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Speaker 1: Hi, I'm Molly John Fast and this is Fast Politics. Well,

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Speaker 1: we discussed the top political headlines with some of today's

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Speaker 1: best minds.

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Speaker 2: And Donald Trump has raged truth, the words that will

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Speaker 2: soon bring him much misery in his existence. I hate

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Speaker 2: Taylor Swift. We have such a great show for you today,

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Speaker 2: Kick Konger and Ryan Max dot By to talk to

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Speaker 2: their new book character Women, how Elon Musk destroyed Twitter.

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Speaker 2: Then the nineteenth Samanda Becker will tell us about her

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Speaker 2: new book You Must Stand Up the Fight for Abortion

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Speaker 2: rights and post dobs America. But first we have the

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Speaker 2: host of the Enemy's List, the Lincoln Project's own Rick Wilson.

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Speaker 1: Welcome back to Fast Politics.

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Speaker 3: For Wilson, why do you sound so for Lauren Kamala

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Speaker 3: Harris kicked the ever loving shit out of Donald Trump

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Speaker 3: up and down that stage just three nights ago, like

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Speaker 3: he was a rented mule. I mean, listen, you should

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Speaker 3: be in a happy place right now. We got dogs

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Speaker 3: and cats living in sin together. Oh, are being eaten

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Speaker 3: one of the two?

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Speaker 1: I don't know, no one's eating dogs. And then they

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Speaker 1: went to well, maybe not dogs, but geese. And then

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Speaker 1: they went to, well, maybe not geese, but there's this woman.

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Speaker 1: They just have nothing.

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Speaker 3: Yeah, they have one crazy person who's not a Haitian.

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Speaker 1: Who in a different town.

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Speaker 3: Yeah right, and a differency may have done something with

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Speaker 3: a cat. But I mean the whole thing is, I know,

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Speaker 3: you're shocked to hear this, a farrago of lies and horseshit.

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Speaker 1: Shocking, shocking, Yes, was shocked because those guys are known

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Speaker 1: for being legit, and there's some possibility that taking Laura

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Speaker 1: Lumer with you.

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Speaker 3: Listen, Laura Lumer. I like to think of her now

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Speaker 3: as the campaign's general consultant, which I really like. I

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Speaker 3: think it's going to make the campaign much more efficient.

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Speaker 3: If Donald Trump really has waited this long to bring

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Speaker 3: Laura Lumer into the family, it's been a mistake. I mean,

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Speaker 3: she is so brilliantly able to catch the mood of America,

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Speaker 3: the sweet spot of American politics. I mean, yesterday calling

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Speaker 3: out Lindsay Graham for being gay, being a nine to

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Speaker 3: eleven truther, being a monstrous shit bag human being the

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Speaker 3: first order. I mean, there's nothing about Laura Lumer honestly

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Speaker 3: that doesn't make America say this is the right choice

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Speaker 3: for president, because he's going to surround himself with the

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Speaker 3: very best people.

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Speaker 1: Also, I would like to add that it was nine

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Speaker 1: to eleven this week and he went to the nine

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Speaker 1: to eleven services he went to.

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Speaker 3: I mean he was there, but was he really there?

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Speaker 1: And she was on the plane, so I don't know

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Speaker 1: that she went to the events.

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Speaker 3: No, she was on the plane for the debate.

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Speaker 1: Also, Yeah, she's been going around to a lot of

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Speaker 1: places with him.

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Speaker 3: Yes, and he's been not wearing his wedding ring. I'm

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Speaker 3: not saying that Alina Habba has anything to worry about,

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Speaker 3: but you know, tongues are wagging. You have me here

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Speaker 3: to perform just that kind of stunt.

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Speaker 1: Rick Wilson, let me ask you a question. I actually

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Speaker 1: am in a bad mood and worried more not in

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Speaker 1: a bad mood, but more worried about a little stay

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Speaker 1: called Pennsylvania. Yeah, so talk me off the ledge about Pennsylvania.

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Speaker 3: I will tell you Pennsylvania is the lynch pen of

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Speaker 3: the campaign. That's real talk. I'm not going to bass

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Speaker 3: you and tell you that Pennsylvania is not super super

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Speaker 3: super important. It is very important. Without Pennsylvania, the world

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Speaker 3: falls apart and we're all living as mutants in a

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Speaker 3: radioactive healthscape that makes Mad Max look like sound of music.

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Speaker 3: It is not great if we don't win Pennsylvania. But

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Speaker 3: I think we're gonna win Pennsylvania.

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Speaker 1: Okay, tell me why.

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Speaker 3: I do not believe that we are in the same

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Speaker 3: spot that they think we are. I think Pennsylvania, particularly

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Speaker 3: in Bucks County, has a big, big number of pro

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Speaker 3: choice Republican women and independent leaning conservative women who are

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Speaker 3: pro choice. And I know we're working very much there

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Speaker 3: at the moment. We're going to be in there with

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Speaker 3: a lot of resources. We're feeling like there's a degree

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Speaker 3: of economic uplift you're going to get when the rate

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Speaker 3: cut kicks in in a week or so, when the

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Speaker 3: Fed kicks the raids down. Pennsylvania's an area that's been

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Speaker 3: hard hit by housing, the explosive housing costs. It's going

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Speaker 3: to be close run thing. I feel much better about

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Speaker 3: Michigan and Wisconsin than I have in the past. I'm

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Speaker 3: optimistic about North Carolina. I'm optimistic about Georgia. Don't quote me,

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Speaker 3: but I'm even starting to feel much better about Nevada,

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Speaker 3: which I was not super happy about for a long time.

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Speaker 1: Yeah, tell us why.

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Speaker 3: I think it's starting to come into a position in Nevada.

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Speaker 3: So Pennsylvania's going to be tough. We got to win it.

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Speaker 3: And there's a lot of angry, pissed off white bros there.

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Speaker 3: That's a real talk, that's a real fact.

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Speaker 1: And she has been going to Pittsburgh, right, Explain to

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Speaker 1: us the thinking there.

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Speaker 3: She's been going to Pittsburgh, She's been going to Philadelphia.

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Speaker 3: She's been in the Tea that area between Pittsburgh and

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Speaker 3: Philadelphia a couple times. Now Walls is there. I think

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Speaker 3: he probably lives there now. He's everything but a permanent resident.

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Speaker 1: Now, well he should because he's the perfect person for there. Right, absolutely,

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Speaker 1: explain to us why he.

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Speaker 3: Scans as a regular suburban, middle class white guy. He

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Speaker 3: does not scan as a Democratic Party elite ready to

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Speaker 3: make you eat insect low for whatever the fuck fantasy

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Speaker 3: of them at the moment. And all of this is

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Speaker 3: going to come down, I think too. You know, do

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Speaker 3: we end up with meaningful African American turnout in the

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Speaker 3: Caller counties around Philadelphia and those Coller counties are Look,

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Speaker 3: Hillary Clinton must be very very blunt. Her campaign did

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Speaker 3: a terrible job activating African Americans in those Coller counties.

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Speaker 3: And that's why Trump won the state. He did not

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Speaker 3: win the state in twenty twenty. I gotta tell you,

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Speaker 3: does anybody believe that there are more Trump voters now

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Speaker 3: than there were in twenty twenty.

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Speaker 1: No? Are there?

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Speaker 3: No, there are not. They are a declining stock. They're

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Speaker 3: a dying breed.

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Speaker 1: Why Biden did better in Pennsylvania is because they knew

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Speaker 1: him in Pennsylvania, because Delaware shares a media market connects.

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Speaker 3: With Pennsylvania voters. And in fact, you've got in Walls

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Speaker 3: a guy who is a familiar flavor. He's biden Esque

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Speaker 3: in that regard. He is somebody they get. They know

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Speaker 3: who this guy is, they know the type of guy

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Speaker 3: he is, and he doesn't scare them. It's real hard,

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Speaker 3: it's real hard for the Trumpers to go. Tim Walls

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Speaker 3: is a communist socialist from liberal California. That Tim Wall's

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Speaker 3: plan to make it to seize the means of production.

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Speaker 3: I mean, come on, get the fuck out of here.

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Speaker 3: That guy does not scare people. And they hate it.

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Speaker 3: But he doesn't scare people. They really hate it. They're like,

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Speaker 3: they're frustrated as shit. They've been looking for, like any

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Speaker 3: opo punch to hit Tim Walls with. For weeks now,

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Speaker 3: and they can't make it happen. They can't make the sell.

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Speaker 1: Do you think that JD. Vance being in the state

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Speaker 1: actually hurts Trump?

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Speaker 3: Well, look, I tell you one thing. JD. Vance being

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Speaker 3: in Ohio, being from Ohio has hurt Trump in Ohio.

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Speaker 3: The number one lesson of every vice presidential candidate is

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Speaker 3: that your job is to go to weddings and funerals.

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Speaker 3: As a general rule, do no harm is the primary

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Speaker 3: mission of a vice presidential candidate. And he has done harm.

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Speaker 3: He has hurt Donald Trump in his home state of Ohio.

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Speaker 3: And for all that Trump is a guy who loves

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Speaker 3: people who suck up to him, what did we see

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Speaker 3: from Trump the other night with JD Vance? He sold

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Speaker 3: him down the river. I didn't talk to jd about that.

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Speaker 3: Who I think he's an intern, maybe a coffee boy.

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Speaker 3: He may have been on the campaign for five minutes,

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Speaker 3: but I've never spoke to him, met him, or know

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Speaker 3: anything about him. That is all.

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Speaker 1: Yeah, so very good. Alec Baldwin doing.

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Speaker 3: Trump Molly, That's all I can do.

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Speaker 1: You know, it's very impressive.

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Speaker 3: I'm a man with many, many severe limitations, and that's

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Speaker 3: one of them. Don't have a good Trump imitation.

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Speaker 1: But here's a question for you. So let's just game

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Speaker 1: this out. North Carolina early voting has been disturbed by

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Speaker 1: the worst person in the world, RFK Junior.

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Speaker 3: Discuss Look, the early voting has been problematic because we

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Speaker 3: have RFK fighting to get off the ballot and to

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Speaker 3: review the ballots right now. You also have today the

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Speaker 3: Trump folks and their allies. They are trying to bring

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Speaker 3: lawsuits to forbid college students and state workers and other

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Speaker 3: people from voting early. The whole thing is, of course,

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Speaker 3: exactly what you would have expected their usual fuckery.

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Speaker 1: Okay, does it work?

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Speaker 3: I don't think it works in the end. You know why.

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Speaker 3: It's September. There's plenty of time to have early voting

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Speaker 3: continue to successfully give us a real, meaningful lift for Harris.

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Speaker 3: I don't think there's anything that's gonna stop that, given that,

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Speaker 3: given that Trump has been such a bad candidate lately,

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Speaker 3: has had no good days. I mean, Trump does not

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Speaker 3: have good days anymore, real talk. I mean this in

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Speaker 3: a serious way, not a dark way. Trump's best day

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Speaker 3: was the day he got shot. That was the last

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Speaker 3: moment that his campaign felt like there was something there

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Speaker 3: beyond just the angry, shitty conspiracy theory, crazy talk bullshit.

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Speaker 1: Well it felt new, yeah, right, Well.

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Speaker 3: It didn't feel it wasn't new, but it felt purposeful

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Speaker 3: at least, right. Yeah, But right now, all it feels

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Speaker 3: like is complaint to palooza and lies and weird shit,

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Speaker 3: because right now it is just weird shit, and weird

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Speaker 3: shit doesn't sell after a certain point.

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Speaker 1: Yeah, so you feel like North Carolina, that's not going

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Speaker 1: to be a huge disturbance.

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Speaker 3: No, I look, I would I like it to be

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Speaker 3: slightly less full of fuckery? Yes, of course, is it

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Speaker 3: disqualifying levels of fuckery? Not really? I think again. Trump

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Speaker 3: has a problem that he cannot solve with women voters.

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Speaker 3: He did not solve it the other night in the

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Speaker 3: debate when he tried to bs his way past and

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Speaker 3: gave probably the most damaging single answer about abortion I

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Speaker 3: could have conceived.

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Speaker 1: Yeah, let's talk about that answer.

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Speaker 3: It was so broken brained. I'm still marveling, because look,

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Speaker 3: what do we know about Trump? He wants to brag.

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Speaker 3: He wants to say, oh, I killed Rivy Wade. I

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Speaker 3: put them on the court. Their active bravery on the

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Speaker 3: court shows how great I am. Sorry, I'm doing it again,

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Speaker 3: you have to just sorry going on with then he

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Speaker 3: knows he Trump's feral animal cunning and his awareness. He

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Speaker 3: knows just how bad, just how shit the the National

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Speaker 3: Band stuff is. He knows just how bad and how

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Speaker 3: dangerous all the things the Republicans have done in the

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Speaker 3: States has become to him. He recognizes that he's stupid,

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Speaker 3: but he's not that stupid, if that makes sense. He

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Speaker 3: is not in a good place, okay, he is in

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Speaker 3: a bad, bad place. That bad place is getting worse

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Speaker 3: by the day, and he understands it. He really does know.

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Speaker 3: He knows how bad it is. But he couldn't get

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Speaker 3: out of his own way with that answer. He couldn't

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Speaker 3: not do the I'm the greatest thing since prepared mustard,

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Speaker 3: I'm the God king answer. And so you know, here

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Speaker 3: we are with Trump pissing off the evangelicals on the

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Speaker 3: one hand and pissing off the rest of America on

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Speaker 3: the other. It was a bad play. It was a

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Speaker 3: terrible play. So that those people are going to make

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Speaker 3: a big difference in Pennsylvania in particular.

