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

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

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

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Speaker 1: without further ado, here is the Weekly Zeitgeist. Well, mister walkin,

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Speaker 1: we are thrilled to be joined.

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Speaker 2: In our third seat Wow by the executive director.

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Speaker 1: Of Civil Rights Corps, which is a nonprofit dedicated to

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Speaker 1: fighting systemic injustice. He's been a civil rights lawyer, a

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Speaker 1: public defender. He was named twenty sixteenth Trial Lawyer of

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Speaker 1: the Year by Public Justice. The author of several books,

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Speaker 1: the incredibly compelling Usual Cruelty and the brand new Ye Coppaganda,

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Speaker 1: which we got to read. An advanced copy of is

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Speaker 1: drop in April fifteenth, So good go pre order right now.

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Speaker 1: We'll be talking about it. Most importantly a great follow

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Speaker 1: on social media, of course, please welcome the brilliant and

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Speaker 1: talented Alan Carcasone.

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Speaker 3: Thank you all for having me back.

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Speaker 2: Oh man, always open door. I got excited when I

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Speaker 2: read the word zeitgeist at.

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Speaker 3: The end of your book. I was like, oh yeah,

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Speaker 3: shout out to the show.

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Speaker 1: Yeah yeah, great word.

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Speaker 4: I was thinking of you guys.

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Speaker 1: Yeah, of course totally.

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Speaker 4: I've been well, I mean, at least as well as

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Speaker 4: can be. I'm excited to have this book out there

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Speaker 4: in the world, and I think the ideas are so

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Speaker 4: important now and at times of rising authoritarianism and a

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Speaker 4: real assault on basic notions of kindness and truth and love.

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Speaker 4: And you know, so I was I was trying to

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Speaker 4: explore the last few years, you know, like what do

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Speaker 4: we make of the mainstream media and how they're leading

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Speaker 4: us to these really dark places? And I'm just glad

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Speaker 4: to be able to be able to talk about it

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Speaker 4: now in public.

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Speaker 3: I'd give them an a minus.

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Speaker 1: I think I think they're mostly nailing it. What do

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Speaker 1: you guess? Sorry, I didn't read the book. But we

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Speaker 1: like the mainstream media, right, They're cool.

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Speaker 3: Yeah, I love the New York Times games.

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Speaker 1: I mean, with with many a guest, we like to

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Speaker 1: get to know them a little bit better and do

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Speaker 1: a search history underrated, overrated, but we got a lot

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Speaker 1: to cover, so we want to just kind of dive

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Speaker 1: right in, if that's all right with you, Alec. Unless

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Speaker 1: you had a search history underrated or overrated that was

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Speaker 1: particularly pressing that you wanted to get off your.

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Speaker 4: Chest, you know, I don't do anything other than think

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Speaker 4: about topaganda. So okay, I said, better just dive right in,

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Speaker 4: you know, all right, let's do it.

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Speaker 3: I was actually it was funny.

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Speaker 2: Yesterday I was hanging out with some people who are

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Speaker 2: from out of town, and like they they hit me

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Speaker 2: with the is LA Safe because like and I was like, you, okay, yeah,

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Speaker 2: Kelly's great, man, what are you talking about? Like, because

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Speaker 2: I see a lot of like you know, the people

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Speaker 2: running into stores and like grabbing stuff and things like that,

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Speaker 2: and I'm like, oh, my sweet sweet.

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Speaker 3: Child, Yeah, this is uh.

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Speaker 2: These are like cherry picked videos that they play over

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Speaker 2: and over again to sort of create that narrative like

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Speaker 2: crime is.

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Speaker 3: Actually going on, Like whoa really?

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Speaker 1: Oh? You know.

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Speaker 4: One of the things, the thing that I just think

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Speaker 4: about a lot is the curation selectively of anecdote.

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Speaker 1: You know.

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Speaker 4: You know, you could you could take a video montage

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Speaker 4: of every missed shot that Michael Jordan took in his career,

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Speaker 4: put them all together, and you make them look like

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Speaker 4: a bad shooter.

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Speaker 1: Sorry, he's the worst player in the history of the NBA.

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Speaker 1: You can't make a friend shot.

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Speaker 4: That's essentially what the mainstream media does with crime. So

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Speaker 4: the most effective propaganda, this is such an important lesson

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Speaker 4: that I learned in my years of studying this. The

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Speaker 4: most effective propaganda is actually based on true anecdotes. Because

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Speaker 4: if you do something that's that's blatantly blatantly false, unless

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Speaker 4: it's something that people can't really figure out is false,

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Speaker 4: you actually lose credibility. But if you use a bunch

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Speaker 4: of true anecdotes to suggest kind of false interpretations or

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Speaker 4: then you actually have much more sophisticated propaganda's harder to

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Speaker 4: tell the difference. And so what we've seen the last

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Speaker 4: few years, as the example like you gave, you know,

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Speaker 4: there was one video that went viral of shoplifting from

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Speaker 4: a Walgreens in San Francisco that itself spawned three hundred

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Speaker 4: and nine articles around the country that one little video.

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Speaker 4: And at the same time, in that one month period

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Speaker 4: that these hundreds of articles are written about it nightly

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Speaker 4: newscasts all over the country, by the thousands, there was

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Speaker 4: virtually no stories about wage theft or tax evasion, things

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Speaker 4: that are happening way way more and that cost orders

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Speaker 4: of magnitude more money for people. And that's the selective

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Speaker 4: creation an anecdote that gets us afraid of of all

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Speaker 4: of the wrong things.

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Speaker 1: And in case people are like yeah, but like it's

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Speaker 1: better like storytelling or it's like more salient to like

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Speaker 1: see the violent thing happening, the wage theft is stealing

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Speaker 1: from you, right, Like those people are stealing from Walgreens,

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Speaker 1: a massive corporation. Uh, they're doing it like on a

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Speaker 1: one off basis, and it's being strapolated into like a

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Speaker 1: massive trend. They're ceiling from a corporation, and the wage

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Speaker 1: stuft is happening to you and it's not getting reported on, right,

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Speaker 1: and the Walgreens story is being you know that we've

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Speaker 1: got to protect Walgreens. And that is why I gave

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Speaker 1: them an A minus instead of a straight a, you know,

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Speaker 1: because they do they have some lapses.

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Speaker 3: Sure, andrew T.

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Speaker 1: We do like to ask our guess, 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 3: All right? Get ready, motherfuckers.

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Speaker 5: So this is no uh, this is probably there's a

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Speaker 5: reasonable chance this has already been one of my search histories.

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Speaker 5: But I'll do it again if I have it, but

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Speaker 5: maybe not. I made a fucking caved and made a

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Speaker 5: viral ish recipe. I made tempora Inoki mushrooms, which were

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Speaker 5: billed as this is like fucking vegan chicken vegan like

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Speaker 5: fried chicken tenders or whatever. But you take an Oki

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Speaker 5: mushrooms such you're in a video episode. You take the

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Speaker 5: nooky mushrooms, cut the little roots off, and you have

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Speaker 5: a bundle of mushrooms that grow long ways, and you

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Speaker 5: cut the bundles into like, I don't know, call it

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Speaker 5: like half a center meter maybe three quarters or of

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Speaker 5: a centimeter like slices, and you batter those motherfuckers so

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Speaker 5: that the Enochi mushrooms are individually little thin mushrooms, but

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Speaker 5: they come become like strands of if you really are.

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Speaker 3: Desperate for it.

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Speaker 2: Fake chickens are just fine on their own, and like

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Speaker 2: we don't have to Yeah.

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Speaker 3: Their lovely. You didn't need to call it that. I'm sure.

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Speaker 5: Throw a little MSG into the recipe I found. It's

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Speaker 5: you know, NBD.

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Speaker 1: But imagine though, as you're biting into them, imagine a

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Speaker 1: chicken dying. Yeah, you just like, you know, like get

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Speaker 1: that cool. Even feel like you're getting the power of

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Speaker 1: another animal.

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Speaker 5: You know, I got the power of I don't know,

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Speaker 5: probably eighty five mushrooms in that one bite. Though, so wow,

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Speaker 5: lot of a lot of a lot of power there.

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Speaker 5: So the the thing I'm pretty sure there's a reasonable

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Speaker 5: chance I've already said, was what my search history is,

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Speaker 5: how do I get rid of all this fucking deep

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Speaker 5: fry oil?

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Speaker 1: Oh? Yeah?

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Speaker 3: Which just throw it? Right?

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Speaker 1: Yeah?

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Speaker 5: I probably The probably sort of distressing thing is by

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Speaker 5: the time I had fried my batch of mushrooms, I

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Speaker 5: made several batches, and I deepried it a little.

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Speaker 3: In another thing, why am I being coy shrimp fitters?

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Speaker 1: Don't be.

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Speaker 3: Escapades.

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Speaker 5: There wasn't that much oil left, so I just kind

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Speaker 5: of like soaked it up with a paper towel and

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Speaker 5: put it into a grocery bag and noted that up.

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Speaker 3: Harbage candidate.

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Speaker 1: I think we'll allow that, all right.

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Speaker 2: I just, like I said, I just throw it out

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Speaker 2: my window. So whatever, this hippie shit you're doing, like

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Speaker 2: fine to each their own, but I.

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Speaker 1: Don't need I just go right down the drain man,

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Speaker 1: right down the kitchen, down the storm drain It's actually

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Speaker 1: the only thing we put in the kitchen, saying because

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Speaker 1: for some reason it's stopped draining water.

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Speaker 3: Yeah. No, you fill them up with water balloons and

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Speaker 3: you throw them into the La River. Oh yeah, fuck it.

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Speaker 3: I honestly can't be worse. There's no EPA monitoring of

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Speaker 3: it anymore.

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Speaker 2: I have people from out of town visiting us, and

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Speaker 2: we like, like, the part of like the value I'm

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Speaker 2: in is right by the La River.

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Speaker 3: So we walked by it and we're like, and the

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Speaker 3: famous La River And.

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Speaker 2: They're like, wait seriously, and I'm like, yep, that's the

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Speaker 2: I'm gonna be.

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Speaker 5: For real, for real, especially because I'm sure municipalities are

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Speaker 5: going to start having to generate their own revenue. LA

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Speaker 5: really needs to have a for I don't know, five

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Speaker 5: hundred dollars, one thousand dollars. You and your friend can

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Speaker 5: ride a motorcycle and a semi down the l I wrote, yeah,

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Speaker 5: or off the bridge you can do that one.

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Speaker 2: Or the drag race from Greece that also happened right

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Speaker 2: there downtown. Yeah, any iconic l A River race.

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Speaker 1: Yeah yeah, just just that killed Keu Reeves in the

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Speaker 1: Rush video back when that was my first celebrity crush.

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Speaker 1: Every every Paul Abdul music video drop was an event

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Speaker 1: in in my little perverted.

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Speaker 3: Was she had the chest of the cheetah.

