, was at

479
00:28:25,400 --> 00:28:28,440
Speaker 1: a press conference being like Donald Trump and JD. Pants

480
00:28:28,960 --> 00:28:32,080
Speaker 1: should shut the fuck up and not say my son's

481
00:28:32,160 --> 00:28:35,280
Speaker 1: name anymore. And then in his name, yeah, not in

482
00:28:35,320 --> 00:28:39,160
Speaker 1: his name exactly. So yeah. In the wake of the accident,

483
00:28:39,280 --> 00:28:43,040
Speaker 1: Haitians were accused by some residents without evidence of drug trafficking,

484
00:28:43,480 --> 00:28:47,600
Speaker 1: retail theft, and disease. People are saying Haitians are occupying

485
00:28:47,680 --> 00:28:52,560
Speaker 1: our land. And then the pet thing happened because of

486
00:28:52,600 --> 00:28:56,680
Speaker 1: a single Facebook post citing a story about and this

487
00:28:56,800 --> 00:29:00,200
Speaker 1: is not an exaggeration. This is a story about the

488
00:29:00,320 --> 00:29:05,080
Speaker 1: user's neighbor's daughter's friend, who the person claimed found her

489
00:29:05,200 --> 00:29:09,440
Speaker 1: cat hanging from a branch at a Haitian neighbor's home

490
00:29:09,560 --> 00:29:13,000
Speaker 1: being carved up to be eaten. But nobody that is

491
00:29:13,120 --> 00:29:20,400
Speaker 1: completely unsourced other than the user's neighbor's dodger's friend, which.

492
00:29:21,320 --> 00:29:26,560
Speaker 3: Meant les boomer credulity here it's so embarrassing.

493
00:29:26,600 --> 00:29:30,360
Speaker 1: I know, it is so embarrassing, and it's he's like

494
00:29:30,800 --> 00:29:34,240
Speaker 1: Trump is kind of like a cartoon liar, you know,

495
00:29:34,400 --> 00:29:37,600
Speaker 1: he's just like a guy who Yeah, he seems like

496
00:29:37,600 --> 00:29:40,320
Speaker 1: a liar. And some people don't seem to mind that,

497
00:29:40,400 --> 00:29:44,040
Speaker 1: but I am just like wondering him falling for that

498
00:29:44,160 --> 00:29:48,640
Speaker 1: makes him look inept, you know, yeah, like just yes, so.

499
00:29:48,840 --> 00:29:51,400
Speaker 3: But in a way that's like that's this is this

500
00:29:51,480 --> 00:29:55,400
Speaker 3: is exactly who his like base loves, Like that's who

501
00:29:55,440 --> 00:29:59,239
Speaker 3: they are. They are falling for it. So what's it's all?

502
00:29:59,360 --> 00:30:01,760
Speaker 3: I just will say, like, you know, as a as

503
00:30:01,760 --> 00:30:04,120
Speaker 3: an Asian person who also you know, is from a

504
00:30:04,160 --> 00:30:08,280
Speaker 3: community that's been accused of eating pets. Sure, Like the

505
00:30:08,480 --> 00:30:12,840
Speaker 3: the other critical thinking thing that's so bizarre is like

506
00:30:13,400 --> 00:30:16,160
Speaker 3: do you know how many straight dogs and cats are?

507
00:30:16,200 --> 00:30:21,400
Speaker 3: They're like even accepting the unbelievably racist premise like why

508
00:30:21,440 --> 00:30:24,080
Speaker 3: would you why would like how stupid do you think

509
00:30:24,080 --> 00:30:26,120
Speaker 3: these people are that they would take a pet.

510
00:30:26,000 --> 00:30:27,560
Speaker 1: They would go into somebody's yard.

511
00:30:27,840 --> 00:30:30,880
Speaker 3: Yeah, Like it's just like like even giving them all

512
00:30:30,960 --> 00:30:34,240
Speaker 3: of the grace possible. The last step of like, well

513
00:30:34,240 --> 00:30:36,760
Speaker 3: why would you bother with a pet is like kind

514
00:30:36,760 --> 00:30:41,800
Speaker 3: of such an insane straw to me, I'm like, why, yeah, yeah, no, stupid,

515
00:30:42,280 --> 00:30:44,640
Speaker 3: It's complete. There's so many other ways to get dogs

516
00:30:44,640 --> 00:30:45,200
Speaker 3: and cats.

517
00:30:45,480 --> 00:30:50,440
Speaker 1: Yeah, oh trust me. Yeah no, sorry, Look, okay, I

518
00:30:50,520 --> 00:30:55,400
Speaker 1: am part of a now maligned group in that I'm irish,

519
00:30:55,480 --> 00:31:00,240
Speaker 1: and so is RFK Junior, and so yes, I'm part

520
00:31:00,280 --> 00:31:03,960
Speaker 1: of a group that is being accused of eating wellco

521
00:31:04,200 --> 00:31:07,840
Speaker 1: dogs and cats. And I guess with better with better

522
00:31:07,880 --> 00:31:11,120
Speaker 1: evidence than this this particular.

523
00:31:11,280 --> 00:31:13,000
Speaker 3: That is why you people do. As far as I

524
00:31:13,000 --> 00:31:13,520
Speaker 3: can tell.

525
00:31:14,840 --> 00:31:18,120
Speaker 1: There is a picture of one of our most famous

526
00:31:18,280 --> 00:31:23,040
Speaker 1: people with like standing over a dog, like pretending to

527
00:31:23,040 --> 00:31:26,600
Speaker 1: be about to eat it, also standing over a bear

528
00:31:26,800 --> 00:31:29,720
Speaker 1: pretending to be about to eat it. So kind of

529
00:31:29,800 --> 00:31:34,520
Speaker 1: kind of interesting that they've chosen this, this rumor to

530
00:31:34,600 --> 00:31:37,240
Speaker 1: fixate on that. I feel like they might be a

531
00:31:37,240 --> 00:31:40,120
Speaker 1: little bit like the fact that it was jd Vance,

532
00:31:40,360 --> 00:31:44,880
Speaker 1: you know, who started this, and he like there has

533
00:31:44,920 --> 00:31:50,040
Speaker 1: to be some sort of sibling rivalry like insecurity with

534
00:31:50,160 --> 00:31:54,760
Speaker 1: jd Vance and RFK Junior, right, because like when RFK

535
00:31:54,880 --> 00:31:59,600
Speaker 1: Junior like brought up the possibility of joining the Trump campaign,

536
00:31:59,680 --> 00:32:01,280
Speaker 1: everyone was like, oh my god, that would have been

537
00:32:01,280 --> 00:32:04,800
Speaker 1: such a better pick. And JD fans like, hey, so

538
00:32:05,480 --> 00:32:08,320
Speaker 1: I don't know how it worked out that JD. Vance

539
00:32:08,400 --> 00:32:12,640
Speaker 1: then was like I hear they're eating dogs and cats

540
00:32:13,240 --> 00:32:16,320
Speaker 1: like some other guy that you might have heard of.

541
00:32:16,680 --> 00:32:21,080
Speaker 1: But it's interesting. It's interesting. No, nobody really knows what's

542
00:32:21,080 --> 00:32:23,960
Speaker 1: going on with JD. Vance at this point. I feel

543
00:32:24,040 --> 00:32:28,280
Speaker 1: like he's got to be in a weird place right now.

544
00:32:28,360 --> 00:32:31,280
Speaker 3: I mean, his his eyeliner guy's got to have some

545
00:32:31,520 --> 00:32:33,640
Speaker 3: you know that that's where you really get the tea

546
00:32:33,960 --> 00:32:37,719
Speaker 3: when you're like really putting on JD's face, Like, yeah,

547
00:32:38,000 --> 00:32:38,680
Speaker 3: I really.