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Speaker 1: The thing that gets me incensed about Trump and Trump

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Speaker 1: is and the way that people talk about Trump, including

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Speaker 1: in the media, industrial complex of which we are members.

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Speaker 1: Is that they always say, like, he just needs to

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Speaker 1: get disciplined. Well, he just has to stick to script.

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Speaker 1: Has no one been here for the last decade?

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Speaker 3: Right, Welcome to the Year of Our Lord twenty twenty four.

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Speaker 3: We are nine years and five months into this fucking bullshit.

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Speaker 3: Have you not noticed that Donald Trump has the personal

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Speaker 3: discipline of an incontinent drunkard. Donald Trump cannot focus on

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Speaker 3: anything for longer than it gives him an erection, a

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Speaker 3: belief he's going to make money off of this, or

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Speaker 3: some sort of cruelty. Nothing. In Trump's world. He is

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Speaker 3: the ultimate add candidate. He has no ability to focus.

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Speaker 3: There is no better Trump. There is no better iteration

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Speaker 3: of Trump. There is no Donald Trump where where he

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Speaker 3: wakes up in the morning and says, why yes, I

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Speaker 3: believe I should sit down with my policy advisors and

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Speaker 3: develop a coherent rationale for this argument. Instead, it's like

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Speaker 3: Laura Lumer tell me they're eating the cats, they're eating

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Speaker 3: the dogs, and Stephen Miller, I think he has some

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Speaker 3: blood in his mouth. He may have eaten the dog,

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Speaker 3: but I believe him. And it's all fucking crazy town.

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Speaker 3: He will never be better no Republican who is empowered

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Speaker 3: and enabled and kissed his ass for as long as

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Speaker 3: they have has any excuse at all whatsoever. There is

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Speaker 3: no excuse there. It is over, it is done. He

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Speaker 3: has poisoned them in a way that they don't even

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Speaker 3: understand how deep the rot runs in their own brains

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Speaker 3: and their own party, because they're still rationalizing it. They're

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Speaker 3: still trying to say, like, well, one of the people

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Speaker 3: in the Reuters focus group was undecided and said that

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Speaker 3: Harris didn't have enough granularity on her policy. Trump's policy

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Speaker 3: was to stand up there and talk about eating dogs

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Speaker 3: and executing the Central Park five. The madness levels here

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Speaker 3: are off the charts.

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Speaker 1: And I would say the false equivalency here is pretty amazing,

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Speaker 1: like continually always expected to be normal, and Republicans are

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Speaker 1: allowed to be insane, not.

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Speaker 3: Just allowed to be insane mally. Let's remember the mainstream

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Speaker 3: media has consistently said things like Trump's energetic and transgressive performance,

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Speaker 3: this appeal to middle class voters. You know what, There

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Speaker 3: are some voters that they have appeals to, But there

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Speaker 3: are some people that like to go to the zoo

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Speaker 3: and watch monkeys throw shit at them.

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Speaker 1: So one of the things that got me upset was

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Speaker 1: there was a Republican senator I've never heard of, which

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Speaker 1: in itself has not nothing. He's in I think North

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Speaker 1: Dakota maybe, and he was saying that he wasn't sure

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Speaker 1: they were going to certify the election.

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Speaker 3: That's Mark Wayne Mullen. That's all one name, Mark Wayne.

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Speaker 4: Yeah.

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Speaker 3: Who the fuck is that Mark Wayne Mullen. Okay, first off,

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Speaker 3: let's be very clear. Mark is a staggering, epic, world

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Speaker 3: class dipshit the worst Mago cuckoo pants people are like, yo,

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Speaker 3: Mark Wayne dial batshit back man, that's too much, brother.

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Speaker 3: He is one of the worst human beings in that body.

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Speaker 1: Has he been there a long time? I never even

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Speaker 1: heard of him.

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Speaker 3: No, he's only been there like four years now.

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Speaker 1: Okay.

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Speaker 3: He is a guy you can count on Mark Wayne

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Speaker 3: to be the shittiest human being on earth. He's a

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Speaker 3: guy who ran and cowered under a bench on one

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Speaker 3: to six, but now is like the heroes of January sixth,

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Speaker 3: must fight against the deep state imprisoning our beloved allies.

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Speaker 3: Fuck this guy. He is a epic, epic piece of shit.

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Speaker 3: There is a real possibility these people will vote to

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Speaker 3: not certify the election. There's a very real possibility that

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Speaker 3: that's what's going to happen. I think I think it's

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Speaker 3: like a seventy percent possibility. You're going to have people

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Speaker 3: in the Senate who do not certify the election. We're

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Speaker 3: going to have a constitutional crisis because Donny dipshit will

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Speaker 3: not say to his people, I lost. Okay, you're right,

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Speaker 3: you got me. I lost, and we're going to end

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Speaker 3: up with an absolute horror show going into next year

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Speaker 3: is going to be terrible. And everybody right now who

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Speaker 3: thinks somehow that Donald Trump will be dignified, correct proper,

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Speaker 3: that he will say, why, yes, you're right. For the

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Speaker 3: good of the country, I must declare that I I

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Speaker 3: have lost this election and accept my defeat. On the

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Speaker 3: field of battle. She could beat him by four hundred

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Speaker 3: Electoral College votes, and that fucker is going to go

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Speaker 3: out and go It was stolen. Stalin stolen. One of

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Speaker 3: those things I like the dictator and the pastry. Also,

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Speaker 3: the election was stalin.

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Speaker 1: Are we worried that this will kip to the Supreme

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Speaker 1: Court and that they'll overturn the election results.

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Speaker 3: Yes, we should be. That is why the defeat has

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Speaker 3: to be in such a scope it does have to

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Speaker 3: be like Obama McCain, not like Biden Trump. She has

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Speaker 3: to beat Trump in a scope well beyond how Biden

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Speaker 3: beat him. I hate saying that because it's hard.

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Speaker 1: Do you think that can happen?

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Speaker 3: No, wait, it's going to be very difficult to get there.

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Speaker 3: I wanted to. I'm working every day to make it happen.

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Speaker 3: But we should not mistake that this is a very

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Speaker 3: precarious moment in our history where you have a party

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Speaker 3: and a president who will do anything to win. They

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Speaker 3: will lie, cheat, and steel. We have to fight them

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Speaker 3: every step of the way. Harris is running a great campaign,

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Speaker 3: running the best campaign that we've seen in our lifetimes.

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Speaker 3: That doesn't mean that Trump will not try to steal it.

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Speaker 3: He will try to steal it. There is no chance

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Speaker 3: on earth that Donald Trump. Just like people say, oh well,

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Speaker 3: Donald Trump needs to be mature, someone says Donald Trump

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Speaker 3: needs to be a person who respects the constitution of

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Speaker 3: the rule of law. Will good luck with that? Have

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Speaker 3: fun storm in that.

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Speaker 1: Castle, Rick Wilson, Now I'm stressed out again.

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Speaker 3: Well, Molly, all we can do is just keep kicking

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Speaker 3: his ass.

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Speaker 2: We have even more tour dates for you. Did you

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Speaker 2: know the Lincoln Projects, Rick Willson have Fast Politics Bali

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Speaker 2: Jug Faster. Are heading out on tour to bring you

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Speaker 2: a night of laughs for our dark political landscape. Join

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Speaker 2: us on August twenty sixth at San Francisco at the

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Speaker 2: Swedish American Hall, or in la on August twenty seventh

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Speaker 2: at the Regent Theater. Then we're headed to the Midwest.

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Speaker 2: We'll be at the Vivarium in Milwaukee on the twenty

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Speaker 2: first of September and on the twenty second will be

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Speaker 2: in Chicago with City Wiry. Then we're going to hit

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Speaker 2: the East coast September thirtieth, We'll be in Boston at

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Speaker 2: Arts at the Armory. On the first of October, we'll

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00:17:04,480 --> 00:17:07,040
Speaker 2: be in Affiliates City Winery, and then DC on the

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Speaker 2: second at the Miracle Theater. And today we just announced

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Speaker 2: that we'll be in New York on the fourteenth of

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Speaker 2: October at Citywinery. If you need to laugh as we

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Speaker 2: get through this election and hopefully never hear from a

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Speaker 2: guy who lives in a golf club again, we got

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Speaker 2: you covered. Join us in our surprise guests to help

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Speaker 2: you laugh instead of cry your way through this election

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Speaker 2: season and give you the inside analysis of what's really

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Speaker 2: going on right now. Buy your tickets now by heading

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Speaker 2: to Politics as Unusual dot bio. That's Politics as Unusual

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Speaker 2: dot bio. Ryan Mack and Kate Kunger are the authors

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Speaker 2: of Character Limit, How Elon Musk Destroyed Twitter.

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Speaker 1: Welcome to Fast Politics Cade. Thank you and Ryan.

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Speaker 5: Hey, Molly, So you.

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Speaker 1: Guys did a book together. First of all, just give

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Speaker 1: us everything about this book.

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Speaker 6: Well you said earlier twenty minutes right.

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Speaker 7: So the book is Character Limit, How Elon Musk Destroyed Twitter,

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Speaker 7: and we take you through the saga that has been

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Speaker 7: Twitter over the last several years, starting with sort of

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Speaker 7: the twilight era of the company, the end of Jack

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Speaker 7: Dorsey's tenure as CEO and Perogue Algarol's brief period as

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Speaker 7: a CEO, and then Elon Musk of course swoops in,

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Speaker 7: makes it offer to buy it, says he doesn't want

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Speaker 7: to buy it anymore, says he wants to buy it again,

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Speaker 7: and then we finished the story with the aftermath of

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Speaker 7: Elon's takeover in the way that he changed and transformed

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Speaker 7: the company.

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Speaker 1: Yes, so this feels like the kind of book where

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Speaker 1: you get going on it and you're shocked at how

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Speaker 1: much worse it really is then you even thought is

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Speaker 1: it discussed?

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Speaker 5: So the genesis of this book was we're reporters at

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Speaker 5: the New York Times. Kate is the b reporter for

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Speaker 5: Twitter now x I cover accountability and cover you know,

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Speaker 5: billionaires and figures in Silicon Valley, and we reported on

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Speaker 5: this deal from its start to the takeover, and yeah,

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Speaker 5: we were getting so much reporting that we simply couldn't

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Speaker 5: put it into every single one of our stories.

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Speaker 3: Like we just couldn't.

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Speaker 5: We couldn't do a justice, you know, right, And there

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Speaker 5: were things we'd love to expand upon from you know,

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Speaker 5: elon try to pull out of the deal, for example,

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Speaker 5: or even after the deal, the types of cuts he made,

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Speaker 5: things like not having toilet paper in the bathrooms because

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Speaker 5: he cut the janitorial service, Like just details like that

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Speaker 5: that we're just so confounding to us and so bizarre

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Speaker 5: and so unprecedented in a takeover like this where one

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Speaker 5: man could you know, essentially buy a company for forty

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Speaker 5: four billion dollars, you know, at the sap of his fingers.

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Speaker 1: Did you think it would happen? And were you shocked?

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Speaker 1: It's sort of the process that it took, you know.

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Speaker 7: This is so funny because I think if you had

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Speaker 7: asked me on any given day during that summer of

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Speaker 7: twenty twenty two whether or not the deal was going

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Speaker 7: to close, I would have given a different answer. And

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Speaker 7: even up to the moment that everyone's seen online where

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Speaker 7: Elon walked into the Twitter building with a sink and

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Speaker 7: made that hokey joke about let that sink in, I

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Speaker 7: was still not sure that he was actually going to

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Speaker 7: do it, because at that point it had just been

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Speaker 7: such a chaotic process that it seemed like he might

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Speaker 7: still pull the rug out from under the whole thing.

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Speaker 7: And I think a lot of the people on the

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Speaker 7: Twitter side of the deal felt that way too. You know,

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Speaker 7: even as he was there in the building saying he

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Speaker 7: was about to buy the company, they weren't willing to

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Speaker 7: leave him until they saw the money in the bank account.

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Speaker 1: Yeah. This reminds me a little bit of the Trump administration.

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Speaker 1: It seemed improbable, impossible, undoable. It got done, but in

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Speaker 1: a very haphazard way with a lot of unhappy people involved.

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Speaker 1: Is it similar in that way?

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Speaker 3: Yeah? I don't know.

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Speaker 5: I didn't do much reporting on the Trump administration. I

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Speaker 5: know the stories of like you know, he didn't expect

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Speaker 5: to win, and then they find themselves in twenty sixteen

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Speaker 5: having to run a government. If that's the framework that

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Speaker 5: we're thinking of, then with musks takeover, there are a

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Speaker 5: lot of similarities. Like you made an offer kind of

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Speaker 5: on a whim. He tried to pull out. Everyone remembers

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Speaker 5: the bot argument. You know, there's so many bots, there's fraud.

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Speaker 3: On this platform.

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Speaker 5: I'm not going to buy it. He tries to back out.

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Speaker 5: He's then forced to buy it at the price he

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Speaker 5: admits to overpaying for forty four billion dollars, and he

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Speaker 5: kind of comes in shooting from the hip. There's really

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Speaker 5: no plan here for what he's going to do with

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Speaker 5: this thing. He's he's talking about these kind of pie

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Speaker 5: in the sky concepts, building a quote unquote everything app

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Speaker 5: wanting to bring in payments to Twitter, but fundamentally misunderstanding

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Speaker 5: that this is a social platform and a very human platform.

