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Speaker 1: Hello, and welcome to Fast Politics. I'm Molly Jongfes, and

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Speaker 1: we have such a great show for you today. We

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Speaker 1: have the fight agency's Rebecca Katz to talk about what

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Speaker 1: it's like to run underdog candidates. Spoiler, some of her

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Speaker 1: has won. But first, we have the New Yorker's Evan

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Speaker 1: Osnos on how Donald Trump achieved regime change at home. Welcome, welcome,

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Speaker 1: Evan Osnos.

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Speaker 2: Thank you, Molly. Always a pleasure to see you.

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Speaker 1: We're here to talk about the midterms where Donald Trump

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Speaker 1: and his party got a shellacking like you can't believe

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Speaker 1: you've been doing this for a while. Were you surprised

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Speaker 1: at the level of shellack?

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Speaker 2: The thumping, the shellack, the I go back. I like

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Speaker 2: my I like the early work when it comes to

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Speaker 2: metaphors on being crushed in a midterm. I will say

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Speaker 2: And I've never said the following words out loud, but

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Speaker 2: I agree with Ted Cruz, which is that this was

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Speaker 2: an ass kicking. Yeah. Now, I will note that he

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Speaker 2: said that because of his Cruzian ways, meaning that he

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Speaker 2: had some self-interested reason for saying so. But even an

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Speaker 2: objective analysis will say, and I have come to strongly

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Speaker 2: endorse the view which has been expressed around the world

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Speaker 2: in multiple places. that Donald Trump set out to achieve

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Speaker 2: regime change in Iran, and he managed to achieve regime

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Speaker 2: change in America. That's a good one. Yeah, that is

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Speaker 2: a good one. Look, I will tell you my core

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Speaker 2: feeling about this is that this was a rebuke of

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Speaker 2: so many things, not just the kind of criminally inept

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Speaker 2: candidates like Ken Paxton, but really fundamentally a rebuke of

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Speaker 2: the obscenity of so many things, the obscenity of the

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Speaker 2: ICE raids, the obscenity of Russian wedding parties, the obscenity

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Speaker 2: of $ 4 gas. I mean, you just go down the line.

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Speaker 2: And at a certain point, people said, no, thank you.

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Speaker 1: Is it a rebuke of the anti-DEI? Like, you have

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Speaker 1: voters in, we just talked to a data journalist from

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Speaker 1: The Economist who was talking about And now we talk

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Speaker 1: to you from The New Yorker because we're here for fancy.

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Speaker 2: We can't even spell data at The New Yorker. Go on.

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Speaker 1: We can't even spell the– I can't even spell The Economist.

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Speaker 1: But we're talking about like in the Rio Grande Valley,

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Speaker 1: voters turned out in huge numbers. In the south, black

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Speaker 1: voters came back from Calais and said like you're not

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Speaker 1: taking away our vote.

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Speaker 3: That's right.

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Speaker 1: Our right to vote. So is this like a referendum

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Speaker 1: on anti-DEI?

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Speaker 2: Yeah. I think on some level– that phenomenon, that backlash

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Speaker 2: effect is profound. I've take, for example, the fact that

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Speaker 2: you had Latino voters who rejected the cruelty of the

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Speaker 2: anti-immigration crusade. And these were voters who had been really

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Speaker 2: interested in what Trump had to say in 2024 and

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Speaker 2: thought that he was going to bring down costs. And

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Speaker 2: on the, One example after another, you saw people just

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Speaker 2: being offended by what he stood for. And that's the idea,

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Speaker 2: what he stood for. I will say also, this was

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Speaker 2: also anti-Doge. You saw former federal workers being elected.

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Speaker 1: Virginia is a good example of that.

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Speaker 2: Exactly. Yeah. You had a smokejumper in Montana get elected.

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Speaker 2: I mean, you had people.

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Speaker 1: Sam Forstad, who was on this podcast months ago.

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Speaker 2: Yes, go on. Well, I mean, I think in... we

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Speaker 2: were all, so I didn't, I'll tell you the moment

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Speaker 2: for me that things started to snap into focus. I

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Speaker 2: was watching this the other night at home. We had

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Speaker 2: friends over for dinner on the night of November 3rd,

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Speaker 2: which is a risky thing because it can be a

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Speaker 2: cast of Paul over the evening if things go the

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Speaker 2: other way. But a couple of New Yorker pals, Atul

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Speaker 2: Gawande was here and Charles Duhigg and We kind of

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Speaker 2: had the TV in the other room so you could

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Speaker 2: go in and you could see it and then come

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Speaker 2: back and either announce good news or bad news.

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Speaker 1: Isn't Charles from Georgia?

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Speaker 2: No, Charles is from California. That's Charles Bethea, you're thinking.

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Speaker 2: But interestingly, it was Maine that got me because in

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Speaker 2: some ways... Like, I'd been obsessed with Iowa. I'd been

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Speaker 2: thinking a lot about Iowa following Josh Turek's race. If

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Speaker 2: he was going to win, it was very interesting. And

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Speaker 2: it was early in the night, actually.

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Speaker 1: Josh Turek, by the way, just such a story for,

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Speaker 1: you know, an athlete, paraplegic. You're gone.

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Speaker 2: And I will say, just to stay on that for

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Speaker 2: a moment, Josh Turek's... The message of his campaign, which

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Speaker 2: really should stay with us, is that.

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Speaker 2: He was born with spina bifida.

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Speaker 1: Because of his father's exposure to Agent Orange during Vietnam. Yeah,

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Speaker 1: go on.

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Speaker 2: Exactly. And I think he had an ad that went

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Speaker 2: viral at one point. And the first words out of

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Speaker 2: the ad were, I was born an underdog. And that

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Speaker 2: idea turned out to be a dominant theme in this

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Speaker 2: election across the board. And if you listen to James

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Speaker 2: Tallarico's victory speech, he said, I'm an underdog. And I

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Speaker 2: think this is what, this was not a left-right thing.

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Speaker 2: This was an underdog-overdog election. And, you know, there is

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Speaker 2: no greater overdog Doge than Elon Musk, right? And he.

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Speaker 1: He's the richest man in the world.

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Speaker 4: Rich.

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Speaker 2: I've noticed he is a trillionaire. And Americans said, no,

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Speaker 2: thank you. And that's, that for me is a huge

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Speaker 2: message because this was an election in which we were

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Speaker 2: over and over again that there were forces beyond our control.

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Speaker 2: There was a kind of inevitability to this whole thing.

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Speaker 2: Here was this guy who's going to pour half a

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Speaker 2: billion dollars into the election. And as he reminded us

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Speaker 2: in 2024, without him, he said Donald Trump wouldn't be president.

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Speaker 2: And so people actually said, no, as a matter of fact,

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Speaker 2: we have free will and we're going to reject a

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Speaker 2: lot of the things that are being told to us

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Speaker 2: that are inevitable. Like data centers and this.

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Speaker 1: I was actually going to say, I was about to

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Speaker 1: say like data centers.

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Speaker 2: Yeah.

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Speaker 1: And AI taking all our jobs.

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Speaker 2: Exactly. I think they're of a piece here. I think

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Speaker 2: that's the theme.

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Speaker 1: Josh Turek is such an interesting candidate because, and we

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Speaker 1: interviewed him a gazillion years ago, but one of the

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Speaker 1: things that I was struck by with Josh Turek is,

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Speaker 1: and this is something I'd like to talk about for

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Speaker 1: a minute, is Josh Turek was not I'm so used

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Speaker 1: to interviewing senators where you're like, oh, they're fancy. They're

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Speaker 1: a senator. There's a class thing. We don't talk about

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Speaker 1: class because we're not supposed to talk about class. And

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Speaker 1: I think part of it is because people don't tend

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Speaker 1: to do jobs that they don't know exist.

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Speaker 3: Yeah.

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Speaker 1: Right. And so it tends to be that a certain

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Speaker 1: class of people know that a certain type of job

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Speaker 1: exists and end up keeping doing that jobs, hence nepotism.

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Speaker 1: And so one of the things as a beneficiary of nepotism,

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Speaker 1: I feel contractually obligated to talk about it all the time.

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Speaker 1: But one of the things that I was struck by

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Speaker 1: when I interviewed Turek like a million years ago was

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Speaker 1: I was like, oh, this guy is hardcore. Like I

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Speaker 1: was like, He is not, there was none of that

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Speaker 1: Susan Collins-y, like, oh dear, you know, what I was

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Speaker 1: hanging out with Prescott. And I wondered, there are a

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Speaker 1: lot of really exciting candidates who are, you know, like,

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Speaker 1: not Susan Collins. Like, so I want to talk to

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Speaker 1: you about this theme of working people.

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Speaker 2: Man, I mean, you know, this is something I think

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Speaker 2: a lot about, write about, Molly, there's Americans are allergic to.

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Speaker 1: Talking about class. You grew up in Greenwich and I

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Speaker 1: grew up on the Upper East Side. So we both

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Speaker 1: understand just how much class is actually caste.

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Speaker 2: Oh, completely true. And Americans are allergic to talking about

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Speaker 2: it in the foreground. There was a great book called

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Speaker 2: Class by Paul Fussell, a very funny and acerbic and

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Speaker 2: insightful writer. And he once said that when he told

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Speaker 2: people he was writing a book on class in America,

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Speaker 2: they would respond as if he said he was writing

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Speaker 2: a book about killing baby seals. It was just so

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Speaker 2: awkward for Americans. And yet it is a primary fact

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Speaker 2: of our lives. And you raised Susan Collins a moment ago,

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Speaker 2: and she would tell stories about hanging out with, you know,

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Speaker 2: Pemberton Thunderwick III or whatever. And she just lost... to

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Speaker 2: a man from Allagash, Maine, Troy Jackson. I will tell you, Allagash,

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Speaker 2: there are not many people out of Maine who have

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Speaker 2: been to Allagash. I have been there. It's five hours

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Speaker 2: north of Portland. It is right up on the Canadian border.

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Speaker 2: It's got about 250 people, way more moose than humans

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Speaker 2: in Allagash. And he won in part, he's a guy,

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Speaker 2: a former logger, as everybody knows, but he won also

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Speaker 2: by speaking to this feeling of being disrespected. She got

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Speaker 2: up on the stage at the debate and she called

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Speaker 2: him by his first name and sort of treated him

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Speaker 2: with a real disdain. And this guy ran- Like he

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Speaker 2: worked for her. Like he worked for her. And he

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Speaker 2: had four months to run and he beat a five-term incumbent.

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Speaker 2: So there's a lesson there, a real lesson there.

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Speaker 1: And I want to talk about the last advertisement in

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Speaker 1: that campaign. The the guy, the fancy broadcaster from Maine

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Speaker 1: who said, I'm not going to vote. Right. I mean,

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Speaker 1: that was we're not voting for Troy Jackson because we

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Speaker 1: don't need a cord of wood.