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Speaker 2: That was yeah, obviously yeah, yeah, yeah, I remember just

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Speaker 2: being a kid like that's the cheat, oh cat, And

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Speaker 2: they're like, no, that's true, and I'm like, it is

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Speaker 2: that that's the cheat cat?

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Speaker 1: It was. It was actually problematic that you can't tell

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Speaker 1: them apart, man, don't find me cheating. What is something,

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Speaker 1: Kristen that you think is underrated?

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Speaker 6: Aging is underrated?

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Speaker 1: I agree with this.

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Speaker 6: I think people, you know what, like I was raised

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Speaker 6: I'm an elder millennial, okay, but I identify as gen X,

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Speaker 6: which is the most gen Z thing I can do.

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Speaker 6: But I grew up hanging out with gen xers, and

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Speaker 6: so we were raised by boomers and they don't age.

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Speaker 6: They don't want to age. Nobody wants to be called

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Speaker 6: grandma anymore. You know, like they're getting new tits and

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Speaker 6: licks into their seventies, and I feel like, no, who's

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Speaker 6: knitting the baby blankets. No one's knitting, no one's aging,

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Speaker 6: you know. I feel like we should embrace that. Our

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Speaker 6: generation should embrace aging, be proud to be a grandparent,

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Speaker 6: be proud to you know, retire, play golf whatever. And

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Speaker 6: these people they don't want to retire, they don't I

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Speaker 6: want to stop fucking. They're out with boner pills, like

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Speaker 6: on the loose.

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Speaker 1: Crazy right on the boner pills. Yeah, I'm always suspicious

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Speaker 1: of like singular explanations of like what is wrong with

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Speaker 1: this country. But there's I think I think somebody wrote

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Speaker 1: a book that was like Generation Sociopaths, just about how

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Speaker 1: like Boomers were all all like these narcissistic sociopaths, and

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Speaker 1: they made it checked a lot of boxes for me.

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Speaker 1: Let's let's put it that way. They're I don't think

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Speaker 1: they're all uniformly like that, and I don't think it's

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Speaker 1: the only problem we've got, but like having one generation

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Speaker 1: that is hoarding all the money and incapable of, you know,

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Speaker 1: being concerned about the generations that come after them, Like

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Speaker 1: if like that seems weird. It seems like we're currently

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Speaker 1: taking it as like humanity just like wasn't able to

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Speaker 1: deal with the challenge of climate change. What if it

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Speaker 1: was just like a very selfish generation. It was just like, actually,

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Speaker 1: we just don't give a fuck about Once we're gone,

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Speaker 1: it's gonna be okay.

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Speaker 2: There's another book that kind of touched on that called

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Speaker 2: The Death of the grown Up by Diana West, and

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Speaker 2: that was sort of talking about how like in the

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Speaker 2: sixties all of like the rock and roll shit kind

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Speaker 2: of like cows, like like the boomers are like I'm

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Speaker 2: never leaving this like mental state either where it's about

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Speaker 2: like forever young and all these other things that occur,

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Speaker 2: I mean, your wealth.

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Speaker 1: And being uninhibited and like not inhibiting other people's sexual

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Speaker 1: Oh sorry, no, not those parts, just just the irresponsibility part.

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Speaker 2: Guy, young young, I get to not care about other people.

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Speaker 6: The only thing they were retained out of woodstock was

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Speaker 6: rolling around in mud.

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Speaker 1: Yeah yeah, and like one thing yeah.

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Speaker 6: No, they did for the generation that like hated their

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Speaker 6: parents so much. It's like they really rebelled against aging.

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Speaker 6: They invented the term anti aging. There was no creams

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Speaker 6: or anything that that phrase didn't exist. And I feel

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Speaker 6: like it's embarrassing to watch them just refuse to go

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Speaker 6: off into the night.

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Speaker 1: It's like, sit down, Why accept the forward movement of time?

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Speaker 1: You know? Why? When I'm supposed to just sit here

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Speaker 1: and let it happen to me.

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Speaker 2: Come on, I'm gonna shoot up my grandkid's blood right

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Speaker 2: into my veins. I'm gonna use my grand kid as

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Speaker 2: a goddamn blood bank.

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Speaker 3: Right, how far we've come.

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Speaker 2: It's like, we used to have children to help, you know,

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Speaker 2: in the fields for agricultural reasons, and now it's like

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Speaker 2: now you gotta have like fourteen blood bags.

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Speaker 1: That's right, Kyle, what's up? Do you think is overrated? Uh?

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Speaker 3: Fucking robots, robots, real robots. I hate them. I hate robots.

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Speaker 2: You just watch electric age too, man, electric states or whatever.

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Speaker 7: I'm like, I don't want you to have to I

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Speaker 7: don't want anything that does two things.

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Speaker 3: I don't like it. I hate you.

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Speaker 7: Like when you see posts now that's like, well we

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Speaker 7: asked chat GPT what it thinks about.

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Speaker 3: I don't care. I don't want to know.

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Speaker 7: There's no value to that to me. It doesn't know words.

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Speaker 7: I'm so tired of it all. Just yeah, I want

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Speaker 7: people to do stuff. I don't want my car to

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Speaker 7: have no driver in it. I am just tired. If

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Speaker 7: we didn't make any more of them.

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Speaker 1: Yeah, every anything.

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Speaker 2: I think the rule of thumb now in our era

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Speaker 2: feels like anything that keeps us away from interacting with

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Speaker 2: people is the devil and we should be cast in

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Speaker 2: a way because it's obviously like helpful methods and and

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Speaker 2: things that robots do that help people and everyone's abilities

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Speaker 2: are limited in different ways, and there's positives to this,

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Speaker 2: but generally, generally, I'm just like the.

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Speaker 3: Robots that deliver food. I hate you.

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Speaker 7: Every everyone skirting in the shortcuts are But this is

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Speaker 7: all stemming from me just seeing a bunch of stuff

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Speaker 7: that's like, well, I asked, we asked chat GPT who

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Speaker 7: should be president, and it said, I don't care.

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Speaker 3: It's not real if it agrees with you, and what Yeah,

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Speaker 3: it's none of it. It's why are we giving it

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Speaker 3: weight like it has an opinion.

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Speaker 7: It's a calculator. The only word calculators should know is

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Speaker 7: boobs upside down. Yeah, and other or go to hell,

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Speaker 7: go to hell. Yes for my t I eighty nine hackers. Yeah,

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Speaker 7: I'm old. Other than that, I'm just like I've had

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Speaker 7: enough of the robots. I'm just I'm sorry. I don't

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Speaker 7: think we they're even fine. They don't need equal.

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Speaker 3: Weight two people.

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Speaker 2: Yeah yeah, yeah, well, I mean those articles are such

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Speaker 2: like those are just coming from like chat GPT shill yeah.

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Speaker 3: Out.

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Speaker 7: You just see whole sports articles that are all written

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Speaker 7: by a robot, and you're just like, you know, we

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Speaker 7: can tell, I can tell when the art is the

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Speaker 7: robot art. I don't know what you think. Yeah, you're

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Speaker 7: not tricking anyone. It's a gradient. Yeah sorry.

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Speaker 1: Three caps are really have really been taken over by

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Speaker 1: robots a way that I'm so tired sucks.

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Speaker 2: Oh that And now they're just like just summarizing bodycam

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Speaker 2: footage video and under like a true crime genre on YouTube.

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Speaker 2: And it's like on March eighteenth, twenty twenty two, this

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Speaker 2: man was like, it's they're just basically putting like chat

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Speaker 2: gpt ai narration over other videos and just making a

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Speaker 2: ton It's that.

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Speaker 7: Whole when you google something and it's like, hey, just

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Speaker 7: so you know, now we lead off with incorrect misinformation

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Speaker 7: and then we'll get to the links and I'm like,

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Speaker 7: I don't want I don't I know I can turn

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Speaker 7: it off.

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Speaker 3: I don't want to.

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Speaker 7: I don't want to have to take extra steps to

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Speaker 7: stop a robot from telling.

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Speaker 3: Me what to do.

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Speaker 7: Yeah, just use a different webs This episode is going

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Speaker 7: to be how I Die Like this, I'll be in

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Speaker 7: front of the Robot Tribunal and yeah yeah yeah, probably

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Speaker 7: three years and they'll be playing this back out of

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Speaker 7: one of their stomachs.

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Speaker 2: That's like did you see like have you I just

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Speaker 2: I don't know, just I was fucking bored as ship.

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Speaker 3: I was falling asleep.

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Speaker 2: But I turned on that Chris Pratt movie The Electric

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Speaker 2: State that was with like a big flop on Netflix,

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Speaker 2: and that whole premise is about the fucking robot wars

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Speaker 2: and how like there had to be a truce between

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Speaker 2: human and robot because they were going to rise up

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Speaker 2: against the people, and there's like all this anti robot

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Speaker 2: and I was just like, dude, this is so stupid.

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Speaker 3: I just turned it off, like.

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Speaker 1: Anti robot propaganda, like we're hearing from Kyle here.

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Speaker 2: Yeah, yeah, someone, Yeah.

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Speaker 1: I was. Somebody was saying that the robot car that

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Speaker 1: drives you around way moo. I think like they I've

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Speaker 1: never been in one, and they're like, oh, you gotta

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Speaker 1: go in one, And I was like, what's good about it?

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Speaker 1: Other like, I think it's slow from the gimmick, it's slower,

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Speaker 1: and they're like, oh, you know, like you don't have

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Speaker 1: to like talk to the driver. I was like, that

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Speaker 1: is that? Yeah, that's not that that.

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Speaker 7: Yeah, I understand that that is the thing that that

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Speaker 7: that people have anxiety about, and ultimately it feels short

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Speaker 7: sighted to be like I think that that we shouldn't

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Speaker 7: remove that simply because you know, I'm just like, I

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Speaker 7: don't know, it's all.

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Speaker 3: I don't know.

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Speaker 2: Person shouldn't have a job because I don't want to

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Speaker 2: talk to them.

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Speaker 7: Yeah, I know that there are every time I say

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Speaker 7: anything on the internet, I'm like, I understand that there's

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Speaker 7: three people who have specific limitations that this could really

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Speaker 7: really affect. And I just I want to earnestly be like,

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Speaker 7: but I don't mean this if for you, like if

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Speaker 7: you are if you have something something that is like

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Speaker 7: really affecting you in a real way. I ultimately I

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Speaker 7: just am so. I saw a way most stopped in

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Speaker 7: Los Angeles because a guy laid on the hood and

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Speaker 7: covered the cameras while it was at a light and

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Speaker 7: it couldn't go anywhere and the person had to sit

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Speaker 7: in it and wait for this guy to get off it.

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Speaker 3: Yeah, they do that.

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Speaker 2: There's another thing I saw in like San Francisco, people

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Speaker 2: are putting his traffic cones on the hood and that

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Speaker 2: would just completely fucking fry it.

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Speaker 3: And they're like, aha, bricked it. Yeah, I'm just so

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Speaker 3: tired of it. I'm just so tired of everything being

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Speaker 3: a robot.