548
00:32:38,400 --> 00:32:43,920
Speaker 1: Think, oh man, yeah, just the like untold documentary where

549
00:32:44,160 --> 00:32:47,760
Speaker 1: about like JD Vance's eyeliner guy that we're gonna get

550
00:32:48,120 --> 00:32:51,120
Speaker 1: is gonna be so good. But yeah, I mean one

551
00:32:51,160 --> 00:32:54,800
Speaker 1: of the stories that's also rising up in Springfield is

552
00:32:54,880 --> 00:33:00,120
Speaker 1: that they're taking the housing in the town. But of

553
00:33:00,120 --> 00:33:03,880
Speaker 1: course this is just an issue of landlords raising prices

554
00:33:04,040 --> 00:33:07,760
Speaker 1: because landlords suck, and because you know a bunch of

555
00:33:07,840 --> 00:33:10,400
Speaker 1: people were coming to town who were willing to pay

556
00:33:10,520 --> 00:33:14,440
Speaker 1: higher rent because they were, you know, filling jobs that

557
00:33:14,520 --> 00:33:17,720
Speaker 1: needed to be filled. So yeah, according to the director

558
00:33:17,760 --> 00:33:22,880
Speaker 1: of Springfield's Housing Authority, the new homeless in Springfield are

559
00:33:23,000 --> 00:33:25,160
Speaker 1: people who can't afford to pay two thousand or three

560
00:33:25,200 --> 00:33:28,160
Speaker 1: thousand dollars a month in rent, which is not the

561
00:33:28,280 --> 00:33:33,440
Speaker 1: version that you're being told about in right wing check groups,

562
00:33:33,520 --> 00:33:34,960
Speaker 1: which I spent a lot of time in because I

563
00:33:35,000 --> 00:33:39,480
Speaker 1: am on Decente. So but yeah, so just a story

564
00:33:39,680 --> 00:33:45,520
Speaker 1: about the values of immigration, you know, like immigration working

565
00:33:45,600 --> 00:33:50,360
Speaker 1: to help prop up and improve the economy of a

566
00:33:50,480 --> 00:33:55,680
Speaker 1: fading town, and that is obviously not how the right

567
00:33:55,720 --> 00:33:56,600
Speaker 1: has chosen to see it.

568
00:33:56,720 --> 00:33:57,360
Speaker 3: Yeah.

569
00:33:57,440 --> 00:34:01,760
Speaker 5: I was reading this newsletter by w camal Bell, who's

570
00:34:01,800 --> 00:34:05,960
Speaker 5: you know, the amazing comedian, and he does these interviews

571
00:34:06,000 --> 00:34:10,320
Speaker 5: with the ACLU and some folks from the ACLU Immigrant

572
00:34:10,400 --> 00:34:14,320
Speaker 5: Rights Project or division we're talking about. There was a

573
00:34:14,360 --> 00:34:17,520
Speaker 5: statistic that they threw out that really stuck in my mind.

574
00:34:17,520 --> 00:34:21,080
Speaker 5: They were saying that the right spends around thirty million

575
00:34:21,200 --> 00:34:26,799
Speaker 5: dollars a year in anti immigrant like to prop up

576
00:34:26,840 --> 00:34:32,280
Speaker 5: and propagate anti immigrant narratives, and like the pro immigrant narratives,

577
00:34:32,280 --> 00:34:35,040
Speaker 5: you get about seven hundred thousand dollars a year being

578
00:34:35,080 --> 00:34:39,000
Speaker 5: spent like intentionally in that way. And so there's just

579
00:34:39,080 --> 00:34:44,240
Speaker 5: like a massive, massive, massive campaign going on to exploit

580
00:34:44,280 --> 00:34:48,200
Speaker 5: this issue. And I feel like if like with the

581
00:34:48,200 --> 00:34:51,560
Speaker 5: the situation of the school bus and the cats and dogs.

582
00:34:51,600 --> 00:34:57,080
Speaker 5: It's like any little any little story that can be

583
00:34:57,239 --> 00:35:02,640
Speaker 5: manipulated will kind of flow into these this like structure,

584
00:35:02,719 --> 00:35:07,200
Speaker 5: these structures that have been created to propagate these narratives.

585
00:35:07,320 --> 00:35:09,759
Speaker 5: And so we just have a lot of work to

586
00:35:09,800 --> 00:35:13,320
Speaker 5: do on the side of you know, telling pro immigrant stories,

587
00:35:13,640 --> 00:35:16,640
Speaker 5: which are actually just human stories of people being people.

588
00:35:17,120 --> 00:35:20,640
Speaker 5: But there's a deeper problem going on if the if

589
00:35:20,680 --> 00:35:24,800
Speaker 5: these little stories can like take hold in a town,

590
00:35:25,880 --> 00:35:30,759
Speaker 5: then it wasn't okay even before the stories came up, right, Like,

591
00:35:31,160 --> 00:35:33,359
Speaker 5: it's like we have so we have so much work

592
00:35:33,400 --> 00:35:33,560
Speaker 5: to do.

593
00:35:33,960 --> 00:35:37,120
Speaker 1: Yeah, I do feel like it's also a failure of

594
00:35:37,400 --> 00:35:43,000
Speaker 1: the mainstream media and they really just take the talking

595
00:35:43,040 --> 00:35:46,440
Speaker 1: point that like immigration is a problem.

596
00:35:46,200 --> 00:35:48,480
Speaker 5: A problem absolutely, as.

597
00:35:48,000 --> 00:35:51,160
Speaker 1: Like the starting point of the conversation around immigrants, yes,

598
00:35:51,280 --> 00:35:53,960
Speaker 1: rather than and I'd say, like also a problem with

599
00:35:54,000 --> 00:35:57,680
Speaker 1: the mainstream Democratic Party that like this feels like a

600
00:35:57,840 --> 00:36:01,120
Speaker 1: thing that you could take and be like, actually, we're

601
00:36:01,120 --> 00:36:03,920
Speaker 1: not even going to engage you on this idea of

602
00:36:04,160 --> 00:36:07,359
Speaker 1: immigration being a problem. We're gonna say it's actually one

603
00:36:07,400 --> 00:36:10,600
Speaker 1: of our greatest strengths, and we're going to say that

604
00:36:10,680 --> 00:36:14,680
Speaker 1: like here's how to like just account for it and

605
00:36:15,080 --> 00:36:19,480
Speaker 1: like expect it and make sure the infrastructure is in place.

606
00:36:19,760 --> 00:36:23,359
Speaker 1: So yeah, there so that there is enough housing when

607
00:36:23,680 --> 00:36:27,000
Speaker 1: there's an influx of people coming to fill jobs that

608
00:36:27,080 --> 00:36:27,800
Speaker 1: need to be filled.

609
00:36:28,320 --> 00:36:31,840
Speaker 3: But every every like complaint from right wing people about

610
00:36:32,000 --> 00:36:35,080
Speaker 3: like like this stuff always boils down to too much

611
00:36:35,120 --> 00:36:38,440
Speaker 3: conservative shit is happening, Like if there was rent control

612
00:36:39,600 --> 00:36:46,080
Speaker 3: right right right, It's God, it's such an unbelievable bummer. Yeah.