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Speaker 2: That was so. And then, of course, Troy Jackson smartly

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Speaker 2: dressed up as a cord of wood for Halloween.

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Speaker 1: Yeah.

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Speaker 2: And I in some ways, Donald Trump forgot the lesson

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Speaker 2: that got him elected in 2016, which is a lot

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Speaker 2: of Americans are feel dumped on, and they feel that

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Speaker 2: decisions are being made without their input, without their control.

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Speaker 2: And when you do that, this country historically will come

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Speaker 2: back and burn you, and it'll teach you, it'll remind

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Speaker 2: you that you have to put people into the foreground.

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Speaker 2: And I, you know, this happens to be the subject

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Speaker 2: of my next book, which we will talk about for months,

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Speaker 2: because I'm going to be thinking about it. But this

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Speaker 2: is like, Wait, what's the subject? This is one of

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Speaker 2: the great lessons. The subject is about a workers' rebellion

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Speaker 2: in 1921. Oh, wow. And a century ago, in some ways,

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Speaker 2: we often forget and have to be reminded of this fact,

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Speaker 2: that when we have moments when it feels as if

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Speaker 2: history and technology and the economy is surging forward, that

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Speaker 2: if you don't pay attention to people who are getting

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Speaker 2: chewed up and screwed at the same time, they will

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Speaker 2: actually bend the curve of history, whether you like it

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Speaker 2: or not.

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Speaker 1: And we do have a moment here where history has

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Speaker 1: bent towards justice in a way that I think very

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Speaker 1: few of us thought would come. And one of the

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Speaker 1: things I want to talk about is, and again, working

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Speaker 1: class voters, we have a lot of working class, working

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Speaker 1: class is the wrong word, working people in the Senate,

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Speaker 1: a place that was meant for the very fancy, that

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Speaker 1: was our House of Lords. And they are gonna do

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Speaker 1: some really cool shit. And I'm very excited to see

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Speaker 1: what they do. But I wanna talk about the way

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Speaker 1: in which women of color took back the narrative because

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Speaker 1: as someone who watched I think the 24 cycle with

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Speaker 1: a lot of heart because I felt like it really

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Speaker 1: echoed the 16 cycle of like women getting very excited

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Speaker 1: about a candidate who they very much identified with and

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Speaker 1: then having that candidate once again just get creamed by

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Speaker 1: a very mediocre white guy. I wondered if you could

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Speaker 1: talk about in 26, we had like Keisha Lance Bottoms.

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Speaker 1: Like we had, you know, a Mexican literally took Greg

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Speaker 1: Abbott's job, you know, with Gina Hinojosa. Like we really

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Speaker 1: did see real, I mean, just exciting. And gubernatorials for

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Speaker 1: women are really like being in the executive is a

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Speaker 1: big deal. And one of the things we saw in

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Speaker 1: 2016 was that, and one of the things that hurt

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Speaker 1: Harris in 2024 was that, you know, Pennsylvania had never

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Speaker 1: had a woman in the executive. So like there were

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Speaker 1: states where you just couldn't imagine them voting for a woman.

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Speaker 2: Totally. And I mean, I think you say you had

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Speaker 2: Deb Haaland win in New Mexico, who's going to be

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Speaker 2: the first Native American woman to lead a U.S. state.

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Speaker 2: You know, you've got a moment in which, and we

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Speaker 2: learned this in 2018 and then also in 2020, when

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Speaker 2: the obscenity of the Trump administration the first time around

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Speaker 2: became so unbearable that we were able as a country

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Speaker 2: to punch through the crust of that history and say, actually,

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Speaker 2: this is the moment where we say this is this, this,

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Speaker 2: we cannot tolerate it any longer. And I think that's

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Speaker 2: sort of what happens. If you, if you look at

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Speaker 2: the patterns throughout history, it is at the moments when

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Speaker 2: it feels like it has reached its most obscene that

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Speaker 2: you get the possibility for a, for a really profound change.

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Speaker 2: So I, you know, I'm, I think that the Democratic

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Speaker 2: Party is now going to face a question of how

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Speaker 2: to make sure that this slingshot election, which is what

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Speaker 2: this really feels like, meaning such a profound backlash against

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Speaker 2: everything that Trump represented, how to make sure that it

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Speaker 2: is not a two-year phenomenon. And look, we are living

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Speaker 2: in a country in which things are so divided that

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Speaker 2: it feels as if every election is your last chance

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Speaker 2: to do anything. And that's dangerous, actually, because if you

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Speaker 2: overread the results and you say, okay, now we can

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Speaker 2: go for everything, that's risky too. And you run the

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Speaker 2: risk of re-empowering the very people who have been pushed

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Speaker 2: out of power in this moment. So in a way,

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Speaker 2: the Democratic Party has a lot of negotiating to do internally.

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Speaker 2: And I think they have reasons to be ambitious. But

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Speaker 2: I also think the biggest lesson for me is listen

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Speaker 2: closely to what the public is saying. Listen really closely

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Speaker 2: because they will make their positions known one way or another.

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Speaker 2: And I hope that we, as a country, listen closely

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Speaker 2: to what they're saying over the course of the next

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Speaker 2: two years.

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Speaker 1: Yeah, I think that's really important. I also do think

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Speaker 1: like when we look at this referendum on the incumbent,

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Speaker 1: which it really is ultimately, um, One of the things

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Speaker 1: that I think really hurt Republicans was that they refused

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Speaker 1: to run a check on Trump. Right. You couldn't make

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Speaker 1: a case for Susan. Like Susan Collins had this thing

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Speaker 1: where she said, you know, I'm the most they were

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Speaker 1: you would towards the end when they started getting these

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Speaker 1: internals that were clearly just them all losing. They would

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Speaker 1: be these things like you look on their website to

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Speaker 1: be no like they'd be green.

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Speaker 4: Right.

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Speaker 1: They wouldn't be red because like and they would say

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Speaker 1: like the most independent member of Congress or the most.

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Speaker 1: So Susan Collins, just to get back to her, because

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Speaker 1: I think this is relevant. She had this statistic that

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Speaker 1: she had voted against Trump a bunch of times, but

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Speaker 1: it had never, ever sunk any of Trump's stuff.

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Speaker 2: That's right. And in a way, look, Washington and particularly

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Speaker 2: Republicans in Washington flunked the test of checking Trump, of

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Speaker 2: pushing back his worst impulses, of rejecting the culture that

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Speaker 2: he represented. And so finally, after a certain point, it

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Speaker 2: was up to the non-Washington America, the rest of the country.

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Speaker 2: And I think what's really important too is we spend

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Speaker 2: so much time talking about the people at the top

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Speaker 2: that we have actually neglected to focus as much on

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Speaker 2: the fact that down ballot, there were some really important

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Speaker 2: rejections too. You go to Kansas where Chris Kobach, the

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Speaker 2: election denier, was chucked out. And perhaps even most importantly,

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Speaker 2: maybe the most important race too that nobody talks about

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Speaker 2: is the fact that the attorney general in Texas, who

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Speaker 2: of course had been Ken Paxton, is out. And Texas

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Speaker 2: had been a place where Republicans could go, conservatives could

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Speaker 2: go and sort of cultivate a laboratory of obstruction and

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Speaker 2: resistance and creating all of this rogue activity and then

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Speaker 2: running it through the Texas courts. That's no longer going

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Speaker 2: to be an option right now.

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Speaker 1: Yeah. And that was like, we saw that with Metapristo

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Speaker 1: and remember that they would try to get that Republican

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Speaker 1: district because the judge there was super anti-choice. I'd love to,

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Speaker 1: I just, we're almost out of time, but when we

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Speaker 1: talk about Texas for a minute, the person who was

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Speaker 1: probably one of the largest boogeymen, rightly so, not even

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Speaker 1: a boogeyman, the largest villains of the midterm cycle, Elon Musk, uh,

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Speaker 1: He lives in Texas and has a very we haven't

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Speaker 1: probably seen the last of the reporting on this, but,

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Speaker 1: you know, he has a town. He tried to buy

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Speaker 1: a beach. He he's sort of a.

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Speaker 2: Very relatable American experience. Right.

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Speaker 1: He's a landed baron of Texas. And I wonder, there's

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Speaker 1: a new sheriff, AG, governor, secretary of state in town. Like,

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Speaker 1: what do you think happens there?

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Speaker 2: I think that the only business that is thriving this

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Speaker 2: week is law firms that are experienced in investigative and

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Speaker 2: regulatory action. So to borrow a phrase, there is a

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Speaker 2: lot of lawyering up going on. And I think even

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Speaker 2: before the election, my great colleague at The New Yorker,

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Speaker 2: Antonia Hitchens, who was hanging out with a lot of

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Speaker 2: MAGA types was... I read that piece. I'm moving to Argentina.

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Speaker 2: So I think we're going to see a terrain that

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Speaker 2: had seemed friendly is going to become quite inhospitable. And

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Speaker 2: in the end, this is what's known as doing the

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Speaker 2: people's business. This election reminded us of the fact that

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Speaker 2: the people have a voice in this process. And it

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Speaker 2: is not just the sort of giant megadons that are

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Speaker 2: walking the political landscape. And three cheers for that.

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Speaker 1: Yeah. Evan Osnos, thank you, thank you, thank you.

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Speaker 2: My great pleasure, Molly. Great to see you.

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Speaker 4: Molly, it is time for the headlines. So we had

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Speaker 4: a $ 2. 5 trillion AI stock wipeout yesterday, but our jobs

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Speaker 4: numbers actually look better than we thought they would, at

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Speaker 4: least until the revisements which seems to plague the trump.

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Speaker 1: Administration so here's the story uh ai it turns out

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Speaker 1: that uh the nasdaq plunged of five and a half

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Speaker 1: points uh They chipmaker sank on fears that Democratic control

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Speaker 1: of Congress would trigger tougher regulation and data center regulation.

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Speaker 1: You know, it's like, don't you know, don't threaten me

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Speaker 1: with a good time. Voters handed Democrats control of the

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Speaker 1: House and the Senate. You know why? Because Republicans were terrible.

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Speaker 1: So chips and you know what? A really bad day

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Speaker 1: in the markets. This is like delicious. You may remember

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Speaker 1: him as being the richest man in the world and

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Speaker 1: also the worst person in the world. The arc of

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Speaker 1: justice bends towards Elon Musk not being a trillionaire anymore.

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Speaker 1: And in fact, it's been a very bad week for

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Speaker 1: Elon Musk, father of many.

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Speaker 4: The Alex Gibney doc went up on parental today.

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Speaker 1: Yes, the Alex Gibney doc. You can, in fact, get

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Speaker 1: the Alex Gibney doc. I think you can also buy it.

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Speaker 4: Yeah, my preorder hit my email this morning.

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Speaker 1: Alex does God's work. It's four hours long, but it's

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Speaker 1: worth it. Oh, you haven't seen it yet, right?