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Speaker 2: Yeah, no, damn, bro, I know you sound like the

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Speaker 2: beginning of the Electric State right now.

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Speaker 3: When they.

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Speaker 7: Viral marketing for the Electric State, we're actually we're rebooting it.

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Speaker 7: We're doing a live action version of that live.

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Speaker 2: Action movie, live or action or even live or action.

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Speaker 3: Just cleaning it up a little bit. It's liver action.

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Speaker 3: All right, let's take a quick break, we'll come back.

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Speaker 1: We'll talk about what's going on in the news. We'll

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Speaker 1: be right back. And we're back. And there's this one moment,

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Speaker 1: as Trump was like taking office and dropping a bunch

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Speaker 1: of like fascist executive orders and just attacking trans people,

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Speaker 1: the Wall Street Journal had another op ed on the

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Speaker 1: front page of their paper that was all about how

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Speaker 1: Colleges two woke and I was just I was kind

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Speaker 1: of amazed at that, but it it really just was

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Speaker 1: kind of an illustration of how relentless they have been

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Speaker 1: and continued to be in painting these institutions. We're going

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Speaker 1: to talk about Harvard and Columbia. The New York Times

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Speaker 1: and like other mainstream media outlets that the democratic part

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Speaker 1: like these institutions that I guess benefit from being like, yeah,

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Speaker 1: we are actually very liberal and just basically they are

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Speaker 1: there to perpetuate the existing power structures and you know,

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Speaker 1: wealth distribution. But yeah, I wanted to start with Harvard.

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Speaker 1: There's Harvard gets mentioned a few times in the book. Specifically,

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Speaker 1: you talk about the study where two academics who claim

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Speaker 1: to be coming from the left and to be like

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Speaker 1: really bummed out by the conclusion that they arrive at

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Speaker 1: are like, guys, sorry, we were just calling balls and

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Speaker 1: strikes here. We're just re doing the math. And unfortunately,

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Speaker 1: the only way for us to move forward as a

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Speaker 1: progressive society is to like double the number the already

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Speaker 1: record high number of cops that we have on the street.

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Speaker 1: We just need to like become even more of a

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Speaker 1: police state, but just more generally, you write in the book,

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Speaker 1: I learned over time the most important qualification for teaching students.

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Speaker 1: Oh no, this was actually a tweet of yours. You wrote,

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Speaker 1: over time the most important qualification for teaching students at

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Speaker 1: elite schools willingness to use your mind, position, and power

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Speaker 1: to preserve distributions of wealth and misery. But you just

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Speaker 1: talk about like kind of what was this surprising to

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Speaker 1: you at a certain point that these institutions like Harvard

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Speaker 1: and Columbia were just so in the bag for the

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Speaker 1: existing kind of power structure.

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Speaker 4: I was pretty naive when I when I got to

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Speaker 4: you know, I went to college at Yale, at the

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Speaker 4: law school at Harvard, and I was pretty naive when

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Speaker 4: I got to these places.

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Speaker 3: You know.

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Speaker 4: I was sort of thinking, Oh, these are institutions of learning,

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Speaker 4: and they're so prestigious, and like people who come here

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Speaker 4: must be smarter, and they must be you know, really

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Speaker 4: rigorous thinkers. And that's really not what goes on at

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Speaker 4: these places at all. I don't want to speak too

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Speaker 4: broad and brushstroke because, like you know, I've a lot

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Speaker 4: of incredible friends and relationships that I've made there. There

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Speaker 4: are a lot of scholars at these institutions, just like

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Speaker 4: at many other universities and colleges and non academic institutions

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Speaker 4: around the country. There are people doing amazing scholarship and

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Speaker 4: scholarship matters like research and matters. I'm not saying that

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Speaker 4: those things are not important. That's not the lesson of

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Speaker 4: the book. But one of the key functions of a

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Speaker 4: place like Harvard is to launder policies and ideas that

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Speaker 4: are divigned to preserve existing distributions of wealth and power

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Speaker 4: in our society launder them with a veneer of academic

440
00:22:48,560 --> 00:22:52,479
Speaker 4: backing and of rigorous thought and things like that. And

441
00:22:52,720 --> 00:22:55,879
Speaker 4: you learned very quickly at a place like Harvard, what

442
00:22:55,960 --> 00:22:59,000
Speaker 4: does it take not only to succeed there as a student,

443
00:22:59,040 --> 00:23:01,320
Speaker 4: but what does it take to become professor? What does

444
00:23:01,359 --> 00:23:05,040
Speaker 4: it take to get tenure? And everybody who it's very political,

445
00:23:05,359 --> 00:23:09,240
Speaker 4: and everybody who's there understands that certain kinds of research

446
00:23:10,119 --> 00:23:13,199
Speaker 4: that benefits certain kinds of people in our society and

447
00:23:13,200 --> 00:23:16,000
Speaker 4: certain institutions is going to be a ticket to success,

448
00:23:16,000 --> 00:23:20,080
Speaker 4: and other kinds of research and views are not. And

449
00:23:20,080 --> 00:23:23,639
Speaker 4: and Harvard is a you know, has endownment worth tens

450
00:23:23,640 --> 00:23:27,159
Speaker 4: of billions of dollars, is a huge industry, and so

451
00:23:27,840 --> 00:23:31,000
Speaker 4: a lot of the scholarship that is produced at a

452
00:23:31,000 --> 00:23:33,919
Speaker 4: place like Harvard is skewed by these other incentives. In

453
00:23:33,960 --> 00:23:36,640
Speaker 4: other words, it's not this kind of pure place where

454
00:23:36,640 --> 00:23:39,680
Speaker 4: like the best ideas are espoused and the people who

455
00:23:39,680 --> 00:23:42,959
Speaker 4: are the smartest and best researchers with the kindest souls

456
00:23:42,960 --> 00:23:45,760
Speaker 4: are the ones who succeeded. It's, you know, it's not

457
00:23:45,800 --> 00:23:48,560
Speaker 4: how it works. And I think the example that I

458
00:23:48,600 --> 00:23:50,199
Speaker 4: use in my book, I have a whole chapter devoted

459
00:23:50,240 --> 00:23:51,879
Speaker 4: to this, I think it's one of the funniest. And

460
00:23:51,920 --> 00:23:54,720
Speaker 4: I try to, by the way, throughout the book, I

461
00:23:54,760 --> 00:23:57,440
Speaker 4: try to talk about all these issues with humor and

462
00:23:57,520 --> 00:24:00,120
Speaker 4: get a little bit of joy, because to me, like

463
00:24:00,359 --> 00:24:02,119
Speaker 4: life is not worth living unless you can laugh a

464
00:24:02,160 --> 00:24:04,639
Speaker 4: little bit about these horrific things.

465
00:24:05,200 --> 00:24:08,159
Speaker 1: And these are definitely teetering on the edge of laughter

466
00:24:08,359 --> 00:24:11,560
Speaker 1: or crying one or the other. Yeah.

467
00:24:11,640 --> 00:24:16,040
Speaker 4: And if you can't laugh about two Harvard professors, you know,

468
00:24:16,119 --> 00:24:20,520
Speaker 4: sort of proposing the greatest expansion of policing in modern

469
00:24:20,560 --> 00:24:24,480
Speaker 4: world history by adding five hundred thousand police officers to

470
00:24:24,560 --> 00:24:28,679
Speaker 4: you as based on rudimentary errors that they made that

471
00:24:28,720 --> 00:24:31,960
Speaker 4: are somewhat comical, then you can't laugh about anything, right.

472
00:24:32,040 --> 00:24:35,160
Speaker 4: And these two guys are particularly funny to me because

473
00:24:35,200 --> 00:24:39,800
Speaker 4: they portray themselves not only as progressives but as socialists,

474
00:24:40,400 --> 00:24:45,359
Speaker 4: and they understand something really important, which is very good

475
00:24:45,520 --> 00:24:48,600
Speaker 4: for your future as a scholar at Harvard. If you

476
00:24:48,640 --> 00:24:51,080
Speaker 4: can be seen as one of those, you know, it's

477
00:24:51,080 --> 00:24:53,800
Speaker 4: almost like a false flag operation, right, It's like you're

478
00:24:53,840 --> 00:24:57,840
Speaker 4: parading around as a leftist socialist progressive, but the ideas

479
00:24:57,880 --> 00:25:02,560
Speaker 4: you're promoting are right and serving the interests of the

480
00:25:02,600 --> 00:25:07,600
Speaker 4: people who financially back Harvard and their social circles, et cetera.

481
00:25:08,080 --> 00:25:11,880
Speaker 4: So it's very smart, actually if what you were thinking

482
00:25:12,000 --> 00:25:14,280
Speaker 4: was that you wanted to advance your career. But anyway,

483
00:25:14,280 --> 00:25:17,320
Speaker 4: these guys propose, and I think one especially comical thing

484
00:25:17,359 --> 00:25:21,480
Speaker 4: about it is that in journal that they published this

485
00:25:21,680 --> 00:25:25,120
Speaker 4: call for five hundred thousand more police. And by the way,

486
00:25:25,400 --> 00:25:28,080
Speaker 4: the call for more police is, as I talk about

487
00:25:28,119 --> 00:25:31,720
Speaker 4: in the book, it's absurd they made this proposal without

488
00:25:31,840 --> 00:25:35,119
Speaker 4: counting the social costs of more police. You can't, you know,

489
00:25:35,560 --> 00:25:39,640
Speaker 4: say I want it. It'd be like installing a new

490
00:25:39,680 --> 00:25:42,360
Speaker 4: heater in your house and not only getting the measurements

491
00:25:42,400 --> 00:25:46,159
Speaker 4: wrong for the heater, but you neglected to include that

492
00:25:46,240 --> 00:25:50,200
Speaker 4: this heater spews carbon monoxide into your house, you know, And.

493
00:25:50,119 --> 00:25:53,840
Speaker 8: So you're saying this heater, yeah, They're like, well, you know,

494
00:25:53,880 --> 00:25:56,439
Speaker 8: we're installing this heater in this house and it's going

495
00:25:56,520 --> 00:25:59,400
Speaker 8: to increase you know, the heat by a degree over

496
00:25:59,480 --> 00:26:02,040
Speaker 8: other heaters, you know, And not only did they get

497
00:26:02,040 --> 00:26:04,680
Speaker 8: that measurement wrong, but they also just neglected to tell

498
00:26:04,680 --> 00:26:05,320
Speaker 8: you it's also going.

499
00:26:05,280 --> 00:26:06,200
Speaker 3: To kill your whole family.

500
00:26:06,359 --> 00:26:08,960
Speaker 1: Right, So you can't promote, you don't.

501
00:26:08,720 --> 00:26:12,159
Speaker 4: Make a social policy proposal without even asking the question of, like,

502
00:26:12,640 --> 00:26:15,680
Speaker 4: what are some of the costs and downsides to the proposal?