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Speaker 5: You're connecting humans to talk to each other, and when

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Speaker 5: people talk to each other, there's all these messy things

436
00:21:40,200 --> 00:21:44,920
Speaker 5: that happen, And you know, I think that misunderstanding is

437
00:21:45,200 --> 00:21:47,960
Speaker 5: led to the cast that we see today, from the

438
00:21:48,080 --> 00:21:52,199
Speaker 5: multiple rounds of layoffs to the issues with governments all

439
00:21:52,240 --> 00:21:56,040
Speaker 5: around the world to just the loss of advertisers and

440
00:21:56,080 --> 00:21:58,399
Speaker 5: the decrease in valuation. I mean the company is now

441
00:21:58,720 --> 00:22:01,840
Speaker 5: worth internally less than twenty billion dollars. You know, that

442
00:22:01,960 --> 00:22:06,640
Speaker 5: is a stunning kind of devaluation over two years, done

443
00:22:06,720 --> 00:22:08,000
Speaker 5: by the man who's supposed to be one of the

444
00:22:08,000 --> 00:22:09,080
Speaker 5: best businessmen in the world.

445
00:22:09,359 --> 00:22:09,800
Speaker 1: Yeah.

446
00:22:09,960 --> 00:22:13,280
Speaker 7: I think there are just a lot of parallels between

447
00:22:13,840 --> 00:22:16,000
Speaker 7: him and Trump. I mean, both of them just sort

448
00:22:16,040 --> 00:22:19,400
Speaker 7: of command Internet attention. Both of them have this sort

449
00:22:19,440 --> 00:22:23,120
Speaker 7: of rotating cast of characters surrounding them, where people are

450
00:22:23,440 --> 00:22:26,399
Speaker 7: in one day and then cast out the next. And

451
00:22:26,440 --> 00:22:29,760
Speaker 7: there's even this one photo I think it is from

452
00:22:30,359 --> 00:22:33,560
Speaker 7: when Elon Musk attended the met gala, but he's in

453
00:22:33,640 --> 00:22:36,280
Speaker 7: a tux and he's given the thumbs up post.

454
00:22:36,320 --> 00:22:38,840
Speaker 6: Yeah, and I saw.

455
00:22:38,600 --> 00:22:41,480
Speaker 7: That photo, I was like, whoa, they look eerily early

456
00:22:41,560 --> 00:22:44,040
Speaker 7: similar when he's in the right costume and doing the

457
00:22:44,119 --> 00:22:46,800
Speaker 7: right post. I think there's a lot of parallels between

458
00:22:47,080 --> 00:22:48,560
Speaker 7: the way that they conduct themselves.

459
00:22:48,800 --> 00:22:51,280
Speaker 1: Yeah, I mean I knew some people inside Twitter. I

460
00:22:51,320 --> 00:22:53,879
Speaker 1: mean there was a sense there it felt to me

461
00:22:54,040 --> 00:22:56,720
Speaker 1: like they had sort of built this little paradise and

462
00:22:56,760 --> 00:22:59,720
Speaker 1: that it couldn't be destroyed so easily. And then it

463
00:22:59,720 --> 00:23:03,800
Speaker 1: really was what is the feeling of the people who

464
00:23:03,960 --> 00:23:06,639
Speaker 1: are in there or were in there because most of

465
00:23:06,680 --> 00:23:07,240
Speaker 1: them are gone.

466
00:23:07,359 --> 00:23:09,320
Speaker 5: Probably part of the reason why we wanted to write

467
00:23:09,320 --> 00:23:11,600
Speaker 5: this book is we wanted to kind of dispel this

468
00:23:11,680 --> 00:23:15,320
Speaker 5: myth that like everything at Twitter was perfect Elon.

469
00:23:15,640 --> 00:23:16,120
Speaker 3: This was a.

470
00:23:16,080 --> 00:23:19,399
Speaker 5: Company that had a lot of issues, from its balance

471
00:23:19,440 --> 00:23:23,919
Speaker 5: sheet and its business to employee happiness, I guess, and

472
00:23:24,400 --> 00:23:27,000
Speaker 5: content moderation decisions. You know, this is a company that

473
00:23:27,040 --> 00:23:31,080
Speaker 5: had to deal with COVID misinformation and the platforming of

474
00:23:31,119 --> 00:23:33,520
Speaker 5: President Trump. I mean, these were the New York Post

475
00:23:33,640 --> 00:23:38,159
Speaker 5: article about Hunter Biden, monumental decisions that drew it a

476
00:23:38,200 --> 00:23:40,200
Speaker 5: lot of flack. And you know, the company was a

477
00:23:40,280 --> 00:23:43,520
Speaker 5: lightning rod constantly. So I don't know if i'd call

478
00:23:43,560 --> 00:23:46,040
Speaker 5: it a paradise. I mean, it certainly wasn't what it

479
00:23:46,080 --> 00:23:49,280
Speaker 5: is today. You know, this kind of mess under Elon,

480
00:23:49,440 --> 00:23:51,639
Speaker 5: But it wasn't a happy place. It was, you know,

481
00:23:51,640 --> 00:23:53,480
Speaker 5: a lot more stable. It was a place where you

482
00:23:53,480 --> 00:23:54,320
Speaker 5: could build a career.

483
00:23:54,760 --> 00:23:57,639
Speaker 7: Yeah, and I think Twitter is such a unique place

484
00:23:57,760 --> 00:24:01,960
Speaker 7: even within the tech industry, and I think pretty much

485
00:24:01,960 --> 00:24:05,720
Speaker 7: any tech job you get the founders or the CEO

486
00:24:05,920 --> 00:24:07,679
Speaker 7: is going to try to convince you to come with

487
00:24:07,760 --> 00:24:09,919
Speaker 7: a real sense of mission, right.

488
00:24:10,320 --> 00:24:12,880
Speaker 6: But I think you know, a lot.

489
00:24:12,720 --> 00:24:17,000
Speaker 7: Of Google employees, for instance, feel pretty divorced from the

490
00:24:17,040 --> 00:24:20,200
Speaker 7: notion that they are there to like help people access

491
00:24:20,240 --> 00:24:23,960
Speaker 7: and catalog the world's information, Right. But I think Twitter

492
00:24:24,000 --> 00:24:27,760
Speaker 7: was the place where people who worked there really grasped

493
00:24:27,840 --> 00:24:30,920
Speaker 7: onto the mission and believed in it in a way

494
00:24:31,240 --> 00:24:33,439
Speaker 7: a lot of other tech employees are just kind of

495
00:24:33,480 --> 00:24:36,560
Speaker 7: like rolling their eyes. And I think Bright and I

496
00:24:36,640 --> 00:24:40,040
Speaker 7: shared that sense that there was something uniquely important about

497
00:24:40,040 --> 00:24:42,080
Speaker 7: what Twitter was doing as a company. In the way

498
00:24:42,480 --> 00:24:48,520
Speaker 7: that it was creating this platform for political speech was

499
00:24:48,560 --> 00:24:52,639
Speaker 7: really fascinating. But as Ryan said, we also wanted to

500
00:24:53,320 --> 00:24:56,160
Speaker 7: portray some of the bumps and lumps that were in there,

501
00:24:56,760 --> 00:24:59,240
Speaker 7: problems that were going on at the company, because that

502
00:24:59,400 --> 00:25:02,919
Speaker 7: narrative just got very distilled down into like good Twitter

503
00:25:03,040 --> 00:25:05,760
Speaker 7: and evil Elon and and we want to take that

504
00:25:05,880 --> 00:25:06,360
Speaker 7: a little bit.

505
00:25:06,680 --> 00:25:09,040
Speaker 5: I don't think we can discount how much there were

506
00:25:09,200 --> 00:25:11,560
Speaker 5: a group of employees in the company who were excited

507
00:25:11,560 --> 00:25:13,919
Speaker 5: about Elon's takeover, or at least wanted to give him

508
00:25:13,920 --> 00:25:15,560
Speaker 5: a chance. You know, this is a company that had

509
00:25:15,960 --> 00:25:19,600
Speaker 5: stagnated with product production, like they couldn't launch a product

510
00:25:19,600 --> 00:25:21,359
Speaker 5: if their life depended on it in some way, you know.

511
00:25:21,600 --> 00:25:24,120
Speaker 5: He Compared to some of the other tech companies out there,

512
00:25:24,480 --> 00:25:27,560
Speaker 5: things were going really slow. There was little innovation, and

513
00:25:27,600 --> 00:25:30,000
Speaker 5: they thought that Elon was the guy who would kickstart

514
00:25:30,000 --> 00:25:32,359
Speaker 5: all this. You know, he had electrified cars, he had

515
00:25:32,359 --> 00:25:35,840
Speaker 5: put rockets into space. Why couldn't we do better things

516
00:25:35,920 --> 00:25:37,720
Speaker 5: under him? And they saw this as kind of the

517
00:25:37,800 --> 00:25:40,240
Speaker 5: lightning bolt that they needed to get things going again.

518
00:25:40,320 --> 00:25:42,600
Speaker 5: But it was the lightning bolt that essentially started an

519
00:25:42,600 --> 00:25:45,000
Speaker 5: inferno inside the company and burn the place down.

520
00:25:45,160 --> 00:25:48,400
Speaker 1: So yeah, right, it totally did. And I know there

521
00:25:48,560 --> 00:25:51,280
Speaker 1: was a lot of that. And also like fundamentally the

522
00:25:51,320 --> 00:25:53,320
Speaker 1: business was not profitable, right.

523
00:25:53,400 --> 00:25:54,800
Speaker 6: Yes, that's pretty much true.

524
00:25:55,000 --> 00:25:58,720
Speaker 7: There was a period of profitability that the company had,

525
00:25:58,920 --> 00:26:02,600
Speaker 7: I want to say, pre COVID, and then during COVID

526
00:26:02,720 --> 00:26:06,600
Speaker 7: actually they were doing fairly well and then had kind

527
00:26:06,600 --> 00:26:09,919
Speaker 7: of fallen off of their numbers by the time Elon

528
00:26:10,000 --> 00:26:11,720
Speaker 7: came in and proposed the acquisition.

529
00:26:12,320 --> 00:26:14,800
Speaker 5: Yeah, I mean, this is a company that gets compared

530
00:26:14,840 --> 00:26:17,400
Speaker 5: to Facebook or Meta a lot, you know, and that's

531
00:26:17,440 --> 00:26:20,560
Speaker 5: a company that has more than a billion I think

532
00:26:20,560 --> 00:26:23,879
Speaker 5: it's close to two billion users across all its platforms now.

533
00:26:24,040 --> 00:26:26,679
Speaker 5: Twitter at its peak had hundreds of millions. You know,

534
00:26:26,840 --> 00:26:28,760
Speaker 5: this was a company that wasn't drawing in the ad

535
00:26:28,800 --> 00:26:31,840
Speaker 5: revenue that a meta draws in, and it was just

536
00:26:31,880 --> 00:26:35,240
Speaker 5: kind of this kind of floating business. It was stagnant

537
00:26:35,280 --> 00:26:37,920
Speaker 5: in some ways, it floated along, and it was kind

538
00:26:37,920 --> 00:26:39,680
Speaker 5: of ripe for the picking for someone like you On

539
00:26:39,840 --> 00:26:41,919
Speaker 5: to come and swoop in and make an offer.

540
00:26:42,200 --> 00:26:45,000
Speaker 1: The large request I feel like with Elon was to

541
00:26:45,040 --> 00:26:47,920
Speaker 1: try to make or as Trump call him Leon, to

542
00:26:47,960 --> 00:26:52,760
Speaker 1: try to make Twitter profitable, right, And he's done a

543
00:26:52,840 --> 00:26:56,000
Speaker 1: number of different things to try to do that. He

544
00:26:56,119 --> 00:26:59,120
Speaker 1: has done a lot of businesses, and many of them

545
00:26:59,320 --> 00:27:04,280
Speaker 1: have struggled with like actual profitability or being priced for

546
00:27:04,359 --> 00:27:07,640
Speaker 1: what their ebuita is. To talk to us about that, sure.

547
00:27:07,440 --> 00:27:10,600
Speaker 5: I mean Tesla was not profitable for years. I mean,

548
00:27:10,720 --> 00:27:13,840
Speaker 5: the car business is incredibly hard. It's why a lot

549
00:27:13,880 --> 00:27:17,160
Speaker 5: of car startups fail. And he went through periods where

550
00:27:17,200 --> 00:27:19,639
Speaker 5: he was on the verge of bankruptcy almost to his

551
00:27:19,720 --> 00:27:22,679
Speaker 5: last dollar, and was able to pull it out. You know,

552
00:27:22,760 --> 00:27:24,920
Speaker 5: I think as something like twenty eighteen, for example, where

553
00:27:24,960 --> 00:27:28,480
Speaker 5: he can't manufacture enough Model three cars, this kind of

554
00:27:28,520 --> 00:27:32,159
Speaker 5: low cost car, mass market car, and is in this

555
00:27:32,200 --> 00:27:34,640
Speaker 5: thing called production hell. And he's really, you know, kind

556
00:27:34,680 --> 00:27:37,439
Speaker 5: of at his wits end, sleeping on the factory floor

557
00:27:37,640 --> 00:27:39,800
Speaker 5: or on the couch in the factory, trying to make

558
00:27:39,920 --> 00:27:42,199
Speaker 5: enough cars. You know, it's part of his mythos. And

559
00:27:42,240 --> 00:27:44,800
Speaker 5: he gets out of that period and the company survives,

560
00:27:44,880 --> 00:27:47,000
Speaker 5: and you know, the stock price shoots to the roof,

561
00:27:47,080 --> 00:27:49,360
Speaker 5: and he is where he is today with this net

562
00:27:49,400 --> 00:27:51,800
Speaker 5: worth of more than two hundred and fifty billion dollars.