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Speaker 4: I have not seen it yet. I preordered it to

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Speaker 4: support him because he's one of my favorite directors.

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Speaker 1: So I saw it. I will preorder it, too. I

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Speaker 1: feel like you're like silent shaming me here.

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Speaker 4: But that was your own shame there, Biz.

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Speaker 1: I saw it. It's four hours long. Basically, what I

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Speaker 1: think is important about the movie and it's two really

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Speaker 1: two movies is that it has in it like this

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Speaker 1: case that that Elon has been NDAing everyone who has

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Speaker 1: either had it NDAing and paying off. Everyone who's had

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Speaker 1: a child with him and everyone who's died in a

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Speaker 1: Tesla auto driver accident, of which there are probably many

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Speaker 1: more than we know. And that is those are the

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Speaker 1: two top lines that have made Elon so nuts. But

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Speaker 1: it's really worth seeing the movie because beyond the fact

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Speaker 1: that it has these two really you know, that, that

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Speaker 1: Elon is doing two really catastrophic things. Um, it just,

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Speaker 1: it really explains how wealthy people can manipulate the system

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Speaker 1: in ways that are pretty, uh, important and problematic. Yeah.

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Speaker 4: Well, uh, I feel bad for all the people who

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Speaker 4: are losing out and have their, uh, 401ks tied to

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Speaker 4: some of these stocks because that really does suck. But, uh, also, um,

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Speaker 4: These things have consequences. And one of the things that

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Speaker 4: I think is interesting is when you have bad ideas,

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Speaker 4: they have consequences. And Tesla's pulling the plug on the

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Speaker 4: Cybertruck after years of poor sales.

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Speaker 1: So let's talk about the Cybertruck. Cybertruck sucks. It is humongous.

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Speaker 1: It is hideous. And it also has lots of problems.

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Speaker 1: I know this from some lawyers I know that it

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Speaker 1: has a lot of problems with the doors, like if

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Speaker 1: you opening the doors, et cetera. It's designed. It was

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Speaker 1: basically outlawed. from what I understand, sort of designed by,

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Speaker 1: it struck me that it was designed as a sort

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Speaker 1: of like very MAGA, very MAGA, MAGA kind of, you know,

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Speaker 1: just hideousness.

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Speaker 4: Well, I mean, one of the things that they told

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Speaker 4: you is they rushed it too much and that there

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Speaker 4: was all these people who bought the demonstration hype because

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Speaker 4: Elon always lies during the demonstrations. And they were like, look,

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Speaker 4: it won't break my arm if I put my arm

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Speaker 4: in the trunk and then it breaks the person's arm. Which, like,

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Speaker 4: if I'm being real, that is as America as it

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Speaker 4: gets right now these days. It's just exactly where we're

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Speaker 4: at these days.

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Speaker 1: Yeah. So that is really good. great example about a

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Speaker 1: really good example about why like this was just always

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Speaker 1: a terrible book a terrible book always a terrible car

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Speaker 1: um and uh musk so basically it has had recalls

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Speaker 1: quality problems price revisions its design is hideous it's enormous

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Speaker 1: and it is like i really think that What it

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Speaker 1: is in a weird way is like it's MAGA as

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Speaker 1: a car, right? It's humongous. It's like badly designed. It

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Speaker 1: takes up like 10 parking spaces. It feels very dangerous, right?

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Speaker 1: It's got all this metal. Like you would never want

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Speaker 1: to put your kid in it. And so MAGA as

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Speaker 1: a car is going bye-bye. And MAGA as a movement

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Speaker 1: is probably going bye-bye.

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Speaker 4: Molly, I'm going to ask you something that I'm imagining

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Speaker 4: you're not going to know. uh do you do you

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Speaker 4: perhaps remember the pickup artist and the term peacocking.

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Speaker 1: I wish i didn't know about this but i actually do.

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Speaker 3: Know okay okay and i hate you for.

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Speaker 4: It but but that that is what i think of

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Speaker 4: every time i see is i think of the guy

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Speaker 4: mystery who wore the big hat and the pickup artist

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Speaker 4: but that as a car that's every time i see

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Speaker 4: it and it's also who often gets out of it

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Speaker 4: in my neighborhood We have a lot of them here.

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Speaker 1: My favorite thing about the Cybertruck is that, like, it's just–

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Speaker 1: by the way, the best part of it is, like,

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Speaker 1: the National Highway Traffic Safety Administration opened an audit in September, like,

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Speaker 1: because it may actually not be safe. And, like, we

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Speaker 1: know that, like, pieces of it have come off, that

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Speaker 1: people have gotten stuck. So I do think it is

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Speaker 1: just such a good and hilarious and amazing metaphor for

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Speaker 1: the whole MAGA experiment.

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Speaker 3: Mm-hmm.

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Speaker 2: Yeah, the meth house.

432
00:26:23,600 --> 00:26:27,000
Speaker 1: The meth house that we live in. It's like Canada

433
00:26:27,380 --> 00:26:30,320
Speaker 1: may be living over a meth house, but we are

434
00:26:30,480 --> 00:26:33,380
Speaker 1: in the meth house, and they're making it in the bathtub.

435
00:26:33,440 --> 00:26:37,200
Speaker 4: One of Jeet here's great quotes. Okay, so somebody who

436
00:26:37,240 --> 00:26:41,580
Speaker 4: left the meth house, Thomas Massey, he gave Pete Hegseth

437
00:26:41,600 --> 00:26:44,359
Speaker 4: a choice. Resign or get impeached, dog.

438
00:26:45,000 --> 00:26:45,280
Speaker 2: Yeah.

439
00:26:46,109 --> 00:26:51,220
Speaker 1: I mean, again, Thomas Massey, like Tucker Carlson, like Marjorie

440
00:26:51,240 --> 00:26:56,439
Speaker 1: Taylor Greene is a, is a really great example of

441
00:26:56,510 --> 00:27:03,270
Speaker 1: like how you can have alliances with people who are useful,

442
00:27:03,910 --> 00:27:08,230
Speaker 1: but perhaps not people you want to, you know, go

443
00:27:08,250 --> 00:27:10,310
Speaker 1: to bed with, you know what I mean? Like, I

444
00:27:10,350 --> 00:27:13,090
Speaker 1: think that's a, not to put too, you know, obviously

445
00:27:13,150 --> 00:27:15,600
Speaker 1: no one's having sex with Thomas Massey, but, Like, the

446
00:27:15,660 --> 00:27:21,180
Speaker 1: point here is, speak for yourself, right? Yeah, somebody is.

447
00:27:21,859 --> 00:27:25,140
Speaker 1: But the point is that, like, he is doing something here,

448
00:27:25,200 --> 00:27:29,700
Speaker 1: which is, like, really cool. which is because he gives

449
00:27:29,760 --> 00:27:32,600
Speaker 1: no fucks, because like he's doing the stuff that I'd

450
00:27:32,640 --> 00:27:36,710
Speaker 1: love to see like John Cornyn do, right? You give

451
00:27:36,750 --> 00:27:39,869
Speaker 1: no fucks. And so it's not like you're a newly

452
00:27:39,950 --> 00:27:42,950
Speaker 1: elected Democrat who's about to have a long career. You

453
00:27:43,150 --> 00:27:47,870
Speaker 1: are like, you're on your way out. And and so

454
00:27:47,910 --> 00:27:51,669
Speaker 1: this is really cool. He unveiled eight articles of impeachment

455
00:27:51,890 --> 00:27:59,639
Speaker 1: against Whiskey Pete. And he said there's one contingency in

456
00:27:59,680 --> 00:28:03,240
Speaker 1: which I won't reintroduce the articles of impeachment. And that's

457
00:28:03,260 --> 00:28:08,679
Speaker 1: Hegsess resigned. Now, here's the deal. They have the numbers

458
00:28:08,940 --> 00:28:13,160
Speaker 1: during the lame duck to impeach and then they will

459
00:28:13,180 --> 00:28:16,240
Speaker 1: begin a trial during the lame duck. And here is

460
00:28:16,300 --> 00:28:19,550
Speaker 1: a question for you about a trial. You have a

461
00:28:19,770 --> 00:28:23,550
Speaker 1: number of senators who have literally lost their jobs. And

462
00:28:23,609 --> 00:28:27,570
Speaker 1: in fact, like he flipped the seat, right? Tillis, that's

463
00:28:27,630 --> 00:28:29,770
Speaker 1: going to, Tillis is going to be replaced by a Democrat.

464
00:28:30,869 --> 00:28:37,950
Speaker 1: John Cornyn replaced by a Democrat. Bill Cassidy, not replaced

465
00:28:37,970 --> 00:28:40,720
Speaker 1: by a Democrat, but like, but could have been, I mean,

466
00:28:41,040 --> 00:28:43,340
Speaker 1: very close to it. So there are a lot of

467
00:28:43,560 --> 00:28:49,420
Speaker 1: seats where, The Republicans have fucking lost their seats because

468
00:28:50,640 --> 00:28:55,270
Speaker 1: even Susan Collins with her concern on pearls. So there

469
00:28:55,330 --> 00:28:59,190
Speaker 1: is a real sense in which you could, I mean,

470
00:28:59,210 --> 00:29:01,770
Speaker 1: maybe you don't see removal, but a trial itself, like

471
00:29:01,790 --> 00:29:05,620
Speaker 1: that is all about Donald Trump's appetite for pain, right?

472
00:29:06,280 --> 00:29:10,220
Speaker 1: And embarrassment. And we know Whiskey Pete, we already know

473
00:29:10,280 --> 00:29:15,060
Speaker 1: that Whiskey Pete's wife may have been lobbying, right? So

474
00:29:15,140 --> 00:29:20,380
Speaker 1: and Jennifer had been going to meetings with Whiskey Pete

475
00:29:20,900 --> 00:29:24,160
Speaker 1: meetings and trips that where a wife usually does not go.

476
00:29:24,260 --> 00:29:28,100
Speaker 1: Not that wives aren't great, but she technically is not

477
00:29:28,200 --> 00:29:32,950
Speaker 1: the secretary for she's, you know. So I think this

478
00:29:32,990 --> 00:29:37,920
Speaker 1: will be really interesting to see what happens here.

479
00:29:37,920 --> 00:29:38,270
Speaker 3: Yep.