613
00:36:46,120 --> 00:36:49,960
Speaker 1: The reason that Springfield was such a hotbed for like

614
00:36:50,000 --> 00:36:52,440
Speaker 1: the way they were able to like pitch themselves to

615
00:36:53,320 --> 00:36:57,040
Speaker 1: these companies was that they said they touted the low

616
00:36:57,080 --> 00:37:00,560
Speaker 1: cost of living combined with its location on two interstate

617
00:37:00,640 --> 00:37:04,760
Speaker 1: highways between Columbus and Dayton. I'm just because Andrew, you're

618
00:37:04,840 --> 00:37:08,680
Speaker 1: from bro Midwest, the Midwest, that Midwest, we think that

619
00:37:08,920 --> 00:37:11,840
Speaker 1: is how So I grew up in Dayton for like

620
00:37:11,960 --> 00:37:16,000
Speaker 1: five years, and we were always talking about the crime

621
00:37:16,080 --> 00:37:19,480
Speaker 1: rate like being really high because of like because we're

622
00:37:19,520 --> 00:37:22,560
Speaker 1: on these like two major interstate highways. Like people in

623
00:37:22,600 --> 00:37:26,600
Speaker 1: the Midwest talk about highways the way like a wildlife

624
00:37:26,640 --> 00:37:29,279
Speaker 1: expert talks about a river. You know, they're just like

625
00:37:29,520 --> 00:37:33,879
Speaker 1: on the banks of the Grand three four Freeway, an

626
00:37:33,880 --> 00:37:38,560
Speaker 1: oasis burst to life of shopping, balls and drug given.

627
00:37:39,400 --> 00:37:42,960
Speaker 3: It's like what our version, the Midwest version of like

628
00:37:43,000 --> 00:37:46,440
Speaker 3: a port is is the highway exit? Yeah, right, like

629
00:37:46,600 --> 00:37:51,439
Speaker 3: all the benefits and the you know, alleged like detriments

630
00:37:51,480 --> 00:37:54,920
Speaker 3: are you know, oh, just because too much stuff's coming

631
00:37:54,960 --> 00:37:58,840
Speaker 3: in and out, godless, godless immigrants are teaching whatever.

632
00:37:59,239 --> 00:38:02,440
Speaker 1: Okay, guess it was actually always that it was coming

633
00:38:02,480 --> 00:38:05,960
Speaker 1: down from Detroit and you're like, yeah, we're like the

634
00:38:07,239 --> 00:38:10,719
Speaker 1: murder capital per capita in the US. I think that

635
00:38:10,800 --> 00:38:13,560
Speaker 1: was like true for a year, and people who lived

636
00:38:13,600 --> 00:38:16,239
Speaker 1: in Dayton were really proud of that fact. But they

637
00:38:16,280 --> 00:38:18,440
Speaker 1: would also always be like, yeah, it's just you know,

638
00:38:18,480 --> 00:38:21,560
Speaker 1: we're right on those two highways coming down from Detroit.

639
00:38:21,760 --> 00:38:25,880
Speaker 3: So I just need one like high school level statistics

640
00:38:25,920 --> 00:38:28,720
Speaker 3: class to be taught, like get the fuck out of here,

641
00:38:28,760 --> 00:38:33,320
Speaker 3: Like oh yeah, might as a statistically significant sure, dudes,

642
00:38:34,320 --> 00:38:36,440
Speaker 3: that's right, Like the hell out of here.

643
00:38:36,960 --> 00:38:39,719
Speaker 1: All right, let's uh, let's take a quick break and

644
00:38:39,960 --> 00:38:55,279
Speaker 1: we'll be right back. And we're back. And so, as

645
00:38:55,280 --> 00:38:59,319
Speaker 1: we talked about on yesterday's trending, there seems to be

646
00:38:59,480 --> 00:39:02,880
Speaker 1: a lot of people on you know, everybody on the

647
00:39:02,920 --> 00:39:07,040
Speaker 1: left is like complete disaster for Trump. It was any

648
00:39:07,080 --> 00:39:11,880
Speaker 1: anything from like the Trump equivalent of Biden's debate performance

649
00:39:11,920 --> 00:39:15,280
Speaker 1: the first time around two I've seen it called Trump's

650
00:39:15,320 --> 00:39:19,640
Speaker 1: nine to eleven. I guess maybe because it was the

651
00:39:19,719 --> 00:39:25,840
Speaker 1: day before, but it's, yeah, like the the idea that

652
00:39:25,880 --> 00:39:31,480
Speaker 1: it was disastrous, like is sometimes being openly acknowledged by

653
00:39:31,480 --> 00:39:36,000
Speaker 1: both sides disastrous for Trump, and sometimes being implicitly acknowledged

654
00:39:36,000 --> 00:39:40,200
Speaker 1: by the right who are claiming that the event was rigged.

655
00:39:40,719 --> 00:39:45,480
Speaker 1: And of course there's the complaint that the moderators that

656
00:39:45,640 --> 00:39:49,239
Speaker 1: he's out there playing three on one like they're they're

657
00:39:49,280 --> 00:39:51,600
Speaker 1: trying to kill him by like fact checking the things

658
00:39:51,600 --> 00:39:56,959
Speaker 1: he's saying. And then there's also the conspiracy theory that

659
00:39:57,520 --> 00:40:02,319
Speaker 1: Kamala Harris's jewelry was telling her the right answers the

660
00:40:02,320 --> 00:40:05,719
Speaker 1: whole time. Oh my god, I feel like would be

661
00:40:05,800 --> 00:40:08,879
Speaker 1: so confusing, Like would be such a would be such

662
00:40:08,920 --> 00:40:12,799
Speaker 1: a difficult thing to do, to have somebody in your

663
00:40:12,840 --> 00:40:15,040
Speaker 1: ear telling you what to say as you're.

664
00:40:16,200 --> 00:40:19,239
Speaker 3: It's a difficult skill to have the the whatever the

665
00:40:19,239 --> 00:40:21,400
Speaker 3: fuck it is called that your ear wiged.

666
00:40:21,120 --> 00:40:27,080
Speaker 1: Or Yeah lives that famous play that's about that it's

667
00:40:27,120 --> 00:40:33,640
Speaker 1: like somebody's name is the spy movies. Definitely superducer. Victors

668
00:40:33,640 --> 00:40:36,400
Speaker 1: think spy movies make it look easy, but there was

669
00:40:36,840 --> 00:40:39,360
Speaker 1: I'll find it by the end of the story. But anyways,

670
00:40:39,719 --> 00:40:44,759
Speaker 1: people think that no way someone would be that articulate

671
00:40:45,400 --> 00:40:48,600
Speaker 1: good at debating. And it must have been the result

672
00:40:48,760 --> 00:40:52,799
Speaker 1: of earphones embedded in her ear rings. Again, like just

673
00:40:53,160 --> 00:40:57,560
Speaker 1: they're like girl ear rings. That's weird. What she must

674
00:40:57,560 --> 00:41:02,120
Speaker 1: be cheating, which provided her with stealth coaching during the debate,

675
00:41:02,680 --> 00:41:07,600
Speaker 1: because the earrings she was wearing clearly resembled the Nova

676
00:41:08,239 --> 00:41:14,440
Speaker 1: h one audio earrings, which were sold on Kickstarter last

677
00:41:14,480 --> 00:41:20,520
Speaker 1: year and sold as supposedly the first and only wireless

678
00:41:20,560 --> 00:41:26,040
Speaker 1: earphones embedded in a pair of pearl ear rings. If

679
00:41:26,040 --> 00:41:30,440
Speaker 1: you look at the news story with the headline I

680
00:41:30,560 --> 00:41:33,160
Speaker 1: use these new smart earrings to listen to music. They

681
00:41:33,239 --> 00:41:36,239
Speaker 1: totally surprised me, and you will be like, oh, yeah,

682
00:41:36,560 --> 00:41:40,960
Speaker 1: these are the same fake news stories that you see

683
00:41:41,120 --> 00:41:44,200
Speaker 1: on the side of like when you're at any sort

684
00:41:44,239 --> 00:41:46,680
Speaker 1: of news site that's like a local news site that

685
00:41:46,719 --> 00:41:49,160
Speaker 1: has just been like we give up on, you know,

686
00:41:49,280 --> 00:41:53,080
Speaker 1: any sort of quality control. We're just we will take

687
00:41:53,280 --> 00:41:56,319
Speaker 1: any ad that you want to give us. Dentists don't

688
00:41:56,320 --> 00:41:58,719
Speaker 1: want you to know this one secret. You'll never have

689
00:41:58,800 --> 00:41:59,880
Speaker 1: to brush your teeth again.