563
00:27:52,040 --> 00:27:54,240
Speaker 5: But he's kind of lived life on the edge and

564
00:27:54,280 --> 00:27:57,120
Speaker 5: making these big bets on the idea that these companies,

565
00:27:57,160 --> 00:27:59,480
Speaker 5: you know, are going to be worth hundreds of billions

566
00:27:59,480 --> 00:28:02,520
Speaker 5: of dollars. SpaceX the same way. It's still private, but

567
00:28:02,840 --> 00:28:05,080
Speaker 5: it went through periods where was on the bridge of bankruptcy.

568
00:28:05,119 --> 00:28:07,800
Speaker 5: If you know, the next rocket exploded, they wouldn't have

569
00:28:07,840 --> 00:28:10,600
Speaker 5: any more capital to build the next one. And he

570
00:28:10,680 --> 00:28:14,359
Speaker 5: sees himself as this kind of hero, this kind of

571
00:28:14,400 --> 00:28:17,600
Speaker 5: generational entrepreneur who can do things better and more efficient

572
00:28:17,760 --> 00:28:19,800
Speaker 5: than the last. That's how he came into Twitter. He

573
00:28:19,800 --> 00:28:24,280
Speaker 5: thought Twitter's past management was inept, corrupt, fraudulent, and he

574
00:28:24,359 --> 00:28:27,040
Speaker 5: thought he could do these things way more efficiently and

575
00:28:27,080 --> 00:28:29,800
Speaker 5: just simply cut and cut and cut and cut and

576
00:28:29,920 --> 00:28:32,520
Speaker 5: get things to where he thought they needed to be,

577
00:28:32,960 --> 00:28:37,160
Speaker 5: as well as you know, generate these unthought of business lines,

578
00:28:37,560 --> 00:28:40,240
Speaker 5: things like selling the verification badges, which he thought he

579
00:28:40,240 --> 00:28:42,760
Speaker 5: could do for millions of people. He thought millions of

580
00:28:42,760 --> 00:28:45,280
Speaker 5: people would buy these badges at eight dollars a month

581
00:28:45,280 --> 00:28:47,600
Speaker 5: and create this new revenue stream for the company, And

582
00:28:47,720 --> 00:28:52,120
Speaker 5: that's just been a complete disaster and miscalculation. This idea

583
00:28:52,760 --> 00:28:56,200
Speaker 5: of must being a generational entrepreneur has become under fire

584
00:28:56,520 --> 00:28:59,320
Speaker 5: with this takeover. You know, it's kind of chewed away

585
00:28:59,360 --> 00:28:59,920
Speaker 5: at as mythos.

586
00:29:00,440 --> 00:29:03,479
Speaker 1: Yeah, it seems to me that he was doing a

587
00:29:03,560 --> 00:29:07,680
Speaker 1: lot lot better before he bought Twitter because there was

588
00:29:07,720 --> 00:29:10,760
Speaker 1: this sort of deniability about his political beliefs. It was

589
00:29:10,800 --> 00:29:14,040
Speaker 1: a certain right. I mean, now it's all out there.

590
00:29:14,360 --> 00:29:19,000
Speaker 1: Do you think that this myth kind of has hurt him?

591
00:29:19,400 --> 00:29:21,960
Speaker 7: It has to some extent, you know, I think in

592
00:29:22,120 --> 00:29:25,480
Speaker 7: buying Twitter, I think, you know, Elon sort of put

593
00:29:25,520 --> 00:29:28,600
Speaker 7: himself in the dangerous position that a lot of people

594
00:29:28,600 --> 00:29:31,280
Speaker 7: in the media industry that have done where now spending

595
00:29:31,280 --> 00:29:31,880
Speaker 7: time on Twitter.

596
00:29:31,880 --> 00:29:33,200
Speaker 6: It's supposedly part of his.

597
00:29:33,160 --> 00:29:36,000
Speaker 1: Job, right, It's true. It is a good point.

598
00:29:36,120 --> 00:29:38,040
Speaker 5: You know, Hey, that is it is part of my job.

599
00:29:38,080 --> 00:29:39,840
Speaker 5: Don't take that red from my kit, but it is.

600
00:29:40,200 --> 00:29:42,800
Speaker 1: He really does need to sort of touch grass, right,

601
00:29:42,840 --> 00:29:43,920
Speaker 1: I mean that's what you're saying.

602
00:29:44,080 --> 00:29:47,960
Speaker 7: It's on Twitter all day, every day, and I have

603
00:29:48,040 --> 00:29:51,160
Speaker 7: notifications turned on for his account, and it got to

604
00:29:51,200 --> 00:29:53,640
Speaker 7: the point I used to have like an older iPhone,

605
00:29:53,720 --> 00:29:56,240
Speaker 7: like a iPhone eight or something, and I had to

606
00:29:56,320 --> 00:29:58,600
Speaker 7: upgrade because it was killing my battery because I got

607
00:29:58,600 --> 00:30:02,400
Speaker 7: so many notifications about you. He's prolific, and I think

608
00:30:02,400 --> 00:30:06,480
Speaker 7: it has hurt his public image. I think it's also

609
00:30:07,000 --> 00:30:12,120
Speaker 7: really started to chip away at his fan base within Tesla,

610
00:30:12,200 --> 00:30:15,560
Speaker 7: which is at financial risk for him. You know, his

611
00:30:15,680 --> 00:30:19,680
Speaker 7: Tesla shareholders are not happy about the amount of time

612
00:30:19,720 --> 00:30:23,560
Speaker 7: and attention that he spends on x rather than at Tesla.

613
00:30:24,200 --> 00:30:30,080
Speaker 7: And because he is such a big PERSDA, that's a

614
00:30:30,120 --> 00:30:34,680
Speaker 7: big draw for retail investors to Tesla, and so Tesla

615
00:30:34,720 --> 00:30:37,880
Speaker 7: actually has a disproportionate number of retail investors who are

616
00:30:37,880 --> 00:30:39,960
Speaker 7: invested in the company because they like Elon.

617
00:30:40,040 --> 00:30:42,120
Speaker 6: They're drawn to him, they think he's cool.

618
00:30:42,000 --> 00:30:45,080
Speaker 7: And so as he chips away at that public persona

619
00:30:45,360 --> 00:30:48,920
Speaker 7: and kind of bursts the bubble for people. That affects

620
00:30:49,080 --> 00:30:51,719
Speaker 7: Tesla's stock a little bit more than it might if

621
00:30:52,080 --> 00:30:53,080
Speaker 7: it was a different company.

622
00:30:53,320 --> 00:30:56,280
Speaker 5: I also think, you know, this has hurt his mythos,

623
00:30:56,320 --> 00:30:59,440
Speaker 5: this idea that he is this great entrepreneur. And I

624
00:30:59,480 --> 00:31:00,920
Speaker 5: don't think we can and take this away from him

625
00:31:00,960 --> 00:31:04,560
Speaker 5: that he ushered in the electric car revolution in the

626
00:31:04,640 --> 00:31:07,560
Speaker 5: United States, and he put rockets in a space and

627
00:31:07,640 --> 00:31:10,800
Speaker 5: privatize the space industry. But just because you can do

628
00:31:10,880 --> 00:31:15,320
Speaker 5: those things and build kind of generational companies doesn't necessarily

629
00:31:15,400 --> 00:31:17,760
Speaker 5: make you the best fit to run a social media company.

630
00:31:18,360 --> 00:31:21,200
Speaker 5: And I think it was that hubris that he came

631
00:31:21,240 --> 00:31:23,600
Speaker 5: in with, you know, why can't I do this? Why

632
00:31:23,640 --> 00:31:26,440
Speaker 5: can't I run Twitter? It must be so much easier

633
00:31:26,480 --> 00:31:29,560
Speaker 5: than putting a rocket into space and have it land

634
00:31:29,600 --> 00:31:32,280
Speaker 5: on a platform. These are different problems, and these are

635
00:31:32,440 --> 00:31:34,720
Speaker 5: you know, some some are engineering problems. You know, some

636
00:31:34,760 --> 00:31:37,040
Speaker 5: are electrical engineering problems. But this is a human problem.

637
00:31:37,080 --> 00:31:39,560
Speaker 5: This is a social interaction problem, and I think not

638
00:31:39,680 --> 00:31:43,040
Speaker 5: grasping that has really hurt his image in the public eye.

639
00:31:43,200 --> 00:31:46,040
Speaker 1: Yeah, for sure, Explain to me where the company is

640
00:31:46,080 --> 00:31:47,440
Speaker 1: now and where he is now.

641
00:31:47,560 --> 00:31:50,719
Speaker 7: I mean, I think X is in very precarious shape

642
00:31:50,760 --> 00:31:55,440
Speaker 7: at this point, and he obviously cut the company back

643
00:31:55,480 --> 00:32:00,080
Speaker 7: a lot, and now I think his engineering time, energy

644
00:32:00,120 --> 00:32:03,960
Speaker 7: resources are somewhat split between x the social media platform

645
00:32:04,040 --> 00:32:08,400
Speaker 7: and xai, the sort of AI company slash chatbot that

646
00:32:08,440 --> 00:32:11,640
Speaker 7: he's trying to build. And you know, the platform itself

647
00:32:11,800 --> 00:32:17,880
Speaker 7: is increasingly partisan and focused on Musk's politics in particular,

648
00:32:18,160 --> 00:32:22,400
Speaker 7: and it's also in you know, pretty decayed financial shape.

649
00:32:22,480 --> 00:32:23,480
Speaker 6: You know, we've seen.

650
00:32:23,520 --> 00:32:26,360
Speaker 7: AD revenue continue to fall and fall on fall. Some

651
00:32:26,440 --> 00:32:29,840
Speaker 7: of these big projects that the company has tried in

652
00:32:29,960 --> 00:32:33,760
Speaker 7: order to bring back AD dollars have sort of faltered.

653
00:32:33,840 --> 00:32:34,000
Speaker 6: You know.

654
00:32:34,040 --> 00:32:38,040
Speaker 7: One of those was doing these long form video shows

655
00:32:38,080 --> 00:32:42,280
Speaker 7: with big personalities, and I think Tucker Carlson is probably

656
00:32:42,320 --> 00:32:45,680
Speaker 7: the only example there of someone who's had a successful

657
00:32:45,720 --> 00:32:47,880
Speaker 7: show built on X and even he has kind of

658
00:32:47,960 --> 00:32:50,720
Speaker 7: migrated and started airing his show on his own platform

659
00:32:50,800 --> 00:32:53,280
Speaker 7: around his own website now, I believe, so they really

660
00:32:53,320 --> 00:32:57,400
Speaker 7: struggled to figure out a way to counteract that decline

661
00:32:57,400 --> 00:33:00,280
Speaker 7: in revenue, and at the same time, you know, I

662
00:33:00,280 --> 00:33:02,680
Speaker 7: think there's there's a core set of users who are

663
00:33:02,840 --> 00:33:07,760
Speaker 7: attracted to the partisanship of the platform, but more and

664
00:33:07,840 --> 00:33:12,160
Speaker 7: more that alienates people and drives them to competitors like threads.

665
00:33:13,400 --> 00:33:16,000
Speaker 1: Can he just keep this going forever? Will he need

666
00:33:16,040 --> 00:33:18,680
Speaker 1: to sell the platform at some point? Or can he

667
00:33:18,840 --> 00:33:20,120
Speaker 1: just ride into the bottom?

668
00:33:21,080 --> 00:33:23,040
Speaker 6: I think Ryan and I disagree about this.

669
00:33:23,840 --> 00:33:26,320
Speaker 1: Good, Yeah, just tell us about that and then we're.

670
00:33:26,520 --> 00:33:29,520
Speaker 7: I kind of don't know if there is a bottom, honestly.

671
00:33:29,640 --> 00:33:32,320
Speaker 7: I mean, I think he can just keep going should

672
00:33:32,360 --> 00:33:36,240
Speaker 7: he choose to do so, and has the financial resources

673
00:33:36,280 --> 00:33:38,480
Speaker 7: to do it, and certainly the interests.

674
00:33:38,120 --> 00:33:40,120
Speaker 6: Right, and then that's not a lot of seat fail.

675
00:33:40,280 --> 00:33:42,600
Speaker 6: But I don't know. I think Ryan disagrees a little

676
00:33:42,600 --> 00:33:43,440
Speaker 6: bit on this that.

677
00:33:43,400 --> 00:33:45,480
Speaker 5: There is no bottom, or there is a bottom.

678
00:33:45,280 --> 00:33:47,160
Speaker 1: That he'll have to sell it at some point.

679
00:33:47,520 --> 00:33:49,600
Speaker 5: I don't think he'll have to sell. You know, we

680
00:33:49,880 --> 00:33:52,040
Speaker 5: haven't really talked about the debt payments that he has.

681
00:33:52,080 --> 00:33:54,320
Speaker 5: By the way, he raised billions of dollars in debt

682
00:33:54,320 --> 00:33:54,600
Speaker 5: he has.

683
00:33:54,760 --> 00:33:55,200
Speaker 6: I think he.

684
00:33:55,320 --> 00:33:57,920
Speaker 5: Services to service that debt. He pays more than a

685
00:33:57,920 --> 00:34:00,520
Speaker 5: billion dollars in interest payments a year, which.

686
00:34:00,360 --> 00:34:00,840
Speaker 4: Is kind of this.