480
00:29:38,270 --> 00:29:41,030
Speaker 4: Um so my we have very great news and uh

481
00:29:41,070 --> 00:29:43,990
Speaker 4: news i'm particularly excited about so um for listeners who

482
00:29:44,040 --> 00:29:48,360
Speaker 4: don't know we made uh an episode about how citizens

483
00:29:48,440 --> 00:29:50,620
Speaker 4: united is the first step we need to do to

484
00:29:50,860 --> 00:29:53,800
Speaker 4: unfuck this country and uh we found this guy tom

485
00:29:53,840 --> 00:29:56,140
Speaker 4: moore from the center for american progress who has a

486
00:29:56,160 --> 00:29:58,060
Speaker 4: plan he uses a lot of people said you can't

487
00:29:58,100 --> 00:30:01,150
Speaker 4: get rid of citizens united he figured out that every

488
00:30:01,210 --> 00:30:03,890
Speaker 4: state grants the permissions for corporations and that you could

489
00:30:04,240 --> 00:30:09,260
Speaker 4: jigger that. And so Montana and Michigan both approved a

490
00:30:09,300 --> 00:30:12,460
Speaker 4: proposal to get the money out of politics and citizens

491
00:30:12,560 --> 00:30:14,950
Speaker 4: united in their states. We'd love to see this go

492
00:30:14,970 --> 00:30:15,550
Speaker 4: to more states.

493
00:30:16,190 --> 00:30:18,930
Speaker 1: So this is like your thing where you get really excited.

494
00:30:19,290 --> 00:30:19,530
Speaker 3: I do.

495
00:30:19,690 --> 00:30:23,100
Speaker 1: I do. I mean, So basically, the idea here is

496
00:30:23,140 --> 00:30:28,260
Speaker 1: if you can't overturn Citizens United, the congressional level, though,

497
00:30:28,360 --> 00:30:31,340
Speaker 1: I think now they have the votes, maybe. Like, make

498
00:30:31,400 --> 00:30:35,570
Speaker 1: Republicans vote for money in politics. Like, make that a thing.

499
00:30:36,110 --> 00:30:39,050
Speaker 1: But this is the idea is you do it at

500
00:30:39,070 --> 00:30:43,330
Speaker 1: the state level. So we have Montana. Montana that just

501
00:30:43,430 --> 00:30:51,420
Speaker 1: elected Sam Forstad with 53.1 points. Fifty fifty point three

502
00:30:51,480 --> 00:30:56,220
Speaker 1: percent of the vote. Sam Forstad is the first Democrat

503
00:30:56,760 --> 00:31:00,760
Speaker 1: in Montana in 30 years. He came on this podcast.

504
00:31:00,800 --> 00:31:08,400
Speaker 1: He's a fire jumper, smoke jumper, firefighter. Just a fucking rules.

505
00:31:08,550 --> 00:31:12,710
Speaker 1: Also quite handsome. I would like to add.

506
00:31:14,050 --> 00:31:14,970
Speaker 4: That's what I keep hearing.

507
00:31:20,250 --> 00:31:23,840
Speaker 1: Rebecca Katz is a Democratic strategist and the founder of

508
00:31:23,890 --> 00:31:27,380
Speaker 1: the Fight Agency. Welcome, welcome, Rebecca Katz.

509
00:31:27,520 --> 00:31:28,850
Speaker 3: Good to be here. Thanks for having me.

510
00:31:29,120 --> 00:31:33,730
Speaker 1: So I wanted you to come on to talk about

511
00:31:34,010 --> 00:31:36,870
Speaker 1: Gina and Ahosa, but also a lot of the other

512
00:31:36,950 --> 00:31:40,510
Speaker 1: candidates that you guys, did all your candidates win?

513
00:31:40,830 --> 00:31:41,820
Speaker 3: Almost, yes, basically.

514
00:31:41,840 --> 00:31:44,560
Speaker 1: Yeah. It's like I said this on Twitter, not to

515
00:31:44,640 --> 00:31:46,940
Speaker 1: quote myself, but like, which I hate it when people

516
00:31:46,960 --> 00:31:49,160
Speaker 1: do that. But it's like it is like the rapture,

517
00:31:49,260 --> 00:31:50,660
Speaker 1: like all the Republicans are gone.

518
00:31:51,000 --> 00:31:52,900
Speaker 3: Well, yeah, there's a there's a couple left. I mean,

519
00:31:52,940 --> 00:31:56,790
Speaker 3: Ted Cruz is the last Republican standing in Texas, one

520
00:31:56,810 --> 00:31:59,410
Speaker 3: statewide elected there. But no, it was it was a

521
00:31:59,450 --> 00:32:02,250
Speaker 3: great night for Democrats and we should be very excited

522
00:32:02,290 --> 00:32:02,570
Speaker 3: about it.

523
00:32:02,670 --> 00:32:05,670
Speaker 1: I want you to talk about what it's like. I mean,

524
00:32:05,690 --> 00:32:10,350
Speaker 1: first of all, Governor Abbott was not unpopular the way

525
00:32:10,450 --> 00:32:13,830
Speaker 1: that Paxton was like you had a real race. So

526
00:32:13,910 --> 00:32:15,250
Speaker 1: talk us through how you did.

527
00:32:15,390 --> 00:32:20,460
Speaker 3: Abbott was not underwater. I would say I. Gina was

528
00:32:20,860 --> 00:32:24,680
Speaker 3: an interesting candidate to me, and she had an interesting

529
00:32:24,740 --> 00:32:28,880
Speaker 3: theory of the case. She was putting together a coalition

530
00:32:29,060 --> 00:32:31,600
Speaker 3: of people who were really pissed off about public schools

531
00:32:31,660 --> 00:32:34,160
Speaker 3: in Texas, starting with that. Because as you may know

532
00:32:34,220 --> 00:32:37,790
Speaker 3: that over 150 school districts in Texas only have a

533
00:32:37,810 --> 00:32:41,170
Speaker 3: four-day school, right? Because that's taking so much money from them.

534
00:32:41,190 --> 00:32:44,130
Speaker 3: So there's the pissed off moms, right? And then there's

535
00:32:44,490 --> 00:32:47,090
Speaker 3: the data center, and how, especially in rural areas, they

536
00:32:47,110 --> 00:32:50,860
Speaker 3: were really mad about that. plus like the surging Latino

537
00:32:50,880 --> 00:32:54,400
Speaker 3: population in Texas. You know, Beto won with 64% of

538
00:32:54,540 --> 00:32:58,330
Speaker 3: the Latino vote. If we had just that for 2026,

539
00:32:58,330 --> 00:33:02,110
Speaker 3: we would win just because of population growth. But on

540
00:33:02,130 --> 00:33:05,630
Speaker 3: the other side of this was Abbott, who had been

541
00:33:05,730 --> 00:33:09,140
Speaker 3: governor for three terms and was running for an unprecedented

542
00:33:09,200 --> 00:33:12,340
Speaker 3: fourth term. And this year felt like a change election.

543
00:33:12,520 --> 00:33:16,140
Speaker 3: And even though he had $ 100 million at the start

544
00:33:16,160 --> 00:33:19,040
Speaker 3: of this cycle, he still seemed a little bit beatable

545
00:33:19,080 --> 00:33:21,580
Speaker 3: to me. And to the folks who decided to take

546
00:33:21,600 --> 00:33:25,530
Speaker 3: the jump on Team Gina, led by our amazing campaign manager,

547
00:33:25,590 --> 00:33:27,430
Speaker 3: Amy Friedman, who's the best of the best.

548
00:33:27,650 --> 00:33:31,870
Speaker 1: So one of the things that when I was reading

549
00:33:31,910 --> 00:33:36,450
Speaker 1: about this, that either I think Gina went to Beto. No,

550
00:33:36,750 --> 00:33:42,050
Speaker 1: I think James went to Beto and asked him about running, right?

551
00:33:42,130 --> 00:33:42,830
Speaker 1: Or was it Gina?

552
00:33:43,350 --> 00:33:44,490
Speaker 3: Well, I think that there was a there were a

553
00:33:44,490 --> 00:33:48,040
Speaker 3: lot of conversations happening. But he agreed that the best

554
00:33:48,100 --> 00:33:50,540
Speaker 3: way to win in Texas is to have a strong ticket.

555
00:33:50,640 --> 00:33:50,780
Speaker 1: Right.

556
00:33:50,820 --> 00:33:51,100
Speaker 2: Yes.

557
00:33:51,600 --> 00:33:52,240
Speaker 3: One candidate.

558
00:33:52,400 --> 00:33:53,780
Speaker 1: And you had to run together.

559
00:33:53,800 --> 00:33:57,240
Speaker 3: And they really like Gina and James knew each other

560
00:33:57,400 --> 00:34:01,190
Speaker 3: in the legislature and they were friends. And they did

561
00:34:01,230 --> 00:34:03,930
Speaker 3: a lot this campaign season together. They had rallies together.

562
00:34:03,990 --> 00:34:06,950
Speaker 3: They supported each other. I mean, there were just, you know,

563
00:34:07,030 --> 00:34:09,690
Speaker 3: even the tweets, like, let's, you know, from Tallarico saying, like,

564
00:34:09,730 --> 00:34:11,950
Speaker 3: let's get Gina over the line. She'll be a great governor.

565
00:34:12,390 --> 00:34:14,410
Speaker 3: Or even election night, just the two of them going

566
00:34:14,430 --> 00:34:17,319
Speaker 3: to Whataburger. Like, it was, they both wanted each other

567
00:34:17,390 --> 00:34:19,880
Speaker 3: to win in a way that was just really great,

568
00:34:20,080 --> 00:34:23,260
Speaker 3: you know? And so they knew if there was a ticket,

569
00:34:23,320 --> 00:34:26,399
Speaker 3: and I, you know, would put Bobby Pulido on that ticket, too. Like,

570
00:34:26,540 --> 00:34:29,009
Speaker 3: you know, all the way down. they knew that they

571
00:34:29,030 --> 00:34:30,930
Speaker 3: would have a better chance of winning. And that's what

572
00:34:30,969 --> 00:34:33,360
Speaker 3: happened for Democrats up and down the ticket in Texas.

573
00:34:33,600 --> 00:34:36,620
Speaker 1: Bobby Polito is a really interesting candidate. He is a

574
00:34:36,660 --> 00:34:40,700
Speaker 1: Takano singer who ran in an R plus 21 district.

575
00:34:40,900 --> 00:34:41,980
Speaker 1: Is that right? Yes.

576
00:34:42,120 --> 00:34:45,180
Speaker 3: And I will tell you, so I was at the rally.

577
00:34:45,480 --> 00:34:47,529
Speaker 3: When was it? A week or two ago in the valley,

578
00:34:47,640 --> 00:34:50,509
Speaker 3: rally in the valley that Voto Latino put on. And

579
00:34:50,530 --> 00:34:53,650
Speaker 3: it was with Polito. It was with Gina. And Gina's

580
00:34:53,710 --> 00:34:58,440
Speaker 3: obviously from the valley and Rico. And there were 8,000

581
00:34:58,440 --> 00:35:01,540
Speaker 3: people there and people were fired up. They were so

582
00:35:01,640 --> 00:35:05,440
Speaker 3: happy for this ticket and they were showing like that

583
00:35:05,460 --> 00:35:06,940
Speaker 3: they were going to make sure they went over the

584
00:35:06,980 --> 00:35:08,819
Speaker 3: finish line. It was hard not to be there that

585
00:35:08,900 --> 00:35:10,319
Speaker 3: night and not think we were going to win.