503
00:26:16,080 --> 00:26:16,919
Speaker 3: Right and anyway.

504
00:26:17,000 --> 00:26:19,280
Speaker 4: But I think like the funniest part about it to

505
00:26:19,320 --> 00:26:23,320
Speaker 4: me is that the journal they published this in was

506
00:26:23,560 --> 00:26:26,520
Speaker 4: created in the wake of the twenty twenty George Floyd

507
00:26:26,600 --> 00:26:32,080
Speaker 4: uprisings by Harvard as a way of pacifying student unrest.

508
00:26:32,080 --> 00:26:33,800
Speaker 4: They were like, we're going to create this Journal of

509
00:26:34,520 --> 00:26:37,280
Speaker 4: Law and Inequality, and it's going to be this new

510
00:26:37,359 --> 00:26:40,240
Speaker 4: thing that people publish to like confront these issues of

511
00:26:40,240 --> 00:26:42,719
Speaker 4: our day. And then just a short time later, that

512
00:26:42,840 --> 00:26:44,840
Speaker 4: Journal of Law and in Equal or whatever they call

513
00:26:44,920 --> 00:26:50,320
Speaker 4: it is already publishing bold please by Harvard professors to

514
00:26:50,840 --> 00:26:52,560
Speaker 4: add five hundred thousand cops.

515
00:26:52,880 --> 00:26:55,480
Speaker 1: Yes, I remember when it came out and we were like,

516
00:26:55,640 --> 00:26:58,680
Speaker 1: oh my god, the mainstream media is going to eat

517
00:26:58,760 --> 00:27:03,240
Speaker 1: this shit, and sure enough they did. There's also just

518
00:27:03,480 --> 00:27:07,040
Speaker 1: a little detail in that because you kind of you know,

519
00:27:07,160 --> 00:27:09,840
Speaker 1: came at this study and pointed out all the ways

520
00:27:09,880 --> 00:27:13,520
Speaker 1: that it was ridiculous and speaking of these two Harvard

521
00:27:13,520 --> 00:27:16,640
Speaker 1: professors as comical figures, just the fact that they were

522
00:27:16,760 --> 00:27:20,920
Speaker 1: using their students to try to construct arguments against your

523
00:27:21,000 --> 00:27:25,120
Speaker 1: critiques of their book was pretty pretty wild. Sure.

524
00:27:25,760 --> 00:27:28,600
Speaker 2: Oh wait, so they were asking the students to basically

525
00:27:28,720 --> 00:27:31,040
Speaker 2: like defend this against evil alec.

526
00:27:31,600 --> 00:27:33,359
Speaker 4: Well, no, I mean I think I don't know that

527
00:27:33,400 --> 00:27:35,760
Speaker 4: they were. I don't think that they were, you know,

528
00:27:35,800 --> 00:27:38,800
Speaker 4: specifically talking about me. But they were instead of like

529
00:27:39,200 --> 00:27:42,360
Speaker 4: testing students on like the basics of first year criminal law.

530
00:27:42,440 --> 00:27:45,320
Speaker 4: You know, one of the professors during the exam was

531
00:27:45,359 --> 00:27:49,000
Speaker 4: apparently asking and the students you know sent me these

532
00:27:49,160 --> 00:27:51,840
Speaker 4: exam questions, they sent in the screenshots of them to me,

533
00:27:52,480 --> 00:27:56,600
Speaker 4: and he was asking for their help developing counter arguments

534
00:27:56,600 --> 00:28:00,600
Speaker 4: to his proposal more police. You know, It's weren't me

535
00:28:00,760 --> 00:28:02,200
Speaker 4: the wrong way about it, but I thought was really

536
00:28:02,200 --> 00:28:05,040
Speaker 4: funny about it. Was like he was he was asking

537
00:28:05,160 --> 00:28:07,800
Speaker 4: students for help with this research project, you know.

538
00:28:08,119 --> 00:28:14,240
Speaker 1: Just like crowdsourcing, like the fleshing out of research project.

539
00:28:14,480 --> 00:28:17,080
Speaker 4: Yeah, and it turned out like the students you know

540
00:28:17,200 --> 00:28:20,080
Speaker 4: told me that like we had like raised a number

541
00:28:20,080 --> 00:28:23,520
Speaker 4: of the objections that you've d helpfully with them privately

542
00:28:23,560 --> 00:28:25,560
Speaker 4: before they So it's not like these people just like

543
00:28:26,080 --> 00:28:29,960
Speaker 4: had like a huge brain fart and forgot about, you know,

544
00:28:30,080 --> 00:28:32,719
Speaker 4: a lot of the critiques and they just like didn't

545
00:28:33,040 --> 00:28:36,800
Speaker 4: care to respond or even rigorously address in the first

546
00:28:36,800 --> 00:28:39,840
Speaker 4: place these things. And that's why I think the copaganda

547
00:28:39,880 --> 00:28:41,960
Speaker 4: book that is full of this stuff.

548
00:28:42,040 --> 00:28:42,160
Speaker 3: Right.

549
00:28:42,200 --> 00:28:44,280
Speaker 4: What I try to do is take some of the

550
00:28:44,280 --> 00:28:49,560
Speaker 4: most outrageous, funniest examples of kind of mainstream liberal institutions,

551
00:28:49,560 --> 00:28:53,320
Speaker 4: whether it's it's professors or news organizations and journalists or

552
00:28:54,080 --> 00:28:59,560
Speaker 4: whatever nonprofits sometimes and illustrate stuff that is actually really

553
00:28:59,600 --> 00:29:01,960
Speaker 4: important that we might miss if we just look at

554
00:29:02,000 --> 00:29:04,720
Speaker 4: the news. Because it's not just these Harvard professors are

555
00:29:04,720 --> 00:29:07,280
Speaker 4: publishing this stuff. It's how does that stuff then get

556
00:29:07,280 --> 00:29:13,280
Speaker 4: translated into the mainstream news that you consume as conventional wisdom, right,

557
00:29:13,440 --> 00:29:17,120
Speaker 4: And that's that's the process that I really try to

558
00:29:17,360 --> 00:29:20,640
Speaker 4: examine in the book with some humor, because you know,

559
00:29:20,720 --> 00:29:23,760
Speaker 4: this is it's you know, there's a lot of upsetting

560
00:29:23,760 --> 00:29:26,760
Speaker 4: stuff in here about about how we're being lied to

561
00:29:26,840 --> 00:29:29,360
Speaker 4: and misled and how many of the institutions that we're

562
00:29:29,360 --> 00:29:32,920
Speaker 4: told to trust are actually really untrustworthy and we need

563
00:29:32,920 --> 00:29:35,880
Speaker 4: to develop better mechanisms of critical thinking. And that's another

564
00:29:35,920 --> 00:29:39,680
Speaker 4: reason why you know this book. We've raised money so

565
00:29:39,760 --> 00:29:43,000
Speaker 4: that any teacher who wants to teach any part of

566
00:29:43,000 --> 00:29:45,400
Speaker 4: this book in school, any person in prison. You know,

567
00:29:45,480 --> 00:29:48,760
Speaker 4: we have free copies of the book available with the people, like,

568
00:29:49,120 --> 00:29:51,320
Speaker 4: obviously I hope that you support the book and and

569
00:29:51,680 --> 00:29:53,680
Speaker 4: all the I don't make any money off the book,

570
00:29:53,720 --> 00:29:56,320
Speaker 4: that any copy that sold, all the royalties go to

571
00:29:56,360 --> 00:29:59,880
Speaker 4: the Stop LAPD Spying Coalition, which organizes on house people

572
00:29:59,880 --> 00:30:02,719
Speaker 4: and skid row in Los Angeles. But even if if

573
00:30:02,720 --> 00:30:04,960
Speaker 4: you can't afford the book, you know, we can get

574
00:30:05,000 --> 00:30:07,000
Speaker 4: free copies to people because we just want people to

575
00:30:08,080 --> 00:30:10,240
Speaker 4: have a more critical understanding of the news that they're

576
00:30:10,280 --> 00:30:10,840
Speaker 4: being shown.

577
00:30:11,200 --> 00:30:13,480
Speaker 2: Yeah, which is like interesting that the way you bring

578
00:30:13,560 --> 00:30:16,240
Speaker 2: up Harvard it immediately relates to the newsroom, right, like

579
00:30:16,280 --> 00:30:19,560
Speaker 2: the way professors and academics can ascend. It's not that

580
00:30:19,600 --> 00:30:22,720
Speaker 2: there's like marching orders, but you can pretty clearly into it.

581
00:30:22,760 --> 00:30:24,760
Speaker 2: You're like, Okay, if I talk about this in this way,

582
00:30:24,840 --> 00:30:26,960
Speaker 2: this is how I get my things pop in, which

583
00:30:27,000 --> 00:30:28,800
Speaker 2: I feel like is another criticism we have of a

584
00:30:28,840 --> 00:30:32,520
Speaker 2: lot of journalism too, where clearly other journalists now, Okay,

585
00:30:32,560 --> 00:30:34,840
Speaker 2: if I write from this perspective or stories with this

586
00:30:34,960 --> 00:30:38,120
Speaker 2: sort of bend to it, that's how I'm able to ascend,

587
00:30:38,240 --> 00:30:40,720
Speaker 2: and that's how it further just sort of reinforces this

588
00:30:40,880 --> 00:30:42,680
Speaker 2: kind of thing, and I think, you know, the New

589
00:30:42,760 --> 00:30:45,280
Speaker 2: York Times is probably a great example of this kind

590
00:30:45,280 --> 00:30:45,600
Speaker 2: of thinking.

591
00:30:45,760 --> 00:30:48,120
Speaker 1: Yeah, that was really You have a great quote from

592
00:30:48,200 --> 00:30:50,000
Speaker 1: David Graeber, who we talk about a lot on this

593
00:30:50,120 --> 00:30:54,480
Speaker 1: show about like the Superman and Batman where they're both

594
00:30:54,560 --> 00:31:00,000
Speaker 1: essential essentially like instituting fascist policies with their superpowers because

595
00:31:00,560 --> 00:31:03,080
Speaker 1: that's all we can imagine. And like that kind of

596
00:31:03,120 --> 00:31:05,440
Speaker 1: tied back to how I think about the mainstream media

597
00:31:05,840 --> 00:31:08,840
Speaker 1: point that Miles was talking about, where it's like they're

598
00:31:08,880 --> 00:31:12,480
Speaker 1: playing to the audience, like the audience wants to see

599
00:31:12,520 --> 00:31:14,320
Speaker 1: I think there's like a part, at least a part

600
00:31:14,320 --> 00:31:16,280
Speaker 1: of the audience that kind of wants to see this

601
00:31:16,640 --> 00:31:20,719
Speaker 1: fucking fascist like that. That's why Batman does that, That's

602
00:31:20,760 --> 00:31:24,080
Speaker 1: why Superman does that, and so they know they're going

603
00:31:24,120 --> 00:31:27,280
Speaker 1: to get readers, and it's just this like cowardly thing

604
00:31:27,600 --> 00:31:30,479
Speaker 1: that's you know, pleasing their corporate overlords. But I think

605
00:31:30,520 --> 00:31:35,479
Speaker 1: it's also like pleasing some dark, horrible part of the

606
00:31:35,520 --> 00:31:39,040
Speaker 1: audience too that is like, yeah, I want to believe

607
00:31:39,480 --> 00:31:42,280
Speaker 1: that this is the police, good guy, right.