687
00:34:01,520 --> 00:34:04,440
Speaker 5: Yeah, can you I don't if you can relate to that.

688
00:34:04,440 --> 00:34:05,400
Speaker 5: That seems like a lot of money.

689
00:34:05,440 --> 00:34:07,920
Speaker 6: Yeah, yeahs are lower.

690
00:34:09,280 --> 00:34:10,920
Speaker 5: I don't know what your credit card bill feels are

691
00:34:10,920 --> 00:34:14,120
Speaker 5: looking like, but but yeah, I mean, like I think

692
00:34:14,239 --> 00:34:16,840
Speaker 5: that that's if we're talking about pain points for Elon,

693
00:34:17,239 --> 00:34:18,759
Speaker 5: that is certainly one of them. This is not a

694
00:34:18,800 --> 00:34:21,640
Speaker 5: guy who has a lot of cash on handy. I mean,

695
00:34:21,680 --> 00:34:23,520
Speaker 5: he has a high net worse, don't get me wrong,

696
00:34:23,600 --> 00:34:25,480
Speaker 5: more than two to fifty billion dollars. But he's not

697
00:34:25,480 --> 00:34:27,400
Speaker 5: liquid on that. That's a lot of tied up in

698
00:34:27,440 --> 00:34:31,239
Speaker 5: a lot of equities in Tesla at SpaceX. He leverages

699
00:34:31,560 --> 00:34:36,160
Speaker 5: his Tesla shares to raise money and cap and cash essentially,

700
00:34:36,360 --> 00:34:39,040
Speaker 5: which is allowed. But you know, we're talking about unrealized

701
00:34:39,080 --> 00:34:42,600
Speaker 5: gains right now in the election. It's exactly that, and

702
00:34:43,120 --> 00:34:45,640
Speaker 5: I think he'll start to feel those pain points. Whether

703
00:34:45,760 --> 00:34:48,520
Speaker 5: or not he'll sell it is another question. I think

704
00:34:48,520 --> 00:34:51,040
Speaker 5: he still personally drives a lot of value from the platform.

705
00:34:51,040 --> 00:34:51,520
Speaker 3: I think the.

706
00:34:51,480 --> 00:34:53,799
Speaker 5: Platform is exactly what he wants it to be. I

707
00:34:53,800 --> 00:34:56,120
Speaker 5: don't know what price you can put on that kind

708
00:34:56,120 --> 00:34:57,560
Speaker 5: of personal enjoyment.

709
00:34:58,480 --> 00:35:01,120
Speaker 1: In the end. Do you think there's some conclusion to

710
00:35:01,200 --> 00:35:03,600
Speaker 1: this or do you think this is just him like

711
00:35:03,680 --> 00:35:04,480
Speaker 1: having a hobby.

712
00:35:05,000 --> 00:35:08,439
Speaker 7: You know, I think in all of our reporting, we

713
00:35:08,440 --> 00:35:11,360
Speaker 7: were never able to find what the greater plan was

714
00:35:11,440 --> 00:35:14,239
Speaker 7: for Twitter. In fact, over and over again, you see

715
00:35:14,239 --> 00:35:19,640
Speaker 7: these moments of just his sort of profound impulsivity and unpreparedness,

716
00:35:20,080 --> 00:35:24,480
Speaker 7: the things that he encounters during the deal. And I

717
00:35:24,520 --> 00:35:28,000
Speaker 7: think that his political leanings are similar of just this

718
00:35:28,040 --> 00:35:31,880
Speaker 7: is something that interests him and fascinates him, and he's

719
00:35:32,040 --> 00:35:34,759
Speaker 7: just kind of dabbling and going with the flow. And

720
00:35:34,960 --> 00:35:39,480
Speaker 7: I don't think that there is some sort of grand

721
00:35:39,600 --> 00:35:41,799
Speaker 7: master plan tacked up on the wall with all the

722
00:35:41,840 --> 00:35:42,879
Speaker 7: red yarn behind him.

723
00:35:42,920 --> 00:35:45,640
Speaker 5: I think he's just I think the way to view

724
00:35:45,719 --> 00:35:48,880
Speaker 5: him is how we view him when he build his companies.

725
00:35:49,120 --> 00:35:52,440
Speaker 5: There is this kind of centered hero complex. He is

726
00:35:52,480 --> 00:35:54,960
Speaker 5: the man who's going to bring us to Mars. But

727
00:35:55,200 --> 00:35:57,480
Speaker 5: if you view it, you know, in this, in this relationship,

728
00:35:57,560 --> 00:35:59,360
Speaker 5: he is the man who is going to help stop

729
00:35:59,480 --> 00:36:02,560
Speaker 5: the immigration crisis at the southern border. He's the man

730
00:36:02,600 --> 00:36:06,160
Speaker 5: that's going to solve election fraud and all these and

731
00:36:06,239 --> 00:36:08,720
Speaker 5: wokeness and all these things that he thinks ails the company,

732
00:36:09,120 --> 00:36:11,840
Speaker 5: and the way to do that is by supporting Donald Trump.

733
00:36:12,040 --> 00:36:14,480
Speaker 5: And it's that hero complex that has serving well in

734
00:36:14,480 --> 00:36:16,879
Speaker 5: the past, and it's how he views the world, and

735
00:36:16,920 --> 00:36:18,120
Speaker 5: I think it's how it's going to serve him in

736
00:36:18,120 --> 00:36:20,800
Speaker 5: the future. So I think that's the kind of number

737
00:36:20,800 --> 00:36:23,520
Speaker 5: one way to analyze what he's thinking about this election.

738
00:36:24,719 --> 00:36:27,839
Speaker 1: So interesting. Thanks you guys, Thank you so much.

739
00:36:27,880 --> 00:36:28,760
Speaker 6: This is really great.

740
00:36:29,880 --> 00:36:32,680
Speaker 1: Are you concerned about Project twenty twenty five and how

741
00:36:32,719 --> 00:36:36,400
Speaker 1: awful Trump's second term could be? Well, so are we,

742
00:36:36,680 --> 00:36:38,960
Speaker 1: which is why we teamed up with iHeart to make

743
00:36:39,000 --> 00:36:42,600
Speaker 1: a limited series with the experts on what a disaster

744
00:36:43,080 --> 00:36:46,799
Speaker 1: Project twenty twenty five would be for America's future. Right now,

745
00:36:46,840 --> 00:36:50,120
Speaker 1: we have just released the final episode of this five

746
00:36:50,200 --> 00:36:54,240
Speaker 1: episode series. They're all available by looking up Molly Jong

747
00:36:54,360 --> 00:36:57,840
Speaker 1: Fast Project twenty twenty five on YouTube, and if you

748
00:36:57,920 --> 00:37:01,560
Speaker 1: are more of a podcast person and not say a YouTuber,

749
00:37:02,040 --> 00:37:04,360
Speaker 1: you can hit play and put your phone in the

750
00:37:04,400 --> 00:37:07,480
Speaker 1: lock screen and it will play back just like the podcast.

751
00:37:07,760 --> 00:37:11,120
Speaker 1: All five episodes are online now. We need to educate

752
00:37:11,120 --> 00:37:14,759
Speaker 1: Americans on what Trump's second term would or could do

753
00:37:14,880 --> 00:37:17,719
Speaker 1: to this country, so please watch it and spread the word.

754
00:37:20,000 --> 00:37:23,120
Speaker 2: Amanda Becker is the Washington correspondent for the nineteenth and

755
00:37:23,320 --> 00:37:25,960
Speaker 2: author of You Must Stand Up, The Fight for Abortion

756
00:37:26,080 --> 00:37:27,560
Speaker 2: Rights and Postdobs America.

757
00:37:28,400 --> 00:37:33,040
Speaker 1: Welcome Back, too fast politics. Amanda Becker, Hi, thanks for

758
00:37:33,040 --> 00:37:36,360
Speaker 1: having me. We're delighted to have you. I want you

759
00:37:36,400 --> 00:37:39,360
Speaker 1: to talk about the Nineteenth for a minute. Sort of

760
00:37:39,440 --> 00:37:41,960
Speaker 1: just give us this landscape of the Nineteenth. A lot

761
00:37:42,000 --> 00:37:44,200
Speaker 1: of us know about it and have been big fans

762
00:37:44,280 --> 00:37:47,360
Speaker 1: of it for a long time, but not everyone. So explain.

763
00:37:47,680 --> 00:37:52,920
Speaker 4: Yeah, So, The Nineteenth is a nonprofit, independent newsroom focused

764
00:37:52,920 --> 00:37:56,880
Speaker 4: on the intersection of politics, policy, and gender. We started

765
00:37:57,360 --> 00:37:59,919
Speaker 4: about four and a half years ago and officially law

766
00:38:00,280 --> 00:38:03,160
Speaker 4: about four years ago, and I have been there since

767
00:38:03,200 --> 00:38:07,920
Speaker 4: the beginning as the Nineteenth Washington Correspondent. So we are

768
00:38:08,000 --> 00:38:14,160
Speaker 4: really focused on the women and LGBTQ people in politics,

769
00:38:14,520 --> 00:38:18,640
Speaker 4: on the policies that matter to them. And so, you know,

770
00:38:18,680 --> 00:38:22,320
Speaker 4: we're covering everything from you know, the fall of Row

771
00:38:22,360 --> 00:38:25,719
Speaker 4: and kind of the chaos that has followed that, it's

772
00:38:25,840 --> 00:38:29,600
Speaker 4: impact on the elections, all of the anti trans legislation

773
00:38:29,719 --> 00:38:33,360
Speaker 4: in the States, and really anything that's important to women

774
00:38:33,520 --> 00:38:34,760
Speaker 4: and LGBTQ people.

775
00:38:35,080 --> 00:38:39,560
Speaker 1: You know, Harris is a Democratic presidential candidate. She's a woman.

776
00:38:40,239 --> 00:38:44,280
Speaker 1: There has been some discussion about the sort of shift

777
00:38:44,520 --> 00:38:47,600
Speaker 1: from Hillary Clinton, you know, when she was the candidate.

778
00:38:47,600 --> 00:38:50,200
Speaker 1: She's also a woman. We live in this country of

779
00:38:50,239 --> 00:38:53,560
Speaker 1: sort of hyper misogyny that a lot of it's really,

780
00:38:54,040 --> 00:38:57,600
Speaker 1: I think, institutionalized. And how have you seen a change

781
00:38:58,160 --> 00:38:59,640
Speaker 1: since twenty sixteen?

782
00:39:00,080 --> 00:39:01,960
Speaker 4: You know, I've been thinking about this a lot, Molly,

783
00:39:02,200 --> 00:39:04,560
Speaker 4: And actually my family was texting me when I was

784
00:39:04,600 --> 00:39:07,719
Speaker 4: standing on the floor at the Democratic National Convention kind

785
00:39:07,719 --> 00:39:11,600
Speaker 4: of watching Vice President Kamala Harris give her a speech.

786
00:39:12,000 --> 00:39:15,759
Speaker 4: It just felt different. And I've been trying to explain

787
00:39:15,960 --> 00:39:18,920
Speaker 4: to myself even kind of what felt different about it.

788
00:39:19,000 --> 00:39:21,440
Speaker 4: And you might have some ideas about this as well,

789
00:39:21,480 --> 00:39:25,520
Speaker 4: but I think that she is felt as like a

790
00:39:25,560 --> 00:39:29,160
Speaker 4: new generation of leaders. You know, she's not that much

791
00:39:29,239 --> 00:39:33,160
Speaker 4: younger than Clinton, but I think that that difference matters

792
00:39:33,640 --> 00:39:36,600
Speaker 4: in terms of their ages. And it just felt really different.

793
00:39:36,640 --> 00:39:39,400
Speaker 4: I mean, I was with Clinton following her campaign for

794
00:39:39,440 --> 00:39:43,040
Speaker 4: two years in twenty fifteen and twenty sixteen, and now

795
00:39:43,040 --> 00:39:45,279
Speaker 4: I'm covering this election, you know, a little bit less

796
00:39:45,360 --> 00:39:47,279
Speaker 4: day to day as I was then, as I was

797
00:39:47,320 --> 00:39:49,279
Speaker 4: at a wire service at the time, But it just

798
00:39:49,360 --> 00:39:51,960
Speaker 4: felt really different. And I think that women in this

799
00:39:52,120 --> 00:39:57,759
Speaker 4: country have realized the impact of Donald Trump being elected

800
00:39:58,040 --> 00:40:01,520
Speaker 4: over Hillary Clinton in a very real way, right because

801
00:40:01,800 --> 00:40:04,520
Speaker 4: it led to the loss of forty nine years of

802
00:40:04,560 --> 00:40:09,200
Speaker 4: abortion rights in this country, and there's really no denying

803
00:40:09,719 --> 00:40:11,879
Speaker 4: that that is evident in the way people are thinking

804
00:40:11,880 --> 00:40:14,359
Speaker 4: about voting now, and so I think that also has

805
00:40:14,400 --> 00:40:18,160
Speaker 4: something to do with the enthusiasm for Kamala Harris's candidacy.

806
00:40:18,400 --> 00:40:21,720
Speaker 1: I agree, but I'm just trying to sort of figure

807
00:40:21,760 --> 00:40:25,839
Speaker 1: this out. If this is true, which you know, this

808
00:40:25,880 --> 00:40:28,920
Speaker 1: would mean sort of that the women can make up

809
00:40:28,960 --> 00:40:31,239
Speaker 1: the difference. Like one of the things I've seen in

810
00:40:31,280 --> 00:40:34,080
Speaker 1: this campaign is Trump has gone like all in on misogyny.