586
00:35:10,540 --> 00:35:14,820
Speaker 1: Interesting. So what do you think like the secret besides

587
00:35:14,860 --> 00:35:17,530
Speaker 1: the fact that it was just a very good environment? Right.

588
00:35:17,620 --> 00:35:20,120
Speaker 1: What do you think this Texas is like? It's just

589
00:35:20,219 --> 00:35:24,640
Speaker 1: such an unprecedented switch. You know, it's such a profound switch.

590
00:35:24,719 --> 00:35:27,000
Speaker 1: What do you think the key elements were?

591
00:35:27,120 --> 00:35:29,520
Speaker 3: These were there, you know, like the indicators were there

592
00:35:29,560 --> 00:35:31,859
Speaker 3: for a while. But like, you know, everybody had been

593
00:35:31,890 --> 00:35:34,049
Speaker 3: burned on Texas so many times that it was like,

594
00:35:34,070 --> 00:35:36,390
Speaker 3: I believe that this year is going to be the year.

595
00:35:36,510 --> 00:35:40,469
Speaker 3: But we had like the environment was terrible for Republicans.

596
00:35:40,489 --> 00:35:44,080
Speaker 3: Like you can't. You can't fool voters into thinking something

597
00:35:44,100 --> 00:35:46,540
Speaker 3: they don't see with their own eyes. It was like,

598
00:35:46,600 --> 00:35:48,580
Speaker 3: and that is whether that's at the gas pump or

599
00:35:48,820 --> 00:35:52,040
Speaker 3: groceries or with medical debt, like whatever it is, like

600
00:35:52,239 --> 00:35:55,319
Speaker 3: there was no chance to breathe. You know, like they,

601
00:35:55,719 --> 00:35:58,580
Speaker 3: like costs were just soaring. And we had these candidates

602
00:35:59,000 --> 00:36:01,080
Speaker 3: who wanted to do something about it. So, I mean,

603
00:36:01,380 --> 00:36:04,180
Speaker 3: I just think it, there was that piece of it, right?

604
00:36:04,219 --> 00:36:06,200
Speaker 3: So just like being sour with what was happening in

605
00:36:06,219 --> 00:36:08,610
Speaker 3: the national mood. There were these other issues, whether it

606
00:36:08,630 --> 00:36:10,870
Speaker 3: was data centers, whether it was schools that were happening.

607
00:36:11,340 --> 00:36:15,120
Speaker 3: And there was a growing electorate of Texans who had

608
00:36:15,160 --> 00:36:18,010
Speaker 3: never really been represented in government. I think it's something

609
00:36:18,070 --> 00:36:22,690
Speaker 3: like 6% of the Texas population is Asian or South Asian.

610
00:36:22,730 --> 00:36:26,580
Speaker 3: You had obviously a very huge Latino population. You just

611
00:36:26,600 --> 00:36:30,160
Speaker 3: didn't have that statewide before. I think everybody just... the

612
00:36:30,219 --> 00:36:32,700
Speaker 3: Democrats went to vote and the independents went to vote.

613
00:36:33,180 --> 00:36:35,299
Speaker 3: And a lot of, a lot of the core MAGA

614
00:36:35,320 --> 00:36:36,350
Speaker 3: Republicans stayed home.

615
00:36:36,590 --> 00:36:41,710
Speaker 1: So I want to ask you now about Dr. Al-Said

616
00:36:42,030 --> 00:36:45,569
Speaker 1: in Michigan, because that was another race and there was

617
00:36:45,650 --> 00:36:49,280
Speaker 1: a lot. So the primary, he was, we saw polls

618
00:36:49,320 --> 00:36:51,739
Speaker 1: that had him way ahead. He sort of eked it out.

619
00:36:51,980 --> 00:36:55,650
Speaker 1: She spent a gazillion she got a gazillion dollars from

620
00:36:55,710 --> 00:36:58,169
Speaker 1: a pack uh now he's gonna win by i think

621
00:36:58,230 --> 00:37:00,110
Speaker 1: about seven points right when.

622
00:37:00,050 --> 00:37:02,730
Speaker 3: It's done seven points just like he said yes.

623
00:37:02,610 --> 00:37:08,020
Speaker 1: Right underperforming a little bit the governor by three points no.

624
00:37:07,900 --> 00:37:10,300
Speaker 3: I think it's only about a point and a half

625
00:37:10,400 --> 00:37:12,640
Speaker 3: right now and he was okay but like you know

626
00:37:12,840 --> 00:37:15,500
Speaker 3: hundreds of millions of dollars against him and.

627
00:37:15,380 --> 00:37:20,150
Speaker 1: No i know and the MAGA machine was pretty focused

628
00:37:20,330 --> 00:37:20,629
Speaker 1: on him.

629
00:37:20,710 --> 00:37:23,030
Speaker 3: And when they came in, first of all, money doesn't

630
00:37:23,070 --> 00:37:26,950
Speaker 3: go as far as it did before. People get their

631
00:37:27,010 --> 00:37:28,710
Speaker 3: news in different ways. You can't just say, I'm going

632
00:37:28,730 --> 00:37:31,800
Speaker 3: to put $ 100 million on TV and you're going to

633
00:37:31,840 --> 00:37:34,900
Speaker 3: win an election, right? I think one of the biggest

634
00:37:34,940 --> 00:37:37,439
Speaker 3: indicators to a lot of these candidates that could really

635
00:37:37,460 --> 00:37:42,080
Speaker 3: withstand these attacks is how much they had their own

636
00:37:42,180 --> 00:37:45,480
Speaker 3: following on social media. Whether millions of people, hundreds of

637
00:37:45,520 --> 00:37:47,750
Speaker 3: thousands of people If you can get out into the

638
00:37:47,790 --> 00:37:50,740
Speaker 3: world what you stand for and what you're doing, that's

639
00:37:50,800 --> 00:37:52,640
Speaker 3: a whole way that people get their news. And I

640
00:37:52,700 --> 00:37:55,180
Speaker 3: think like last year you saw in New York that

641
00:37:55,219 --> 00:37:58,540
Speaker 3: people went after Mamdani because he was just like he

642
00:37:58,560 --> 00:38:01,930
Speaker 3: was talented and just good at social media without understanding

643
00:38:01,989 --> 00:38:04,970
Speaker 3: that for a lot of millennials, Gen Z, and also

644
00:38:05,350 --> 00:38:07,640
Speaker 3: everybody else, like They don't just get their news by

645
00:38:07,680 --> 00:38:11,529
Speaker 3: watching like the Walter Cronkite every night. You know, they

646
00:38:11,570 --> 00:38:14,170
Speaker 3: get it in different ways, like even here today. And

647
00:38:14,210 --> 00:38:17,830
Speaker 3: I just think the candidates that really overperformed got to

648
00:38:17,890 --> 00:38:20,170
Speaker 3: do it a little bit more because they had an

649
00:38:20,230 --> 00:38:21,250
Speaker 3: audience that they could reach.

650
00:38:21,550 --> 00:38:25,569
Speaker 1: He also is the first Muslim senator. And as you know,

651
00:38:25,610 --> 00:38:28,660
Speaker 1: I've written a lot about Harris, like Being the first

652
00:38:28,840 --> 00:38:31,180
Speaker 1: is not necessarily a fun experience.

653
00:38:31,300 --> 00:38:33,700
Speaker 3: And the hatred that came with it was really over

654
00:38:33,739 --> 00:38:34,080
Speaker 3: the top.

655
00:38:34,260 --> 00:38:36,219
Speaker 1: No, I'm unbelievably horrible.

656
00:38:36,260 --> 00:38:38,740
Speaker 3: He was called anti-Semitic every single day of the campaign.

657
00:38:38,800 --> 00:38:39,920
Speaker 2: No, I know.

658
00:38:40,280 --> 00:38:43,799
Speaker 3: The guy who grew up in a 40% Jewish area,

659
00:38:44,110 --> 00:38:48,850
Speaker 3: he spent his entire tween years going to barn bat mitzvahs.

660
00:38:48,969 --> 00:38:51,750
Speaker 3: It was so far from the truth. And just because

661
00:38:51,790 --> 00:38:54,790
Speaker 3: he had a different view on what was happening in

662
00:38:54,830 --> 00:38:58,360
Speaker 3: the Mideast doesn't make him any... Does not make him anti-Semitic.

663
00:38:58,480 --> 00:39:00,720
Speaker 3: And I thought a lot of people just really conflated

664
00:39:00,739 --> 00:39:01,180
Speaker 3: the two things.

665
00:39:01,400 --> 00:39:04,640
Speaker 1: Yeah. I mean, I think that we've seen that, like

666
00:39:04,700 --> 00:39:09,540
Speaker 1: the Netanyahu government really wants people to conflate anti-Zionism with anti-Semitism,

667
00:39:09,580 --> 00:39:12,480
Speaker 1: which only hurts the diaspora.

668
00:39:12,840 --> 00:39:14,580
Speaker 3: I would argue that is bad for the Jews to

669
00:39:14,680 --> 00:39:15,219
Speaker 3: conflate the two.

670
00:39:15,239 --> 00:39:18,850
Speaker 1: Yes. Yeah, no question. But it also, he did, you know, he,

671
00:39:19,150 --> 00:39:20,969
Speaker 1: in a certain way, he broke a glass ceiling, which

672
00:39:21,030 --> 00:39:23,250
Speaker 1: meant that he was going to get a lot of abuse.

673
00:39:23,469 --> 00:39:26,049
Speaker 1: You know, that's just part of what it is now.

674
00:39:26,390 --> 00:39:28,850
Speaker 1: And I think he dealt with it really well. What

675
00:39:28,890 --> 00:39:31,629
Speaker 1: was the, what was the? thinking there when you're running

676
00:39:31,670 --> 00:39:34,380
Speaker 1: that candidate? Because he's like a doctor. And one of

677
00:39:34,390 --> 00:39:36,450
Speaker 1: the things I was a little bit curious by was

678
00:39:36,489 --> 00:39:39,879
Speaker 1: he's a PhD MD. And I was surprised you guys

679
00:39:39,940 --> 00:39:41,180
Speaker 1: didn't talk about that more.

680
00:39:41,200 --> 00:39:43,540
Speaker 3: I mean, we did talk about it. I mean, he did.

681
00:39:43,560 --> 00:39:45,940
Speaker 3: I mean, he wrote a book on Medicare for all.

682
00:39:45,980 --> 00:39:47,219
Speaker 3: We talked about that a lot, too.

683
00:39:47,260 --> 00:39:47,460
Speaker 1: Right.