690
00:42:01,640 --> 00:42:03,840
Speaker 5: My five year old definitely wants to hear that secret.

691
00:42:03,960 --> 00:42:07,560
Speaker 3: Yeah, oh yeah, God, which celebrity was hideous?

692
00:42:07,960 --> 00:42:11,600
Speaker 1: Right? Yeah, you'll never, guess you'll never. When you see

693
00:42:11,640 --> 00:42:14,759
Speaker 1: what she looks like today, your jaw will drop and

694
00:42:14,800 --> 00:42:18,960
Speaker 1: then like a picture of Sarah Jessica Parker or like something,

695
00:42:19,120 --> 00:42:21,160
Speaker 1: I know what they look like? Uh McLean? Why do

696
00:42:21,360 --> 00:42:24,320
Speaker 1: children hate brushing their teeth so much? So much?

697
00:42:24,520 --> 00:42:27,560
Speaker 5: It's like it's like they feel tortured by it. It's like, not,

698
00:42:28,040 --> 00:42:30,360
Speaker 5: don't you like the feeling of having a clean mouth?

699
00:42:30,680 --> 00:42:31,200
Speaker 5: I guess not.

700
00:42:31,920 --> 00:42:35,120
Speaker 1: They hate it. They fucking hate that feeling. My five

701
00:42:35,239 --> 00:42:38,480
Speaker 1: or my six year old this morning was like demanding

702
00:42:38,520 --> 00:42:41,680
Speaker 1: to be paid for brushing his teeth. I was like, whoa, yeah,

703
00:42:42,040 --> 00:42:44,920
Speaker 1: you talk. He's like, I mean, I don't you asked

704
00:42:44,960 --> 00:42:52,480
Speaker 1: me every day to do this? Like he was, yeah, exactly,

705
00:42:52,680 --> 00:42:55,600
Speaker 1: and be like it's called bitcoin mining. You want to

706
00:42:55,600 --> 00:42:58,960
Speaker 1: earn your keep around here, stop going to school and

707
00:42:59,000 --> 00:43:05,800
Speaker 1: get this. It's easy.

708
00:43:05,840 --> 00:43:06,320
Speaker 3: It's easy.

709
00:43:06,440 --> 00:43:08,640
Speaker 1: Just gamify. You make it fun for the little fuckers.

710
00:43:08,719 --> 00:43:09,200
Speaker 1: It's great.

711
00:43:09,280 --> 00:43:10,880
Speaker 3: Oh my god. Yeah.

712
00:43:10,920 --> 00:43:16,520
Speaker 1: So the theory of the case here is that this

713
00:43:16,880 --> 00:43:20,719
Speaker 1: the vice president of the United States, somebody who presumably

714
00:43:21,440 --> 00:43:24,880
Speaker 1: has a campaign that has hundreds of millions of dollars,

715
00:43:25,280 --> 00:43:31,839
Speaker 1: has access to like NASA and Pentagon Technology, went went

716
00:43:31,880 --> 00:43:36,399
Speaker 1: on Kickstarter to buy a pair of Nova each one

717
00:43:36,560 --> 00:43:40,640
Speaker 1: audio earrings because I guess they were both pearl earrings.

718
00:43:40,800 --> 00:43:46,319
Speaker 1: Is the thing that Laura Lumer was pointing out, Laura Laura, Yeah,

719
00:43:46,600 --> 00:43:50,040
Speaker 1: Total Nazi showed a picture of Kamala Harris's ear ring

720
00:43:50,520 --> 00:43:54,279
Speaker 1: next to the I used these new smart earrings like

721
00:43:54,640 --> 00:43:58,360
Speaker 1: spam ad So I don't know what do we think, guys,

722
00:44:00,040 --> 00:44:02,880
Speaker 1: do we take away any credit we had been giving

723
00:44:03,560 --> 00:44:06,400
Speaker 1: Kamala Harris for her performance because she cheats.

724
00:44:07,160 --> 00:44:09,880
Speaker 5: Here's the thing. Here's the thing about the performance of

725
00:44:09,920 --> 00:44:13,040
Speaker 5: the debate, Like there was all the things she said,

726
00:44:13,800 --> 00:44:15,040
Speaker 5: and then there were the.

727
00:44:15,080 --> 00:44:18,040
Speaker 1: Looks on her face, yeah.

728
00:44:17,280 --> 00:44:20,560
Speaker 5: Right, like like she there was this one point especially

729
00:44:20,600 --> 00:44:24,680
Speaker 5: where she gave this like oh poor Donald kind of

730
00:44:24,760 --> 00:44:28,200
Speaker 5: look where like and he hates that, like it's it's

731
00:44:28,360 --> 00:44:31,200
Speaker 5: they are these you know, it's almost a different form

732
00:44:31,239 --> 00:44:33,680
Speaker 5: of a zinger h when it's like oh.

733
00:44:34,840 --> 00:44:39,560
Speaker 3: So yeah, that was yeah, and it can't what like

734
00:44:39,880 --> 00:44:43,200
Speaker 3: that's why would you think you would need coaching for that?

735
00:44:43,440 --> 00:44:51,120
Speaker 3: And this like there donald face tech wearable I mean,

736
00:44:51,160 --> 00:44:53,840
Speaker 3: I will say also this is likely a version of

737
00:44:54,120 --> 00:44:57,920
Speaker 3: the like eating pets thing, which is like how stupid

738
00:44:57,920 --> 00:45:00,359
Speaker 3: do you think Kamala Harris is like why And she'd

739
00:45:01,040 --> 00:45:04,239
Speaker 3: like do this in this way even if you you know,

740
00:45:04,680 --> 00:45:08,960
Speaker 3: accept your racist ass premise, like like you can't. She

741
00:45:09,000 --> 00:45:13,040
Speaker 3: could easily have a invisible your piece like or whatever,

742
00:45:13,280 --> 00:45:16,239
Speaker 3: like any number of better ways.

743
00:45:16,400 --> 00:45:21,920
Speaker 1: Yeah, yeah, exactly. Why get a kickstarter piece of wearable

744
00:45:22,000 --> 00:45:25,799
Speaker 1: tech that, by the way, some people looked into it

745
00:45:26,280 --> 00:45:30,520
Speaker 1: and nobody has ever gotten what like the people who

746
00:45:30,560 --> 00:45:34,799
Speaker 1: invested in the kickstarter never got the kickstarter hearings.

747
00:45:36,160 --> 00:45:37,160
Speaker 3: Bottom all India.

748
00:45:37,440 --> 00:45:41,320
Speaker 1: Yeah, that's why she cornered the market, isn't it?

749
00:45:41,400 --> 00:45:46,520
Speaker 5: Also like this thing that like somebody's Facebook message that

750
00:45:46,719 --> 00:45:51,520
Speaker 5: of their friends, neighbor's daughter can start an entire series

751
00:45:51,640 --> 00:45:55,160
Speaker 5: of like multiple like yeah, can get the former president

752
00:45:55,200 --> 00:45:57,680
Speaker 5: to say something, can start like all these other news

753
00:45:57,719 --> 00:46:01,480
Speaker 5: stories and this person like post sing something on Facebook

754
00:46:01,600 --> 00:46:05,239
Speaker 5: or whatever platform truth social they were using, suddenly has

755
00:46:05,400 --> 00:46:08,040
Speaker 5: us talking about it like we're talking about it. She

756
00:46:08,200 --> 00:46:10,880
Speaker 5: did it, She got she got it out there in

757
00:46:10,920 --> 00:46:15,120
Speaker 5: this way. And so it's also like how these little,

758
00:46:15,920 --> 00:46:19,719
Speaker 5: you know, pieces of information can be considered some kind

759
00:46:19,719 --> 00:46:23,880
Speaker 5: of a some kind of a you know, thing to investigate.