811
00:40:34,520 --> 00:40:38,640
Speaker 1: So like he's going on these far right podcasts of

812
00:40:38,719 --> 00:40:42,520
Speaker 1: young podcasters, he's selling NFTs. I mean that I think

813
00:40:42,840 --> 00:40:45,879
Speaker 1: maybe more scamm than misogyny. But like you know, he's

814
00:40:45,920 --> 00:40:49,040
Speaker 1: got Whule Cogan. So many of the speakers at the

815
00:40:49,200 --> 00:40:53,160
Speaker 1: RNC had like allegations of domestic violence, right, Like this

816
00:40:53,360 --> 00:40:55,480
Speaker 1: was like if you were it was like big divorce

817
00:40:55,640 --> 00:40:59,839
Speaker 1: dad energy, right. Bill Ackman is his greatest champion. Like

818
00:41:00,080 --> 00:41:03,000
Speaker 1: you know, anyone on their first wife is not going

819
00:41:03,080 --> 00:41:05,880
Speaker 1: to necessarily respond to this message. But how do you

820
00:41:05,960 --> 00:41:10,000
Speaker 1: think that, Like, I mean, I think his calculus is

821
00:41:10,040 --> 00:41:12,640
Speaker 1: that he's going to be able to run up the

822
00:41:12,760 --> 00:41:16,440
Speaker 1: numbers with divorce Dad energy and that he'll get enough

823
00:41:16,480 --> 00:41:19,680
Speaker 1: white women so he'll be able to win. There certainly

824
00:41:19,719 --> 00:41:22,239
Speaker 1: are white women who identify more with their race than

825
00:41:22,280 --> 00:41:24,520
Speaker 1: their gender. I mean, do you think that's shifting.

826
00:41:25,040 --> 00:41:26,880
Speaker 4: So if you look at polling, and I know pulling

827
00:41:26,960 --> 00:41:32,640
Speaker 4: is incredibly imperfect, we are seeing shifts among white women

828
00:41:32,840 --> 00:41:36,239
Speaker 4: and among married women, and that would be different than

829
00:41:36,280 --> 00:41:39,000
Speaker 4: in twenty sixteen or even in twenty twenty because right

830
00:41:39,080 --> 00:41:42,839
Speaker 4: white women have broken for Trump the past two elections.

831
00:41:43,080 --> 00:41:45,080
Speaker 4: And I know that's hard for a lot of liberal

832
00:41:45,120 --> 00:41:48,240
Speaker 4: white women's stomach, but that is the truth. I remember

833
00:41:48,239 --> 00:41:50,279
Speaker 4: getting hate mail in twenty twenty when I wrote about

834
00:41:50,280 --> 00:41:53,120
Speaker 4: how white women back to Trump. That is something. Honestly,

835
00:41:53,360 --> 00:41:57,040
Speaker 4: I think that that is one of the stories of

836
00:41:57,080 --> 00:42:00,600
Speaker 4: this election. Unfortunately, you know, it takes six months or

837
00:42:00,680 --> 00:42:03,359
Speaker 4: so to get the validated voter data, so all we're

838
00:42:03,360 --> 00:42:05,319
Speaker 4: going to have close to the election to kind of

839
00:42:05,320 --> 00:42:08,520
Speaker 4: look at what happened is except pulling, which is you know,

840
00:42:08,760 --> 00:42:11,319
Speaker 4: just as imperfect as any other type of poll. I

841
00:42:11,360 --> 00:42:14,840
Speaker 4: think this election has the potential to be the one

842
00:42:15,280 --> 00:42:18,799
Speaker 4: in which white women break for the Democratic candidate. And

843
00:42:19,080 --> 00:42:21,480
Speaker 4: I don't know how much of that will end up

844
00:42:21,520 --> 00:42:25,000
Speaker 4: being because it's Kamala Harris. I don't know how much

845
00:42:25,040 --> 00:42:26,719
Speaker 4: of that will end up being because this is the

846
00:42:26,719 --> 00:42:29,960
Speaker 4: first presidential election without Roe v. Wade in place, or

847
00:42:29,960 --> 00:42:33,560
Speaker 4: a combination of things, including what we're hearing from the Republicans,

848
00:42:33,920 --> 00:42:37,319
Speaker 4: which is you have Trump's kind of strain of how

849
00:42:37,360 --> 00:42:40,479
Speaker 4: he looks at gender and masculinity, and then you also

850
00:42:40,520 --> 00:42:44,040
Speaker 4: have Jad Vancis, when this is somebody who has talked

851
00:42:44,120 --> 00:42:48,080
Speaker 4: openly about what he sees as the negative impacts of

852
00:42:48,120 --> 00:42:52,160
Speaker 4: the sexual revolution. He thinks no fault divorce can be

853
00:42:52,200 --> 00:42:55,600
Speaker 4: a bad thing. And these reflect larger trends in the

854
00:42:55,680 --> 00:42:57,960
Speaker 4: right wing of the Republican Party that you know has

855
00:42:58,000 --> 00:43:01,480
Speaker 4: floated legislation and states to kind of roll back no

856
00:43:01,600 --> 00:43:04,160
Speaker 4: fault divorce and a variety of other things that we're

857
00:43:04,200 --> 00:43:07,440
Speaker 4: seen as empowering women over the past fifty years.

858
00:43:07,680 --> 00:43:10,040
Speaker 1: Yeah, I mean, vance was really a choice to double

859
00:43:10,080 --> 00:43:12,120
Speaker 1: down on that. I think a lot of us wondered

860
00:43:12,239 --> 00:43:16,840
Speaker 1: if picking someone like Nikki Haley might give again, Like

861
00:43:16,960 --> 00:43:20,320
Speaker 1: so much of this election on the hair side, anyway,

862
00:43:20,360 --> 00:43:24,360
Speaker 1: has been about creating a permission structure to give Republicans

863
00:43:24,440 --> 00:43:26,959
Speaker 1: to vote for a Democrat. So, for example, Dick Cheney

864
00:43:27,040 --> 00:43:30,880
Speaker 1: endorsement is not for liberals, It's not even necessarily for

865
00:43:31,120 --> 00:43:34,839
Speaker 1: normal voters. It's for very far right people who can't

866
00:43:34,840 --> 00:43:38,200
Speaker 1: stop MacDonald Trump and who you know, see Dick Cheney

867
00:43:38,239 --> 00:43:40,759
Speaker 1: as that permission. So on the Hair side, we've seen

868
00:43:40,800 --> 00:43:42,879
Speaker 1: a lot of that. On the Trump side, we've seen

869
00:43:43,000 --> 00:43:45,759
Speaker 1: none of that. So the question is right, like, he

870
00:43:45,800 --> 00:43:47,759
Speaker 1: could have picked Nicki Haley as a vice president and

871
00:43:47,760 --> 00:43:50,239
Speaker 1: he was like new go. He did nothing to sort

872
00:43:50,239 --> 00:43:54,160
Speaker 1: of grow the electorate. Now there's a world in which

873
00:43:54,160 --> 00:43:56,879
Speaker 1: that doesn't matter. But don't you think that eventually that

874
00:43:56,880 --> 00:43:58,279
Speaker 1: comes back to bite him.

875
00:43:58,840 --> 00:44:01,120
Speaker 4: I had to say, I was shocked when he chose

876
00:44:01,239 --> 00:44:04,480
Speaker 4: jd Vance. I'm from Ohio, I grew up there, so

877
00:44:04,560 --> 00:44:08,040
Speaker 4: I followed the politics there pretty closely still. Actually his

878
00:44:08,200 --> 00:44:11,040
Speaker 4: grandparents are from the same region of Kentucky that my

879
00:44:11,120 --> 00:44:15,440
Speaker 4: grandparents are from. They settled in Cincinnati, has settled in Middletown,

880
00:44:15,560 --> 00:44:18,560
Speaker 4: which is about, you know, twenty five miles away from

881
00:44:18,600 --> 00:44:22,440
Speaker 4: where my family is. And so I followed his story

882
00:44:22,560 --> 00:44:25,520
Speaker 4: right over the years. And his story has shifted, of course,

883
00:44:25,560 --> 00:44:27,560
Speaker 4: and he frames it in different ways.

884
00:44:27,560 --> 00:44:31,719
Speaker 1: Yes, like his name, his story has shifted, yes.

885
00:44:31,600 --> 00:44:36,560
Speaker 4: Yes, And when Trump chose him I just thought to myself, frankly,

886
00:44:36,960 --> 00:44:41,640
Speaker 4: what is he thinking, Because from my perspective, picking jd

887
00:44:41,800 --> 00:44:46,120
Speaker 4: Vance did nothing and not only didn't hunting to bring

888
00:44:46,160 --> 00:44:49,960
Speaker 4: in any more people who weren't already backing Trump. It

889
00:44:50,200 --> 00:44:54,759
Speaker 4: was a play to like the base. It was potentially

890
00:44:54,760 --> 00:44:58,000
Speaker 4: a move that would alienate people in the middle. And

891
00:44:58,200 --> 00:44:59,880
Speaker 4: I have to just think it was related to the

892
00:45:00,040 --> 00:45:03,799
Speaker 4: assassination attempt and kind of like flying high after recovering

893
00:45:03,840 --> 00:45:06,839
Speaker 4: from that and coming out of that and feeling invincible,

894
00:45:06,880 --> 00:45:08,839
Speaker 4: and of course Joe Biden was still on the top

895
00:45:08,880 --> 00:45:11,680
Speaker 4: of the Democratic ticket, thens they just thought this was

896
00:45:11,719 --> 00:45:16,399
Speaker 4: a lock. But I don't see how jd Vance boosts

897
00:45:16,480 --> 00:45:19,560
Speaker 4: Trump's chances in any way in November, and could potentially

898
00:45:19,800 --> 00:45:21,000
Speaker 4: in fact hurt them.

899
00:45:21,239 --> 00:45:24,120
Speaker 1: Yeah, I mean that's my sense. But again, it just

900
00:45:24,160 --> 00:45:26,600
Speaker 1: seems like a calculus with no thought of growing the electorate.

901
00:45:26,680 --> 00:45:28,680
Speaker 1: But Trump has never had much interest in growing the

902
00:45:28,719 --> 00:45:29,399
Speaker 1: elector right.

903
00:45:29,640 --> 00:45:31,439
Speaker 4: No, I think what they're going to have to rely

904
00:45:31,560 --> 00:45:34,360
Speaker 4: on is the loyalist and churning out as many of

905
00:45:34,400 --> 00:45:37,040
Speaker 4: them as possible, you know, running kind of a fear

906
00:45:37,160 --> 00:45:41,680
Speaker 4: campaign based on immigration in particular and the economy as well.

907
00:45:41,680 --> 00:45:44,359
Speaker 4: To a certain extent, and hope that that's enough and

908
00:45:44,400 --> 00:45:46,520
Speaker 4: that they can get enough men and drive up enough

909
00:45:46,520 --> 00:45:48,520
Speaker 4: men and maybe get some men of color in there

910
00:45:48,560 --> 00:45:50,520
Speaker 4: that didn't support.

911
00:45:50,200 --> 00:45:51,040
Speaker 6: Them the last time.

912
00:45:51,280 --> 00:45:54,160
Speaker 4: I think we could potentially see a bigger gender gap

913
00:45:54,239 --> 00:45:56,359
Speaker 4: than we've ever seen. And there's been a gender gap

914
00:45:56,400 --> 00:45:59,399
Speaker 4: in politics and it's been growing, and I think that

915
00:45:59,400 --> 00:46:01,719
Speaker 4: that will only be exacerbated this year.

916
00:46:02,080 --> 00:46:03,680
Speaker 1: So talk to me about your book.

917
00:46:03,840 --> 00:46:06,239
Speaker 4: Yes, So, it came out last week. It's called You

918
00:46:06,320 --> 00:46:08,839
Speaker 4: Must Stand Up The Fight for Abortion Rights and Post

919
00:46:08,920 --> 00:46:12,520
Speaker 4: Dobbs America. It is the first year. It opens in

920
00:46:12,600 --> 00:46:16,200
Speaker 4: June twenty twenty two at a clinic in Tuscaloosa as

921
00:46:16,280 --> 00:46:19,399
Speaker 4: the Dobbs decision comes out, and it ends a year

922
00:46:19,480 --> 00:46:23,160
Speaker 4: later at that same clinic talking about kind of what

923
00:46:23,280 --> 00:46:28,960
Speaker 4: could be next. It's primary geographic settings along with Tuscaloosa, Alabama,

924
00:46:29,000 --> 00:46:32,839
Speaker 4: our Phoenix, Arizona to a slightly lesser extent Maryland, and

925
00:46:32,880 --> 00:46:37,280
Speaker 4: then also some chapters from places like Wisconsin, Kentucky, Massachusetts,

926
00:46:37,360 --> 00:46:40,919
Speaker 4: Ohio to just show the absolute chaos in the.

927
00:46:40,800 --> 00:46:42,640
Speaker 1: First year after Dobbs.

928
00:46:42,880 --> 00:46:47,040
Speaker 4: Told from the perspectives of doctors, healthcare providers, people working

929
00:46:47,040 --> 00:46:53,680
Speaker 4: at clinics, but also lawyers, students, moms, friends, everyday voters

930
00:46:53,760 --> 00:46:57,839
Speaker 4: who were mobilizing trying to protect abortion rights in kind

931
00:46:57,880 --> 00:47:00,319
Speaker 4: of that pivotal first year after we lost to the

932
00:47:00,360 --> 00:47:01,560
Speaker 4: federal right to an abortion.