608
00:31:42,240 --> 00:31:45,680
Speaker 2: And this is it It's an easy solve, Yeah, yeah,

609
00:31:45,840 --> 00:31:48,080
Speaker 2: I think there's there's definitely part of that, but I

610
00:31:48,400 --> 00:31:50,960
Speaker 2: don't know, because I mean, I think it's easy to

611
00:31:51,040 --> 00:31:53,960
Speaker 2: envision you could have really compelling local news story.

612
00:31:54,040 --> 00:31:57,800
Speaker 4: Like, for example, if instead of of installing a reporter

613
00:31:58,240 --> 00:32:00,760
Speaker 4: to read all the police press releases and regurgitate them

614
00:32:00,760 --> 00:32:02,840
Speaker 4: for the news every day, what if you had a

615
00:32:02,880 --> 00:32:06,160
Speaker 4: reporter whose job was to report on like landlord tenant

616
00:32:06,160 --> 00:32:08,280
Speaker 4: court and every day you had like a bad landlord

617
00:32:08,320 --> 00:32:11,760
Speaker 4: of the day article or a bad employer of the

618
00:32:11,840 --> 00:32:14,480
Speaker 4: day absolute Like I think people would watch and be

619
00:32:14,560 --> 00:32:17,160
Speaker 4: interested in finding out what landlords are doing to their

620
00:32:17,200 --> 00:32:20,960
Speaker 4: tenants across la or across Chicago or across in the country.

621
00:32:21,000 --> 00:32:23,760
Speaker 4: I think if you if you installed somebody at all

622
00:32:23,760 --> 00:32:26,360
Speaker 4: of the sites that are that are figuring out who's

623
00:32:26,360 --> 00:32:30,760
Speaker 4: polluting our drinking water, who is emitting dangerous gases that

624
00:32:30,800 --> 00:32:32,960
Speaker 4: are hurting our children. And you know, by the way,

625
00:32:33,000 --> 00:32:35,440
Speaker 4: air pollution kills one hundred thousand people in the US

626
00:32:35,480 --> 00:32:39,360
Speaker 4: every year. Okay, that's four to five times all homicide

627
00:32:39,400 --> 00:32:42,560
Speaker 4: can buy. There are people every single day in all

628
00:32:42,640 --> 00:32:45,440
Speaker 4: these cities who are actually just emitting stuff that's killing

629
00:32:45,440 --> 00:32:52,080
Speaker 4: our children and people. Yeah, and I think I think

630
00:32:52,120 --> 00:32:54,120
Speaker 4: people would. I mean, I hear what you're saying. I

631
00:32:54,120 --> 00:32:56,560
Speaker 4: definitely think there's part of like they feel like they're

632
00:32:56,840 --> 00:33:00,320
Speaker 4: they're playing down you know, it's like reality TV or

633
00:33:00,440 --> 00:33:03,560
Speaker 4: Law and Order. Like they definitely think they're what they think,

634
00:33:03,600 --> 00:33:06,800
Speaker 4: they know what their audience wants. But I believe audiences

635
00:33:06,800 --> 00:33:09,960
Speaker 4: would tolerate different types of villains and different types of

636
00:33:10,000 --> 00:33:13,320
Speaker 4: stories about who is causing us harm that were more

637
00:33:13,360 --> 00:33:15,760
Speaker 4: consistent with reality. And that's another thing I talk about

638
00:33:15,760 --> 00:33:16,240
Speaker 4: in the book.

639
00:33:16,880 --> 00:33:20,200
Speaker 1: Yeah, yeah, I mean you have so there's so many

640
00:33:20,240 --> 00:33:24,520
Speaker 1: wild an like what while the Walgreens shoplifting panic was happening,

641
00:33:24,560 --> 00:33:27,360
Speaker 1: they had to settle a massive lawsuit about like wage theft.

642
00:33:27,880 --> 00:33:31,000
Speaker 1: But get that gets you You make a good point

643
00:33:31,040 --> 00:33:36,760
Speaker 1: about how anytime there are multiple, you know, cases of

644
00:33:36,800 --> 00:33:41,000
Speaker 1: shoplifting or just like viral videos of shoplifting, that becomes

645
00:33:41,040 --> 00:33:44,920
Speaker 1: a wave or a trend or you know, out of control.

646
00:33:45,240 --> 00:33:50,040
Speaker 1: But these things, the you know, the Walgreens wage theft

647
00:33:50,280 --> 00:33:53,880
Speaker 1: settlement is treated as a one off thing. And then

648
00:33:53,920 --> 00:33:57,960
Speaker 1: when you talked to editors, they were like, well, we

649
00:33:58,000 --> 00:34:01,320
Speaker 1: reported on a different company doing that last month, so

650
00:34:01,400 --> 00:34:05,800
Speaker 1: we can't like that that story's been done essentially, which

651
00:34:05,880 --> 00:34:10,279
Speaker 1: is so so wild and don't doesn't really doesn't really

652
00:34:10,280 --> 00:34:14,360
Speaker 1: make any sense unless you're taking into account. Yeah, but

653
00:34:14,440 --> 00:34:17,640
Speaker 1: we can't like make them mad because they are the

654
00:34:17,680 --> 00:34:19,320
Speaker 1: big corporate overlord.

655
00:34:19,760 --> 00:34:21,239
Speaker 4: Yeah, I mean, I think you have to understand also

656
00:34:21,280 --> 00:34:23,400
Speaker 4: how the news media works. And there's lots of scholarship

657
00:34:23,440 --> 00:34:27,239
Speaker 4: written about how the media functions. But they it organizes

658
00:34:27,320 --> 00:34:32,279
Speaker 4: things into news themes, and it categorizes things so that

659
00:34:32,280 --> 00:34:35,440
Speaker 4: people can understand them. And so I give this great

660
00:34:35,600 --> 00:34:40,040
Speaker 4: example from the late seventies early eighties of this person

661
00:34:40,040 --> 00:34:43,320
Speaker 4: who in this supporter, who was embedded in a newsroom,

662
00:34:43,719 --> 00:34:47,320
Speaker 4: and he watched the creation of a supposed crime wave

663
00:34:47,840 --> 00:34:52,799
Speaker 4: by youth of color against old people. And there was

664
00:34:52,800 --> 00:34:55,880
Speaker 4: this panic that emerged that all these young people of

665
00:34:55,920 --> 00:35:00,000
Speaker 4: color were robbing and stealing from and mugging and hurting

666
00:35:00,040 --> 00:35:04,600
Speaker 4: old people. And every time that happened during this period,

667
00:35:05,040 --> 00:35:07,440
Speaker 4: it was seen as further evidence of this trend. But

668
00:35:07,560 --> 00:35:09,719
Speaker 4: when you take a step back and look several years later,

669
00:35:09,760 --> 00:35:12,440
Speaker 4: you actually see that the only thing that had been

670
00:35:12,480 --> 00:35:16,440
Speaker 4: created was this news categorization, and this news trend. Actually

671
00:35:16,600 --> 00:35:20,000
Speaker 4: incidents of young people stealing and mugging and robbing old

672
00:35:20,000 --> 00:35:23,640
Speaker 4: people were actually down during that period, and this is

673
00:35:24,280 --> 00:35:27,440
Speaker 4: it's very hard for people to understand. But sometimes the news,

674
00:35:27,760 --> 00:35:31,280
Speaker 4: you know, for example, after the East Palestine trained derailment

675
00:35:31,400 --> 00:35:34,279
Speaker 4: in Ohio a couple of years ago, there were there's

676
00:35:34,320 --> 00:35:37,120
Speaker 4: more of an interest in the news media in covering

677
00:35:37,160 --> 00:35:40,880
Speaker 4: train derailments. Those are things that are happening all the time, right,

678
00:35:40,920 --> 00:35:43,920
Speaker 4: there's like, but now that you're focused on it and

679
00:35:43,960 --> 00:35:46,160
Speaker 4: it's a theme of your news, it's going to play

680
00:35:46,280 --> 00:35:49,399
Speaker 4: into this idea and the same thing is happening. There

681
00:35:49,440 --> 00:35:53,640
Speaker 4: was a massive society wide panic about shoplifting and retail theft,

682
00:35:53,719 --> 00:35:56,480
Speaker 4: even though retail theft we now know is down, And

683
00:35:56,560 --> 00:35:59,480
Speaker 4: so how we categorize things, it's.

684
00:35:59,320 --> 00:36:00,640
Speaker 1: Just so it's like the.

685
00:36:00,600 --> 00:36:04,840
Speaker 4: Michael Jordan example I gave earlier. If you're looking for it,

686
00:36:04,880 --> 00:36:08,040
Speaker 4: and if you just only document the missed shots, you're

687
00:36:08,040 --> 00:36:10,759
Speaker 4: going to not capture the full story. And that's what

688
00:36:10,800 --> 00:36:14,440
Speaker 4: makes the media they have such an awesome responsibility because

689
00:36:15,000 --> 00:36:17,840
Speaker 4: they have a choice every single day, out of the

690
00:36:18,080 --> 00:36:20,680
Speaker 4: millions and millions of things that have happened in the world,

691
00:36:21,000 --> 00:36:23,000
Speaker 4: they're going to tell us about ten or fifteen of them,

692
00:36:23,280 --> 00:36:26,440
Speaker 4: or twenty of them. And also they're going to suggest

693
00:36:26,560 --> 00:36:29,400
Speaker 4: in their coverage, how should we think about this? In

694
00:36:29,440 --> 00:36:34,040
Speaker 4: other words, is that wildfire connected to climate change? Or

695
00:36:34,080 --> 00:36:38,880
Speaker 4: is it some isolated event? Is that airline crash connected

696
00:36:38,920 --> 00:36:44,239
Speaker 4: to DEI programs? At you know, like Trump said, right,

697
00:36:44,320 --> 00:36:45,000
Speaker 4: you know, yeah?

698
00:36:45,160 --> 00:36:46,000
Speaker 1: Or like?