684
00:39:48,320 --> 00:39:49,759
Speaker 3: I guess I want to go. I want to take

685
00:39:49,780 --> 00:39:52,700
Speaker 3: you back a little further, because I remember at the

686
00:39:52,739 --> 00:39:56,490
Speaker 3: beginning of this cycle, you know, Democrats in a really

687
00:39:56,570 --> 00:39:59,810
Speaker 3: bad place. We had just lost Harris. We're just felt

688
00:39:59,850 --> 00:40:02,040
Speaker 3: defeated all over. And it was like, well, now we

689
00:40:02,080 --> 00:40:04,800
Speaker 3: have we have to win these hard races. Gary Peters

690
00:40:04,860 --> 00:40:07,200
Speaker 3: is going to retire. Like, what are we going to do? And,

691
00:40:07,219 --> 00:40:10,480
Speaker 3: you know, we, I'm a media consultant. Like, we meet

692
00:40:10,520 --> 00:40:14,420
Speaker 3: with lots of people. I remember meeting with Abdul and

693
00:40:14,520 --> 00:40:18,969
Speaker 3: just being really, really impressed. Like, he seemed like he

694
00:40:19,030 --> 00:40:22,299
Speaker 3: had everything. Um, and he was, and I, I did

695
00:40:22,380 --> 00:40:24,440
Speaker 3: believe that we could win a general election and we

696
00:40:24,480 --> 00:40:26,340
Speaker 3: got into a lot of, like, we got a lot

697
00:40:26,390 --> 00:40:28,710
Speaker 3: of pushback. They were just, you know, there were constant

698
00:40:28,730 --> 00:40:31,410
Speaker 3: stories about like, well, if you nominate, if you nominate Abdul,

699
00:40:31,770 --> 00:40:33,969
Speaker 3: you know, Michigan will go red, you know? And it

700
00:40:34,010 --> 00:40:36,340
Speaker 3: was like a constant thing. And he had to, um,

701
00:40:36,940 --> 00:40:40,239
Speaker 3: show everyone that he could win. And that's what he did.

702
00:40:40,360 --> 00:40:44,560
Speaker 3: And he grabbed that attention economy and he went on

703
00:40:44,860 --> 00:40:48,250
Speaker 3: every outlet he could find and really showed up everywhere

704
00:40:48,270 --> 00:40:49,870
Speaker 3: and went after every single voter.

705
00:40:50,130 --> 00:40:53,230
Speaker 1: And I mean, he earned it and won the primary.

706
00:40:54,070 --> 00:40:57,370
Speaker 1: I wonder if you could talk about, like, he was

707
00:40:57,430 --> 00:40:58,569
Speaker 1: not the establishment pick.

708
00:40:59,160 --> 00:40:59,359
Speaker 3: No.

709
00:41:00,820 --> 00:41:02,160
Speaker 1: That's like the understatement of the year.

710
00:41:02,700 --> 00:41:05,239
Speaker 3: A billion dollars trying to defeat him in a primary.

711
00:41:05,760 --> 00:41:09,850
Speaker 1: Yeah. And when you found out he wasn't the establishment pick,

712
00:41:09,910 --> 00:41:13,350
Speaker 1: did you change the way you ran that campaign? And

713
00:41:13,450 --> 00:41:14,109
Speaker 1: if so, how?

714
00:41:14,610 --> 00:41:17,670
Speaker 3: He wouldn't be the establishment pick. The establishment doesn't go

715
00:41:17,710 --> 00:41:22,529
Speaker 3: for candidates like Abdul. That's not... Once he won the primary,

716
00:41:22,710 --> 00:41:27,310
Speaker 3: I think, you know, the DSCC, SMP, the Senate majority,

717
00:41:27,489 --> 00:41:29,930
Speaker 3: like they were all in. They were all in. But

718
00:41:30,070 --> 00:41:31,790
Speaker 3: we had to win first. And that was on our

719
00:41:31,930 --> 00:41:33,129
Speaker 3: own with a lot of people.

720
00:41:33,150 --> 00:41:37,270
Speaker 1: I guess. You have had candidates who were not the

721
00:41:37,310 --> 00:41:42,089
Speaker 1: establishment pick before who have really done amazingly well. And

722
00:41:42,350 --> 00:41:45,310
Speaker 1: I wonder if you could talk about why you think...

723
00:41:45,670 --> 00:41:49,490
Speaker 1: the establishment picks certain candidates. Like we've seen this cycle.

724
00:41:49,930 --> 00:41:53,450
Speaker 1: We've seen recently Schumer has done a whole thing about

725
00:41:53,489 --> 00:41:56,150
Speaker 1: how he picked a lot of great candidates. And he

726
00:41:56,200 --> 00:41:59,090
Speaker 1: certainly picked some really good candidates, but he also picked

727
00:41:59,140 --> 00:42:01,779
Speaker 1: some candidates who didn't make it. Do you think that

728
00:42:02,060 --> 00:42:08,200
Speaker 1: candidates are rewarded for being not as passionate? I think

729
00:42:08,260 --> 00:42:10,820
Speaker 1: about some of these candidates that aren't the establishment picks

730
00:42:10,880 --> 00:42:14,529
Speaker 1: seem a little more authentic or a little more like

731
00:42:14,550 --> 00:42:17,150
Speaker 1: they're willing to put themselves out there in a way.

732
00:42:17,170 --> 00:42:21,109
Speaker 1: And I wonder if you could talk us through that.

733
00:42:21,200 --> 00:42:24,239
Speaker 1: And you've had candidates, too, who have been the establishment pick.

734
00:42:24,280 --> 00:42:27,870
Speaker 1: I mean, I think, I mean, Gina, you know, people

735
00:42:27,910 --> 00:42:31,610
Speaker 1: where they weren't pushing so, the establishment wasn't pushing so hard.

736
00:42:32,390 --> 00:42:34,310
Speaker 3: I mean, that's, I was, first of all, Molly, you're

737
00:42:34,330 --> 00:42:36,200
Speaker 3: going to get me into trouble because I'm trying so

738
00:42:36,239 --> 00:42:38,420
Speaker 3: hard to play nice right now. So I'm going to

739
00:42:38,440 --> 00:42:42,820
Speaker 3: make my work. But I would say that I'd rather

740
00:42:42,940 --> 00:42:46,220
Speaker 3: win and lose by one less point to have someone

741
00:42:46,260 --> 00:42:51,629
Speaker 3: who's going to go make big things happen. Just like

742
00:42:51,730 --> 00:42:54,989
Speaker 3: get these wins and then do nothing once we have power.

743
00:42:55,130 --> 00:42:57,830
Speaker 3: Like I am, we have had 10 years of this

744
00:42:57,890 --> 00:43:01,410
Speaker 3: like Trump regime. It is time to get people into

745
00:43:01,469 --> 00:43:03,710
Speaker 3: power who are going to do something about.

746
00:43:03,510 --> 00:43:03,850
Speaker 1: It, right?

747
00:43:04,030 --> 00:43:07,239
Speaker 3: And I just, I don't think it's about getting, you know,

748
00:43:07,340 --> 00:43:10,379
Speaker 3: an elected figure who's just going to vote the right way. Yes,

749
00:43:10,420 --> 00:43:12,460
Speaker 3: you need to vote to get into the majority. I

750
00:43:12,500 --> 00:43:15,260
Speaker 3: understand that. But you also need people who once they

751
00:43:15,560 --> 00:43:17,880
Speaker 3: are going there are going to grab their power. and

752
00:43:17,910 --> 00:43:19,980
Speaker 3: do something and make real change. And I think that's

753
00:43:20,020 --> 00:43:22,520
Speaker 3: what we're seeing. I think you have a lot of great,

754
00:43:23,500 --> 00:43:26,500
Speaker 3: great candidates this cycle who are going to go and

755
00:43:26,540 --> 00:43:29,600
Speaker 3: shake up Washington and is honestly really, really excited because

756
00:43:29,640 --> 00:43:32,819
Speaker 3: it's about time. Like in my mind, this is what

757
00:43:32,860 --> 00:43:36,340
Speaker 3: should have happened in 2018, right? Like if we, if the,

758
00:43:36,360 --> 00:43:38,319
Speaker 3: just hear me out. If the Democrats had actually done

759
00:43:38,360 --> 00:43:42,810
Speaker 3: an autopsy, like a DNC autopsy after Hillary Law to

760
00:43:42,830 --> 00:43:46,109
Speaker 3: figure out like Bernie tapped into and figured out that, wait,

761
00:43:46,530 --> 00:43:49,290
Speaker 3: maybe people do think the system is rigged. Maybe people

762
00:43:49,310 --> 00:43:51,770
Speaker 3: think like, you know, that corporations have too much power, right?

763
00:43:51,969 --> 00:43:54,569
Speaker 3: Because in the last 10 years, all that's happened is

764
00:43:54,590 --> 00:43:57,920
Speaker 3: that whole imbalance has gotten much worse. So this year,

765
00:43:57,980 --> 00:43:59,819
Speaker 3: whether you want to call people, like whatever you want

766
00:43:59,840 --> 00:44:02,700
Speaker 3: to call them on the political spectrum, these candidates were

767
00:44:03,060 --> 00:44:08,160
Speaker 3: all pop, right? They won with big populist agenda. And

768
00:44:08,420 --> 00:44:11,279
Speaker 3: it was much more about like what, like they had

769
00:44:11,380 --> 00:44:13,600
Speaker 3: their villains, whether it was Elon Musk, whether it was

770
00:44:13,620 --> 00:44:17,000
Speaker 3: the corporations. They were saying they have too much power.

771
00:44:17,080 --> 00:44:19,020
Speaker 3: And if I get elected, I'm going to help you.

772
00:44:19,200 --> 00:44:21,310
Speaker 3: We could have been with her for years, Molly. We

773
00:44:21,350 --> 00:44:23,850
Speaker 3: could have been having like popular candidate for years if

774
00:44:23,890 --> 00:44:26,450
Speaker 3: we had just like gotten a little curious about it.

775
00:44:26,650 --> 00:44:30,219
Speaker 1: But we've also had candidates. You've had candidates that have

776
00:44:30,340 --> 00:44:32,660
Speaker 1: flamed out. And I'm talking about Plattner.

777
00:44:33,200 --> 00:44:35,700
Speaker 3: I think the issue with Maine is. and this is

778
00:44:35,739 --> 00:44:38,180
Speaker 3: not something I want to re-litigate, but here we are, right,

779
00:44:38,680 --> 00:44:39,060
Speaker 3: is that.

780
00:44:39,100 --> 00:44:42,609
Speaker 1: I mean, I just... It's a female practice not to... Okay, okay.

781
00:44:42,770 --> 00:44:46,250
Speaker 3: There was a feeling that, like, you could see that the...

782
00:44:46,250 --> 00:44:50,450
Speaker 3: that Washington Democrats wanted to freeze the field and wait

783
00:44:50,469 --> 00:44:52,759
Speaker 3: to see if Janet Mills would get into the ring, right?

784
00:44:53,200 --> 00:44:55,799
Speaker 3: And Janet Mills was a very unpopular government.