760
00:46:23,920 --> 00:46:28,040
Speaker 3: It's it's because it's our pathetic media allow like some

761
00:46:28,120 --> 00:46:31,640
Speaker 3: people saying to be a fact. So then it's like

762
00:46:32,040 --> 00:46:35,640
Speaker 3: this is real, Like this pipeline has been so abused of,

763
00:46:35,719 --> 00:46:40,240
Speaker 3: like a Twitter nazi says a thing that gets repeated

764
00:46:40,280 --> 00:46:43,720
Speaker 3: by Breitbart, which then is like the pipeline to Fox News,

765
00:46:44,360 --> 00:46:46,640
Speaker 3: which is then news. All of a sudden, it's like

766
00:46:46,840 --> 00:46:51,399
Speaker 3: Fox News is like money laundering for racist lies. It's

767
00:46:51,440 --> 00:46:52,919
Speaker 3: just like, because.

768
00:46:52,480 --> 00:46:56,080
Speaker 5: I'm going to use that quote money laundering for racist lies.

769
00:46:56,120 --> 00:47:00,200
Speaker 1: That was a good because accounts of how they like

770
00:47:00,239 --> 00:47:02,600
Speaker 1: look for stories, and it really is that they just

771
00:47:02,600 --> 00:47:06,400
Speaker 1: like go through blogs, racist blogs that they're probably already

772
00:47:06,440 --> 00:47:09,759
Speaker 1: posting on. We saw like the Tucker Carlson's like main

773
00:47:09,880 --> 00:47:13,719
Speaker 1: writer for the entire rise of his career was like

774
00:47:13,800 --> 00:47:17,919
Speaker 1: outed as just being this like horrible racist like shit

775
00:47:18,040 --> 00:47:20,840
Speaker 1: poster on like a law school forum.

776
00:47:21,000 --> 00:47:24,000
Speaker 3: Yeah obviously, yeah, like where else would you get them?

777
00:47:24,800 --> 00:47:28,120
Speaker 3: If I could steal three minutes of podcast time to

778
00:47:28,680 --> 00:47:32,959
Speaker 3: awkwardly pivot into a question for mcleat loosely based off

779
00:47:33,000 --> 00:47:35,640
Speaker 3: of well only because we learned during the break that

780
00:47:35,719 --> 00:47:39,719
Speaker 3: she is up in San Francisco, and since tech wearables

781
00:47:39,760 --> 00:47:43,280
Speaker 3: is in this I have this theory that the recent

782
00:47:43,400 --> 00:47:48,720
Speaker 3: spate of tech wearable failures is down to income inequality

783
00:47:49,360 --> 00:47:53,360
Speaker 3: on the streets of San Francisco, making it such that

784
00:47:53,440 --> 00:47:56,040
Speaker 3: there's no more like teens to tell you you look

785
00:47:56,080 --> 00:47:59,960
Speaker 3: like a nerd? Is that is there? Eddy? Is there

786
00:48:00,080 --> 00:48:01,080
Speaker 3: any truth to that?

787
00:48:01,760 --> 00:48:01,880
Speaker 1: Well?

788
00:48:01,920 --> 00:48:04,600
Speaker 5: Wait a minute, Okay, first, like I'm just going to say, like,

789
00:48:04,880 --> 00:48:07,319
Speaker 5: what do you mean by tech wearbles, Like you're like

790
00:48:07,360 --> 00:48:08,320
Speaker 5: the classes?

791
00:48:08,760 --> 00:48:11,880
Speaker 3: Yeah, yeah, yeah, yeah, okay, because those are all crashing

792
00:48:11,960 --> 00:48:14,800
Speaker 3: and burning. And I just had been beating this pet

793
00:48:14,840 --> 00:48:18,680
Speaker 3: theory to death that I was like, I I was like,

794
00:48:18,840 --> 00:48:21,880
Speaker 3: basically that like loitering could have saved the tech industry,

795
00:48:21,960 --> 00:48:24,319
Speaker 3: but the tech bros are too stupid to realize it.

796
00:48:25,239 --> 00:48:29,399
Speaker 5: I think that there are always teenagers ready to tell

797
00:48:29,440 --> 00:48:33,440
Speaker 5: you that you look like a fucking tweet, that you

798
00:48:33,480 --> 00:48:36,680
Speaker 5: look like like that has not changed, But I will

799
00:48:36,680 --> 00:48:40,160
Speaker 5: say that, you know, San Francisco has the lowest population

800
00:48:40,480 --> 00:48:44,640
Speaker 5: of kids under eighteen, or kids the people under eighteen

801
00:48:44,719 --> 00:48:48,080
Speaker 5: than any major metropolitan city in the United States, and

802
00:48:48,120 --> 00:48:51,640
Speaker 5: it's even increased since the pandemic in back. Next week

803
00:48:51,680 --> 00:48:54,320
Speaker 5: they're going to announce this whole slate of like which

804
00:48:54,480 --> 00:48:58,160
Speaker 5: schools are getting closed and merged because there are so

805
00:48:58,280 --> 00:49:02,280
Speaker 5: many less students there were. So you are in fact

806
00:49:02,440 --> 00:49:06,440
Speaker 5: correct that despite the fact that there are still teenagers

807
00:49:06,800 --> 00:49:08,680
Speaker 5: ready to tell you that you look like a d weep,

808
00:49:08,960 --> 00:49:09,840
Speaker 5: there are less of that.

809
00:49:10,680 --> 00:49:12,040
Speaker 3: So yeah, So but.

810
00:49:12,040 --> 00:49:16,360
Speaker 5: Can I link Can I link that to the fall

811
00:49:16,440 --> 00:49:18,719
Speaker 5: of tech wearables? I don't know if I can make that,

812
00:49:19,400 --> 00:49:21,400
Speaker 5: but maybe if you make a blog post about it, it.

813
00:49:21,400 --> 00:49:22,240
Speaker 1: Will become news.

814
00:49:22,440 --> 00:49:27,080
Speaker 3: I'm here to make that irresponsible, politically motivated claim. But

815
00:49:27,719 --> 00:49:30,239
Speaker 3: I'm not. I'm not. I'm just I was just like, God,

816
00:49:30,239 --> 00:49:32,320
Speaker 3: this has just been a theorians in my brain forever.

817
00:49:32,440 --> 00:49:36,839
Speaker 1: My neighbor's kid actually goes to Berkeley and his roommate's

818
00:49:36,920 --> 00:49:40,520
Speaker 1: sister actually said that this is the case. So I

819
00:49:40,560 --> 00:49:43,680
Speaker 1: think we're pretty hell yeah, yeah, I think back on

820
00:49:43,680 --> 00:49:48,600
Speaker 1: this one. It's also I think a big trend with

821
00:49:49,600 --> 00:49:53,359
Speaker 1: tech companies has been like that they create their own

822
00:49:53,360 --> 00:49:57,560
Speaker 1: campus where, you know, their own like little universes where

823
00:49:57,600 --> 00:49:58,400
Speaker 1: it's like you never.

824
00:49:58,280 --> 00:49:58,839
Speaker 3: Have to leave.