699
00:36:46,360 --> 00:36:52,520
Speaker 4: So this is an awesome responsibility because just by juxtaposing

700
00:36:53,400 --> 00:36:56,120
Speaker 4: two stories together or two ideas together, Like I give

701
00:36:56,120 --> 00:36:59,440
Speaker 4: an example in the book where they claim that a

702
00:36:59,440 --> 00:37:01,600
Speaker 4: certain kind of crime went down in a certain city,

703
00:37:01,640 --> 00:37:04,000
Speaker 4: and they say just they just note that also the

704
00:37:04,000 --> 00:37:07,040
Speaker 4: police had been holding a charity basketball tournament. So it's

705
00:37:07,080 --> 00:37:10,840
Speaker 4: like they're suggesting that, like, and what they didn't report

706
00:37:10,960 --> 00:37:14,120
Speaker 4: is that that particular kind of crime had gone down nationwide,

707
00:37:14,280 --> 00:37:17,239
Speaker 4: not just in the place where there's a police youth

708
00:37:17,320 --> 00:37:22,800
Speaker 4: basketball tournament. So these are all these category categories and

709
00:37:22,840 --> 00:37:28,160
Speaker 4: these causes. They're highly manipulable. And if you take nothing

710
00:37:28,160 --> 00:37:30,000
Speaker 4: else away from the book, I want you to take

711
00:37:30,200 --> 00:37:34,640
Speaker 4: away the idea that there are a lot of very

712
00:37:34,680 --> 00:37:38,400
Speaker 4: smart people being paid billions of dollars a year to

713
00:37:38,600 --> 00:37:43,520
Speaker 4: shape how you think about what is newsworthy, what's happening

714
00:37:43,520 --> 00:37:47,239
Speaker 4: in the world, what the causes are, and therefore what

715
00:37:47,320 --> 00:37:48,480
Speaker 4: should we spend money on.

716
00:37:48,560 --> 00:37:52,080
Speaker 1: As the solutions. Right, Let's take one more quick break

717
00:37:52,160 --> 00:37:53,720
Speaker 1: and then I just want to talk to you about

718
00:37:53,760 --> 00:37:56,680
Speaker 1: like where we go from from here, because obviously things

719
00:37:56,719 --> 00:37:59,960
Speaker 1: are shifting. Some of this fascism that has been ignored

720
00:38:00,120 --> 00:38:02,880
Speaker 1: for a long time is getting louder, and so I

721
00:38:02,960 --> 00:38:05,040
Speaker 1: just want to kind of hear your thoughts on on that.

722
00:38:05,080 --> 00:38:06,040
Speaker 3: We'll be right back.

723
00:38:16,400 --> 00:38:20,360
Speaker 1: And we're back, We are back, and the movie we

724
00:38:20,480 --> 00:38:27,080
Speaker 1: all keep hearing about now the Air Up Now, Sorry, Kevin,

725
00:38:27,120 --> 00:38:30,279
Speaker 1: a classic. That was what we were talking about before.

726
00:38:30,880 --> 00:38:36,400
Speaker 7: Brody in that one, Uh yeah.

727
00:38:37,719 --> 00:38:37,960
Speaker 1: He was.

728
00:38:38,400 --> 00:38:43,360
Speaker 2: He was Sally the African basketball player, the Kyle Airs

729
00:38:43,480 --> 00:38:44,960
Speaker 2: up theres.

730
00:38:44,600 --> 00:38:47,840
Speaker 1: What So I I knew that they were making a

731
00:38:47,920 --> 00:38:49,760
Speaker 1: snow White remake.

732
00:38:49,719 --> 00:38:53,160
Speaker 2: Because of the racist backlash, because of the races backlash.

733
00:38:53,239 --> 00:38:57,520
Speaker 1: That's basically how it came on our radar on this show.

734
00:38:57,560 --> 00:39:00,840
Speaker 1: But yeah, they've been having a lot of success doing

735
00:39:01,320 --> 00:39:03,960
Speaker 1: the Like I was blown a way to see like

736
00:39:04,000 --> 00:39:06,120
Speaker 1: a couple of years after it came out, we were

737
00:39:06,120 --> 00:39:11,080
Speaker 1: looking at like the top ten box office grossers, like, yeah,

738
00:39:11,280 --> 00:39:14,280
Speaker 1: movies of all time, and like the Lion King Live

739
00:39:14,320 --> 00:39:18,080
Speaker 1: action remake was like on there. It's like between Jurassic

740
00:39:18,120 --> 00:39:21,080
Speaker 1: Park and like et it's like the quotation.

741
00:39:20,719 --> 00:39:22,719
Speaker 7: Marks around live action are doing a lot of heavy

742
00:39:22,760 --> 00:39:25,400
Speaker 7: rifting in the animals talk movie. I'm not sure how

743
00:39:25,440 --> 00:39:29,520
Speaker 7: many cameras were there, right, that's right, but the plates

744
00:39:29,560 --> 00:39:30,200
Speaker 7: on cameras.

745
00:39:30,600 --> 00:39:34,040
Speaker 1: Yeah, and it's wild, like a movie that feels like

746
00:39:34,080 --> 00:39:37,000
Speaker 1: it never happened. It's still like one of the most

747
00:39:37,480 --> 00:39:39,759
Speaker 1: financially successful. Like whatever's that.

748
00:39:39,840 --> 00:39:42,080
Speaker 7: You just can't fathom all those people ever being in

749
00:39:42,080 --> 00:39:45,400
Speaker 7: the same room. It's like an ungettable group of people,

750
00:39:46,120 --> 00:39:47,320
Speaker 7: unbookable cast.

751
00:39:47,719 --> 00:39:52,120
Speaker 1: Yeah, but you just get it. Yeah, and they'll do

752
00:39:52,160 --> 00:39:57,480
Speaker 1: it individually in separate you know, trailers or whatever. Everything's computer.

753
00:39:58,360 --> 00:40:01,080
Speaker 3: That's movies.

754
00:40:01,239 --> 00:40:03,080
Speaker 7: I do like the idea of someone like having like

755
00:40:03,160 --> 00:40:06,600
Speaker 7: explaining to Beyonce how to change her input in garage band.

756
00:40:06,840 --> 00:40:07,279
Speaker 3: That's right.

757
00:40:07,640 --> 00:40:09,400
Speaker 7: Oh, I mean she's a recording artist. She probably knows

758
00:40:09,440 --> 00:40:10,960
Speaker 7: how to do that. That's but she's the most famous

759
00:40:10,960 --> 00:40:13,160
Speaker 7: person in the movie. I assume famous people can't use inputs.

760
00:40:14,040 --> 00:40:19,600
Speaker 3: John all of it your second pole, he would be

761
00:40:20,840 --> 00:40:22,080
Speaker 3: to be your second poll from the.

762
00:40:22,080 --> 00:40:24,400
Speaker 1: Second most famous person Beyonce.

763
00:40:26,280 --> 00:40:29,000
Speaker 3: Kevin Bacon, Sabrina Carpenter.

764
00:40:30,600 --> 00:40:34,200
Speaker 2: All Right, so they're somewhat soulless, I guess is what

765
00:40:34,239 --> 00:40:38,560
Speaker 2: I'd say. It feels a little like CGI slot, but

766
00:40:38,640 --> 00:40:43,319
Speaker 2: it's incredibly profitable because so you got the two quadrants,

767
00:40:43,360 --> 00:40:46,520
Speaker 2: all right, You got the people who saw the first

768
00:40:46,600 --> 00:40:50,279
Speaker 2: movie and still like that movie, the first Lion King,

769
00:40:50,600 --> 00:40:53,719
Speaker 2: and then you got these younger audiences who will never

770
00:40:53,880 --> 00:40:58,200
Speaker 2: know the joy of seeing the first movie without having

771
00:40:58,280 --> 00:41:00,759
Speaker 2: this kind of it's like.

772
00:41:00,920 --> 00:41:03,920
Speaker 1: High level AI slop is what it feels like a little.

773
00:41:03,640 --> 00:41:05,640
Speaker 7: Bit that is a good way of like they just

774
00:41:05,760 --> 00:41:08,960
Speaker 7: copy pasted the YouTube link to the first movie.

775
00:41:09,320 --> 00:41:09,920
Speaker 3: Yeah.

776
00:41:09,960 --> 00:41:13,640
Speaker 1: And the other point that people have made is that like,

777
00:41:13,640 --> 00:41:17,120
Speaker 1: like thinking about it like a business person, you're like, well,

778
00:41:17,160 --> 00:41:20,440
Speaker 1: there's a lot of hard work that has already been done,

779
00:41:20,640 --> 00:41:24,080
Speaker 1: Like they've already like designed these shots, and we don't

780
00:41:24,080 --> 00:41:27,480
Speaker 1: have to pay those people at all, Like you don't

781
00:41:27,520 --> 00:41:29,000
Speaker 1: have to pay them get.

782
00:41:28,920 --> 00:41:31,319
Speaker 7: A shot for shot remake of Psycho And we all

783
00:41:31,440 --> 00:41:34,480
Speaker 7: dragged him for it, and then people at Disney were like, yeah,

784
00:41:34,520 --> 00:41:35,920
Speaker 7: but what if we did this with other stuff?

785
00:41:36,080 --> 00:41:38,120
Speaker 1: Yeah? What if he did this like with stuff that

786
00:41:38,440 --> 00:41:42,319
Speaker 1: actually everything we've ever done. Yeah, so this is a

787
00:41:42,320 --> 00:41:44,920
Speaker 1: remake of a nineteen thirty seven film. So I actually

788
00:41:45,719 --> 00:41:47,960
Speaker 1: am skeptical that there's a ton of people who are

789
00:41:48,000 --> 00:41:50,880
Speaker 1: still left over from that initial right.

790
00:41:50,800 --> 00:41:53,840
Speaker 7: We hired a lot of the same animators, right, Yeah,

791
00:41:54,040 --> 00:41:55,719
Speaker 7: the union was stronger back then.

792
00:41:56,040 --> 00:41:58,560
Speaker 3: A lot of people with the same mental state as nineteen.

793
00:41:58,280 --> 00:42:03,279
Speaker 1: Fo Yeah, some reason the history kind of rhymes, but

794
00:42:03,760 --> 00:42:07,480
Speaker 1: apparently it's good. Like the early critical reaction is that

795
00:42:07,520 --> 00:42:10,360
Speaker 1: it's like pretty good for a snow White movie, like

796
00:42:10,480 --> 00:42:15,320
Speaker 1: Rachel Ziggler, who is playing playing the titular snow White

797
00:42:16,360 --> 00:42:19,440
Speaker 1: just you know, her star power shines through, according to

798
00:42:19,560 --> 00:42:24,520
Speaker 1: early critics, and Disney's like burying it, which is weird,

799
00:42:24,680 --> 00:42:27,319
Speaker 1: like that the the very opposite of what they did

800
00:42:27,320 --> 00:42:30,120
Speaker 1: with The Lion King, when when it was just like

801
00:42:30,280 --> 00:42:33,040
Speaker 1: the biggest media event of the year for a thing

802
00:42:33,040 --> 00:42:34,640
Speaker 1: that ended up being kind of nothing.