785
00:44:56,080 --> 00:44:59,620
Speaker 1: And... And very old, too. Very old. She would have

786
00:44:59,640 --> 00:45:02,280
Speaker 1: been 84 when she was done with her first year.

787
00:45:02,360 --> 00:45:05,120
Speaker 3: Yeah, she would have been the oldest freshman of all

788
00:45:05,160 --> 00:45:05,960
Speaker 3: time when she got in.

789
00:45:05,960 --> 00:45:06,120
Speaker 1: Yeah.

790
00:45:06,140 --> 00:45:09,529
Speaker 3: She would have been 79 as a new freshman. And

791
00:45:09,550 --> 00:45:12,870
Speaker 3: the thinking was, there's got to be somebody else besides

792
00:45:12,930 --> 00:45:15,290
Speaker 3: just Janet Mills. And I think that instance, if you

793
00:45:15,330 --> 00:45:18,410
Speaker 3: go back, like I don't think that that Washington should

794
00:45:18,510 --> 00:45:21,800
Speaker 3: bully and try and freeze fields and wait for some

795
00:45:22,000 --> 00:45:24,500
Speaker 3: candidate to come. I think primaries are healthy.

796
00:45:24,739 --> 00:45:24,959
Speaker 1: Right.

797
00:45:25,060 --> 00:45:27,100
Speaker 3: And that it is good to get the best person

798
00:45:27,560 --> 00:45:30,040
Speaker 3: out there. Now, like there were obviously like a lot

799
00:45:30,080 --> 00:45:34,279
Speaker 3: of a lot of challenges with that particular candidate. But

800
00:45:34,320 --> 00:45:37,560
Speaker 3: what I will say is he still defeated and like

801
00:45:37,660 --> 00:45:40,270
Speaker 3: the incumbent governor, you know, and it was because people

802
00:45:40,290 --> 00:45:42,620
Speaker 3: were unhappy and he tapped into something. And it was

803
00:45:42,680 --> 00:45:45,120
Speaker 3: good to see that Troy Jackson, a man who had been,

804
00:45:45,140 --> 00:45:47,859
Speaker 3: you know, Bernie Sanders' state director years before, is now

805
00:45:47,880 --> 00:45:48,239
Speaker 3: a U.S.

806
00:45:48,260 --> 00:45:48,500
Speaker 1: Senator.

807
00:45:48,600 --> 00:45:52,480
Speaker 3: Because it doesn't have to just be like boring, you know,

808
00:45:52,880 --> 00:45:56,509
Speaker 3: centrists who will not do anything to make their party better.

809
00:45:56,710 --> 00:46:00,390
Speaker 1: In some ways, it feels as if you can't have

810
00:46:00,469 --> 00:46:06,890
Speaker 1: Troy Jackson without having Graham Plattner. He represented something that

811
00:46:07,070 --> 00:46:10,680
Speaker 1: really was much more of what Mainers wanted, right? Because

812
00:46:10,780 --> 00:46:14,500
Speaker 1: Troy is really progressive. And Troy Jackson did that too, right?

813
00:46:14,860 --> 00:46:18,420
Speaker 1: I mean, when they went for a nominee, they didn't

814
00:46:18,460 --> 00:46:21,190
Speaker 1: go back to, they went for what they really wanted,

815
00:46:21,230 --> 00:46:24,330
Speaker 1: which was a progressive. So in the end, he ultimately,

816
00:46:24,610 --> 00:46:27,390
Speaker 1: I think, served a good purpose, if that makes any sense.

817
00:46:27,570 --> 00:46:29,989
Speaker 1: And since Troy won in the end.

818
00:46:30,010 --> 00:46:32,310
Speaker 3: People want to hope. Like that was the whole thing.

819
00:46:32,370 --> 00:46:35,989
Speaker 3: I mean, there was a lot of excitement on the

820
00:46:36,030 --> 00:46:39,240
Speaker 3: ground in Maine. Like they wanted something different. People wanted,

821
00:46:39,500 --> 00:46:42,940
Speaker 3: you know, especially after like the Biden debacle, like people

822
00:46:43,120 --> 00:46:46,000
Speaker 3: wanted someone who could speak to what they were. That

823
00:46:46,020 --> 00:46:47,500
Speaker 3: was the law of the country. Yeah.

824
00:46:47,860 --> 00:46:51,620
Speaker 1: One of the things that I appreciate about these candidates

825
00:46:51,800 --> 00:46:54,620
Speaker 1: is that I get very frustrated when I see candidates

826
00:46:54,640 --> 00:46:58,600
Speaker 1: who don't try, you know, and one of the things

827
00:46:58,700 --> 00:47:02,469
Speaker 1: that what we see with a, I mean, I'm very

828
00:47:02,510 --> 00:47:04,750
Speaker 1: excited about Troy Jackson because I feel like he is,

829
00:47:05,010 --> 00:47:07,390
Speaker 1: he actually represents something we don't see enough of in

830
00:47:07,469 --> 00:47:11,550
Speaker 1: the Senate, which is someone who is legitimately working class,

831
00:47:11,710 --> 00:47:12,009
Speaker 1: you know.

832
00:47:12,030 --> 00:47:13,870
Speaker 3: People also mocked him for that too. I mean, that

833
00:47:13,910 --> 00:47:14,250
Speaker 3: was like.

834
00:47:14,310 --> 00:47:17,350
Speaker 1: No, I know. It became a big issue in the campaign.

835
00:47:17,570 --> 00:47:19,569
Speaker 3: But Troy Jackson's a real one, you know what I mean?

836
00:47:19,590 --> 00:47:19,670
Speaker 1: Yeah.

837
00:47:20,050 --> 00:47:21,870
Speaker 3: And I think he'll work hard for the people of Maine.

838
00:47:22,090 --> 00:47:27,690
Speaker 1: And in the end, that last advertisement where Susan Collins

839
00:47:28,090 --> 00:47:32,580
Speaker 1: had that fancy broadcaster saying that it wasn't like buying wood.

840
00:47:32,920 --> 00:47:36,340
Speaker 1: I mean, it was just sort of so it struck

841
00:47:36,380 --> 00:47:38,920
Speaker 1: me as an important moment in that campaign.

842
00:47:39,100 --> 00:47:40,339
Speaker 3: And condescending.

843
00:47:40,520 --> 00:47:41,120
Speaker 1: Yeah. Yeah.

844
00:47:41,180 --> 00:47:41,410
Speaker 2: Yeah.

845
00:47:42,090 --> 00:47:45,020
Speaker 3: And I just, I think people overestimated her. Like, yes,

846
00:47:45,040 --> 00:47:47,400
Speaker 3: she was strong in Maine, but she also wasn't as

847
00:47:47,440 --> 00:47:49,739
Speaker 3: strong as she was a term or two ago. Like

848
00:47:49,780 --> 00:47:51,610
Speaker 3: a lot of these people just like, they're trying so

849
00:47:51,650 --> 00:47:53,930
Speaker 3: hard to hold on to power. And it's like, what

850
00:47:53,950 --> 00:47:55,710
Speaker 3: are you going to do with the power once you're there?

851
00:47:55,810 --> 00:47:58,410
Speaker 3: For Susan Collins, she completely sold out. She, you know,

852
00:47:58,460 --> 00:48:01,020
Speaker 3: she put a lot of, like, moneyed interests ahead of

853
00:48:01,060 --> 00:48:03,100
Speaker 3: the people of Maine, and I think they rejected it

854
00:48:03,140 --> 00:48:03,820
Speaker 3: pretty handily.

855
00:48:04,900 --> 00:48:08,629
Speaker 1: Of the candidates this cycle who you worked with, what

856
00:48:08,650 --> 00:48:12,270
Speaker 1: do you think the sort of way that forward is

857
00:48:12,410 --> 00:48:16,640
Speaker 1: with them? You know, like... I'm just thinking of they

858
00:48:16,700 --> 00:48:20,719
Speaker 1: seem like new candidates in a way. They're good at

859
00:48:20,800 --> 00:48:26,200
Speaker 1: talking to people, willing to go anywhere. They don't speak.

860
00:48:26,300 --> 00:48:28,859
Speaker 1: One of the big complaints I have, sorry, now we're

861
00:48:28,910 --> 00:48:33,350
Speaker 1: going into big complaints I have, is that sometimes you

862
00:48:33,450 --> 00:48:37,410
Speaker 1: interview people and there's a lot of Democratic leadership that's

863
00:48:37,450 --> 00:48:42,299
Speaker 1: like this where they say, things like it's America and

864
00:48:42,320 --> 00:48:45,239
Speaker 1: you put them and you're like, that doesn't mean anything.

865
00:48:45,420 --> 00:48:47,680
Speaker 1: Like if you just don't want to talk, then don't talk.

866
00:48:47,719 --> 00:48:49,990
Speaker 3: You know, I think millennials are going to have their moment,

867
00:48:50,170 --> 00:48:52,170
Speaker 3: this cycle, right? Like we are seeing they're going to

868
00:48:52,190 --> 00:48:54,450
Speaker 3: get into power and they're going to be able to

869
00:48:54,489 --> 00:48:57,650
Speaker 3: be comfortable with the new ways that media work, right?

870
00:48:57,690 --> 00:49:00,030
Speaker 3: Like it is just, it is not about just having,

871
00:49:00,110 --> 00:49:03,230
Speaker 3: I mean, we talked about this with Kamala, right? Like

872
00:49:03,250 --> 00:49:06,250
Speaker 3: that she would do an interview, say something wrong and

873
00:49:06,270 --> 00:49:08,070
Speaker 3: then not do an interview for a month or whatever

874
00:49:08,090 --> 00:49:11,339
Speaker 3: it was. It was I mean, it's not just about

875
00:49:11,380 --> 00:49:14,719
Speaker 3: having these amazing communicators get into office. It's also about

876
00:49:14,760 --> 00:49:17,410
Speaker 3: them being comfortable and being comfortable in their own skin

877
00:49:17,510 --> 00:49:19,270
Speaker 3: and being able to have a real conversation and sound

878
00:49:19,310 --> 00:49:21,910
Speaker 3: like a normal person. person. I just don't think like

879
00:49:22,110 --> 00:49:24,910
Speaker 3: that is something that we've had, especially in the Democratic Party,

880
00:49:24,950 --> 00:49:25,630
Speaker 3: for a very long time.

881
00:49:25,770 --> 00:49:28,210
Speaker 1: Yeah, I mean, I think that's right. So if you're Gina...

882
00:49:29,390 --> 00:49:32,359
Speaker 1: What do you think is sort of the job one

883
00:49:32,590 --> 00:49:34,340
Speaker 1: if you're the governor of Texas?

884
00:49:34,460 --> 00:49:38,640
Speaker 3: Gina Hinojosa's election is probably the most consequential election of

885
00:49:38,700 --> 00:49:41,080
Speaker 3: any candidate in America right now because she is now

886
00:49:41,140 --> 00:49:44,060
Speaker 3: the governor of Texas, right? It is a big deal.