825
00:49:58,920 --> 00:50:01,000
Speaker 1: We have chefs, we have all these things, and like

826
00:50:01,160 --> 00:50:05,160
Speaker 1: I think that that might be the equivalent of like

827
00:50:05,200 --> 00:50:09,880
Speaker 1: when a famous person creates like a a home with

828
00:50:09,960 --> 00:50:13,759
Speaker 1: a name that like they never leave like Neverland or

829
00:50:13,840 --> 00:50:18,440
Speaker 1: Graceland or you know, like a and like that's always

830
00:50:18,440 --> 00:50:21,799
Speaker 1: a bad sign, like they always become insulated from reality

831
00:50:22,080 --> 00:50:26,000
Speaker 1: and start like going in weird directions. And you know,

832
00:50:26,280 --> 00:50:30,799
Speaker 1: I feel like the tech world has that broadly in

833
00:50:30,840 --> 00:50:34,640
Speaker 1: San Francisco, and then like yeah, at a smaller scale,

834
00:50:34,680 --> 00:50:39,640
Speaker 1: like on Google's campus where like it's true, yeah, everybody epic.

835
00:50:40,320 --> 00:50:44,319
Speaker 5: Okay, I did perform. I performed at Google once. This

836
00:50:44,360 --> 00:50:47,080
Speaker 5: is a long time ago, and I will say that,

837
00:50:47,600 --> 00:50:51,520
Speaker 5: you know, it was like the like my biggest well,

838
00:50:51,520 --> 00:50:53,719
Speaker 5: actually I have two big memories of that. The first

839
00:50:53,719 --> 00:50:56,879
Speaker 5: one was the food of like it is our sushi hour.

840
00:50:57,000 --> 00:50:58,440
Speaker 5: May I offer you some sushi?

841
00:50:58,560 --> 00:50:58,799
Speaker 3: Yeah?

842
00:50:58,880 --> 00:51:01,680
Speaker 5: Why yes, you may offer They desire a cookie hour

843
00:51:02,200 --> 00:51:04,120
Speaker 5: and all that. So there was that the food was

844
00:51:04,160 --> 00:51:07,360
Speaker 5: like whoa. And the other thing was that the sound

845
00:51:07,600 --> 00:51:10,600
Speaker 5: system totally failed.

846
00:51:11,239 --> 00:51:14,040
Speaker 1: And I was the technology.

847
00:51:14,120 --> 00:51:18,000
Speaker 5: Yes, the technology failed, and I was so there for it.

848
00:51:18,080 --> 00:51:21,799
Speaker 5: I was like I'm living this irony. I'm soaking it up.

849
00:51:21,840 --> 00:51:23,960
Speaker 5: And then we had a bunch of acoustic instruments. So

850
00:51:24,400 --> 00:51:26,360
Speaker 5: we went from the stage to the grass and we

851
00:51:26,440 --> 00:51:29,200
Speaker 5: made a circle around us and did the rest of

852
00:51:29,239 --> 00:51:32,440
Speaker 5: the performance acoustic, and it was ten times better than

853
00:51:32,480 --> 00:51:35,200
Speaker 5: it would have if been if the but anyway, but

854
00:51:35,280 --> 00:51:37,680
Speaker 5: the other thing was like it it they all of

855
00:51:37,719 --> 00:51:40,880
Speaker 5: this stuff is only meant to keep those people working,

856
00:51:41,719 --> 00:51:43,960
Speaker 5: you know, so they don't have to leave. So it

857
00:51:44,080 --> 00:51:46,520
Speaker 5: is exactly that. That's the same as the you know,

858
00:51:46,600 --> 00:51:49,840
Speaker 5: the buses that that. So you're from your doorstep to

859
00:51:49,880 --> 00:51:53,759
Speaker 5: your doorstep, not interacting with any community around you. You

860
00:51:53,800 --> 00:51:56,680
Speaker 5: are like in the Google world from the minute that

861
00:51:56,760 --> 00:52:01,719
Speaker 5: you your house. Yeh yeah, so you're totally right because I're.

862
00:52:01,520 --> 00:52:05,080
Speaker 1: Inside a giant roving Google like pep rally.

863
00:52:05,719 --> 00:52:08,640
Speaker 3: Yeah yeah, all right. I'll keep working on my thesis.

864
00:52:08,640 --> 00:52:11,919
Speaker 3: But it's bally motivated and highly biased, so I don't

865
00:52:11,960 --> 00:52:13,160
Speaker 3: know what's gonna happen with it.

866
00:52:14,360 --> 00:52:17,840
Speaker 1: I mean, but it seems true, right, it feels correct

867
00:52:18,120 --> 00:52:22,080
Speaker 1: the fact that it's like a historic like a children

868
00:52:22,120 --> 00:52:25,880
Speaker 1: of men for school age children, like where there's just

869
00:52:25,960 --> 00:52:29,040
Speaker 1: like not no, there's no school age children.

870
00:52:29,320 --> 00:52:31,280
Speaker 5: It's it's weird, it's really weird.

871
00:52:31,600 --> 00:52:33,040
Speaker 3: That's that is so wild.

872
00:52:33,360 --> 00:52:36,439
Speaker 1: Yeah, that's so interesting. Did they like bust them off

873
00:52:36,480 --> 00:52:39,520
Speaker 1: once they started making fun of their shitty Google glasses?

874
00:52:39,520 --> 00:52:41,359
Speaker 1: Where they like, we got to get these kids out

875
00:52:41,360 --> 00:52:47,680
Speaker 1: of here. Amazing? Well, mclet, what a pleasure having you

876
00:52:47,880 --> 00:52:51,640
Speaker 1: on the daily zeitgeist. Where can people find you? Follow you,

877
00:52:51,760 --> 00:52:53,080
Speaker 1: hear you all that good stuff?

878
00:52:53,239 --> 00:52:56,440
Speaker 5: You can find me follow me at macleat music dot com.

879
00:52:56,440 --> 00:52:59,160
Speaker 5: That's also my all my handles are the same. It's

880
00:52:59,320 --> 00:53:02,960
Speaker 5: m e k lit music dot com.

881
00:53:03,080 --> 00:53:05,560
Speaker 1: Amazing. Yeah, And is there a work Amedia that you've

882
00:53:05,560 --> 00:53:06,200
Speaker 1: been enjoying.

883
00:53:06,880 --> 00:53:11,160
Speaker 5: Okay, I'm gonna be super selfish here, yes a moment,

884
00:53:11,360 --> 00:53:14,520
Speaker 5: and I'm gonna call out the latest episode that we

885
00:53:14,680 --> 00:53:17,759
Speaker 5: just released of the podcast Movement with My Fleet, because

886
00:53:17,800 --> 00:53:20,600
Speaker 5: it features this woman who is a legend and who

887
00:53:20,680 --> 00:53:24,279
Speaker 5: everybody should know. Her name is June Millington. She was

888
00:53:24,400 --> 00:53:28,520
Speaker 5: the co creator and lead guitarist of the band Fanny,

889
00:53:28,920 --> 00:53:31,279
Speaker 5: which was the first all women band to get a

890
00:53:31,280 --> 00:53:33,880
Speaker 5: major label record deal, the first all women band to

891
00:53:33,960 --> 00:53:37,759
Speaker 5: be on the Billboard Top forty. She herself is Filipina

892
00:53:37,840 --> 00:53:42,200
Speaker 5: American and is like, uh, she's an absolute legend of

893
00:53:43,000 --> 00:53:50,000
Speaker 5: American music and she's spent decades supporting women songwriters performers,

894
00:53:50,400 --> 00:53:53,600
Speaker 5: founded a music school with Angela Davis like she's a genius,

895
00:53:53,719 --> 00:53:56,640
Speaker 5: and it's just been mind boggling to me how people

896
00:53:56,760 --> 00:53:59,279
Speaker 5: not enough people know about her. So the latest episode

897
00:53:59,560 --> 00:54:02,000
Speaker 5: of our podcast features Jude Millington, so.