933
00:47:01,960 --> 00:47:04,120
Speaker 1: Sometimes I take a moment to talk about how I

934
00:47:04,160 --> 00:47:06,719
Speaker 1: was right and glowed. This is one of them. I

935
00:47:06,840 --> 00:47:10,040
Speaker 1: was right, and I hate it when the Supreme Court

936
00:47:10,160 --> 00:47:14,520
Speaker 1: let SBA ride right when they took it on the

937
00:47:14,560 --> 00:47:18,760
Speaker 1: shadow dock it and did not overturn it in Texas.

938
00:47:18,960 --> 00:47:22,520
Speaker 1: When Texas overturned Row a year before the Supreme Court

939
00:47:22,600 --> 00:47:26,960
Speaker 1: officially put the knife in the legislation, what did you think?

940
00:47:27,000 --> 00:47:28,560
Speaker 1: Did you think it was the end? I thought it

941
00:47:28,600 --> 00:47:30,359
Speaker 1: was the end, but I'm very negative, so.

942
00:47:30,800 --> 00:47:33,240
Speaker 4: I also thought it was the end. I think anyone

943
00:47:33,360 --> 00:47:36,280
Speaker 4: working in this space, whether they were at a clinic

944
00:47:36,680 --> 00:47:39,880
Speaker 4: and in a clinical setting or you know, working on

945
00:47:39,960 --> 00:47:42,600
Speaker 4: policy at the national level, also thought it was the end.

946
00:47:43,120 --> 00:47:45,200
Speaker 4: The title of my book, You Must Stand Up is

947
00:47:45,239 --> 00:47:48,320
Speaker 4: from a doctor in Arizona, and it's from a blog

948
00:47:48,400 --> 00:47:51,080
Speaker 4: that she wrote in I believe it was twenty eighteen,

949
00:47:51,400 --> 00:47:53,080
Speaker 4: may have been twenty nineteen, but I think it was

950
00:47:53,120 --> 00:47:56,680
Speaker 4: twenty eighteen. So, you know, while the Dobbs decision in

951
00:47:56,719 --> 00:47:59,800
Speaker 4: the fall Row took many kind of rank and file

952
00:47:59,840 --> 00:48:03,279
Speaker 4: on the Americans by surprise, for the people who are

953
00:48:03,360 --> 00:48:06,120
Speaker 4: working in this space or in space is adjacent to

954
00:48:06,840 --> 00:48:11,000
Speaker 4: reproductive health. They saw this coming even before SB eight,

955
00:48:11,120 --> 00:48:14,960
Speaker 4: but certainly when the Supreme Court let sbight go into effect.

956
00:48:15,200 --> 00:48:17,759
Speaker 4: That is when, for example, the chapter I have set

957
00:48:17,800 --> 00:48:20,640
Speaker 4: in Massachusetts, which is really a chapter about how can

958
00:48:20,680 --> 00:48:24,360
Speaker 4: a state where the vast, vast majority of its residents

959
00:48:24,560 --> 00:48:27,879
Speaker 4: support abortion rights, how can they make sure that they're

960
00:48:27,880 --> 00:48:30,719
Speaker 4: delivering for the people in their state and giving them

961
00:48:30,719 --> 00:48:35,120
Speaker 4: what they want. Massachusetts SB eight was when they mobilized,

962
00:48:35,200 --> 00:48:37,799
Speaker 4: was when they got a coalition back together that had

963
00:48:37,840 --> 00:48:41,080
Speaker 4: worked on this issue before, because they thought, you know,

964
00:48:41,239 --> 00:48:43,960
Speaker 4: this is the end, and it doesn't matter that this

965
00:48:44,000 --> 00:48:46,359
Speaker 4: is in Texas, it doesn't matter that it's going to be,

966
00:48:46,719 --> 00:48:49,480
Speaker 4: you know, worse in red states. They're going to try

967
00:48:49,520 --> 00:48:52,000
Speaker 4: to come for people in Massachusetts too. And so they

968
00:48:52,000 --> 00:48:55,000
Speaker 4: got together and passed one of the strongest abortion provider

969
00:48:55,080 --> 00:48:56,480
Speaker 4: shield laws we have in this country.

970
00:48:56,520 --> 00:48:56,919
Speaker 6: Right now.

971
00:48:57,040 --> 00:49:01,000
Speaker 1: Yes, when you look at this, how this happened, What

972
00:49:01,080 --> 00:49:05,040
Speaker 1: do you think the greatest driver was of the push

973
00:49:05,200 --> 00:49:07,440
Speaker 1: to get rid of row overturning Row?

974
00:49:07,560 --> 00:49:10,480
Speaker 4: Yeah, I think it really depends whether you're talking about

975
00:49:10,840 --> 00:49:15,279
Speaker 4: the average person who might oppose abortion or be represented

976
00:49:15,280 --> 00:49:17,000
Speaker 4: by some of the groups or some of the kind

977
00:49:17,040 --> 00:49:20,400
Speaker 4: of behind the scenes architects. There are many people in

978
00:49:20,400 --> 00:49:24,719
Speaker 4: this country, including women, who believe abortion is wrong, and

979
00:49:24,880 --> 00:49:27,680
Speaker 4: they you know, it has been a fifty year project

980
00:49:27,840 --> 00:49:31,960
Speaker 4: to overturn Row. The anti abortion movement wanted to establish

981
00:49:32,040 --> 00:49:35,240
Speaker 4: beetle personhood back when Roe was decided. That is still

982
00:49:35,280 --> 00:49:37,680
Speaker 4: their end goal, and they've been working for that, and

983
00:49:37,719 --> 00:49:40,000
Speaker 4: they truly believe that they are working on kind of

984
00:49:40,000 --> 00:49:43,480
Speaker 4: like the moral fight of their time. Now I am

985
00:49:43,520 --> 00:49:47,960
Speaker 4: not convinced. Let's say that a lot of the funders

986
00:49:48,120 --> 00:49:53,000
Speaker 4: of this are motivated by that same moral conviction necessarily

987
00:49:53,080 --> 00:49:55,880
Speaker 4: and a lot of arts behind the scenes. I think

988
00:49:55,960 --> 00:49:59,399
Speaker 4: what this is about is power. Who has power, who

989
00:49:59,400 --> 00:50:02,560
Speaker 4: can amass more power, who can take away power from

990
00:50:02,560 --> 00:50:05,480
Speaker 4: other people? And it's about politics. And you know, abortion

991
00:50:05,640 --> 00:50:08,120
Speaker 4: was not a political issue until the eighties and even

992
00:50:08,160 --> 00:50:10,439
Speaker 4: into the nineties. You know, it is when it really

993
00:50:10,480 --> 00:50:14,000
Speaker 4: took hold, and that was a story about power, right

994
00:50:14,080 --> 00:50:18,440
Speaker 4: they were reorganizing. It was a few Republican strategists who

995
00:50:18,440 --> 00:50:23,040
Speaker 4: were trying to make evangelicals a reliable voting block for

996
00:50:23,280 --> 00:50:27,680
Speaker 4: Republican candidates, and it's related to kind of the decline

997
00:50:27,960 --> 00:50:32,719
Speaker 4: in the acceptability of fighting for segregated schools. And they

998
00:50:32,760 --> 00:50:35,520
Speaker 4: came up with, you know, being anti abortion as a

999
00:50:35,560 --> 00:50:39,400
Speaker 4: new way to appeal to those voters. Now, that was

1000
00:50:39,480 --> 00:50:42,200
Speaker 4: just about winning races, right, So it all comes back

1001
00:50:42,239 --> 00:50:44,440
Speaker 4: to power, and so they knew if they kind of

1002
00:50:44,520 --> 00:50:49,680
Speaker 4: combined that new voting block of Evangelical Christians with US

1003
00:50:49,840 --> 00:50:54,759
Speaker 4: Catholics that had traditionally been Democrats or trended democrat, they

1004
00:50:54,960 --> 00:50:58,600
Speaker 4: could get a lock on power. And so this is

1005
00:50:58,640 --> 00:51:00,600
Speaker 4: a story about power, and this is story about what

1006
00:51:00,640 --> 00:51:03,000
Speaker 4: it means to be an American and what rights you

1007
00:51:03,080 --> 00:51:03,920
Speaker 4: have as an American.

1008
00:51:04,400 --> 00:51:08,319
Speaker 1: So interesting and I think really important. There's so much

1009
00:51:08,400 --> 00:51:12,800
Speaker 1: in this story, but what's the solution to the real problem.

1010
00:51:13,080 --> 00:51:15,840
Speaker 4: So if you are someone who supports abortion rights and

1011
00:51:15,840 --> 00:51:19,400
Speaker 4: would like to see something row like back in place,

1012
00:51:19,960 --> 00:51:23,040
Speaker 4: that's kind of what my book is about. The second half.

1013
00:51:23,080 --> 00:51:26,040
Speaker 4: It's people trying to figure out. I like to think

1014
00:51:26,080 --> 00:51:29,120
Speaker 4: of it as the book is a story about democratic

1015
00:51:29,200 --> 00:51:32,120
Speaker 4: erosion and how we got to a place where much

1016
00:51:32,160 --> 00:51:34,560
Speaker 4: of the country has laws in place that the citizens

1017
00:51:34,600 --> 00:51:38,640
Speaker 4: do not support. But also it's showing you people in

1018
00:51:38,680 --> 00:51:42,160
Speaker 4: a variety of roles who have figured out what their

1019
00:51:42,360 --> 00:51:46,040
Speaker 4: role is to play in strengthening our democracy. So it's

1020
00:51:46,120 --> 00:51:51,520
Speaker 4: everything from doctors and even medical students realizing that it's

1021
00:51:51,560 --> 00:51:55,759
Speaker 4: not an option to not be an advocate or an activist.

1022
00:51:56,400 --> 00:51:59,480
Speaker 4: It's actually an essential part of your practice if you're

1023
00:51:59,480 --> 00:52:01,560
Speaker 4: going to be an bgyn and want to take care

1024
00:52:01,600 --> 00:52:06,760
Speaker 4: of patients. It is young mothers in Kentucky who found

1025
00:52:06,760 --> 00:52:09,560
Speaker 4: out about how they could get involved through a Jewish

1026
00:52:09,560 --> 00:52:13,800
Speaker 4: women's group and started trying to defeat a ballot measure

1027
00:52:13,840 --> 00:52:16,800
Speaker 4: there that would have you know, abortion is already banned

1028
00:52:16,800 --> 00:52:19,560
Speaker 4: in Kentucky, but there was an effort to actually add

1029
00:52:19,560 --> 00:52:21,719
Speaker 4: that to the constitution. They were able to push back

1030
00:52:21,719 --> 00:52:24,120
Speaker 4: on that amendment and defeat it. They actually took to

1031
00:52:24,160 --> 00:52:26,279
Speaker 4: the streets, one of them for the first time, to

1032
00:52:26,320 --> 00:52:28,920
Speaker 4: go canvassing and door knocking for that issue. I think

1033
00:52:28,960 --> 00:52:31,359
Speaker 4: if you talk to the people who the legal strategists,

1034
00:52:31,480 --> 00:52:34,000
Speaker 4: they say that we're in a kind of spaghetti at

1035
00:52:34,040 --> 00:52:38,040
Speaker 4: the wall moment where, you know, the abortion rights movement

1036
00:52:38,160 --> 00:52:42,200
Speaker 4: on a national level has been accused by some of

1037
00:52:42,200 --> 00:52:45,640
Speaker 4: being kind of gatekeepers on strategy for the past couple decades,

1038
00:52:46,040 --> 00:52:50,200
Speaker 4: and you're seeing kind of the universe of organizations bringing

1039
00:52:50,239 --> 00:52:53,279
Speaker 4: really high profile cases expand beyond kind of the one

1040
00:52:53,360 --> 00:52:55,440
Speaker 4: or two usual groups that were doing that and the

1041
00:52:55,480 --> 00:52:58,400
Speaker 4: build up to Dobbs. A lot of people across the

1042
00:52:58,440 --> 00:53:01,480
Speaker 4: political spectrum, include being a main character in my book,

1043
00:53:01,680 --> 00:53:04,040
Speaker 4: have started likening the period we're in right now to

1044
00:53:04,080 --> 00:53:09,480
Speaker 4: prohibition right and looking the repeal of prohibition as a

1045
00:53:09,560 --> 00:53:13,520
Speaker 4: template for how we could restore abortion rights in this country.

1046
00:53:13,840 --> 00:53:17,360
Speaker 4: Because what we're seeing with these ballot measures right across

1047
00:53:17,400 --> 00:53:20,279
Speaker 4: the country, any time a ballot measure has been put

1048
00:53:20,320 --> 00:53:23,839
Speaker 4: before the people, abortion rights has won. There are ten

1049
00:53:23,920 --> 00:53:26,800
Speaker 4: states that right now that are going to be voting

1050
00:53:27,040 --> 00:53:31,800
Speaker 4: on abortion rights ballot measures in November. And while that's

1051
00:53:31,880 --> 00:53:34,040
Speaker 4: great for the people in those states, and the reason

1052
00:53:34,040 --> 00:53:37,719
Speaker 4: they're doing it is because voters have realized, like legislation

1053
00:53:37,840 --> 00:53:40,480
Speaker 4: can be fleeting, it can be overturned, it can be

1054
00:53:41,320 --> 00:53:43,359
Speaker 4: the best way to protect a right is to have

1055
00:53:43,440 --> 00:53:46,560
Speaker 4: it in your constitution. While that's great for the people

1056
00:53:46,600 --> 00:53:49,960
Speaker 4: in the states who have a citizen referred ballot measure process,

1057
00:53:50,040 --> 00:53:52,360
Speaker 4: not every state has that, So in Alabama that's not

1058
00:53:52,400 --> 00:53:55,280
Speaker 4: an option for them. And so when I was talking

1059
00:53:55,320 --> 00:53:57,000
Speaker 4: to one of the women who works at the clinic

1060
00:53:57,080 --> 00:53:59,640
Speaker 4: in Alabama. She was saying, I actually think you know,

1061
00:54:00,120 --> 00:54:03,400
Speaker 4: ultimately what we need is a constitutional convention and anment

1062
00:54:03,480 --> 00:54:07,160
Speaker 4: to the US Constitution that is related to bodily autonomy.