887
00:49:44,080 --> 00:49:47,430
Speaker 3: And I think she takes that seriously. And she takes

888
00:49:48,170 --> 00:49:51,649
Speaker 3: her promises seriously. She wants to put the people first

889
00:49:51,730 --> 00:49:55,109
Speaker 3: after all these years of the corporations literally having their way.

890
00:49:55,640 --> 00:49:58,340
Speaker 3: I mean, Elon Musk made, what, a half a billion

891
00:49:58,360 --> 00:50:01,440
Speaker 3: dollar bet to move his entire company to Texas to

892
00:50:01,480 --> 00:50:04,560
Speaker 3: get out of California. And now he's got, you know, Governor,

893
00:50:04,600 --> 00:50:06,600
Speaker 3: you know, like, that's great, man.

894
00:50:06,680 --> 00:50:08,299
Speaker 1: That is some.

895
00:50:08,100 --> 00:50:11,720
Speaker 3: Poetic justice right there. I love it. Yeah. Let's see.

896
00:50:11,880 --> 00:50:14,560
Speaker 3: Let's see how she leads. But she's got a big job.

897
00:50:14,760 --> 00:50:16,219
Speaker 3: I think she's going to be fantastic.

898
00:50:16,790 --> 00:50:20,990
Speaker 1: Do you think that there's going to be a leadership election?

899
00:50:21,450 --> 00:50:24,460
Speaker 1: Do you think that it's a secret ballot? Obviously, it's

900
00:50:24,500 --> 00:50:28,340
Speaker 1: kind of a popularity contest. There's already a lot of posturing.

901
00:50:28,800 --> 00:50:32,379
Speaker 1: Like in your mind, how important is leadership? And, you know,

902
00:50:32,719 --> 00:50:33,299
Speaker 1: talk about it.

903
00:50:33,460 --> 00:50:36,640
Speaker 3: So I think it is hard to see a path

904
00:50:36,700 --> 00:50:40,710
Speaker 3: for anyone challenging Schumer or Jeffries just because when you

905
00:50:40,730 --> 00:50:42,830
Speaker 3: win the majority by this much, it's like a, it's

906
00:50:42,850 --> 00:50:44,670
Speaker 3: a hard argument for someone coming against them. And I

907
00:50:44,730 --> 00:50:47,989
Speaker 3: actually don't know of anyone who is, well, there's not,

908
00:50:48,330 --> 00:50:50,410
Speaker 3: we'll see what happens, but there's no one who has

909
00:50:50,640 --> 00:50:53,540
Speaker 3: announced yet to check. But I do hope for both

910
00:50:53,560 --> 00:50:56,920
Speaker 3: of them that they might not hold all the, the,

911
00:50:57,140 --> 00:51:01,900
Speaker 3: the spotlight themselves and let other people take, take the

912
00:51:01,920 --> 00:51:04,480
Speaker 3: helm a little bit on different issues. When Harry Reid

913
00:51:04,520 --> 00:51:09,490
Speaker 3: was Senate leader, he had mocks, Bacchus from Montana being

914
00:51:09,690 --> 00:51:11,310
Speaker 3: you know he tasked him with being in charge of

915
00:51:11,350 --> 00:51:13,710
Speaker 3: saving social security and then Bacchus was the face of

916
00:51:13,750 --> 00:51:17,049
Speaker 3: that right I think uh I think for the big

917
00:51:17,090 --> 00:51:19,259
Speaker 3: projects that Democrats need to focus on I think it'd

918
00:51:19,280 --> 00:51:22,120
Speaker 3: be interesting to have other leaders front and center who

919
00:51:22,180 --> 00:51:26,200
Speaker 3: are really are better communicators and understand the media environment

920
00:51:26,400 --> 00:51:28,660
Speaker 3: and that's I'm not trying to like get into it

921
00:51:28,700 --> 00:51:31,529
Speaker 3: with like our leaders. I just think like everybody is

922
00:51:31,590 --> 00:51:33,969
Speaker 3: good at different things. And when you are in a

923
00:51:33,989 --> 00:51:36,410
Speaker 3: position of power, your job should be to put all

924
00:51:36,430 --> 00:51:38,670
Speaker 3: the people under you in a position to succeed. And

925
00:51:39,690 --> 00:51:42,500
Speaker 3: and I think when especially in the Senate, when you

926
00:51:42,540 --> 00:51:44,759
Speaker 3: had different members of the Senate being out there, I

927
00:51:44,800 --> 00:51:47,060
Speaker 3: think that is good for I think that's good for

928
00:51:47,080 --> 00:51:48,739
Speaker 3: the Democratic Party. And I think that's good for America.

929
00:51:48,920 --> 00:51:52,549
Speaker 1: Do you think there's like a chance of passing legislation

930
00:51:53,010 --> 00:51:56,649
Speaker 1: at this moment, even though Democrats don't control the executive branch?

931
00:51:56,930 --> 00:51:59,890
Speaker 3: I do, because I do think that the president who is, well,

932
00:51:59,930 --> 00:52:02,469
Speaker 3: it's hard to predict anything, but he did write a

933
00:52:02,489 --> 00:52:05,060
Speaker 3: book called The Art of the Deal, right? Like he

934
00:52:05,100 --> 00:52:10,460
Speaker 3: does like making deals. And I think that's actually, I mean, honestly,

935
00:52:10,480 --> 00:52:13,060
Speaker 3: if you go back in time, you know, like Harry

936
00:52:13,140 --> 00:52:16,040
Speaker 3: Reid was a very good minority leader in some ways

937
00:52:16,060 --> 00:52:17,840
Speaker 3: because he could like get into a fight, but he

938
00:52:17,880 --> 00:52:20,440
Speaker 3: also knew how to, as a majority leader, hold power.

939
00:52:20,460 --> 00:52:24,320
Speaker 3: I think, you know, Schumer, one of the biggest problems

940
00:52:24,360 --> 00:52:26,880
Speaker 3: for him coming in in 2017 was that he wasn't

941
00:52:27,380 --> 00:52:30,020
Speaker 3: used to being someone who was like the agitator. He

942
00:52:30,060 --> 00:52:32,529
Speaker 3: wanted to make it. So I'm interested to see if

943
00:52:32,650 --> 00:52:36,190
Speaker 3: just like what happened during COVID and this and that,

944
00:52:36,210 --> 00:52:38,870
Speaker 3: like if there are some ways that Trump would make

945
00:52:38,950 --> 00:52:41,710
Speaker 3: some deals with a Democratic House and Senate.

946
00:52:41,890 --> 00:52:44,230
Speaker 1: Yeah. Rebecca Katz, thank you.

947
00:52:44,489 --> 00:52:45,180
Speaker 3: Thanks for having me.

948
00:52:47,230 --> 00:52:49,879
Speaker 4: And now your moment of suffering.

949
00:52:50,469 --> 00:52:51,560
Speaker 1: Jesse Cannon.

950
00:52:52,120 --> 00:52:52,880
Speaker 2: Bye, Jugfast.

951
00:52:53,500 --> 00:52:55,280
Speaker 4: Your buddy, Harmeet Dhillon.

952
00:52:55,300 --> 00:52:56,060
Speaker 2: I know you love her.

953
00:52:57,000 --> 00:52:57,739
Speaker 3: Ah, Harmeet.

954
00:52:58,360 --> 00:53:01,520
Speaker 4: So the rats are fleeing the ship, Maya. We've seen

955
00:53:01,560 --> 00:53:06,779
Speaker 4: a few people leaving the MAGA train. Prosecutor down in Miami.

956
00:53:07,080 --> 00:53:11,180
Speaker 4: And Harmeet has resigned as the Justice Department's Assistant Attorney

957
00:53:11,219 --> 00:53:15,070
Speaker 4: General for Civil Rights. Now, granted, she was airing grievances

958
00:53:15,130 --> 00:53:18,240
Speaker 4: quite often on Twitter, so... This is not really a surprise.

959
00:53:18,700 --> 00:53:21,460
Speaker 1: One of my favorite things about Hamid is like they

960
00:53:21,480 --> 00:53:23,560
Speaker 1: were not they were like laser focused on what's the

961
00:53:23,739 --> 00:53:25,020
Speaker 1: opposite of civil rights.

962
00:53:25,700 --> 00:53:26,820
Speaker 2: Yes, this is right.

963
00:53:26,900 --> 00:53:30,360
Speaker 1: Like they were if civil rights is making Donald Trump happy,

964
00:53:30,760 --> 00:53:34,490
Speaker 1: then they were focused on it. But if civil rights

965
00:53:35,270 --> 00:53:41,989
Speaker 1: is if civil rights is actually like helping, you know,

966
00:53:43,969 --> 00:53:50,060
Speaker 1: helping People who need like the whole point of the fucking,

967
00:53:50,560 --> 00:53:53,420
Speaker 1: you know, this the Justice Department was to protect the

968
00:53:53,460 --> 00:53:57,290
Speaker 1: American people. And Donald Trump was like, no, it is

969
00:53:57,330 --> 00:54:04,790
Speaker 1: to protect me. So, you know, she is my revenge unit. Yeah, exactly.

970
00:54:04,830 --> 00:54:08,299
Speaker 1: I mean, that's really what he thought. So and we're

971
00:54:08,340 --> 00:54:10,680
Speaker 1: going to see a lot more. The Justice Department expects

972
00:54:10,719 --> 00:54:14,500
Speaker 1: to see several other senior political appointees to leave before

973
00:54:14,560 --> 00:54:20,660
Speaker 1: they go to jail, before they're investigated. So it's very

974
00:54:20,760 --> 00:54:25,750
Speaker 1: sad for her. And you know who's laughing? Pam Bondi.

975
00:54:25,930 --> 00:54:30,830
Speaker 1: She's laughing. But yeah, I'm sure Harmeet will try to

976
00:54:30,890 --> 00:54:37,070
Speaker 1: marry her. Or run off and live somewhere without extradition.

977
00:54:37,090 --> 00:54:41,650
Speaker 1: And she will be, you know, we're going to see

978
00:54:41,670 --> 00:54:44,650
Speaker 1: a lot of this. Like, gas up those planes and

979
00:54:44,690 --> 00:54:48,719
Speaker 1: head to Chile, team. That's it for this episode of

980
00:54:48,820 --> 00:54:55,690
Speaker 1: Fast Politics. Tune in every Monday, Wednesday, Thursday, and Saturday

981
00:54:55,810 --> 00:54:59,130
Speaker 1: to hear the best minds in politics make sense of

982
00:54:59,670 --> 00:55:03,680
Speaker 1: all this chaos. If you enjoy this podcast, please send

983
00:55:03,719 --> 00:55:07,320
Speaker 1: it to a friend and keep the conversation going. Thanks

984
00:55:07,400 --> 00:55:08,100
Speaker 1: for listening.