898
00:54:02,400 --> 00:54:06,000
Speaker 1: We'll allow it. We will allow that selfish plug because

899
00:54:06,200 --> 00:54:09,040
Speaker 1: it sounds awesome. Andrew, where can people find you? And

900
00:54:09,160 --> 00:54:11,400
Speaker 1: is there a work a media that you've been enjoying?

901
00:54:11,920 --> 00:54:15,120
Speaker 3: Man, Andrew t my podcasts, Joss Racist, We have. We've

902
00:54:15,160 --> 00:54:20,280
Speaker 3: been doing premium podcasts on suboptimal pods, dot com and not.

903
00:54:20,600 --> 00:54:23,480
Speaker 3: This is the self plug is I've been doing this

904
00:54:23,960 --> 00:54:28,080
Speaker 3: podcast like a bonus podcast with enemy of my show,

905
00:54:28,320 --> 00:54:31,520
Speaker 3: friend of this show presumably Jessica gal where she and

906
00:54:31,600 --> 00:54:35,240
Speaker 3: I are? She She basically was like, Poker's all chance,

907
00:54:35,400 --> 00:54:39,359
Speaker 3: So we're doing a poker versus scratch off tickets, who

908
00:54:39,360 --> 00:54:42,800
Speaker 3: can win more money challenge on what Gallum blur the

909
00:54:42,880 --> 00:54:48,960
Speaker 3: spoiler alert, I'm curb stopping her. So you are good

910
00:54:48,960 --> 00:54:52,520
Speaker 3: at I'm playing ten cent poker. I'm not good at poker.

911
00:54:52,600 --> 00:54:55,239
Speaker 3: I'm better than the people who play ten cents, better

912
00:54:55,280 --> 00:54:57,879
Speaker 3: than chance at poker. Huh, yes, I'm better than chance

913
00:54:57,920 --> 00:55:01,080
Speaker 3: and scratchers are minus.

914
00:55:00,760 --> 00:55:05,960
Speaker 1: Ev I'm actually better. I'm better than chance at scratchers.

915
00:55:06,120 --> 00:55:09,520
Speaker 3: Yeah, yeah, yeah, yeah, I got a system.

916
00:55:12,000 --> 00:55:14,719
Speaker 1: By the way, for anybody who thinks like we're just

917
00:55:14,760 --> 00:55:19,799
Speaker 1: making that up, you can go and read for days

918
00:55:20,000 --> 00:55:25,160
Speaker 1: about various lotto and scratch ticket buying systems and you

919
00:55:25,200 --> 00:55:26,600
Speaker 1: know the math behind it.

920
00:55:27,040 --> 00:55:30,320
Speaker 3: There are exploits, but those loopholes tend to get closed.

921
00:55:30,360 --> 00:55:34,920
Speaker 3: I will say, do they win? Like these? There you go?

922
00:55:35,120 --> 00:55:40,120
Speaker 3: I mean the to me every time, every time I go,

923
00:55:40,200 --> 00:55:44,840
Speaker 3: because I have weird, like weirdly stupid cousins who are literal,

924
00:55:44,960 --> 00:55:49,480
Speaker 3: like you know, engineers, a literal rocket scientist in my family,

925
00:55:49,719 --> 00:55:52,240
Speaker 3: who when they're in Vegas are like, I got a system,

926
00:55:52,360 --> 00:55:56,480
Speaker 3: and I'm like, the counterpoint to your system is, look

927
00:55:56,520 --> 00:56:00,000
Speaker 3: how nice the bellagio is. Like, if it was possible

928
00:56:00,400 --> 00:56:04,680
Speaker 3: to take money out of this ecosystem by just some

929
00:56:04,840 --> 00:56:08,800
Speaker 3: bozo's method, why exist?

930
00:56:09,239 --> 00:56:11,719
Speaker 5: Why are the rooms here so cheap? Why are the

931
00:56:11,719 --> 00:56:12,839
Speaker 5: flights here so cheap?

932
00:56:13,239 --> 00:56:20,560
Speaker 1: Exactly, so much luxury, so little being officially paid for

933
00:56:20,600 --> 00:56:22,799
Speaker 1: it in the normal way that one would pay for it.

934
00:56:22,880 --> 00:56:26,399
Speaker 3: You don't. You don't have a system, I feel is.

935
00:56:26,440 --> 00:56:30,960
Speaker 1: Kind of a system, right that it's just like annoying

936
00:56:31,040 --> 00:56:32,799
Speaker 1: to do. Some people don't do that.

937
00:56:33,040 --> 00:56:36,080
Speaker 3: I I so, actually this isn't the thing, but I

938
00:56:36,120 --> 00:56:38,920
Speaker 3: have been shooting. I can find it. There's a couple

939
00:56:39,000 --> 00:56:40,520
Speaker 3: car I'm not going to find it. Sorry, there's a

940
00:56:40,560 --> 00:56:43,920
Speaker 3: couple of card counters on YouTube because actually tech wearables

941
00:56:43,920 --> 00:56:47,240
Speaker 3: have gotten small enough that they're able to surreptitiously record

942
00:56:47,280 --> 00:56:53,360
Speaker 3: their blackjack hands and do it over. No, they're not cheating.

943
00:56:53,640 --> 00:56:56,200
Speaker 3: They are They're just like, yeah, it is, and it's

944
00:56:56,560 --> 00:56:59,080
Speaker 3: card counting. I will just say, is trivially easy. The

945
00:56:59,120 --> 00:57:02,120
Speaker 3: hard part is not going to hot and keeping keeping

946
00:57:02,160 --> 00:57:05,640
Speaker 3: the bit up while while you're doing it. I guess

947
00:57:05,719 --> 00:57:08,160
Speaker 3: I won't elaborate. You can YouTube it. The piece of

948
00:57:08,200 --> 00:57:10,840
Speaker 3: media I've been enjoying is there's an anime on Netflix

949
00:57:10,880 --> 00:57:15,040
Speaker 3: called Delicious in Dungeon that is about slaying monsters in

950
00:57:15,080 --> 00:57:17,400
Speaker 3: a dungeon and then cooking and eating them. And there's

951
00:57:17,440 --> 00:57:20,040
Speaker 3: nothing has ever been more up my alley than that.

952
00:57:20,840 --> 00:57:23,400
Speaker 5: Wow, I feel that I know you better now.

953
00:57:23,720 --> 00:57:27,040
Speaker 3: It goes from a sword and sorcery thing into a

954
00:57:27,160 --> 00:57:30,320
Speaker 3: cooking show seamlessly, and it's so insane and I love it.

955
00:57:30,600 --> 00:57:33,280
Speaker 5: That's nice, Okay to just to say a media that

956
00:57:33,640 --> 00:57:35,920
Speaker 5: was not my shameless plug. I was watching I've been

957
00:57:35,920 --> 00:57:40,880
Speaker 5: watching the show Extraordinary on Hulu and it is the funniest,

958
00:57:41,480 --> 00:57:44,240
Speaker 5: funniest show that I've seen in a long long time.

959
00:57:44,280 --> 00:57:47,120
Speaker 5: And I had a super hard August and it was

960
00:57:47,240 --> 00:57:49,800
Speaker 5: like the like the light of my day, just like

961
00:57:49,920 --> 00:57:53,280
Speaker 5: keeping me laughing. It's, you know, Irish. I find Irish

962
00:57:53,320 --> 00:57:56,920
Speaker 5: people very funny, oh really great sense of humor. And

963
00:57:56,960 --> 00:58:00,640
Speaker 5: it's like starring and written by this Irish whose name

964
00:58:00,640 --> 00:58:03,240
Speaker 5: I can't remember, but it's called Extraordinary and it's on

965
00:58:03,360 --> 00:58:05,920
Speaker 5: Hulu and it's like, I really highly recommend it.