1063
00:54:07,280 --> 00:54:10,040
Speaker 1: Yeah, I think that's right. Thank you so much for

1064
00:54:10,120 --> 00:54:10,719
Speaker 1: joining us.

1065
00:54:11,000 --> 00:54:16,040
Speaker 4: Thank you, No, no moment good.

1066
00:54:16,520 --> 00:54:19,000
Speaker 3: My moment of fuckery is one of these classic high

1067
00:54:19,160 --> 00:54:24,520
Speaker 3: order mistakes. New Coke Land War in Asia, Parachute Pants.

1068
00:54:24,920 --> 00:54:27,799
Speaker 3: The magas have decided that they're going to take on

1069
00:54:28,200 --> 00:54:31,080
Speaker 3: the cultural juggernaut that we all know and love. S

1070
00:54:31,160 --> 00:54:34,160
Speaker 3: Taylor Swift. I welcome this decision on their part because

1071
00:54:34,239 --> 00:54:37,560
Speaker 3: it is truly one of the dumbest things I've ever

1072
00:54:37,600 --> 00:54:40,279
Speaker 3: heard in my life. The idea that jadvancewroled and said,

1073
00:54:40,480 --> 00:54:43,200
Speaker 3: I don't think anyone's going to be persuaded by a

1074
00:54:43,239 --> 00:54:46,600
Speaker 3: billionaire entertainer, like, bitch, have you met your boss?

1075
00:54:48,440 --> 00:54:51,000
Speaker 1: But he's not a billionaire. That's somewhat difference.

1076
00:54:51,280 --> 00:54:53,680
Speaker 3: That is an effering and a distinction. But look, she's

1077
00:54:53,719 --> 00:54:56,160
Speaker 3: going to be a force multiplier in this campaign. It's

1078
00:54:56,200 --> 00:54:58,120
Speaker 3: not going to decide the campaign, but she's going to

1079
00:54:58,120 --> 00:55:00,319
Speaker 3: be a major force multiplier in the campaign. It is

1080
00:55:00,360 --> 00:55:02,319
Speaker 3: going to make a big difference because there is a

1081
00:55:02,400 --> 00:55:04,920
Speaker 3: generation of young girls who grew up with Taylor Swift

1082
00:55:05,000 --> 00:55:07,480
Speaker 3: who are now voting age this generation of their moms

1083
00:55:07,600 --> 00:55:09,399
Speaker 3: who grew up with Taylor Swift and taking their daughters

1084
00:55:09,400 --> 00:55:12,160
Speaker 3: to Taylor Swift. You know, Andrew Breitbart was not right

1085
00:55:12,160 --> 00:55:14,600
Speaker 3: about a lot of things, but he said one time

1086
00:55:14,960 --> 00:55:18,239
Speaker 3: that politics is downstream of culture, and that's why Republicans

1087
00:55:18,239 --> 00:55:20,600
Speaker 3: are always so desperate to get any kind of star.

1088
00:55:21,120 --> 00:55:24,560
Speaker 3: And even like these d minus celebrities, We've got Scott Poe,

1089
00:55:26,280 --> 00:55:29,720
Speaker 3: but she is the biggest cultural icon of our time.

1090
00:55:30,000 --> 00:55:33,480
Speaker 3: She is a big deal. And their sense of denial

1091
00:55:33,600 --> 00:55:37,040
Speaker 3: and despair about this is just sweet, sweet maga teers

1092
00:55:37,040 --> 00:55:40,640
Speaker 3: flowing in a beautiful river of fuckery. They're attacking her,

1093
00:55:40,920 --> 00:55:44,920
Speaker 3: they're bitching about her, they're complaining, I'm here for all

1094
00:55:45,040 --> 00:55:46,440
Speaker 3: of it, all of it.

1095
00:55:46,680 --> 00:55:50,720
Speaker 1: Yeah, it does seem like the party that is at

1096
00:55:50,880 --> 00:55:54,840
Speaker 1: war with women right as at war about Troyce.

1097
00:55:55,280 --> 00:55:57,640
Speaker 3: I mean, why not go to war with the most

1098
00:55:57,719 --> 00:56:01,359
Speaker 3: beloved female pop star. Oh, I don't know of you know,

1099
00:56:01,400 --> 00:56:05,120
Speaker 3: since Madonna. It is a painful I'd argue of all time.

1100
00:56:05,280 --> 00:56:07,160
Speaker 3: You can make the all time argument. Yeah, you could

1101
00:56:07,239 --> 00:56:08,000
Speaker 3: you absolutely could.

1102
00:56:08,360 --> 00:56:10,719
Speaker 1: She does a thing that Madonna doesn't do, which is

1103
00:56:10,920 --> 00:56:13,959
Speaker 1: she's wholesome. Yeah, so here they are going to war

1104
00:56:14,120 --> 00:56:17,480
Speaker 1: with the wholesome, self made American billionaire.

1105
00:56:17,640 --> 00:56:22,640
Speaker 3: I'm pro Madonna and I vote, but I take your point. Yeah.

1106
00:56:22,680 --> 00:56:25,279
Speaker 1: If she were even the slightest bit Maga, they would

1107
00:56:25,320 --> 00:56:28,160
Speaker 1: be obsessed with her, right, She's the dream. Remember the

1108
00:56:28,239 --> 00:56:31,439
Speaker 1: news cycle when they thought when they discovered she wasn't

1109
00:56:31,480 --> 00:56:32,720
Speaker 1: Maga and they were so upset.

1110
00:56:33,120 --> 00:56:35,600
Speaker 3: Yeah, well now they're so upset at a level that

1111
00:56:35,719 --> 00:56:39,160
Speaker 3: makes that look like they couldn't find a parking space.

1112
00:56:39,440 --> 00:56:42,080
Speaker 3: They are losing their minds on her. And please, guys,

1113
00:56:42,320 --> 00:56:46,320
Speaker 3: I want you to continue to call her a dumb

1114
00:56:46,400 --> 00:56:49,720
Speaker 3: slut and call her a failure and call her stupid,

1115
00:56:50,000 --> 00:56:53,080
Speaker 3: because nothing could ever go wrong with the Swifties finding

1116
00:56:53,080 --> 00:56:55,560
Speaker 3: out about what Mauga's doing to Taylor. Nothing could ever

1117
00:56:55,640 --> 00:56:58,160
Speaker 3: go wrong there. Guys, y'all just keep tearing it up.

1118
00:56:58,320 --> 00:57:00,640
Speaker 3: You just keep criticizing her all you want, Just go for it.

1119
00:57:00,760 --> 00:57:04,360
Speaker 3: I await your capitulation on the field of battle because

1120
00:57:04,800 --> 00:57:07,360
Speaker 3: she is undefeated.

1121
00:57:07,640 --> 00:57:12,880
Speaker 1: Thank you. Rick Wilson, all right, all right, Jesse Cannon.

1122
00:57:13,040 --> 00:57:16,919
Speaker 2: Molly Jong Fast, jd Vance he's really showing that he's

1123
00:57:16,960 --> 00:57:19,120
Speaker 2: a member of the Trump administration because he's taken on

1124
00:57:19,160 --> 00:57:21,040
Speaker 2: Trump's trade he let the mask slip.

1125
00:57:21,680 --> 00:57:26,160
Speaker 1: No, I disagree. I actually think that by admitting you

1126
00:57:26,240 --> 00:57:29,840
Speaker 1: were lying, that's like not what you're supposed to do.

1127
00:57:30,040 --> 00:57:33,000
Speaker 1: Like you like, what Trump would have done is he

1128
00:57:33,040 --> 00:57:36,240
Speaker 1: would have said, yeah, no, that's what I said. You know,

1129
00:57:36,320 --> 00:57:39,960
Speaker 1: something along the lines of Paul Mattaford right where you'd

1130
00:57:39,960 --> 00:57:43,400
Speaker 1: make enough confusion so that you never really admit that

1131
00:57:43,440 --> 00:57:47,080
Speaker 1: you're lying, even if everyone knows you're lying. This was actually,

1132
00:57:47,360 --> 00:57:50,240
Speaker 1: I think like one of the I mean, I would

1133
00:57:50,320 --> 00:57:53,240
Speaker 1: say it rare, but it's all missteps with Jada Vance,

1134
00:57:53,400 --> 00:57:57,240
Speaker 1: Dana Bash did a really excellent job of asking him

1135
00:57:57,560 --> 00:58:01,800
Speaker 1: a very specific question and then following up.

1136
00:58:02,680 --> 00:58:06,240
Speaker 8: The American media totally ignored this stuff until Donald Trump

1137
00:58:06,240 --> 00:58:09,120
Speaker 8: and I start talking about cat means. If I have to,

1138
00:58:09,480 --> 00:58:13,560
Speaker 8: it's just mean, create stories so that the American media

1139
00:58:13,600 --> 00:58:16,640
Speaker 8: actually pays attention to the suffering of the American people.

1140
00:58:16,880 --> 00:58:19,040
Speaker 8: Then that's what I'm gonna do. Dana, Because you just

1141
00:58:19,040 --> 00:58:21,400
Speaker 8: said that you're creating the public policy.

1142
00:58:21,720 --> 00:58:23,520
Speaker 6: Sorry, you just said that you're creating the story.

1143
00:58:23,680 --> 00:58:26,880
Speaker 1: Is that, Dana, you just said that this is a

1144
00:58:26,880 --> 00:58:31,080
Speaker 1: story that you created, so that the eating dog re acting.

1145
00:58:31,440 --> 00:58:34,920
Speaker 8: Not we are creating, we are, Dana. It comes from

1146
00:58:35,040 --> 00:58:37,840
Speaker 8: first hand accounts from my constituents. I say that we're

1147
00:58:37,880 --> 00:58:41,040
Speaker 8: creating a story, meaning we're creating the American media focusing

1148
00:58:41,080 --> 00:58:41,360
Speaker 8: on it.

1149
00:58:41,480 --> 00:58:44,560
Speaker 1: What I thought was really interesting about this was obviously

1150
00:58:44,600 --> 00:58:48,680
Speaker 1: it's not happening, but it's also they finally just you know,

1151
00:58:48,760 --> 00:58:52,840
Speaker 1: for whatever reason, but Vans couldn't keep it going. And

1152
00:58:52,880 --> 00:58:58,640
Speaker 1: then Mike DeLine, who remembers the governor of Ohio and

1153
00:58:58,840 --> 00:59:03,080
Speaker 1: ran about twenty points ahead of Vance, even though Vance's

1154
00:59:03,120 --> 00:59:08,440
Speaker 1: campaign had to pour money into it. Dwine actually really

1155
00:59:08,640 --> 00:59:12,880
Speaker 1: just set him, you know, really, he said, I think

1156
00:59:12,880 --> 00:59:15,080
Speaker 1: it's unfortunate that this came up. But let me tell

1157
00:59:15,120 --> 00:59:17,960
Speaker 1: you what we do now. We know that Haitians in

1158
00:59:18,080 --> 00:59:21,120
Speaker 1: Springfield are legal. They came to Springfield to work. Ohio

1159
00:59:21,840 --> 00:59:24,240
Speaker 1: is on the move in Springfield has made it great.

1160
00:59:24,720 --> 00:59:28,160
Speaker 1: The point is that this was meant to be a

1161
00:59:28,200 --> 00:59:31,080
Speaker 1: sort of standard other the way Trump often does it,

1162
00:59:31,160 --> 00:59:33,360
Speaker 1: you know, the same as what he did to Muslims,

1163
00:59:33,640 --> 00:59:35,960
Speaker 1: the same as what he did to Mexicans, the same

1164
00:59:35,960 --> 00:59:38,840
Speaker 1: as what he did to all these other ethnic groups.

1165
00:59:38,880 --> 00:59:43,160
Speaker 1: He targets, but instead in this one, they just went

1166
00:59:43,320 --> 00:59:47,960
Speaker 1: too far and they made up a completely insane lie

1167
00:59:48,400 --> 00:59:52,600
Speaker 1: which is now debunked, and that is our moment of

1168
00:59:52,640 --> 00:59:57,320
Speaker 1: fuck Ray. That's it for this episode of Fast Politics.

1169
00:59:57,680 --> 01:00:00,600
Speaker 1: Tune in every Monday, Wednesday, and Friday to hear the

1170
01:00:00,640 --> 01:00:04,080
Speaker 1: best minds in politics makes sense of all this chaos.

1171
01:00:04,360 --> 01:00:06,920
Speaker 1: If you enjoyed what you've heard, please send it to

1172
01:00:06,960 --> 01:00:10,360
Speaker 1: a friend and keep the conversation going. And again, thanks

1173
01:00:10,360 --> 01:00:11,000
Speaker 1: for listening.