803
00:42:35,120 --> 00:42:37,880
Speaker 2: It's been so many contras, like the fans, there's so

804
00:42:37,920 --> 00:42:41,359
Speaker 2: many loud ass hater fans from the beginning that it's

805
00:42:41,400 --> 00:42:44,200
Speaker 2: just like hammered Disney like into a corner. And then

806
00:42:44,239 --> 00:42:47,520
Speaker 2: on top of that, like the the Rachel Zeggler saying

807
00:42:47,560 --> 00:42:49,640
Speaker 2: things about the movie that people are like, how dare

808
00:42:49,719 --> 00:42:53,240
Speaker 2: she talk up characterize a thing from nineteen thirty seven

809
00:42:53,360 --> 00:42:55,000
Speaker 2: is potentially being backwards?

810
00:42:55,200 --> 00:42:57,840
Speaker 3: Yeah, she says one more thing has changed in a

811
00:42:57,960 --> 00:43:01,480
Speaker 3: hundred years. I'm gonna lose my mind in my thirty

812
00:43:01,520 --> 00:43:02,240
Speaker 3: dollars house.

813
00:43:06,640 --> 00:43:11,000
Speaker 1: Yeah, they are doing the thing that they like move

814
00:43:11,280 --> 00:43:14,279
Speaker 1: the Flash that Ezra Miller Flash Movie did, where they like,

815
00:43:14,320 --> 00:43:17,879
Speaker 1: don't let press come to the premiere, Like they're still

816
00:43:17,880 --> 00:43:20,200
Speaker 1: throwing a premiere, but they're not letting press come to it.

817
00:43:20,320 --> 00:43:21,680
Speaker 3: What just happened earlier this week?

818
00:43:21,760 --> 00:43:25,200
Speaker 1: Yeah, Yeah, it's just the movie costs two hundred and

819
00:43:25,280 --> 00:43:26,800
Speaker 1: seventy million dollars.

820
00:43:27,239 --> 00:43:30,200
Speaker 7: Yeah, yeah, is crazy that they're like, you know how

821
00:43:30,239 --> 00:43:32,960
Speaker 7: much snow White costs Fox?

822
00:43:34,160 --> 00:43:35,920
Speaker 3: Right, we bought that Fox.

823
00:43:36,239 --> 00:43:38,720
Speaker 1: We bought that Fox for that that amount of money.

824
00:43:38,719 --> 00:43:41,080
Speaker 2: We bought all of the things a Fox em since

825
00:43:41,440 --> 00:43:44,560
Speaker 2: right right, Yeah, No, it's because even too. The other

826
00:43:44,600 --> 00:43:47,120
Speaker 2: thing that was really telling is like the pre sale

827
00:43:47,200 --> 00:43:49,399
Speaker 2: for the tickets was like only like a week out

828
00:43:49,480 --> 00:43:52,719
Speaker 2: from the premiere, where anytime you have like a supposed

829
00:43:52,800 --> 00:43:55,840
Speaker 2: tent pole film, you're doing more than like a week

830
00:43:56,200 --> 00:43:58,600
Speaker 2: to try and get pre sale figures up. And it

831
00:43:58,600 --> 00:44:01,759
Speaker 2: seemed like very early on maybe people were yeah, yeah,

832
00:44:02,000 --> 00:44:03,520
Speaker 2: already gets to like.

833
00:44:04,080 --> 00:44:07,240
Speaker 3: The Avengers Doomsday movie, right, really.

834
00:44:07,080 --> 00:44:13,040
Speaker 1: Yeah yeah in the twenty thirties, yeah, yeah, yeah, and

835
00:44:13,080 --> 00:44:16,120
Speaker 1: then there, Yeah, there's a controversy. Rachel Diggler has said

836
00:44:16,200 --> 00:44:21,719
Speaker 1: some things hugely controversial about the sexual politics of the

837
00:44:21,719 --> 00:44:24,319
Speaker 1: first movie, which, by the way, like going back and

838
00:44:24,400 --> 00:44:27,480
Speaker 1: watching the first movie. I did this for the Bechdel cast.

839
00:44:28,040 --> 00:44:32,480
Speaker 1: Like snow White is only like anytime she does something

840
00:44:32,520 --> 00:44:35,960
Speaker 1: with her own agency, she like knocks herself out, Like

841
00:44:36,000 --> 00:44:38,960
Speaker 1: she like runs into the woods and like knocks herself out.

842
00:44:39,040 --> 00:44:40,879
Speaker 3: Like brning the buttons to a video game.

843
00:44:41,080 --> 00:44:44,480
Speaker 1: Yeah, the only exactly the only good things that happened

844
00:44:44,480 --> 00:44:48,799
Speaker 1: to her is when she's unconscious, Like literally she's unconscious,

845
00:44:49,000 --> 00:44:51,160
Speaker 1: and then all the animals like fall in love with her.

846
00:44:51,440 --> 00:44:54,359
Speaker 1: She's passed out in like the dwarf's bed, and like

847
00:44:55,000 --> 00:44:57,839
Speaker 1: the they are about to like stab her with an

848
00:44:57,840 --> 00:45:01,360
Speaker 1: ice pick and then or with a X pick axe

849
00:45:01,560 --> 00:45:03,759
Speaker 1: pick axe, and like they just fall in love with

850
00:45:03,800 --> 00:45:07,160
Speaker 1: her because she's so pretty when she's asleep. When she's awake,

851
00:45:07,280 --> 00:45:10,000
Speaker 1: she runs into the woods and like knocks herself out

852
00:45:10,040 --> 00:45:14,360
Speaker 1: by like running into branches. She she eats a poison

853
00:45:14,520 --> 00:45:17,960
Speaker 1: the most clearly poisoned apple of all time in the history.

854
00:45:19,520 --> 00:45:21,720
Speaker 3: Even like even Eve was like whoa.

855
00:45:21,480 --> 00:45:23,560
Speaker 2: Whoa, whoa, whoa, whoa whoa, whoa, whoa.

856
00:45:23,239 --> 00:45:30,879
Speaker 1: Yeah, yeah, yeah, and then the ultimate thing that saves her.

857
00:45:30,920 --> 00:45:33,880
Speaker 1: She's in a coma in like a weird glass coffin

858
00:45:33,960 --> 00:45:37,200
Speaker 1: in the middle of the woods, and like Prince Charman

859
00:45:37,280 --> 00:45:39,640
Speaker 1: comes by and saves her by like falling in love

860
00:45:39,640 --> 00:45:42,399
Speaker 1: with her again because of how pretty she is while

861
00:45:42,440 --> 00:45:46,080
Speaker 1: she's asleep. That that is her superpower is being pretty

862
00:45:46,280 --> 00:45:50,239
Speaker 1: when she's not making decisions or like messing it up

863
00:45:50,320 --> 00:45:55,000
Speaker 1: by being having free will, she's just passed out. It's

864
00:45:55,040 --> 00:45:57,840
Speaker 1: like fucking Bill Cosby wrote the movie. It's really a

865
00:45:57,920 --> 00:46:00,120
Speaker 1: weird and has some weird angles.

866
00:46:00,280 --> 00:46:01,920
Speaker 2: Even like even when she was talking about she's just

867
00:46:01,960 --> 00:46:03,319
Speaker 2: kind of like, yeah, it's a little you know, it's

868
00:46:03,360 --> 00:46:07,000
Speaker 2: like nineteen thirty seven, so yeah, there's some things that yeah, yeah,

869
00:46:07,040 --> 00:46:09,279
Speaker 2: yeah yeah, and it's well again, this goes back to

870
00:46:09,320 --> 00:46:12,680
Speaker 2: the racism because she's you know, Colombian and also Polish.

871
00:46:12,719 --> 00:46:15,360
Speaker 2: That why can't they just focus on the Polish side

872
00:46:15,440 --> 00:46:17,719
Speaker 2: and celebrate that, you know what I mean, Like, yeah,

873
00:46:17,760 --> 00:46:18,919
Speaker 2: it's fine, you know, because they.

874
00:46:18,800 --> 00:46:21,400
Speaker 3: Have joke books in their bathrooms, I know.

875
00:46:23,360 --> 00:46:27,040
Speaker 2: But like you look at too, like even when Homegirl,

876
00:46:27,080 --> 00:46:30,920
Speaker 2: who was Hermione, when she played Belle and Beauty and

877
00:46:30,960 --> 00:46:33,600
Speaker 2: the Beast. She also was saying shit about like how

878
00:46:33,880 --> 00:46:38,759
Speaker 2: you know the new Emma Yes, Emma Watson, Yes, thank you,

879
00:46:38,800 --> 00:46:42,120
Speaker 2: super Catherine. She even had things to say about how

880
00:46:42,239 --> 00:46:44,919
Speaker 2: the remake was like that there were just some things

881
00:46:44,920 --> 00:46:46,560
Speaker 2: in the original that she was like, oh, I don't know,

882
00:46:46,680 --> 00:46:49,279
Speaker 2: but that sort of didn't cause the same kind of controversy.

883
00:46:49,320 --> 00:46:52,520
Speaker 2: But again, because everything is all about yes, they tell.

884
00:46:52,560 --> 00:46:56,960
Speaker 7: The difference in immediate reaction to these two actresses just

885
00:46:57,120 --> 00:46:59,040
Speaker 7: really hard for me to find something that could cause

886
00:46:59,080 --> 00:47:02,480
Speaker 7: a knee jerk reaction from someone ready to be upset online.

887
00:47:02,600 --> 00:47:06,640
Speaker 1: So she is being compared to Gal Gado, who is

888
00:47:06,719 --> 00:47:10,720
Speaker 1: that plays the evil Queen Gal Gadot, who has served

889
00:47:11,040 --> 00:47:13,760
Speaker 1: in the idea, literally served in the idea.

890
00:47:14,040 --> 00:47:16,360
Speaker 3: Hey, Daniel day Lewis got into character early.

891
00:47:16,920 --> 00:47:22,959
Speaker 1: Exactly, has been very outspokenly pro Israel since the start

892
00:47:22,960 --> 00:47:27,000
Speaker 1: of the war in Gaza, and Rachel Ziggler, who's just said,

893
00:47:27,840 --> 00:47:29,920
Speaker 1: you know, pro Palestine.

894
00:47:29,400 --> 00:47:30,719
Speaker 3: There needs to be a ceasefire.

895
00:47:31,400 --> 00:47:33,080
Speaker 7: I think that at least the way they could like

896
00:47:33,120 --> 00:47:35,239
Speaker 7: come together is if Gal could get some sort of

897
00:47:35,239 --> 00:47:39,440
Speaker 7: like vertical video song montage video put together to really

898
00:47:39,520 --> 00:47:41,200
Speaker 7: just let us all unite.

899
00:47:41,640 --> 00:47:43,920
Speaker 1: Try try and imagine a world where that happens.

900
00:47:44,080 --> 00:47:47,200
Speaker 7: Never that I will. I will never forget about that.

901
00:47:47,200 --> 00:47:50,359
Speaker 7: That's the turning point in every in celebrity warship as

902
00:47:50,360 --> 00:47:51,280
Speaker 7: a culture, I believe.

903
00:47:51,800 --> 00:47:55,560
Speaker 1: I similarly think that that was a very important event.