966
00:58:06,000 --> 00:58:07,960
Speaker 1: There you go. You can find me on Twitter at

967
00:58:08,000 --> 00:58:11,760
Speaker 1: Jack Underscore Obrian. The work of media I've been enjoying

968
00:58:12,720 --> 00:58:16,520
Speaker 1: is uh the Simpsons thing. I'll link off to it

969
00:58:16,680 --> 00:58:21,600
Speaker 1: of the episode about the House of Ill Repute opening

970
00:58:21,600 --> 00:58:25,400
Speaker 1: in Springfield and then edited together with Donald Trump. I

971
00:58:25,440 --> 00:58:28,640
Speaker 1: love the clip. I also love just the person who

972
00:58:28,840 --> 00:58:33,760
Speaker 1: tweeted it at fear Gass Kelly. Ferg Gass fear Gass

973
00:58:34,320 --> 00:58:37,800
Speaker 1: s e A r g h A. S Kelly tweeted

974
00:58:37,840 --> 00:58:40,640
Speaker 1: with the thing, I better go super viral for this.

975
00:58:42,840 --> 00:58:43,200
Speaker 1: He did?

976
00:58:43,240 --> 00:58:44,640
Speaker 3: He did, Yeah, he did.

977
00:58:44,840 --> 00:58:44,920
Speaker 2: So.

978
00:58:45,000 --> 00:58:47,440
Speaker 1: He called his shot, but like, I just like that

979
00:58:47,520 --> 00:58:49,960
Speaker 1: as a sentiment to be like, I better fucking blow

980
00:58:50,080 --> 00:58:50,760
Speaker 1: up for this.

981
00:58:50,760 --> 00:58:53,720
Speaker 3: This is awesome work, I will say. Actually, the Peanuts

982
00:58:53,760 --> 00:58:57,360
Speaker 3: one is The Peanuts one was by comedian Noah Garfinkel,

983
00:58:57,480 --> 00:59:01,040
Speaker 3: who is you know, Jack AND's never met you know.

984
00:59:01,640 --> 00:59:04,880
Speaker 1: Yeah, no I know. Yeah, yeah, really good. Yeah, And

985
00:59:04,960 --> 00:59:06,680
Speaker 1: I actually didn't realize that was him.

986
00:59:06,880 --> 00:59:12,000
Speaker 3: Yeah. Rather in a different vibe than this better go viral.

987
00:59:12,560 --> 00:59:14,760
Speaker 3: This one I'm sure has gone more viral than normal

988
00:59:14,800 --> 00:59:17,080
Speaker 3: for Noah, but he does this kind of shit all

989
00:59:17,080 --> 00:59:20,400
Speaker 3: the time. So if you like this vibe, yeh like

990
00:59:20,520 --> 00:59:21,640
Speaker 3: follow him? Yes?

991
00:59:21,840 --> 00:59:25,880
Speaker 1: Absolutely. You can find me on Twitter at Jack Underscore O'Brien.

992
00:59:25,920 --> 00:59:28,240
Speaker 1: You can find us on Twitter at Daily's Zeikeist. We're

993
00:59:28,240 --> 00:59:30,840
Speaker 1: at v daily Zikeeist on Instagram. We have a Facebook

994
00:59:30,880 --> 00:59:33,480
Speaker 1: fanpage and a website Daily zeikeuys dot com, where we

995
00:59:33,520 --> 00:59:36,520
Speaker 1: post our episodes and our footnotes where we look off

996
00:59:36,520 --> 00:59:39,160
Speaker 1: to the information that we talked about in today's episode,

997
00:59:39,240 --> 00:59:43,080
Speaker 1: as well as a song that we think you might enjoy.

998
00:59:43,720 --> 00:59:47,600
Speaker 1: Super producer Justin Connor, is there a song that you

999
00:59:47,760 --> 00:59:49,320
Speaker 1: think people should go check out?

1000
00:59:49,520 --> 00:59:49,680
Speaker 3: Uh?

1001
00:59:49,760 --> 00:59:52,800
Speaker 7: Yeah, I'm actually going back to my hometown in Chicago

1002
00:59:52,920 --> 00:59:56,320
Speaker 7: tomorrow for a little trip, and I usually throw this

1003
00:59:56,400 --> 00:59:59,240
Speaker 7: song on to one of the playlists for the flight

1004
00:59:59,280 --> 01:00:02,360
Speaker 7: because it's really relaxing and chill. And it also works

1005
01:00:02,400 --> 01:00:05,640
Speaker 7: really well with mclete being here because this is by

1006
01:00:05,840 --> 01:00:09,240
Speaker 7: an Ethiopian jazz musician. I think let you mentioned it

1007
01:00:09,280 --> 01:00:12,520
Speaker 7: was Ethiopian New Year's Today the day before.

1008
01:00:12,880 --> 01:00:14,360
Speaker 3: Yeah, Happy New Year everybody.

1009
01:00:14,680 --> 01:00:18,000
Speaker 7: So this is this is a song called teta which

1010
01:00:18,480 --> 01:00:22,800
Speaker 7: pardon the pronunciation, I believe that how okay, which translates

1011
01:00:22,840 --> 01:00:23,640
Speaker 7: to nostalgic.

1012
01:00:23,880 --> 01:00:25,880
Speaker 1: And I just really love this track.

1013
01:00:25,920 --> 01:00:28,080
Speaker 7: I think I heard it in The Bear at some point,

1014
01:00:28,280 --> 01:00:32,320
Speaker 7: but it's very, very relaxing, very technically good jazz music,

1015
01:00:32,480 --> 01:00:36,400
Speaker 7: but just a really nice Sunday evening kind of vibe.

1016
01:00:36,400 --> 01:00:39,760
Speaker 7: So this is Mula and you can find this song

1017
01:00:39,880 --> 01:00:40,640
Speaker 7: in the notes.

1018
01:00:40,760 --> 01:00:42,720
Speaker 1: I don't know if The Bear should be nominated for

1019
01:00:43,080 --> 01:00:46,480
Speaker 1: Best Comedy stuff, but I do think it should be

1020
01:00:46,520 --> 01:00:49,760
Speaker 1: nominated for like best Playlist, because they have.

1021
01:00:50,240 --> 01:00:54,120
Speaker 5: Not yeah, not best comedy. This not best comedy this season.

1022
01:00:54,720 --> 01:00:59,240
Speaker 1: Oh this is intense, but but some good some good

1023
01:00:59,280 --> 01:00:59,880
Speaker 1: tracks in there.

1024
01:01:00,000 --> 01:01:00,600
Speaker 5: What a great show.

1025
01:01:00,720 --> 01:01:03,720
Speaker 1: Yeah, nonetheless, all right, well we will link off to

1026
01:01:03,800 --> 01:01:06,520
Speaker 1: that in the footnotes. The Daily Zeitgeist is a production

1027
01:01:06,560 --> 01:01:10,560
Speaker 1: of iHeartRadio. For more podcasts from iHeartRadio, visit the iHeartRadio app,

1028
01:01:10,600 --> 01:01:12,880
Speaker 1: Apple podcast, or wherever you listen to your favorite shows.

1029
01:01:12,920 --> 01:01:15,680
Speaker 1: That is gonna do it for us this morning, back

1030
01:01:15,720 --> 01:01:18,240
Speaker 1: this afternoon to tell you what is trending, and we

1031
01:01:18,280 --> 01:01:22,560
Speaker 1: will talk to you all then. Bye bye,