904
00:47:56,080 --> 00:47:59,359
Speaker 1: I'm still waiting for the the long read on how

905
00:47:59,360 --> 00:47:59,840
Speaker 1: it came.

906
00:47:59,719 --> 00:48:02,759
Speaker 7: Back to the all of this Snow White reboot is

907
00:48:02,800 --> 00:48:06,600
Speaker 7: shot in front of Galgadot's like pool side cabana where

908
00:48:06,640 --> 00:48:08,120
Speaker 7: she's trying to relate to people.

909
00:48:08,640 --> 00:48:12,759
Speaker 1: That's right. So it also so at the time that

910
00:48:12,800 --> 00:48:15,759
Speaker 1: it came out, Benjapiro was like, I mean, her name

911
00:48:15,800 --> 00:48:19,719
Speaker 1: is snow White, like that, this is white supremacist, and

912
00:48:19,760 --> 00:48:22,799
Speaker 1: that's a good, a good thing, I guess, so much

913
00:48:22,840 --> 00:48:27,240
Speaker 1: so that he started launched his own version of snow

914
00:48:27,239 --> 00:48:31,279
Speaker 1: White because snow White is a public domain starring a

915
00:48:31,640 --> 00:48:33,839
Speaker 1: conservative like YouTube star.

916
00:48:34,440 --> 00:48:37,240
Speaker 3: And I thought you were joking earlier.

917
00:48:37,400 --> 00:48:41,960
Speaker 1: No, no, no. I thought it was like because.

918
00:48:41,680 --> 00:48:43,719
Speaker 7: Bench Sparro likes musicals and stuff, and I remember his

919
00:48:43,760 --> 00:48:46,480
Speaker 7: reaction to Wicked being like a very viral thing. I

920
00:48:46,520 --> 00:48:47,960
Speaker 7: thought you were actually messing with me.

921
00:48:48,239 --> 00:48:50,200
Speaker 1: No no, no, no, no no. He was like, we're gonna

922
00:48:50,320 --> 00:48:52,200
Speaker 1: bring into our own and it's gonna be snow white,

923
00:48:52,239 --> 00:48:54,839
Speaker 1: and she's gonna be really white. By the way, the

924
00:48:54,880 --> 00:48:58,160
Speaker 1: prison just looks the same amount of white as Rachel Tigler.

925
00:48:58,200 --> 00:49:00,640
Speaker 1: It's not like, yeah, it's not like dizzy He's out

926
00:49:00,680 --> 00:49:04,440
Speaker 1: here trying to fight for diversity. But anyways, they put

927
00:49:04,480 --> 00:49:08,680
Speaker 1: out a trailer looked like shit, and that movie fell apart.

928
00:49:09,040 --> 00:49:13,880
Speaker 1: So unfortunately they won't make it. They'd never made it. No, no, no, no, no.

929
00:49:14,120 --> 00:49:17,279
Speaker 2: Of course, of course she's snow white. Other people like

930
00:49:17,480 --> 00:49:20,520
Speaker 2: this is this is an American thing because it's Disney,

931
00:49:20,560 --> 00:49:22,960
Speaker 2: And I'm like, can do you know anything about fucking

932
00:49:23,000 --> 00:49:26,359
Speaker 2: snow white? Do you know who the brother's grim? Are

933
00:49:26,360 --> 00:49:27,160
Speaker 2: they from America?

934
00:49:27,680 --> 00:49:28,440
Speaker 1: Yeah?

935
00:49:28,560 --> 00:49:32,960
Speaker 3: So Cleveland, I believe, Yeah, yeah, Cleveland and Germany. They

936
00:49:33,280 --> 00:49:35,319
Speaker 3: explained the energy I have while I'm there.

937
00:49:35,719 --> 00:49:39,160
Speaker 1: But yeah, like, it really doesn't makes it if I

938
00:49:39,200 --> 00:49:43,000
Speaker 1: had to guess why Disney is just completely burying a

939
00:49:43,040 --> 00:49:46,360
Speaker 1: movie that costs them two hundred and seventy million dollars,

940
00:49:46,800 --> 00:49:52,640
Speaker 1: whether it's their objection to Gal Gadot being pro Israeli

941
00:49:52,840 --> 00:49:58,360
Speaker 1: or because they're ashamed that Rachel Ziggler says pro Palestine

942
00:49:58,440 --> 00:50:01,960
Speaker 1: things every once in a while and because they're trying.

943
00:50:02,000 --> 00:50:07,000
Speaker 1: They're scared of being criticized by the right during this administration.

944
00:50:07,440 --> 00:50:11,640
Speaker 1: I don't know which side is more likely here, but yeah,

945
00:50:11,680 --> 00:50:16,000
Speaker 1: they're they're just it seems somewhat cowardly to me. It's

946
00:50:16,040 --> 00:50:19,840
Speaker 1: also kind of in keeping with their legacy because they've

947
00:50:20,400 --> 00:50:22,840
Speaker 1: kind of been dicks about snow White from the start.

948
00:50:23,520 --> 00:50:27,160
Speaker 1: The actress who voiced snow White was paid twenty dollars

949
00:50:27,239 --> 00:50:30,840
Speaker 1: a day and was not listed, was not listed in

950
00:50:30,880 --> 00:50:36,040
Speaker 1: the credits, and Walt Disney just blackballed her from ever

951
00:50:36,080 --> 00:50:40,760
Speaker 1: appearing in anything ever again, allegedly so that her because

952
00:50:40,800 --> 00:50:43,320
Speaker 1: he was like, that would ruin the snow White illusion.

953
00:50:43,800 --> 00:50:46,239
Speaker 2: Right, That's why I also have her sleeping in this

954
00:50:46,320 --> 00:50:48,560
Speaker 2: glass coffin in the middle of nowhere, and.

955
00:50:48,840 --> 00:50:52,000
Speaker 3: Right every now and then give her water and food. Yeah, yeah,

956
00:50:52,120 --> 00:50:54,040
Speaker 3: I just don't want to destroy the illusion. Yeah.

957
00:50:54,080 --> 00:50:56,520
Speaker 2: That's so wild though too. They like that, like that

958
00:50:56,680 --> 00:50:58,919
Speaker 2: old tiny way of doing this. I mean they're still

959
00:50:58,960 --> 00:51:01,320
Speaker 2: blacklisting actors now, what am I saying? But like twenty

960
00:51:01,360 --> 00:51:03,160
Speaker 2: dollars a day is like it's you can make almost

961
00:51:03,239 --> 00:51:04,200
Speaker 2: double that now.

962
00:51:05,719 --> 00:51:07,080
Speaker 3: For voice acting.

963
00:51:07,440 --> 00:51:11,400
Speaker 2: Yeah, but yeah, just like for our whole equipment that

964
00:51:11,560 --> 00:51:12,160
Speaker 2: texture of.

965
00:51:12,080 --> 00:51:15,359
Speaker 3: Being like you, I own you and the voice.

966
00:51:15,080 --> 00:51:17,880
Speaker 2: And I can disappear you, my pretty and that's the

967
00:51:17,920 --> 00:51:19,080
Speaker 2: fucking end of it anyway.

968
00:51:20,280 --> 00:51:23,120
Speaker 7: So the actor I'm just reading the die the actress

969
00:51:23,239 --> 00:51:26,680
Speaker 7: who voice also about being Adriana Cascilotti. I'm sorry, no

970
00:51:26,719 --> 00:51:29,440
Speaker 7: one seeing her would change the image of snow white, right,

971
00:51:29,440 --> 00:51:31,120
Speaker 7: I don't even know what she looks like. I'm picturing

972
00:51:31,160 --> 00:51:33,960
Speaker 7: Adriana from the Sopranos, but purely based on name.

973
00:51:37,600 --> 00:51:38,480
Speaker 3: The mystique.

974
00:51:38,520 --> 00:51:40,799
Speaker 7: I guess there were eight movies then, you know they

975
00:51:40,800 --> 00:51:43,160
Speaker 7: were like, well whoa whoahoa, no one can ride that train.

976
00:51:43,239 --> 00:51:44,960
Speaker 3: It came at the screen, do you know what I mean?

977
00:51:45,040 --> 00:51:50,759
Speaker 1: Right, they were point there's still that point of like

978
00:51:50,920 --> 00:51:53,720
Speaker 1: movies where they're like, we don't want to ruin the.

979
00:51:53,960 --> 00:51:58,640
Speaker 3: Magic translates what Casablanca turns to. Do you know what

980
00:51:58,680 --> 00:52:00,359
Speaker 3: I mean? He's gonna have some real gripes with where

981
00:52:00,400 --> 00:52:02,560
Speaker 3: that movie took y R Right, what if they.

982
00:52:02,480 --> 00:52:04,880
Speaker 1: Were on the other side, I don't know, just just

983
00:52:04,920 --> 00:52:08,520
Speaker 1: spitball in here, but yeah, they're like that. People are

984
00:52:08,560 --> 00:52:11,799
Speaker 1: if if this woman goes out and goes on like

985
00:52:11,840 --> 00:52:14,959
Speaker 1: one one of these talk shows she's people are gonna

986
00:52:14,960 --> 00:52:18,439
Speaker 1: really like literally, Jack Benny invited her to come on

987
00:52:18,480 --> 00:52:22,120
Speaker 1: his radio show and she was like yeah, and then

988
00:52:22,160 --> 00:52:23,920
Speaker 1: they were like, we checked with Walt. Walt says no,

989
00:52:24,239 --> 00:52:28,840
Speaker 1: because then people will think that the drawing is not singing.

990
00:52:29,000 --> 00:52:31,840
Speaker 1: They'll know that you're an actual person.

991
00:52:32,040 --> 00:52:34,600
Speaker 7: And oh, only Walt Disney knew how dumb people are

992
00:52:34,880 --> 00:52:37,279
Speaker 7: falling her deep fakes now where it isn't even the

993
00:52:37,320 --> 00:52:40,719
Speaker 7: actual real person singing a song. Yeah, show him a

994
00:52:40,760 --> 00:52:43,000
Speaker 7: Snapchat filter. Let's blow his frozen head.

995
00:52:43,360 --> 00:52:47,200
Speaker 3: Oh my god, he's probably fucking he'd probably redie. How

996
00:52:47,320 --> 00:52:51,200
Speaker 3: is that a dog? No? I don't have ears like

997
00:52:51,239 --> 00:52:53,480
Speaker 3: that though, or a big puffy snout.

998
00:52:54,680 --> 00:52:59,359
Speaker 1: All right, that's gonna do it for this week's weekly Zeitgeist.

999
00:52:59,440 --> 00:53:02,920
Speaker 1: Please I can review the show if you like. The

1000
00:53:03,000 --> 00:53:08,240
Speaker 1: show means the world of Miles. He needs your validation, folks.

1001
00:53:08,840 --> 00:53:11,919
Speaker 1: I hope you're having a great weekend and I will

1002
00:53:11,960 --> 00:54:04,200
Speaker 1: talk to you Monday. Bye.